DETAILED ACTION
This office action is in response to the communication filed on April 21, 2026. Claims 1, 3-10, 12-16, and 18-23 are currently pending.
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 .
Response to Arguments
Applicant's arguments filed on April 21, 2026 have been fully considered but they are not persuasive for the following reasons:
Applicant in Pages 12-14 of the Remarks argues that the amended independent claims do not recite an abstract idea because the claimed features do not fall within the category of a mental process and are rather method steps implemented by a specifically programmed computer component, the claims recite additional elements that integrate the alleged abstract idea into a practical application, the claims recite additional elements that amount to significantly more than the alleged abstract idea, and the claims provide an inventive step over existing technologies.
Examiner respectfully disagrees.
It is important to note that the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements (MPEP 2106.05(a)).
If the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement (MPEP 2106.04(d)(1)).
Independent claim 1 and similarly independent claims 10 and 16 covers several steps, such as the determining steps, that recite an abstract idea within the “Mental Processes” grouping of abstract ideas, because a person can mentally or using a pen and paper perform the limitations recited in said steps, which is discussed in detail in the current 101 rejection below.
The remaining steps in the claims that are identified as reciting additional elements, are only adding insignificant extra-solution activity to the judicial exception, are recognized as a well understood, routine, and conventional activity within the field of computer functions, and are applying the exception using generic computer components, which is not sufficient to amount to significantly more than the judicial exception and are not directed to any specific improvement in computer technology.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Applicant in Pages 15-16 of the Remarks argues that Walker and Katardjiev do not teach or even suggest the features "wherein the vertical category tags comprise tags corresponding to different content types”, “consumption data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags”, and “the recommended search term and the vertical category tag that have the association relation in terms of consumption are adjacently presented", as recited in amended independent claim 1 and similarly recited in amended independent claims 10 and 16.
Examiner respectfully disagrees. The previously cited prior art Walker and Katardjiev and the newly cited prior art Chandra alone and/or in combination discloses the argued features.
Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms.
Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds.
Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags.
Therefore, Walker discloses "wherein the vertical category tags comprise tags corresponding to different content”.
Walker does not explicitly disclose “tags comprising tags corresponding to different content types”, but the Chandra reference discloses the feature.
Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms.
Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds are adjacently placed.
Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags.
Therefore, Walker discloses “data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags”, and “the recommended search term and the vertical category tag that have the association relation are adjacently presented".
Walker does not explicitly disclose “consumption data” and “tag that have the association relation in terms of consumption", but the Katardjiev reference discloses the features.
Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags.
Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists.
Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date.
Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria.
Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method.
Therefore, Katardjiev discloses “consumption data” and “tag that have the association relation in terms of consumption".
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Walker and Katardjiev, to have combined Walker and Katardjiev. The motivation to combine Walker and Katardjiev would be to provide a user with a more relevant set of recommendations by implementing a tag-based user directed recommendation.
Walker and Katardjiev discloses tags comprising tags corresponding to different content, however, Walker and Katardjiev do not explicitly disclose:
wherein the…tags comprise tags corresponding to different content types;
Chandra in [0002] and [0109] discloses highlighting positions of content, such as text, images, etc., for better visibility, later retrieval, display, publishing, sharing with one or more other users, users adding tags about the highlight, these tags are used to create more relevant search results for future retrievals.
Chandra in [0074] and [0161] discloses a user may search for content based on tags, which is a user assigned, relevant keyword or term associated with or assigned to a piece of information, like a picture, article, or video clip, user can add personalized tags such as “My tags”, users can add recommended tags to highlight content.
Therefore, Chandra discloses “tags comprising tags corresponding to different content types”.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Walker, Katardjiev, and Chandra, to have combined Walker, Katardjiev, and Chandra. The motivation to combine Walker, Katardjiev, and Chandra would be to enable a user to highlight portions of content for visibility, later retrieval, and sharing based on user assigned recommendation tags.
For the above reasons, Examiner states that rejection of the current Office action is proper.
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.
Claims 1, 3-10, 12-16, and 18-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1:
Independent claims 1, 10, and 16 respectively recite a method, an electronic device, and a non-transitory computer-readable storage medium, which are directed to a statutory category such as a process, machine, or an article of manufacture.
At step 2A, prong one:
Independent claim 1 and similarly independent claims 10 and 16 recites the limitations:
“determining a sequence of filter items obtained after mixing and sorting the plurality of recommended search terms and vertical category tags, wherein the vertical category tags comprise tags corresponding to different content types”;
A person can mentally or using a pen and paper determine a sequence of filter items obtained after mentally or using a pen and paper mixing and sorting a plurality of recommended search terms and vertical category tags, wherein the vertical category tags comprise tags corresponding to different content types.
wherein the determining the sequence of filter items obtained after mixing and sorting the plurality of recommended search terms and the vertical category tags comprises:
“determining an association relation between each of the recommended search terms and each of the vertical category tags, based on consumption data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags, wherein in response to association degree between a certain recommended search term and a certain vertical category tag determined based on the consumption data being greater than a threshold, it is determined that the certain recommended search term and the certain vertical category tag have an association relation in terms of consumption”;
A person can mentally or using a pen and paper determine a sequence of filter items obtained after mentally or using a pen and paper mixing and sorting a plurality of recommended search terms and vertical category tags by mentally or using a pen and paper determining an association relation between each of the recommended search terms and each of the vertical category tags, based on consumption data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags, wherein in response to association degree between a certain recommended search term and a certain vertical category tag determined based on the consumption data being greater than a threshold the person can mentally or using a pen and paper determine that the certain recommended search term and the certain vertical category tag have an association relation in terms of consumption.
“determining the sequence of filter items obtained after mixing and sorting the plurality of recommended search terms and the vertical category tags in accordance with the determined association relation, such that the recommended search term and the vertical category tag that have the association relation in terms of consumption are adjacently presented”.
A person can mentally or using a pen and paper determine a sequence of filter items obtained after mentally or using a pen and paper mixing and sorting a plurality of recommended search terms and vertical category tags by mentally or using a pen and paper determining an association relation between each of the recommended search terms and each of the vertical category tags, based on consumption data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags, wherein in response to association degree between a certain recommended search term and a certain vertical category tag determined based on the consumption data being greater than a threshold the person can mentally or using a pen and paper determine that the certain recommended search term and the certain vertical category tag have an association relation in terms of consumption.
The limitations, as recited above, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
At step 2A, prong two:
This judicial exception is not integrated into a practical application.
Independent claim 1 and similarly independent claims 10 and 16 recites the limitations:
“in response to receiving search information, acquiring a plurality of recommended search terms matched with the search information”, which is a step of acquiring or retrieving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
“presenting the sequence of filter items obtained after the mixing and sorting in a navigation bar of a search page”, which is a step of presenting data. The step is recited at a high level of generality, and amounts to mere data outputting, which is a form of insignificant extra-solution activity.
The additional elements “a method for presenting search filter items, comprising:” and “in a navigation bar of a search page” in the steps in claim 1 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “an electronic device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate via the bus when the electronic device runs, and the machine-readable instructions, when executed by the processor, cause the processor to perform the following operations for presenting search filter items:” and “in a navigation bar of a search page” in the steps in claim 10 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a non-transitory computer-readable storage medium, storing thereon a computer program which, when executed by a processor, causes the processor to perform the following operations for presenting search filter items:” and “in a navigation bar of a search page” in the steps in claim 16 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claim a whole, because it does not impose any meaningful limits on practicing the abstract idea.
At step 2B:
Independent claims 1, 10, and 16 recite the same additional elements as identified in step 2A prong two above. These additional elements are not sufficient to amount to significantly more than the judicial exception.
Independent claim 1 and similarly independent claims 10 and 16 recites the limitations:
“in response to receiving search information, acquiring a plurality of recommended search terms matched with the search information”, which is a step of acquiring or retrieving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
“presenting the sequence of filter items obtained after the mixing and sorting in a navigation bar of a search page”, which is a step of presenting data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of presenting offers and gathering statistics (MPEP 2106.05(d)(II)(iv)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claim is directed to an abstract idea and is not patent eligible.
Dependent claim 3 and similarly dependent claims 12 and 18 recites additional limitations, such as:
wherein the sequence of filter items is generated in accordance with the following steps:
“determining a priority order of the vertical category tags based on the search information”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1, 10, and 16, because a person can mentally or using a pen and paper determine a priority order of vertical category tags based on a search information, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“determining a plurality of prepositive vertical category tags from the vertical category tags in accordance with the priority order”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1, 10, and 16, because a person can mentally or using a pen and paper determine a plurality of prepositive vertical category tags from vertical category tags in accordance with a priority order, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the plurality of recommended search terms as prepositive filter items of the sequence of filter items, and taking the vertical category tags other than the prepositive vertical category tags among the vertical category tags as postpositive filter items of the sequence of filter items”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1, 10, and 16, because a person can mentally or using a pen and paper take what is resulted after mixing and sorting a plurality of prepositive vertical category tags and a plurality of recommended search terms as prepositive filter items of a sequence of filter items, and the person can mentally or using a pen and paper take vertical category tags other than prepositive vertical category tags among the vertical category tags as postpositive filter items of a sequence of filter items, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“wherein the prepositive filter items are used for being arranged before the postpositive filter items”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1, 10, and 16, because a person can mentally or using a pen and paper use prepositive filter items for arrangement before using postpositive filter items, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 4 and similarly dependent claims 13 and 19 recites additional limitation, such as:
wherein the determining the priority order of the vertical category tags based on the search information comprises:
“determining the priority order of the vertical category tags based on consumption data of multimedia content under a search category to which the search information belongs”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1, 10, and 16, because a person can mentally or using a pen and paper determine a priority order of vertical category tags based on consumption data of multimedia content under a search category to which a search information belongs, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 5 and similarly dependent claims 14 and 20 recites additional limitations, such as:
wherein the determining the plurality of prepositive vertical category tags from the vertical category tags in accordance with the priority order comprises:
“selecting a first number of first vertical category tags which are top-ranked, in an order from high to low of the priority order”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1, 10, and 16, because a person can mentally or using a pen and paper select a first number of first vertical category tags which are top-ranked, in an order from high to low of the priority order, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“performing a union process on the first number of first vertical category tags and a plurality of preset second vertical category tags, and taking the vertical category tags in the resulted union as the plurality of prepositive vertical category tags”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1, 10, and 16, because a person can mentally or using a pen and paper perform a union process on a first number of first vertical category tags and a plurality of preset second vertical category tags, and the person can mentally or using a pen and paper take the vertical category tags in the resulted union as a plurality of prepositive vertical category tags, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 and similarly dependent claim 15 recites additional limitation, such as:
wherein the taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the plurality of recommended search terms as prepositive filter items of the sequence of filter items comprises:
“in response to the number of the recommended search terms being greater than a second number, selecting the second number of recommended search terms from the plurality of recommended search terms, in accordance with at least one of degree of correlation between the plurality of recommended search terms and the search information or consumption data of multimedia content corresponding to the plurality of recommended search terms”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1 and 10, because a person can mentally or using a pen and paper select a second number of recommended search terms from a plurality of recommended search terms, in response to a number of the recommended search terms being greater than the second number, in accordance with at least one of degree of correlation between the plurality of recommended search terms and search information or consumption data of multimedia content corresponding to the plurality of recommended search terms, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the second number of recommended search terms as the prepositive filter items of the sequence of filter items”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1 and 10, because a person can mentally or using a pen and paper take what is resulted after mixing and sorting a plurality of prepositive vertical category tags and a second number of recommended search terms as a prepositive filter items of a sequence of filter items, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“the taking the vertical category tags other than the prepositive vertical category tags among the vertical category tags as the postpositive filter items of the sequence of filter items comprises:
taking the vertical category tags other than the prepositive vertical category tags, as well as the recommended search terms other than the second number of recommended search terms among the plurality of recommended search terms, as the postpositive filter items of the sequence of filter items”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1 and 10, because a person can mentally or using a pen and paper take vertical category tags other than prepositive vertical category tags among vertical category tags as postpositive filter items of a sequence of filter items by mentally or using a pen and paper taking the vertical category tags other than the prepositive vertical category tags, as well as recommended search terms other than a second number of recommended search terms among a plurality of recommended search terms, as the postpositive filter items of the sequence of filter items, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 7 recites additional limitations, such as:
wherein prior to mixing and sorting the plurality of recommended search terms and the vertical category tags, the method further comprises:
“performing a semantic match on the plurality of recommended search terms and the vertical category tags, and determining whether there is a recommended search term which is semantically matched with one of the vertical category tags among the plurality of recommended search terms”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper perform a semantic match on a plurality of recommended search terms and vertical category tags to mentally or using a pen and paper determine whether there is a recommended search term which is semantically matched with one of the vertical category tags among the plurality of recommended search terms, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“if yes, deleting the recommended search term which is semantically matched with the one of the vertical category tags”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper delete a recommended search term which is semantically matched with one of the vertical category tags based on a determination, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 8 recites additional limitations, such as:
wherein subsequent to presenting the sequence of filter items obtained after the mixing and sorting, the method further comprises:
“adjusting the order of respective filter items in the sequence of filter items in accordance with trigger operation data for the respective filter items in the sequence of filter items”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper adjust an order of respective filter items in a sequence of filter items in accordance with trigger operation data for respective filter items in the sequence of filter items, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 9 recites additional limitations, such as:
wherein subsequent to presenting the sequence of filter items obtained after the mixing and sorting, the method further comprises at least one of the following steps:
“in response to a trigger operation for a target vertical category tag in the sequence of filter items, setting the vertical category tag to be in a selected state, and presenting respective search results under the vertical category tag for the search information; or
in response to a trigger operation for a recommended search term in the sequence of filter items, setting a comprehensive tag in the search page to be in a selected state, and presenting respective search results matched with the search information and the recommended search term”, which are steps for presenting data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data outputting, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of presenting offers and gathering statistics (MPEP 2106.05(d)(II)(iv)).
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 21 and similarly dependent claims 22 and 23 recites additional limitations, such as:
“wherein the vertical tags comprise pictures, videos, short videos, and users”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claims 1, 10, and 16, because a person can mentally or using a pen and paper determine a sequence of filter items obtained after mentally or using a pen and paper mixing and sorting a plurality of recommended search terms and vertical category tags, wherein the vertical tags comprise pictures, videos, short videos, and users, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, dependent claims 3-9, 12-15, and 18-23 are also directed to abstract idea without significantly more and are 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(s) 1, 3-6, 8-10, 12-16, and 18-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walker (US Pub 2022/0075513) in view of Katardjiev (US Pub 2019/0087435) and in further view of Chandra (US Pub 2022/0164403).
With respect to claim 1, Walker discloses a method for presenting search filter items, comprising:
in response to receiving search information, acquiring a plurality of recommended search terms matched with the search information (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags);
determining a sequence of filter items obtained after…the plurality of recommended search terms and vertical category tags, wherein the vertical category tags comprise tags corresponding to different content… (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds; Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose mixing and sorting and tags comprising tags corresponding to different content types, but the Katardjiev and Chandra references disclose the features, as discussed below);
presenting the sequence of filter items obtained after…in a navigation bar of a search page (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented on a search page with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can navigate and select one of the suggestions to execute a search of the selected keyword or tags, which is interpreted as a navigation bar of a search page; here Walker does not explicitly disclose mixing and sorting, but the Katardjiev reference discloses the feature, as discussed below);
wherein the determining the sequence of filter items obtained after…the plurality of recommended search terms and the vertical category tags (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose mixing and sorting, but the Katardjiev reference discloses the feature, as discussed below) comprises:
determining an association relation between each of the recommended search terms and each of the vertical category tags, based on…data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags, wherein in response to association degree between a certain recommended search term and a certain vertical category tag determined based on the…data…it is determined that the certain recommended search term and the certain vertical category tag have an association relation… (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds are adjacently placed; Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose determining an association based on consumption data, wherein in response to association degree determined based on the consumption data being greater than a threshold, it is determined that the certain tag has an association relation in terms of consumption, but the Katardjiev reference discloses the features, as discussed below); and
determining the sequence of filter items obtained after…the plurality of recommended search terms and the vertical category tags in accordance with the determined association relation, such that the recommended search term and the vertical category tag that have the association relation…are adjacently presented (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds are adjacently placed; Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose mixing and sorting and tag that have the association relation in terms of consumption, but the Katardjiev reference discloses the features, as discussed below).
Walker discloses determining a sequence of filter items obtained after a search term is input in a search bar, which includes a plurality of recommended search terms or keywords and vertical tags, and determining an association relation between each of the recommended search terms and each of the vertical category tags, based on data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags, however, Walker does not explicitly disclose:
…mixing and sorting…;
determining an association…based on consumption data...wherein in response to association degree…determined based on the consumption data being greater than a threshold, it is determined that the certain…tag have an association relation in terms of consumption;
The Katardjiev reference discloses mixing and sorting (Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method);
determining an association based on consumption data, wherein in response to association degree determined based on the consumption data being greater than a threshold, it is determined that the certain tag has an association relation in terms of consumption (Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Walker and Katardjiev, to have combined Walker and Katardjiev. The motivation to combine Walker and Katardjiev would be to provide a user with a more relevant set of recommendations by implementing a tag-based user directed recommendation scheme (Katardjiev: [0001] and [0010]).
Walker and Katardjiev discloses tags comprising tags corresponding to different content, however, Walker and Katardjiev do not explicitly disclose:
wherein the…tags comprise tags corresponding to different content types;
The Chandra reference discloses tags comprising tags corresponding to different content types (Chandra in [0002] and [0109] discloses highlighting positions of content, such as text, images, etc., for better visibility, later retrieval, display, publishing, sharing with one or more other users, users adding tags about the highlight, these tags are used to create more relevant search results for future retrievals; Chandra in [0074] and [0161] discloses a user may search for content based on tags, which is a user assigned, relevant keyword or term associated with or assigned to a piece of information, like a picture, article, or video clip, user can add personalized tags such as “My tags”, users can add recommended tags to highlight content).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Walker, Katardjiev, and Chandra, to have combined Walker, Katardjiev, and Chandra. The motivation to combine Walker, Katardjiev, and Chandra would be to enable a user to highlight portions of content for visibility, later retrieval, and sharing based on user assigned recommendation tags (Chandra: [0002] and [0161]).
With respect to claim 3, Walker in view of Katardjiev and in further view of Chandra discloses the method according to claim 1, wherein the sequence of filter items is generated in accordance with the following steps of:
determining a priority order of the vertical category tags based on the search information; determining a plurality of prepositive vertical category tags from the vertical category tags in accordance with the priority order (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the plurality of recommended search terms as prepositive filter items of the sequence of filter items, and taking the vertical category tags other than the prepositive vertical category tags among the vertical category tags as postpositive filter items of the sequence of filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method);
wherein the prepositive filter items are used for being arranged before the postpositive filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 4, Walker in view of Katardjiev and in further view of Chandra discloses the method according to claim 3, wherein the determining the priority order of the vertical category tags based on the search information comprises:
determining the priority order of the vertical category tags based on consumption data of multimedia content under a search category to which the search information belongs (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 5, Walker in view of Katardjiev and in further view of Chandra discloses the method according to claim 3, wherein the determining the plurality of prepositive vertical category tags from the vertical category tags in accordance with the priority order comprises:
selecting a first number of first vertical category tags which are top-ranked, in an order from high to low of the priority order (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
performing a union process on the first number of first vertical category tags and a plurality of preset second vertical category tags, and taking the vertical category tags in the resulted union as the plurality of prepositive vertical category tags (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 6, Walker in view of Katardjiev and in further view of Chandra discloses the method according to claim 3, wherein the taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the plurality of recommended search terms as the prepositive filter items of the sequence of filter items comprises:
in response to the number of the recommended search terms being greater than a second number, selecting the second number of recommended search terms from the plurality of recommended search terms, in accordance with at least one degree of correlation between the plurality of recommended search terms and the search information or consumption data of multimedia content corresponding to the plurality of recommended search terms (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the second number of recommended search terms as the prepositive filter items of the sequence of filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
the taking the vertical category tags other than the prepositive vertical category tags among the vertical category tags as the postpositive filter items of the sequence of filter items comprises:
taking the vertical category tags other than the prepositive vertical category tags, as well as the recommended search terms other than the second number of recommended search terms among the plurality of recommended search terms, as the postpositive filter items of the sequence of filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 8, Walker in view of Katardjiev and in further view of Chandra discloses the method in accordance with claim 1, wherein subsequent to presenting the sequence of filter items obtained after the mixing and sorting, the method further comprises:
adjusting the order of respective filter items in the sequence of filter items in accordance with trigger operation data for the respective filter items in the sequence of filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 9, Walker in view of Katardjiev and in further view of Chandra discloses the method according to claim 1, wherein subsequent to presenting the sequence of filter items obtained after the mixing and sorting, the method further comprises at least one of the following steps:
in response to a trigger operation for a target vertical category tag in the sequence of filter items, setting the target vertical category tag to be in a selected state, and presenting respective search results under the target vertical category tag for the search information (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); or
in response to a trigger operation for a recommended search term in the sequence of filter items, setting a comprehensive tag in the search page to be in a selected state, and presenting respective search results matched with the search information and the recommended search term (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 10, Walker discloses an electronic device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate via the bus when the electronic device runs, and the machine-readable instructions, when executed by the processor, cause the processor to perform the following operations (Walker in [0161], [0182], and [0183] discloses device comprising processor, memory, and a bus, memory storing instructions executed by the processor, memory communicating with bus) for presenting search filter items:
in response to receiving search information, acquiring a plurality of recommended search terms matched with the search information (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags);
determining a sequence of filter items obtained after…the plurality of recommended search terms and vertical category tags, wherein the vertical category tags comprise tags corresponding to different content… (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds; Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose mixing and sorting and tags comprising tags corresponding to different content types, but the Katardjiev and Chandra references disclose the features, as discussed below);
presenting the sequence of filter items obtained after…in a navigation bar of a search page (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented on a search page with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can navigate and select one of the suggestions to execute a search of the selected keyword or tags, which is interpreted as a navigation bar of a search page; here Walker does not explicitly disclose mixing and sorting, but the Katardjiev reference discloses the feature, as discussed below);
wherein the determining the sequence of filter items obtained after…the plurality of recommended search terms and the vertical category tags (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose mixing and sorting, but the Katardjiev reference discloses the feature, as discussed below) comprises:
determining an association relation between each of the recommended search terms and each of the vertical category tags, based on…data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags, wherein in response to association degree between a certain recommended search term and a certain vertical category tag determined based on the…data…it is determined that the certain recommended search term and the certain vertical category tag have an association relation… (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds are adjacently placed; Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose determining an association based on consumption data, wherein in response to association degree determined based on the consumption data being greater than a threshold, it is determined that the certain tag has an association relation in terms of consumption, but the Katardjiev reference discloses the features, as discussed below); and
determining the sequence of filter items obtained after…the plurality of recommended search terms and the vertical category tags in accordance with the determined association relation, such that the recommended search term and the vertical category tag that have the association relation…are adjacently presented (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds are adjacently placed; Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose mixing and sorting and tag that have the association relation in terms of consumption, but the Katardjiev reference discloses the features, as discussed below).
Walker discloses determining a sequence of filter items obtained after a search term is input in a search bar, which includes a plurality of recommended search terms or keywords and vertical tags, and determining an association relation between each of the recommended search terms and each of the vertical category tags, based on data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags, however, Walker does not explicitly disclose:
…mixing and sorting…;
determining an association…based on consumption data...wherein in response to association degree…determined based on the consumption data being greater than a threshold, it is determined that the certain…tag have an association relation in terms of consumption;
The Katardjiev reference discloses mixing and sorting (Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method);
determining an association based on consumption data, wherein in response to association degree determined based on the consumption data being greater than a threshold, it is determined that the certain tag has an association relation in terms of consumption (Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Walker and Katardjiev, to have combined Walker and Katardjiev. The motivation to combine Walker and Katardjiev would be to provide a user with a more relevant set of recommendations by implementing a tag-based user directed recommendation scheme (Katardjiev: [0001] and [0010]).
Walker and Katardjiev discloses tags comprising tags corresponding to different content, however, Walker and Katardjiev do not explicitly disclose:
wherein the…tags comprise tags corresponding to different content types;
The Chandra reference discloses tags comprising tags corresponding to different content types (Chandra in [0002] and [0109] discloses highlighting positions of content, such as text, images, etc., for better visibility, later retrieval, display, publishing, sharing with one or more other users, users adding tags about the highlight, these tags are used to create more relevant search results for future retrievals; Chandra in [0074] and [0161] discloses a user may search for content based on tags, which is a user assigned, relevant keyword or term associated with or assigned to a piece of information, like a picture, article, or video clip, user can add personalized tags such as “My tags”, users can add recommended tags to highlight content).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Walker, Katardjiev, and Chandra, to have combined Walker, Katardjiev, and Chandra. The motivation to combine Walker, Katardjiev, and Chandra would be to enable a user to highlight portions of content for visibility, later retrieval, and sharing based on user assigned recommendation tags (Chandra: [0002] and [0161]).
With respect to claim 12, Walker in view of Katardjiev and in further view of Chandra discloses the electronic device according to claim 10, wherein the sequence of filter items is generated in accordance with the following operations of:
determining a priority order of the vertical category tags based on the search information (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method);
determining a plurality of prepositive vertical category tags from the vertical category tags in accordance with the priority order (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the plurality of recommended search terms as prepositive filter items of the sequence of filter items, and taking the vertical category tags other than the prepositive vertical category tags among the vertical category tags as postpositive filter items of the sequence of filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method);
wherein the prepositive filter items are used for being arranged before the postpositive filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 13, Walker in view of Katardjiev and in further view of Chandra discloses the electronic device according to claim 12, wherein the determining the priority order of the vertical category tags based on the search information comprises:
determining the priority order of the vertical category tags based on consumption data of multimedia content under a search category to which the search information belongs (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 14, Walker in view of Katardjiev and in further view of Chandra discloses the electronic device according to claim 12, wherein the determining the plurality of prepositive vertical category tags from the vertical category tags in accordance with the priority order comprises:
selecting a first number of first vertical category tags which are top-ranked, in an order from high to low of the priority order (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
performing a union process on the first number of first vertical category tags and a plurality of preset second vertical category tags, and taking the vertical category tags in the resulted union as the plurality of prepositive vertical category tags (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 15, Walker in view of Katardjiev and in further view of Chandra discloses the electronic device according to claim 12, wherein the taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the plurality of recommended search terms as prepositive filter items of the sequence of filter items comprises:
in response to the number of the recommended search terms being greater than a second number, selecting the second number of recommended search terms from the plurality of recommended search terms, in accordance with degree of correlation between the plurality of recommended search terms and the search information or consumption data of multimedia content corresponding to the plurality of recommended search terms (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the second number of recommended search terms as the prepositive filter items of the sequence of filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
the taking the vertical category tags other than the prepositive vertical category tags among the vertical category tags as the postpositive filter items of the sequence of filter items comprises:
taking the vertical category tags other than the prepositive vertical category tags, as well as the recommended search terms other than the second number of recommended search terms among the plurality of recommended search terms, as the postpositive filter items of the sequence of filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 16, Walker discloses a non-transitory computer-readable storage medium, storing thereon a computer program which, when executed by a processor, causes the processor to perform the following operations (Walker in [0161] and [0183] discloses computer readable media storing instructions executed by a processor) for presenting search filter items:
in response to receiving search information, acquiring a plurality of recommended search terms matched with the search information (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags);
determining a sequence of filter items obtained after…the plurality of recommended search terms and vertical category tags, wherein the vertical category tags comprise tags corresponding to different content… (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds; Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose mixing and sorting and tags comprising tags corresponding to different content types, but the Katardjiev and Chandra references disclose the features, as discussed below);
presenting the sequence of filter items obtained after…in a navigation bar of a search page (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented on a search page with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can navigate and select one of the suggestions to execute a search of the selected keyword or tags, which is interpreted as a navigation bar of a search page; here Walker does not explicitly disclose mixing and sorting, but the Katardjiev reference discloses the feature, as discussed below);
wherein the determining the sequence of filter items obtained after…the plurality of recommended search terms and the vertical category tags (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose mixing and sorting, but the Katardjiev reference discloses the feature, as discussed below) comprises:
determining an association relation between each of the recommended search terms and each of the vertical category tags, based on…data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags, wherein in response to association degree between a certain recommended search term and a certain vertical category tag determined based on the…data…it is determined that the certain recommended search term and the certain vertical category tag have an association relation… (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds are adjacently placed; Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose determining an association based on consumption data, wherein in response to association degree determined based on the consumption data being greater than a threshold, it is determined that the certain tag has an association relation in terms of consumption, but the Katardjiev reference discloses the features, as discussed below); and
determining the sequence of filter items obtained after…the plurality of recommended search terms and the vertical category tags in accordance with the determined association relation, such that the recommended search term and the vertical category tag that have the association relation…are adjacently presented (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds are adjacently placed; Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; here Walker does not explicitly disclose mixing and sorting and tag that have the association relation in terms of consumption, but the Katardjiev reference discloses the features, as discussed below).
Walker discloses determining a sequence of filter items obtained after a search term is input in a search bar, which includes a plurality of recommended search terms or keywords and vertical tags, and determining an association relation between each of the recommended search terms and each of the vertical category tags, based on data of multimedia content corresponding to each of the recommended search terms under each of the vertical category tags, however, Walker does not explicitly disclose:
…mixing and sorting…;
determining an association…based on consumption data...wherein in response to association degree…determined based on the consumption data being greater than a threshold, it is determined that the certain…tag have an association relation in terms of consumption;
The Katardjiev reference discloses mixing and sorting (Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method);
determining an association based on consumption data, wherein in response to association degree determined based on the consumption data being greater than a threshold, it is determined that the certain tag has an association relation in terms of consumption (Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Walker and Katardjiev, to have combined Walker and Katardjiev. The motivation to combine Walker and Katardjiev would be to provide a user with a more relevant set of recommendations by implementing a tag-based user directed recommendation scheme (Katardjiev: [0001] and [0010]).
Walker and Katardjiev discloses tags comprising tags corresponding to different content, however, Walker and Katardjiev do not explicitly disclose:
wherein the…tags comprise tags corresponding to different content types;
The Chandra reference discloses tags comprising tags corresponding to different content types (Chandra in [0002] and [0109] discloses highlighting positions of content, such as text, images, etc., for better visibility, later retrieval, display, publishing, sharing with one or more other users, users adding tags about the highlight, these tags are used to create more relevant search results for future retrievals; Chandra in [0074] and [0161] discloses a user may search for content based on tags, which is a user assigned, relevant keyword or term associated with or assigned to a piece of information, like a picture, article, or video clip, user can add personalized tags such as “My tags”, users can add recommended tags to highlight content).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Walker, Katardjiev, and Chandra, to have combined Walker, Katardjiev, and Chandra. The motivation to combine Walker, Katardjiev, and Chandra would be to enable a user to highlight portions of content for visibility, later retrieval, and sharing based on user assigned recommendation tags (Chandra: [0002] and [0161]).
With respect to claim 18, Walker in view of Katardjiev and in further view of Chandra discloses the non-transitory computer-readable storage medium according to claim 16, wherein the sequence of filter items is generated in accordance with the following operations:
determining a priority order of the vertical category tags based on the search information (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method);
determining a plurality of prepositive vertical category tags from the vertical category tags in accordance with the priority order (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
taking what is resulted after mixing and sorting the plurality of prepositive vertical category tags and the plurality of recommended search terms as prepositive filter items of the sequence of filter items, and taking the vertical category tags other than the prepositive vertical category tags among the vertical category tags as postpositive filter items of the sequence of filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method);
wherein the prepositive filter items are used for being arranged before the postpositive filter items (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 19, Walker in view of Katardjiev and in further view of Chandra discloses the non-transitory computer-readable storage medium according to claim 18, wherein the determining the priority order of the vertical category tags based on the search information comprises:
determining the priority order of the vertical category tags based on consumption data of multimedia content under a search category to which the search information belongs (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 20, Walker in view of Katardjiev and in further view of Chandra discloses the non-transitory computer-readable storage medium according to claim 18, wherein the determining the plurality of prepositive vertical category tags from the vertical category tags in accordance with the priority order comprises:
selecting a first number of first vertical category tags which are top-ranked, in an order from high to low of the priority order (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method); and
performing a union process on the first number of first vertical category tags and a plurality of preset second vertical category tags, and taking the vertical category tags in the resulted union as the plurality of prepositive vertical category tags (Walker in [0150] and [0159] and in Figures 6C and 7 discloses receiving a search information in a search bar and generating a pop-up presented with keyword suggestions, keyword suggestions including separate clusters, presenting a cluster of most frequent keywords and/or extracted metadata tags and cluster of most frequent tags, any suitable metric can be used to identify top keywords or metadata tags, user can select one of the suggestions to execute a search of the selected keyword or tags; Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
With respect to claim 21, Walker in view of Katardjiev and in further view of Chandra discloses the method according to claim 1, wherein the vertical category tags comprise pictures, videos, short videos, and users (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds; Chandra in [0074] and [0161] discloses a user may search for content based on tags, which is a user assigned, relevant keyword or term associated with or assigned to a piece of information, like a picture, article, or video clip, user can add personalized tags such as “My tags”).
With respect to claim 22, Walker in view of Katardjiev and in further view of Chandra discloses the electronic device according to claim 10, wherein the vertical category tags comprise pictures, videos, short videos, and users (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds; Chandra in [0074] and [0161] discloses a user may search for content based on tags, which is a user assigned, relevant keyword or term associated with or assigned to a piece of information, like a picture, article, or video clip, user can add personalized tags such as “My tags”).
With respect to claim 23, Walker in view of Katardjiev and in further view of Chandra discloses the non-transitory computer-readable storage medium according to claim 16, wherein the vertical category tags comprise pictures, videos, short videos, and users (Walker in [0069] and [0073] discloses extracting content or action tags from video, audio is analyzed and terms are stored as searchable metadata tags associated with corresponding video segments, storing action tags corresponding to extracted software events, a metadata panel showing transcribed audio and extracted metadata tags for each video segment or cluster of clip atoms; Walker in [0148] discloses related content tags such as outdoor, sunset, sky, sun, clouds; Chandra in [0074] and [0161] discloses a user may search for content based on tags, which is a user assigned, relevant keyword or term associated with or assigned to a piece of information, like a picture, article, or video clip, user can add personalized tags such as “My tags”).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walker (US Pub 2022/0075513) in view of Katardjiev (US Pub 2019/0087435) in view of Chandra (US Pub 2022/0164403) and in further view of Bagwell (US Pub 2016/0012019).
With respect to claim 7, Walker in view of Katardjiev and in further view of Chandra discloses the method according to claim 1, wherein prior to mixing and sorting the plurality of recommended search terms and vertical category tags, the method further comprises:
performing a…match on the plurality of recommended search terms and the vertical category tags, and determining whether there is a recommended search term which is…matched with the vertical category tag among the plurality of recommended search terms (Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method; here Walker and Katardjiev do not explicitly disclose performing a semantic match, but the Bagwell reference discloses the features, as discussed below); and
if yes, deleting the recommended search term which is…matched with the vertical category tag (Katardjiev in [0011] and [0012] discloses obtaining recommendations, correlate the recommendations with tags, correlate the representative items with the tags, sorting the tags, provide the recommendations and tags; Katardjiev in [0053] and [0054] discloses displaying recommendation list including recommended items and tag list including tags, each tag has associated therewith a number of representative items from user’s viewing, watching or other history, tag engine queries the user’s activity database and includes items, tag engine sorts and ranks to recommendations in the list based on a recommended order, likelihood of watching, or activity date; Katardjiev in [0052] and [0088] discloses removing any recommended items that contain tags specified in an excluded list, correlate and update based on inclusion and exclusion lists; Katardjiev in [0055] and [0083] and in Figures 6A and 6B discloses tag engine returns both the recommendations list including the recommended items and the tag list, displaying a row of recommendations and a list of tags, which can be adjusted based on user criteria; Katardjiev in [0080] and [0082] discloses assigning importance to all or some of the tags by giving them a value or sorting them in order of most to least important, or by using a ranking method).
Katardjiev discloses performing a match of recommended items or tags with an exclusion and/or inclusion list and deleting based on the match, however, Walker, Katardjiev, and Chandra do not explicitly disclose:
performing a semantic match…which is semantically matched with the…tag;
The Bagwell reference discloses performing a semantic match which is semantically matched with the tag (Bagwell in [0055], [0070], and [0074] discloses suppressing or ignoring duplicate or near duplicate tags corresponding to the same document, merge or combine duplicate tags or near duplicate tags with similar semantic meaning, determine calculated semantic distance between existing tags and new tag based on a distance threshold, merge, filter, suppress, delete, or ignore the selected new tag based on calculated semantic distance, semantic distance calculated based on closest matching tag subject matter topic).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Walker, Katardjiev, Chandra, and Bagwell, to have combined Walker, Katardjiev, Chandra, and Bagwell. The motivation to combine Walker, Katardjiev, Chandra, and Bagwell would be to suppress or ignore duplicate or near duplicate keywords by identifying keyword strings with similar semantic meaning (Bagwell: [0055] and [0057]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.M/Examiner, Art Unit 2159 /MARC S SOMERS/Primary Examiner, Art Unit 2159
