DETAILED ACTION
This action is in response to a filing filed on February 11th, 2025. Claims 1-20 have been examined in this application.
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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 1-16 is/are drawn to method (i.e., a process), and Claims 17-20 is/are drawn to system (i.e., a manufacture). (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Claim 17: An electronic device, comprising:
a processor; and a memory, wherein the memory stores computer-executable instructions, and wherein the computer-executable instructions, when executed by the process, cause the processor to:
display at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label, wherein the content consumption feature label is generated based on historical consumption data of the publisher for the media content, and wherein the content consumption feature label corresponds to different content consumption feature dimensions;
determine at least one target content consumption feature label in at least one target content consumption feature dimension based on associated data corresponding to the recommended content;
and display the at least one target content consumption feature label in the recommended content.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
The independent claims recite displaying recommended media content, using historical consumption data associated with a publisher to generate a content consumption feature label, determining a target label in a target feature dimension based on associated data corresponding to the recommended content, and displaying the selected label with the recommended content. Claim 16 similarly recites acquiring recommended content, determining a content consumption feature label associated with a user based on historical user-consumption data, determining a target label in a target feature dimension based on the recommended content and a publishing scene, and publishing the recommended content with the label carried in the content. Claim 17 recites an electronic device that performs substantially the same information-gathering, analysis, label-selection, and display operations. Under the broadest reasonable interpretation these limitations amount to organizing and presenting recommendation information by obtaining content/recommendation information, evaluating user or publisher consumption behavior, selecting a descriptive label/reason from a category or feature dimension, and presenting the recommended content with that selected label. Such steps can be characterized as mental processes and/or certain methods of organizing human activity, including recommending media content, categorizing content by user or publisher behavior, and presenting an explanatory reason for the recommendation. The claims do not recite a specific improvement to computer functionality or a particular technological solution to a computer problem. Rather, the computer components are used as tools to perform generic data collection, analysis, selection, and display of recommendation information. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
The dependent claims do not recite a different abstract idea, but instead further limit the abstract idea identified in the independent claims. Claims 2-3, 6-7 and 18-20 further recite determining feature information, acquiring parameter information, determining similarity information, selecting a target content consumption feature dimension, and selecting a target content consumption feature label based on feature information. These limitations are directed to the abstract idea of collecting and evaluating information about recommended content, comparing that information to content-consumption categories or labels, and selecting a descriptive recommendation label based on the comparison. Accordingly, these claims recite a mental process because the claimed operations can be characterized as observing information, evaluating feature relevance, judging similarity, and choosing a label. They also organize human activity by categorizing recommended content according to user or publisher consumption preferences and recommendation reasons. Claims 4-5 further specify that the associated data may include recommendation text, a recommendation image, or a target interactive topic, and that the system identifies a text keyword, an image feature, or a keyword associated with the recommended content. These limitations are directed to the abstract idea of analyzing recommendation-related text, images, or topics to identify descriptive features for classifying and labeling recommended content. Identifying a keyword in text, identifying an image feature, and identifying a topic-related keyword are forms of information observation and classification. These are mental processes because a person could review text, images, or topics and identify descriptive keywords or visual features. Claims 8-10 further recite displaying interactive topic content in a preset page, responding to a user trigger operation, displaying a content display page, displaying recommended content related to a topic, and displaying a preset content recommendation list including a recommendation topic. These claims fall within certain methods of organizing human activity because they manage how users interact with recommendation topics, content lists, and related content displays. They also involve mental processes because the underlying operation is selecting and presenting related information based on a topic or user-triggered interest. Claims 11-13 further recite acquiring historical consumption data of a publisher, determining browsing data and interaction data, using browsing duration, comment content, recommended content published by a publisher, acquiring associated consumption data of a user, determining related browsing behavior between a user and a publisher, and determining a label based on browsing or interaction data. The claimed browsing data, interaction data, comment content, historical consumption data, browsing duration, and related browsing behavior are all types of human activity or preference information used to classify and explain content recommendations. These limitations fall within certain methods of organizing human activity because they organize social, browsing, recommendation, and interaction behavior between users, publishers, and content. They also recite mental processes because they involve observing activity, evaluating behavioral relevance, comparing relationships, and judging which label best describes the recommendation. Claims 14-15 apply the recommendation-label concept to book media content and video media content. The claims further recite recommendation association information such as a book or video to be recommended, recommendation data generated by a publisher, or an interactive topic associated with the recommended content. These limitations are directed to the abstract idea of organizing and presenting recommended content and recommendation topics in a user interface, and showing related content in response to user selection.. Thus, the dependent claims do not add a different abstract idea or recite a technological improvement. Accordingly, the dependent claims do not recite a different abstract idea but instead further define the same abstract idea using generic computer functionality, which falls within a judicial exception under 35 U.S.C. §101.
Independent claim(s) 1 and 16 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claims 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using an artificial intelligence (AI) model, a processor, etc. (Claims 1, 16, and 17) is/are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of using an electronic device, a processor, memory, etc. (Claims 1, 16, and 17 and dependent claims 2-15, and 17-20) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., display, determine, etc. steps performed by using an electronic device, a processor, memory, etc.). This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element(s) recited in claims, beyond the abstract idea, do not integrate the abstract idea into a practical application, as the claims recites a generic electronic device including a processor and memory storing computer-executable instructions for performing generic recommendation-label activity, including displaying recommended content, determining a content consumption feature label based on associated data corresponding to the recommended content, determining a target content consumption feature label in a target content consumption feature dimension, and displaying the target content consumption feature label in the recommended content. These limitations amount to routine data gathering, data analysis/classification, label selection, and data output/display. For example, using associated data corresponding to the recommended content constitutes mere data gathering performed before or during the abstract idea of determining which explanatory recommendation label should be selected. Determining a content consumption feature label and a target content consumption feature label merely analyzes and classifies recommendation-related information according to a selected feature dimension. Displaying the recommended content and displaying the selected target content consumption feature label merely outputs the result of the abstract recommendation-label analysis to a user. Accordingly, these additional elements do not integrate the abstract idea into a practical application and constitute insignificant extra-solution activity (Independent Claims 1, 16, and 17), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claims 2-3, 6-7, and 18-20 merely further specify determining feature information, acquiring parameter information, determining similarity information, selecting a target content consumption feature dimension, and selecting a target content consumption feature label based on feature information. These limitations amount to collecting content-related information, comparing or classifying the information, and selecting a descriptive recommendation label, which are part of the abstract idea of analyzing recommendation information and selecting an explanatory label. Claims 4-5 merely identify a text keyword, image feature, or keyword associated with a target interactive topic. These limitations amount to extracting descriptive information from recommendation text, images, or topics, which is routine information identification and classification used as input to the abstract recommendation-label selection process. Claims 8-10 merely display interactive topic content in a page, respond to a user trigger operation, display a content display page, display related recommended content, and display a preset content recommendation list including a recommendation topic. Claims 11-13 merely acquire publisher historical consumption data, determine browsing data and interaction data, use browsing duration, comment content, recommended content published by a publisher, user-associated consumption data, and related browsing behavior between a user and publisher to determine a content consumption feature label. These limitations amount to collecting user/publisher behavior information, classifying browsing and interaction activity, and using that behavioral information to select a recommendation label, which is part of the abstract idea of evaluating consumption-history and interaction data to explain a recommendation. Claims 14-15 merely apply the recommendation-label concept to book media content and video media content and recite recommendation association information such as a book or video to be recommended, publisher-generated recommendation data, or an interactive topic associated with the recommended content. These additional limitations are collecting and analyzing recommendation-related information to select and present an explanatory content consumption feature label, and post-solution commercial activity. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent Claims 1, 16, and 17 and dependent claims 2-15, and 17-20 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional element(s) of performing generic recommendation-label activity, including displaying recommended content, determining a content consumption feature label based on associated data corresponding to the recommended content, determining a target content consumption feature label in a target content consumption feature dimension, and displaying the target content consumption feature label in the recommended content. These limitations amount to routine data gathering, routine data output, and post-solution transaction processing, additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea), which is similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), “Storing and retrieving information in memory”, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; “Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93, Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own disclosure at paragraph [0040] acknowledges that “The apparatus for displaying content may be coupled to a terminal device, so that at least one target content consumption feature label associated with a publisher of the recommended content may be determined and displayed based on a trigger operation of a user on the terminal device. Alternatively, the apparatus for displaying content may also be coupled to a server in communication connection with the terminal device, so that at least one target content consumption feature label associated with the publisher of the recommended content may be determined and displayed based on the trigger operation of the user on the terminal device, and the terminal device may be controlled to display the recommended content.” This additional element therefore do not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-15 and 17-20 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim).
When viewed as an ordered combination, the additional elements of dependent claims 2-7, 9-14, and 16-20 merely instruct implementation of the abstract idea using generic computer components to receive, analyze, classify, update, store, present, and display commercial inventory information. The claims do not recite any unconventional arrangement of computer components, any specific improvement to AI model architecture or training, any improvement to computer functionality, or any other technological improvement. Instead, the claims recite the use of generic computer functionality and an AI model as tools to perform the commercial inventory-management workflow at a high level of generality. Therefore, the additional elements, individually and as an ordered combination, do not integrate the judicial exception into a practical application and do not amount to significantly more than the abstract idea.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-20 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status:
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6-7, 14-16, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20100042932 (“Lehtiniemi”) in view of U.S. Pat. 8260117 (“Xu”).
As per claims 1 and 17, Lehtiniemi discloses, displaying at least one piece of recommended content for recommending media content (Examiner interprets displaying at least one piece of recommended content for recommending media content because Lehtiniemi teaches providing a graphical symbol indicative of recommendation criteria in association with a recommended content item so that a user can quickly learn why the content item is being recommended. Lehtiniemi further teaches that the disclosed media content is not limited to music and may include movies, books, articles, texts, journals, videos, maps, games, television or radio programs, internet links, files, and the like.) (“provide a mechanism by which a user can quickly learn information regarding why a particular content item is being recommended to the user. In this regard, for example, some embodiments of the present invention may provide for the delivery of a graphical symbol that may be indicative of recommendation criteria to be provided to the user in association with the particular content item. The graphical symbol may, in some instances, be overlaid on top of a graphical representation associated with the content item (e.g., album cover art). However, graphical symbols may also be provided in association with items in a list format. Although an example embodiment will be described below primarily in the context of music related content items, some embodiments could be practiced in the context of other media content items such as movies, books, articles, texts, journals, videos, maps, games, television or radio programs or channels, internet links or sites, files, and/or the like”) (0018, 0024), wherein a publisher of the recommended content is associated with at least one content consumption feature label (Examiner interprets that a publisher/recommender/entity associated with the recommended content is associated with a content consumption feature label because Lehtiniemi’s symbol database stores graphical symbols corresponding to recommendation criteria, including avatars, pictures, symbols, or other indicia associated with a particular individual or entity recommending a content item. Lehtiniemi further teaches that recommendation manager 218 displays recommendation criteria in association with recommended content and provides a graphical symbol to indicate the reason for the recommendation) (“symbol database 236 may include a plurality of graphical symbols in which each symbol corresponds to a particular recommendation criterion. Thus, for example, recommendation criteria indicative of music considered desirable or good for listening while driving may include an image of a car, while a bicycle graphic may be indicative of music considered desirable for listening while bicycling. The graphical symbols may also include avatars, pictures, symbols or other indicia associated with a particular individual or entity recommending a particular content item. For example, if a friend recommends a content item, an image of the friend may be stored in the symbol database 236 for use in identifying content recommended by the friend” and “recommendation manager 218 may be configured to generate an indication of recommendation criteria to be displayed in association with recommended content. In this regard, for example, the recommendation manager 218 may be configured to display (or provide for display of) recommendation criteria for filtered or selected content items associated with a recommendation to be communicated to a user or recommended content to be served to or identified for the user. The recommendation criteria may be indicated by the graphical symbols of the symbol database 236. Thus, for example, a particular file may be played by or provided to a device of the user (e.g., a mobile terminal). Along with the particular file, a graphical symbol may be provided to indicate the reason for the recommendation based on the recommendation criteria”) (0037 and 0043),
and displaying the at least one target content consumption feature label in the recommended content (Examiner interprets displaying the label in the recommended content because the recommendation criteria may be provided as a graphical overlay on album art or other graphical representation associated with the recommended content item, including the example of FIGS. 3A-3B where graphical symbol 302 is overlaid on the album-cover image) (“the recommendation criteria via the graphical symbol, the recommendation manager 218 may provide further information along with the indication of the recommendation criteria. For example, the recommendation manager 218 may access the graphics database 240 to access album art associated with a particular music content item and provide the recommendation criteria as a graphical overlay on the album art. FIG. 3A shows an example of an album cover (e.g., a graphical representation 300 or other visual content associated with a content item being recommended) that may correspond to a particular music content item in accordance with an example embodiment of the invention. FIG. 3B shows the album cover as modified in accordance with an example embodiment of the invention. In this regard, as seen in FIG. 3B, a graphical symbol 302 is included as an overlay on top of the album cover image forming the graphical representation 300 of FIG. 3A. In FIG. 3B, one of the faces of a band member has been replaced (or covered) with a face of the individual recommending the content item to the user”) (0046, also see claims 9 and 11).
Lehtiniemi discloses, specifically doesn’t disclose, wherein the content consumption feature label is generated based on historical consumption data of the publisher for the media content, and wherein the content consumption feature label corresponds to different content consumption feature dimensions, however Xu discloses, wherein the content consumption feature label is generated based on historical consumption data of the publisher for the media content (Examiner interprets generating recommendation explanations based on historical consumption and viewing behavior, including a user having watched, browsed to, or highly rated a video, and teaches collaborative-filtering inputs based on user opinions or behavior and viewing history) (“Video-to-video recommendations refer to video recommendations that are based on a user's indication of interest in a particular video. For example, if a user has watched, browsed to, or highly rated a particular video, it may be assumed that particular video is of interest to the user. Based on that interest, a recommendation system may recommend other videos to the user. For the purpose of explanation, the video in which the user has indicated an interest is referred to herein as the "compared" video.”) (Col. 3 Ln. 21-30), and wherein the content consumption feature label corresponds to different content consumption feature dimensions (Examiner interprets content consumption feature dimensions because Xu uses content-based filtering inputs based on similarity between videos and feature information from videos recently viewed, browsed, or rated by a user, including title, genre, duration, actors, keywords, and tags) (“Content-based filtering inputs 110 are any inputs that relate to how similar the content of the target video is to the content of other videos. For the purpose of explanation, an embodiment shall be described in which video-to-video recommendations are generated. Thus, the content-based filtering inputs focus on the similarity between a video pair comprising the target video and the compared video. However, in alternative embodiments, the content-based filtering inputs 110 may include feature information for any number of other videos, such as the set of videos that the target user has recently viewed, browsed, or rated.”) (Col. 4 Ln. 30-40);
determining at least one target content consumption feature label in at least one target content consumption feature dimension based on associated data corresponding to the recommended content (Examiner interprets determining a target content consumption feature label in a target dimension because Xu selects an explanation based on the features that most positively affected the ML score, including explanations such as “This video is similar to X,” “Viewers of X frequently view this video,” “This video is one of the most viewed videos among all users,” and explanations based on genre, actors, theme, tags, duration, or music score.) (“Based on the features that most positively affected the ML score 150, indicated by output 152, the recommendation system may select a "explanation" for why the corresponding video was recommended to the target user (i.e. why the ML score 150 was sufficiently high to justify recommending the target video). For example: If the CBF score 130 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "This video is similar to X" (where X is the compared video). If the CF score 124 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "Viewers of X frequently view this video" (where X is the compared video). If the other inputs 112 indicate that the target video is one of the most viewed videos among the entire population of viewers, and this feature had a significant effect on the ML score 150…”) (Col. 6 Ln. 7-40).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, wherein the content consumption feature label is generated based on historical consumption data of the publisher for the media content, and wherein the content consumption feature label corresponds to different content consumption feature dimensions and displaying the at least one target content consumption feature label in the recommended content, as taught by Xu for the purpose for to determine the reason for a recommendation based on the feature or feature dimension that most positively affected a recommendation score where the a recommendation display that selects and displays the most relevant content-consumption feature label, thereby improving transparency, user understanding, and perceived relevance of the recommendation.
As per claims 2 and 18, Lehtiniemi discloses, wherein determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the associated data corresponding to the recommended content comprises: determining feature information corresponding to the associated data (Examiner interprets “feature information” broadly as metadata, recommendation criteria, category information, content features, user-history features, or extracted features associated with the recommended content, including genre, artist, context, mood, title, actor, keyword, tag, duration, similarity information, or recommendation score-contribution information.) (“determining the recommendation criteria may include determining the recommendation criteria based on metadata associated with the recommended content item or determining with which category a particular music content item corresponds in relation to at least one of genre, artist, context or mood”) (0062).
Lehtiniemi discloses, specifically doesn’t disclose, determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information, however Xu discloses, and determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information (Examiner interprets determining the target content consumption feature label based on feature information because Xu selects an explanation for why a video was recommended based on features that most positively affected the ML score.) (“Based on the features that most positively affected the ML score 150, indicated by output 152, the recommendation system may select a "explanation" for why the corresponding video was recommended to the target user (i.e. why the ML score 150 was sufficiently high to justify recommending the target video). For example: If the CBF score 130 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "This video is similar to X" (where X is the compared video). If the CF score 124 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "Viewers of X frequently view this video" (where X is the compared video). If the other inputs 112 indicate that the target video is one of the most viewed videos among the entire population of viewers, and this feature had a significant effect on the ML score 150…”) (Col. 6 Ln. 7-40).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information, as taught by Xu for the purpose for selecting which criterion or explanation should be displayed based on the features most responsible for the recommendation score.
As per claims 3, Lehtiniemi discloses, and determining the feature information associated with the at least one piece of media content based on the first parameter information, wherein the feature information comprises category information, creator information, and content type information corresponding to the at least one piece of media content (Examiner interprets “first parameter information” as basic content data and historical interaction data, including content metadata, video metadata, user logs, viewing history, browsing history, rating history, interested-user lists, CF metrics, CBF metrics, and video-pair metrics. “Category information” reads on genre/tag/context/topic; “creator information” reads on artist/actor/director/author/source; and “content type information” reads on media type, title, duration, metadata, or classification) (“determining the recommendation criteria may include determining the recommendation criteria based on metadata associated with the recommended content item or determining with which category a particular music content item corresponds in relation to at least one of genre, artist, context or mood”) (0062).
Lehtiniemi specifically doesn’t disclose, one piece of media content to be recommended that corresponds to the recommended content, acquiring first parameter information associated with the at least one piece of media content, wherein the first parameter information comprises basic information and historical interaction information corresponding to the at least one piece of media content and determining the feature information associated with the at least one piece of media content based on the first parameter information, however Xu discloses, wherein the associated data comprises at least one piece of media content to be recommended that corresponds to the recommended content (Examiner interprets that the data comprising at least one piece of media content corresponding to the recommended content because Xu’s video-to-video recommendation uses a target video and a compared video, where the compared video is a video in which the user has indicated interest) (“Video-to-video recommendations refer to video recommendations that are based on a user's indication of interest in a particular video. For example, if a user has watched, browsed to, or highly rated a particular video, it may be assumed that particular video is of interest to the user. Based on that interest, a recommendation system may recommend other videos to the user. For the purpose of explanation, the video in which the user has indicated an interest is referred to herein as the "compared" video.”) (Col. 3 Ln. 21-30);
acquiring first parameter information associated with the at least one piece of media content, wherein the first parameter information comprises basic information and historical interaction information corresponding to the at least one piece of media content (Examiner interprets that acquiring first parameter information including basic information and historical interaction information because Xu stores user logs, video metadata, interested-user lists, CF metrics, CBF metrics, and video-pair metrics, and folds user behavior into a user history summarizing historical watching behavior in a user/video/intensity tuple) (“according to one embodiment, the recommendation system incrementally folds new user behavior data into a user history that summarizes all historical watching behavior in the form of a (user, video, intensity) tuple. In addition, the recommendation system distinguishes new information from old information, and only updates the video-pair-centric metrics 244 of video pairs that have new user activities … ne embodiment, the video-centric CF metrics for a particular video are only updated in response to an new expression of interest in the particular video. Similarly, the video-pair-centric metrics for a particular video pair are only updated in response to a new expression of interest in at least one of the videos that belong to the video pair. Because many videos may experience long periods during which no user expresses an interest, the percentage of video-centric metrics that need to recalculated is reduced, thereby lowering the processing required to maintain the video-centric metrics 242 and video-pair-centric metrics 244 up to date in real time.”) (Col. 9 Ln. 20-59);
and determining the feature information associated with the at least one piece of media content based on the first parameter information (Examiner interprets determining feature information including category, creator, and content-type information because Xu uses title, genre, duration, actors, keywords, and tags, and Lehtiniemi uses metadata such as genre, artist, context, and mood) (“Content-based filtering inputs 110 are any inputs that relate to how similar the content of the target video is to the content of other videos. For the purpose of explanation, an embodiment shall be described in which video-to-video recommendations are generated. Thus, the content-based filtering inputs focus on the similarity between a video pair comprising the target video and the compared video. However, in alternative embodiments, the content-based filtering inputs 110 may include feature information for any number of other videos, such as the set of videos that the target user has recently viewed, browsed, or rated.”) (Col. 4 Ln. 30-40).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, one piece of media content to be recommended that corresponds to the recommended content, acquiring first parameter information associated with the at least one piece of media content, wherein the first parameter information comprises basic information and historical interaction information corresponding to the at least one piece of media content and determining the feature information associated with the at least one piece of media content based on the first parameter information, as taught by Xu for the purpose to use video metadata, user-log, and historical interaction data as the feature information thus to enrich recommendation criteria using historical viewing behavior and content-based features, thereby improving the accuracy and relevance of the displayed recommendation label.
As per claims 6, Lehtiniemi specifically doesn’t disclose, determining, for each content consumption feature label, similarity information between the content consumption feature label and the feature information; and determining at least one content consumption feature label with the similarity information satisfying a preset condition as the at least one target content consumption feature label, however Xu discloses, wherein determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information comprises (Examiner interprets that the) (Examiner interprets “similarity information” as a score, comparison, overlap, match, or distance between feature information and a candidate label/dimension. The Examiner interprets “preset condition” as a threshold, high score, heavily weighted feature, sufficient similarity, or feature contribution sufficient to select an explanation/label) (“Content-based filtering inputs 110 are any inputs that relate to how similar the content of the target video is to the content of other videos. For the purpose of explanation, an embodiment shall be described in which video-to-video recommendations are generated. Thus, the content-based filtering inputs focus on the similarity between a video pair comprising the target video and the compared video. However, in alternative embodiments, the content-based filtering inputs 110 may include feature information for any number of other videos, such as the set of videos that the target user has recently viewed, browsed, or rated.”) (Col. 4 Ln. 30-40);
determining, for each content consumption feature label, similarity information between the content consumption feature label and the feature information (Examiner interprets determining similarity information because Xu’s CBF inputs relate to how similar the target video is to other videos, including similarity between a target video and compared video based on features such as title, genre, duration, actors, keywords, and tags) (“Content-based filtering inputs 110 are any inputs that relate to how similar the content of the target video is to the content of other videos. For the purpose of explanation, an embodiment shall be described in which video-to-video recommendations are generated. Thus, the content-based filtering inputs focus on the similarity between a video pair comprising the target video and the compared video. However, in alternative embodiments, the content-based filtering inputs 110 may include feature information for any number of other videos, such as the set of videos that the target user has recently viewed, browsed, or rated.”) (Col. 4 Ln. 30-59);
and determining at least one content consumption feature label with the similarity information satisfying a preset condition as the at least one target content consumption feature label (Examiner interprets selecting a label satisfying a preset condition because Xu selects an explanation when a score or feature strongly affects the ML score, such as where the CBF score is high and heavily weighted, where the CF score is high and heavily weighted, or where popularity/interest-burst inputs significantly affect the ML score) (“Based on the features that most positively affected the ML score 150, indicated by output 152, the recommendation system may select a "explanation" for why the corresponding video was recommended to the target user (i.e. why the ML score 150 was sufficiently high to justify recommending the target video). For example: If the CBF score 130 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "This video is similar to X" (where X is the compared video). If the CF score 124 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "Viewers of X frequently view this video" (where X is the compared video). If the other inputs 112 indicate that the target video is one of the most viewed videos among the entire population of viewers, and this feature had a significant effect on the ML score 150, then the user may be presented with the explanation: "This video is one of the most viewed videos among all users". If the other inputs 112 indicate that the target video is currently experience an interest boost, and this feature had a significant effect on the ML score 150”) (Col. 6 Ln. 7-40).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label, as taught by Lehtiniemi, determining, for each content consumption feature label, similarity information between the content consumption feature label and the feature information; and determining at least one content consumption feature label with the similarity information satisfying a preset condition as the at least one target content consumption feature label, as taught by Xu for the purpose to use similarity-score and threshold/weight-based explanation selection to provide the display mechanism for presenting that explanation as a graphical recommendation label.
As per claims 7, Lehtiniemi specifically doesn’t disclose, determining, from a plurality of content consumption feature dimensions, a target content consumption feature dimension matching at least partial feature information based on the at least partial feature information; and determining, from the at least one content consumption feature label corresponding to the target content consumption feature dimension, at least one target content consumption feature label matching remaining feature information based on the remaining feature information, however Xu discloses, wherein determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information comprises (Examiner interprets “determining, from a plurality of content consumption feature dimensions, a target content consumption feature dimension” as selecting one reason category from multiple possible recommendation-reason categories, such as CBF similarity, CF viewer behavior, popularity, interest burst, genre, actors, theme, tags, duration, or viewing-history-based interest.) (“Based on the features that most positively affected the ML score 150, indicated by output 152, the recommendation system may select a "explanation" for why the corresponding video was recommended to the target user (i.e. why the ML score 150 was sufficiently high to justify recommending the target video). For example: If the CBF score 130 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "This video is similar to X" (where X is the compared video). If the CF score 124 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "Viewers of X frequently view this video" (where X is the compared video). If the other inputs 112 indicate that the target video is one of the most viewed videos among the entire population of viewers, and this feature had a significant effect on the ML score 150, then the user may be presented with the explanation: "This video is one of the most viewed videos among all users". If the other inputs 112 indicate that the target video is currently experience an interest boost, and this feature had a significant effect on the ML score 150”) (Col. 6 Ln. 7-40);
determining, from a plurality of content consumption feature dimensions, a target content consumption feature dimension matching at least partial feature information based on the at least partial feature information (Examiner interprets selecting a target dimension from multiple dimensions because Xu considers multiple inputs and score components, including content-based filtering, collaborative filtering, popularity, interest burst, and other inputs. Xu then selects an explanation corresponding to the feature or score component that most positively affected the ML score) (“Based on the features that most positively affected the ML score 150, indicated by output 152, the recommendation system may select a "explanation" for why the corresponding video was recommended to the target user (i.e. why the ML score 150 was sufficiently high to justify recommending the target video). For example: If the CBF score 130 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "This video is similar to X" (where X is the compared video). If the CF score 124 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "Viewers of X frequently view this video" (where X is the compared video). If the other inputs 112 indicate that the target video is one of the most viewed videos among the entire population of viewers, and this feature had a significant effect on the ML score 150, then the user may be presented with the explanation: "This video is one of the most viewed videos among all users". If the other inputs 112 indicate that the target video is currently experience an interest boost, and this feature had a significant effect on the ML score 150”) (Col. 6 Ln. 7-40);
and determining, from the at least one content consumption feature label corresponding to the target content consumption feature dimension, at least one target content consumption feature label matching remaining feature information based on the remaining feature information (Examiner interprets selecting a label within the selected dimension because the selected explanation changes depending on the selected dimension: CBF similarity leads to “This video is similar to X,” CF behavior leads to “Viewers of X frequently view this video,” popularity leads to “This video is one of the most viewed videos among all users,” and interest burst leads to a “new and hot” style explanation) (“Based on the features that most positively affected the ML score 150, indicated by output 152, the recommendation system may select a "explanation" for why the corresponding video was recommended to the target user (i.e. why the ML score 150 was sufficiently high to justify recommending the target video). For example: If the CBF score 130 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "This video is similar to X" (where X is the compared video). If the CF score 124 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "Viewers of X frequently view this video" (where X is the compared video). If the other inputs 112 indicate that the target video is one of the most viewed videos among the entire population of viewers, and this feature had a significant effect on the ML score 150, then the user may be presented with the explanation: "This video is one of the most viewed videos among all users". If the other inputs 112 indicate that the target video is currently experience an interest boost, and this feature had a significant effect on the ML score 150”) (Col. 6 Ln. 7-40).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label, as taught by Lehtiniemi, determining, from a plurality of content consumption feature dimensions, a target content consumption feature dimension matching at least partial feature information based on the at least partial feature information; and determining, from the at least one content consumption feature label corresponding to the target content consumption feature dimension, at least one target content consumption feature label matching remaining feature information based on the remaining feature information, as taught by Xu for the purpose for choosing a recommendation explanation based on the most influential feature dimension resulted in displayed label that corresponds to the most relevant selected dimension.
As per claims 14, Lehtiniemi discloses, wherein the method further comprises: displaying at least one piece of recommended content for recommending book media content, wherein the publisher of the recommended content is associated with at least one content consumption feature label related to a browsing behavior for the book media content (Examiner interprets “book media content” broadly as digital book content, articles, texts, journals, or other written media. The Examiner interprets “recommendation association information” as information connecting the recommendation to a book/content item, publisher-generated recommendation data, metadata, topic, source, or interactive topic.) (“users may desire to know some information about recommended content items. Accordingly, embodiments of the present invention may provide a mechanism by which a user can quickly learn information regarding why a particular content item is being recommended to the user. In this regard, for example, some embodiments of the present invention may provide for the delivery of a graphical symbol that may be indicative of recommendation criteria to be provided to the user in association with the particular content item. The graphical symbol may, in some instances, be overlaid on top of a graphical representation associated with the content item (e.g., album cover art). However, graphical symbols may also be provided in association with items in a list format. Although an example embodiment will be described below primarily in the context of music related content items, some embodiments could be practiced in the context of other media content items such as movies, books, articles, texts, journals, videos, maps, games, television or radio programs or channels, internet links or sites, files, and/or the like.”) (0018);
determining at least one target content consumption feature label in at least one target content consumption feature dimension based on recommendation association information associated with each piece of recommended content, wherein the recommendation association information comprises at least one of the followings: at least one book to be recommended in the recommended content, recommendation data generated by the publisher based on the at least one book to be recommended, and an interactive topic associated with the at least one piece of recommended content (Examiner interprets that recommendation association information because the search/filter engine parses recommendation messages and provides corresponding recommendation information to recommendation manager 218 so that a graphical recommendation criterion may be generated) (“the search/filter engine 220 may be configured to parse recommendation messages in the recommendation database 234 for a particular user and, for recommendation messages that are indicative of a recommendation that complies with the user's profile, provide information to the recommendation manager 218 regarding the corresponding recommendation (e.g., an indication of a recommendation) to enable the recommendation manager 218 to generate an indication of recommendation criteria (e.g., via a graphical symbol associated with the recommendation criteria) for the corresponding recommendation. However, in some cases, compliance with the user's profile may not be a factor. The indication may then be served to the user or posted generally in a manner accessible to the user. In other words, in some embodiments, the search/filter engine 220 may be configured to filter recommendations from other users or entities regarding recommendations, in some cases further based on a particular user's interests (e.g., as specified in the user profile database 230). In alternative embodiments, the search/filter engine 220 may be configured to search information available via a service and generate indications of recommendations for use by the recommendation manager 218 as described below …”) (0041-0045);
and displaying the at least one target content consumption feature label in a display region associated with the recommended content, so that a user views the recommended content based on the at least one target content consumption feature label (Examiner interprets displaying the label in a display region associated with the recommended content because graphical symbols may be overlaid on graphical representations or displayed in recommended-content lists.) (“providing the indication of the recommendation criteria via the graphical symbol, the recommendation manager 218 may provide further information along with the indication of the recommendation criteria. For example, the recommendation manager 218 may access the graphics database 240 to access album art associated with a particular music content item and provide the recommendation criteria as a graphical overlay on the album art. FIG. 3A shows an example of an album cover (e.g., a graphical representation 300 or other visual content associated with a content item being recommended) that may correspond to a particular music content item in accordance with an example embodiment of the invention. FIG. 3B shows the album cover as modified in accordance with an example embodiment of the invention. In this regard, as seen in FIG. 3B, a graphical symbol 302 is included as an overlay on top of the album cover image forming the graphical representation 300 of FIG. 3A. In FIG. 3B, one of the faces of a band member has been replaced (or covered) with a face of the individual recommending the content item to the user …”) (0046-0051).
As per claims 15, Lehtiniemi discloses, and displaying the at least one target content consumption feature label in a display region associated with the recommended content, so that a user views the recommended content based on the at least one target content consumption feature label (Examiner interprets “video media content” as video assets, movies, television shows, home videos, how-to videos, music videos, streamed videos, or other video content. The Examiner interprets “recommendation association information” as target video, compared video, recommendation score, CF/CBF data, metadata, or explanation data.) (“providing the indication of the recommendation criteria via the graphical symbol, the recommendation manager 218 may provide further information along with the indication of the recommendation criteria. For example, the recommendation manager 218 may access the graphics database 240 to access album art associated with a particular music content item and provide the recommendation criteria as a graphical overlay on the album art. FIG. 3A shows an example of an album cover (e.g., a graphical representation 300 or other visual content associated with a content item being recommended) that may correspond to a particular music content item in accordance with an example embodiment of the invention. FIG. 3B shows the album cover as modified in accordance with an example embodiment of the invention. In this regard, as seen in FIG. 3B, a graphical symbol 302 is included as an overlay on top of the album cover image forming the graphical representation 300 of FIG. 3A. In FIG. 3B, one of the faces of a band member has been replaced (or covered) with a face of the individual recommending the content item to the user …”) (0046-0051, 0064).
Lehtiniemi specifically doesn’t disclose, determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information, however Xu discloses, wherein the method further comprises: displaying at least one piece of recommended content for recommending video media content, wherein the publisher of the recommended content is associated with at least one content consumption feature label related to a browsing behavior for the video media content (Examiner interprets that teaches video media recommendation, including selecting which video assets to recommend to users based on ML scores generated from collaborative-filtering and content-based-filtering inputs.) (“It has become increasingly common for content, such as video assets, to be delivered to users over a network. Movies, television shows, home videos, how-to videos, and music videos are merely a few examples of the types of video assets that are currently provided over the Internet, telephone networks, intranets, cable connections, etc. Video assets that are obtained over a network can be played after the entire video assets have been downloaded, or as the video assets are being delivered (streamed) … Given the plethora of video assets that are available for consumption, it has become increasingly important to help users identify those video assets that are most likely to interest them. For example, a user will have a much better experience with a system that recommends to the user ten videos in which the user is highly interested, than with a system that merely provides tools for searching for those same ten videos among a million less interesting videos.”) (Col. 1 Ln. 20-55, Col. 3 Ln. 1-20);
determining at least one target content consumption feature label in at least one target content consumption feature dimension based on recommendation association information associated with each piece of recommended content, wherein the recommendation association information comprises at least one of the followings: at least one video to be recommended in the recommended content, recommendation data generated by the publisher based on the at least one video to be recommended, and an interactive topic associated with the at least one piece of recommended content (Examiner interprets browsing behavior for video media content because video-to-video recommendations are based on a user having watched, browsed to, or highly rated a video, and collaborative-filtering inputs are based on user opinions or behavior and viewing history) (“Video-to-video recommendations refer to video recommendations that are based on a user's indication of interest in a particular video. For example, if a user has watched, browsed to, or highly rated a particular video, it may be assumed that particular video is of interest to the user. Based on that interest, a recommendation system may recommend other videos to the user. For the purpose of explanation, the video in which the user has indicated an interest is referred to herein as the "compared" video … Collaborative filtering inputs 108 are any inputs that relate to user opinions or behavior. Collaborative filtering is based on the assumption that two people that have similar opinions about one thing are likely to have similar opinions about another thing. In the context of predicting user interest in video assets, collaborative filtering assumes that if a target user has enjoyed viewing several of the same videos as another user, then the target user is likely to enjoy viewing a video that the other user enjoyed, but that the target user has not yet seen.”) (Col. 3 Ln. 21-60, Col. 4 Ln. 30-59).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, in response to a trigger operation performed by a user for any recommendation topic, displaying a content display page associated with the recommendation topic and displaying the at least one piece of recommended content related to the recommendation topic in the content display page, as taught by Xu for the purpose to allowed a video recommendation to carry a displayed label/reason selected based on video browsing behavior, video metadata, and feature-score contribution.
As per claims 16, Lehtiniemi discloses, acquiring recommended content to be published (Examiner interprets “acquiring recommended content to be published” as receiving, selecting, identifying, or obtaining recommended content for presentation. The Examiner interprets “recommendation data” as recommendation criteria, metadata, score data, explanation data, CF/CBF feature data, topic data, or reason data. The Examiner interprets “publishing scene” as a display context, feed context, post context, recommendation page, content page, playlist, media-service context, or user-interface scenario in which the recommended content is presented) (“method, apparatus and computer program product are therefore provided that may enable the provision of a graphic symbol to indicate recommendation criteria with respect to recommended content items. Thus, for example, a user may be able to know something about a recommend content item based on the recommendation criteria.”) (0004-0005), wherein the recommended content to be published comprises at least one piece of media content to be recommended and recommendation data generated based on the at least one piece of media content to be recommended (Examiner interprets acquiring recommended content to be published because it receives an indication of a recommended content item, determines recommendation criteria, selects a graphical symbol, and provides the symbol for display with the recommended content item) (“symbol database 236 may include a plurality of graphical symbols in which each symbol corresponds to a particular recommendation criterion. Thus, for example, recommendation criteria indicative of music considered desirable or good for listening while driving may include an image of a car, while a bicycle graphic may be indicative of music considered desirable for listening while bicycling. The graphical symbols may also include avatars, pictures, symbols or other indicia associated with a particular individual or entity recommending a particular content item. For example, if a friend recommends a content item, an image of the friend may be stored in the symbol database 236 for use in identifying content recommended by the friend” and “recommendation manager 218 may be configured to generate an indication of recommendation criteria to be displayed in association with recommended content. In this regard, for example, the recommendation manager 218 may be configured to display (or provide for display of) recommendation criteria for filtered or selected content items associated with a recommendation to be communicated to a user or recommended content to be served to or identified for the user. The recommendation criteria may be indicated by the graphical symbols of the symbol database 236. Thus, for example, a particular file may be played by or provided to a device of the user (e.g., a mobile terminal). Along with the particular file, a graphical symbol may be provided to indicate the reason for the recommendation based on the recommendation criteria”) (0037 and 0043-0045);
and publishing the recommended content, wherein the at least one target content consumption feature label is carried in the recommended content (Examiner interprets “in the recommended content” broadly as displaying the label in, on, over, next to, or in association with the recommended content. Lehtiniemi teaches providing recommendation criteria as a graphical overlay on album art associated with a recommended music content item. Lehtiniemi’s FIGS. 3A and 3B show a graphical symbol overlaid on top of album cover art, including an example where a band member’s face is replaced or covered with the face of the individual recommending the content item.) (“the recommendation criteria via the graphical symbol, the recommendation manager 218 may provide further information along with the indication of the recommendation criteria. For example, the recommendation manager 218 may access the graphics database 240 to access album art associated with a particular music content item and provide the recommendation criteria as a graphical overlay on the album art. FIG. 3A shows an example of an album cover (e.g., a graphical representation 300 or other visual content associated with a content item being recommended) that may correspond to a particular music content item in accordance with an example embodiment of the invention. FIG. 3B shows the album cover as modified in accordance with an example embodiment of the invention. In this regard, as seen in FIG. 3B, a graphical symbol 302 is included as an overlay on top of the album cover image forming the graphical representation 300 of FIG. 3A. In FIG. 3B, one of the faces of a band member has been replaced (or covered) with a face of the individual recommending the content item to the user”) (0046-0051, also see claims 9 and 11).
Lehtiniemi discloses, specifically doesn’t disclose, wherein the content consumption feature label is generated based on historical consumption data of the publisher for the media content, and wherein the content consumption feature label corresponds to different content consumption feature dimensions, however Xu discloses, determining at least one content consumption feature label associated with a user (Examiner interprets determining a content consumption feature label associated with a user based on historical consumption data of the user because Xu uses watched, browsed, rated, and viewing-history information to generate recommendations and explanations) (“Based on the features that most positively affected the ML score 150, indicated by output 152, the recommendation system may select a "explanation" for why the corresponding video was recommended to the target user (i.e. why the ML score 150 was sufficiently high to justify recommending the target video). For example: If the CBF score 130 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "This video is similar to X" (where X is the compared video). If the CF score 124 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "Viewers of X frequently view this video" (where X is the compared video). If the other inputs 112 indicate that the target video is one of the most viewed videos among the entire population of viewers, and this feature had a significant effect on the ML score 150, then the user may be presented with the explanation: "This video is one of the most viewed videos among all users". If the other inputs 112 indicate that the target video is currently experience an interest boost, and this feature had a significant effect on the ML score 150, then the user may be presented with the explanation”) (Col. 6 Ln. 7-40), wherein the content consumption feature label is generated based on historical consumption data of the user for the media content (Examiner interprets) (“Video-to-video recommendations refer to video recommendations that are based on a user's indication of interest in a particular video. For example, if a user has watched, browsed to, or highly rated a particular video, it may be assumed that particular video is of interest to the user. Based on that interest, a recommendation system may recommend other videos to the user. For the purpose of explanation, the video in which the user has indicated an interest is referred to herein as the "compared" video.”) (Col. 3 Ln. 21-60);
determining at least one target content consumption feature label in at least one target content consumption feature dimension based on the recommended content and a publishing scene associated with the recommended content (Examiner interprets a target label in a target dimension because the selected explanation depends on which feature dimension most positively affected the ML score, such as CBF similarity, CF viewer behavior, popularity, interest burst, genre, actors, theme, tags, duration, or music score) (“Based on the features that most positively affected the ML score 150, indicated by output 152, the recommendation system may select a "explanation" for why the corresponding video was recommended to the target user (i.e. why the ML score 150 was sufficiently high to justify recommending the target video). For example: If the CBF score 130 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "This video is similar to X" (where X is the compared video). If the CF score 124 is high, and was heavily weighted by the machine learning engine 102, then the user may be presented with the explanation "Viewers of X frequently view this video" (where X is the compared video). If the other inputs 112 indicate that the target video is one of the most viewed videos among the entire population of viewers, and this feature had a significant effect on the ML score 150…”) (Col. 6 Ln. 7-40).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, determining at least one content consumption feature label associated with a user, wherein the content consumption feature label is generated based on historical consumption data of the user for the media content; determining at least one target content consumption feature label in at least one target content consumption feature dimension based on the recommended content and a publishing scene associated with the recommended content, as taught by Xu for the purpose for determining the reason for a recommendation based on the features that most positively affected a recommendation score, including consumption history, viewing history, collaborative-filtering behavior, and content-based feature dimensions hereby improving user understanding of why the recommended content is being presented.
Claims 4-5, 8-13 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20100042932 (“Lehtiniemi”) in view of U.S. Pat. 8260117 (“Xu”) in view of U.S. Pat. 9442989 (“Jackson”)
As per claims 4, Lehtiniemi discloses, and determining the feature information corresponding to the associated data comprises: identifying a text keyword in the recommendation text, and/or identifying an image feature corresponding to the recommendation image (Examiner notes that underlined limitation is disclosed by another prior art. Examiner interprets “recommendation text” as textual content, post text, query text, recommendation message text, or topic text associated with a recommendation. The Examiner interprets “recommendation image” as album art, thumbnail, cover image, graphical representation, or other image associated with the recommended content. The Examiner interprets “image feature” as a face, shape, edge, text region, empty region, object, or other recognized visual feature.) (“e recommendation manager 218 may be configured to determine at which location of the graphical representation 300 of the content item the graphical symbol 302 is to be placed. As such, for example, the recommendation manager 218 may be configured to access the graphical representation 300 (e.g., from the graphics database 240) and determine or detect different objects or features such as faces, shapes, edges, text regions, relatively empty regions, and/or the like using image feature recognition techniques. The recommendation manager 218 may then determine at what location and/or in what color, size, font, style, etc., the graphical symbol 302 should be overlaid onto the graphical representation 300. Rules regarding the conditions under which certain characteristics (e.g., size, location, font, color, and/or the like) for the graphical symbol 302 are to be employed may be stored, for example, in the memory device 216. As an alternative, either a user receiving recommendations or an entity providing recommendations may select the type and appearance of the graphical symbol 302”) (0048-0049).
Lehtiniemi specifically doesn’t disclose, determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information, however Jackson discloses, wherein the associated data comprises recommendation data in the recommended content, wherein the recommendation data comprises a recommendation text and/or a recommendation image (Examiner interprets that the identifying a text keyword in recommendation text because Jackson generates saved queries from sets of words or alphanumeric characters identified as topically relevant to content previously generated or viewed by a user, and analyzes content by identifying words common to sources such as queries, landing pages, emails, posts, and combinations thereof) (“posts are generated for a particular user based on one or more queries that are saved for the particular user in a saved queries repository 536. Each query can be a set of words or alpha-numeric characters that are identified as topically relevant to content that the particular user has previously generated or viewed … the queries are generated based on user-selected options. For example, upon a particular user (e.g., Bill) opening his account or going into a settings page for his account, Bill may be presented with a survey or series of topical categories that Bill may identify as categories of interest. A word associated with each category may be added to a saved query for the user. For example, Bill may select a “Cars” option in a settings page and, in response, be presented with a list of various types of cars. Bill may select “Ford” as a sub-option. In response, the saved query for Bill may include the word “Ford.””) (Col. 23 Ln. 25-59, Col. 24 Ln. 1-60);
and determining the feature information corresponding to the associated data comprises: identifying a text keyword in the recommendation text (Examiner interprets that the “Target interactive topic” reads on micro-blogging topic, saved query topic, same-topic recommendation, or post topic.) (“The content may be analyzed by identifying a set of words that are common to multiple different sources of content (e.g., multiple different queries, landing pages, emails, posts, or a combination of these). Words from a repository of truncated words may be removed from the set of words (e.g., the words “the,” “a,” and “their” may be removed as the truncated words). The repository of truncated words may include words that are commonly used in a language or dialect (e.g., the repository may include a 500 most commonly used words in a particular language). The repository of truncated words may also include words that were not determined to be of interest to users. For example, if the word “box” is not a commonly used word, but posts that are recommended based on the use of the word “box” are rarely “liked,” @replied, viewed in expanded format, or commented on, the word box may be added to the truncated repository.”) (Col. 24 Ln. 1-60).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, the recommendation data comprises a recommendation text and/or a recommendation image; and determining the feature information corresponding to the associated data comprises: identifying a text keyword in the recommendation text, as taught by Jackson for the purpose to provide a known way to extract topical keywords from recommendation-related text to allow recommendation labels to be selected based on text keywords and image features, thereby improving the relevance of the displayed recommendation reason.
As per claims 5, Lehtiniemi specifically doesn’t disclose, wherein the associated data comprises a target interactive topic associated with the at least one piece of recommended content, however Jackson discloses, wherein the associated data comprises a target interactive topic associated with the at least one piece of recommended content (Examiner interprets that the “target interactive topic” as a topic, post topic, saved-query topic, same-topic recommendation, micro-blogging topic, source topic, or recommendation topic associated with the recommended content) (“a post is selected as a recommended post based on a high score, and in response to the selection of the post, an analysis is performed of the post (or the post's score) to identify the primary criteria that resulted in the high score. … The “Popular” recommendation dialog boxes 402 may appear with posts that are recommended based on a high amount of post activity. For example, most of the recommended dialog boxes are displayed to a post recipient when the post is associated with a significant number of likes, comments, and @replies. The post may be “popular” with people in general (as with dialog box 410), or popular specific to a particular type of additional criteria. The additional criteria may be that the activity is provided by subscribed users (as with dialog box 404). The additional criteria may be that the activity was popular with people having a similar interest (as with dialog box 406). The similar interest may be identified based on standing queries, which are discussed below in this document.”) (Col. 17 Ln. 1-65, Col. 18 Ln. 1-20);
and determining the feature information corresponding to the associated data comprises: determining at least one keyword associated with the recommended content in the target interactive topic (Examiner interprets that the “Target interactive topic” reads on micro-blogging topic, saved query topic, same-topic recommendation, or post topic, further the associated data comprising a target interactive topic because Jackson selects recommended posts based on same-topic, same-source, popular, top-poster, and past-importance criteria, and identifies primary criteria that resulted in a high post score) (“posts are generated for a particular user based on one or more queries that are saved for the particular user in a saved queries repository 536. Each query can be a set of words or alpha-numeric characters that are identified as topically relevant to content that the particular user has previously generated or viewed … the queries are generated based on user-selected options. For example, upon a particular user (e.g., Bill) opening his account or going into a settings page for his account, Bill may be presented with a survey or series of topical categories that Bill may identify as categories of interest. A word associated with each category may be added to a saved query for the user. For example, Bill may select a “Cars” option in a settings page and, in response, be presented with a list of various types of cars. Bill may select “Ford” as a sub-option. In response, the saved query for Bill may include the word “Ford.””) (Col. 23 Ln. 35-65 to Col. 24 Ln. 1-60);
and determining the at least one keyword as the feature information (Examiner interpretation determining a keyword associated with recommended content in the target interactive topic because saved queries are generated from words identified as topically relevant to content previously generated or viewed, and posts may be selected as recommendations if they include query terms and satisfy a threshold) (“The content may be analyzed by identifying a set of words that are common to multiple different sources of content (e.g., multiple different queries, landing pages, emails, posts, or a combination of these). Words from a repository of truncated words may be removed from the set of words (e.g., the words “the,” “a,” and “their” may be removed as the truncated words). The repository of truncated words may include words that are commonly used in a language or dialect (e.g., the repository may include a 500 most commonly used words in a particular language). The repository of truncated words may also include words that were not determined to be of interest to users. For example, if the word “box” is not a commonly used word, but posts that are recommended based on the use of the word “box” are rarely “liked,” @replied, viewed in expanded format, or commented on, the word box may be added to the truncated repository.”) (Col. 24 Ln. 1-60, Col. 23 Ln. 35-65).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, the recommendation data comprises a recommendation text and/or a recommendation image; and determining the feature information corresponding to the associated data comprises: identifying a text keyword in the recommendation text, as taught by Jackson for the purpose to identify topical relevance in social/recommended content, to produce recommendation labels based on topical keywords associated with an interactive recommendation topic
As per claims 8 and 19, Lehtiniemi specifically doesn’t disclose, wherein displaying the at least one piece of recommended content for recommending the media content comprises: displaying at least one piece of interactive topic content in a preset interactive page in response to a trigger operation performed by a user for any piece of the interactive topic content, displaying a content display page associated with the interactive topic content and displaying the at least one piece of recommended content related to the interactive topic content in the content display page, however Jackson discloses, wherein displaying the at least one piece of recommended content for recommending the media content comprises: displaying at least one piece of interactive topic content in a preset interactive page (Examiner interprets that the displaying interactive topic content in a preset page because users view streams of posts in a micro-blogging service, including posts authored by subscribed users and recommended posts authored by users the viewer has not followed) (“The other people that subscribe to the user may view the user's post by logging into a website that displays, for each individual other person, a stream of posts that the micro-blogging service pushes to the individual person (i.e., a computing device that is associated with the user). Each stream of posts may include posts that are authored by other users that the other person “follows” or “subscribes.” Also, the stream of posts can include posts that the micro-blogging service recommends for the other person, even though the other person has not affirmatively agreed to “follow” an author of the recommended posts.”) (Col. 8 Ln. 25-65);
in response to a trigger operation performed by a user for any piece of the interactive topic content, displaying a content display page associated with the interactive topic content (Examiner interprets that a trigger operation and display of a content display page because a user may select or expand a post, comment on a post, or interact with a recommendation indicator, which causes the interface to display additional post content, comments, or recommendation explanations in an expanded form or pop-up/dialog box) (“The “Expand” option 144 may enable Bill to expand John's post 104 so that all content associated with the post 104 (e.g., all content that he submitted, all comments, etc.) may be viewed at a single time in an expanded form. The post may increase in size within the interface 100 or may appear as a separate “pop-up” box that is overlaid on the interface 100. In some examples, the post 104 displays all users that are subscribed to the post, and whether the users”) (Col. 15 Ln. 1-20, Col. 14 Ln. 15-65);
and displaying the at least one piece of recommended content related to the interactive topic content in the content display page (Examiner interpretation displaying recommended content related to an interactive topic because recommended posts and recommendation dialog boxes are based on criteria such as popular, same topic, same source, top poster, past importance, likes, comments, and @replies) (“a post is selected as a recommended post based on a high score, and in response to the selection of the post, an analysis is performed of the post (or the post's score) to identify the primary criteria that resulted in the high score. … The “Popular” recommendation dialog boxes 402 may appear with posts that are recommended based on a high amount of post activity. For example, most of the recommended dialog boxes are displayed to a post recipient when the post is associated with a significant number of likes, comments, and @replies. The post may be “popular” with people in general (as with dialog box 410), or popular specific to a particular type of additional criteria. The additional criteria may be that the activity is provided by subscribed users (as with dialog box 404). The additional criteria may be that the activity was popular with people having a similar interest (as with dialog box 406). The similar interest may be identified based on standing queries, which are discussed below in this document.”) (Col. 17 Ln. 1-65, Col. 18 Ln. 1-20).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, wherein displaying the at least one piece of recommended content for recommending the media content comprises: displaying at least one piece of interactive topic content in a preset interactive page in response to a trigger operation performed by a user for any piece of the interactive topic content, displaying a content display page associated with the interactive topic content and displaying the at least one piece of recommended content related to the interactive topic content in the content display page, as taught by Jackson for the purpose for a conventional user interface for interacting with recommended social/media content, including expanded displays, pop-ups, and recommendation reasons in response to a user trigger operation within an interactive page, improving user control and understanding of recommended content.
As per claims 9 and 20, Lehtiniemi specifically doesn’t disclose, wherein displaying the at least one piece of recommended content for recommending the media content comprises: displaying at least one piece of interactive topic content historically published by a user in a preset information page in response to a trigger operation performed by a user for any piece of the interactive topic content, displaying a content display page associated with the interactive topic content and displaying the at least one piece of recommended content related to the interactive topic content in the content display page, however Jackson discloses, wherein displaying the at least one piece of recommended content for recommending the media content comprises: displaying at least one piece of interactive topic content historically published by a user in a preset information page (Examiner interprets that the displaying recommended content related to an interactive topic because recommended posts and recommendation dialog boxes are based on criteria such as popular, same topic, same source, top poster, past importance, likes, comments, and @replies) (“The other people that subscribe to the user may view the user's post by logging into a website that displays, for each individual other person, a stream of posts that the micro-blogging service pushes to the individual person (i.e., a computing device that is associated with the user). Each stream of posts may include posts that are authored by other users that the other person “follows” or “subscribes.” Also, the stream of posts can include posts that the micro-blogging service recommends for the other person, even though the other person has not affirmatively agreed to “follow” an author of the recommended posts.”) (Col. 8 Ln. 25-65);
in response to a trigger operation performed by a user for any piece of the interactive topic content, displaying a content display page associated with the interactive topic content (Examiner interprets that displaying interactive topic content in a preset page because users view streams of posts in a micro-blogging service, including posts authored by subscribed users and recommended posts authored by users the viewer has not followed) (“The messaging interface 100 includes mechanisms for Bill to interact with John's post. For example, Bill may select the comment interface element 132, and in response, user interface elements and controls may appear that enable Bill to generate and submit textual or multimedia content for inclusion in the post. The comment is distributed to all users that received the post so that when these other users view the post they see Bill's comment. The post may be updated for all users, whether the users have viewed the post previously or not”) (Col. 14 Ln. 15-65, Col. 15 Ln. 1-20);
and displaying the at least one piece of recommended content related to the interactive topic content in the content display page (“The messaging interface 100 includes mechanisms for Bill to interact with John's post. For example, Bill may select the comment interface element 132, and in response, user interface elements and controls may appear that enable Bill to generate and submit textual or multimedia content for inclusion in the post. The comment is distributed to all users that received the post so that when these other users view the post they see Bill's comment. The post may be updated for all users, whether the users have viewed the post previously or not”) (Col. 14 Ln. 15-65, Col. 15 Ln. 1-20).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, wherein displaying the at least one piece of recommended content for recommending the media content comprises: displaying at least one piece of interactive topic content historically published by a user in a preset information page in response to a trigger operation performed by a user for any piece of the interactive topic content, displaying a content display page associated with the interactive topic content and displaying the at least one piece of recommended content related to the interactive topic content in the content display page, as taught by Jackson for the purpose for an interactive feed/page environment use a conventional user interface for interacting with recommended social/media content, including expanded displays, pop-ups, and recommendation reasons and presented in response to a user trigger operation within an interactive page, improving user control and understanding of recommended content.
As per claims 10, Lehtiniemi discloses, wherein displaying the at least one piece of recommended content for recommending the media content comprises: displaying a preset content recommendation list, wherein the content recommendation list comprises at least one recommendation topic (“the functionality of the recommendation manager 218 may be employed with respect to recommended music or other content items being played at a device (e.g., the media player 205). As such, for example, if the media player 205 is in a mode that enables a recommended content item to be automatically played or added to a playlist, the recommendation manager 218 may operate to present album cover art (or other graphical representations associated with a content item to be played) as modified by inclusion of the graphical symbol 302 in response to the content item beginning to play. However, as an alternative, listings of recommended songs or other content items (e.g., as provided by an online music store or other music service) may be provided in which one or more (or even all) of the recommended songs or other content items include respective graphical symbols indicative of the recommendation criteria for each corresponding recommended song. In this regard, for example, FIG. 4, in accordance with an example embodiment of the invention, shows an example web page associated with a music related service (e.g., service 100) in which various recommended content items may be displayed. As shown in FIG. 4, some (or all) of the recommended content items include respective graphical symbols to indicate the recommendation criteria associated with each recommended content item”) (0051).
Lehtiniemi specifically doesn’t disclose, in response to a trigger operation performed by a user for any recommendation topic, displaying a content display page associated with the recommendation topic and displaying the at least one piece of recommended content related to the recommendation topic in the content display page, however Jackson discloses, in response to a trigger operation performed by a user for any recommendation topic, displaying a content display page associated with the recommendation topic (“The messaging interface 100 includes mechanisms for Bill to interact with John's post. For example, Bill may select the comment interface element 132, and in response, user interface elements and controls may appear that enable Bill to generate and submit textual or multimedia content for inclusion in the post. The comment is distributed to all users that received the post so that when these other users view the post they see Bill's comment. The post may be updated for all users, whether the users have viewed the post previously or not”) (Col. 14 Ln. 15-65, Col. 15 Ln. 1-20);
and displaying the at least one piece of recommended content related to the recommendation topic in the content display page (“The messaging interface 100 includes mechanisms for Bill to interact with John's post. For example, Bill may select the comment interface element 132, and in response, user interface elements and controls may appear that enable Bill to generate and submit textual or multimedia content for inclusion in the post. The comment is distributed to all users that received the post so that when these other users view the post they see Bill's comment. The post may be updated for all users, whether the users have viewed the post previously or not”) (Col. 14 Ln. 15-65, Col. 15 Ln. 1-20).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, in response to a trigger operation performed by a user for any recommendation topic, displaying a content display page associated with the recommendation topic and displaying the at least one piece of recommended content related to the recommendation topic in the content display page, as taught by Jackson for the purpose for interactive feed/page environment user interface for interacting with recommended social/media content, including expanded displays, pop-ups, and recommendation reasons to be presented in response to a user trigger operation within an interactive page, improving user control and understanding of recommended content.
As per claims 11, Lehtiniemi discloses, wherein after displaying the at least one piece of recommended content for recommending the media content, the method further comprises: acquiring, for each piece of recommended content, the historical consumption data of the publisher corresponding to the recommended content (Examiner interprets that the acquiring recommendation information associated with other users or entities because its search/filter engine filters recommendations from other users or entities and provides indication data to the recommendation manager to generate graphical recommendation criteria) (“the search/filter engine 220 may be configured to parse recommendation messages in the recommendation database 234 for a particular user and, for recommendation messages that are indicative of a recommendation that complies with the user's profile, provide information to the recommendation manager 218 regarding the corresponding recommendation (e.g., an indication of a recommendation) to enable the recommendation manager 218 to generate an indication of recommendation criteria (e.g., via a graphical symbol associated with the recommendation criteria) for the corresponding recommendation. However, in some cases, compliance with the user's profile may not be a factor. The indication may then be served to the user or posted generally in a manner accessible to the user. In other words, in some embodiments, the search/filter engine 220 may be configured to filter recommendations from other users or entities regarding recommendations, in some cases further based on a particular user's interests (e.g., as specified in the user profile database 230). In alternative embodiments, the search/filter engine 220 may be configured to search information available via a service and generate indications of recommendations for use by the recommendation manager 218 as described below”) (0041-0042).
Lehtiniemi specifically doesn’t disclose, determining browsing data and interaction data of the publisher for the media content based on the historical consumption data, however Xu discloses, determining browsing data and interaction data of the publisher for the media content based on the historical consumption data (Examiner interprets that determining browsing data based on historical consumption data because Xu stores user behavior data as historical watching behavior in the form of a user/video/intensity tuple and updates video-centric and video-pair-centric metrics in response to new user activities) (“the recommendation system incrementally folds new user behavior data into a user history that summarizes all historical watching behavior in the form of a (user, video, intensity) tuple. In addition, the recommendation system distinguishes new information from old information, and only updates the video-pair-centric metrics 244 of video pairs that have new user activities … the video-centric CF metrics for a particular video are only updated in response to an new expression of interest in the particular video. Similarly, the video-pair-centric metrics for a particular video pair are only updated in response to a new expression of interest in at least one of the videos that belong to the video pair. Because many videos may experience long periods during which no user expresses an interest, the percentage of video-centric metrics that need to recalculated is reduced, thereby lowering the processing required to maintain the video-centric metrics 242 and video-pair-centric metrics 244 up to date in real time.”) (Col. 9 Ln. 15-52), wherein the browsing data at least comprises a browsing duration corresponding to each piece of media content (Examiner interprets browsing duration because Xu discusses normalizing scores based on users’ overall viewing behavior, including an example where users watched a video pair for 50 minutes) (“A normalization factor is another example of a model tuning parameter that may be used by some models. A normalization factor generally tries to normalize scores based on users' overall viewing behavior. For example, user A and user B both watched video pair (v1, v2) for 50 minutes. However, user A only watched videos v1 and v2, whereas user B watched hundreds of other videos. User B has more divided attention relative to the video pair. The endorsement from user A for the relatedness of video v1 and v2 should be more important than that from user B. This is in the same spirit of PageRank used in Web page ranking, where the weight given to an out-going link depends in part on the number of out-going links each web page may have.”) (Col. 14 Ln. 32-44).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, determining browsing data and interaction data of the publisher for the media content based on the historical consumption data, wherein the browsing data at least comprises a browsing duration corresponding to each piece of media content, as taught by Xu for the purpose to provide historical browsing/consumption data to improve the accuracy and transparency of the recommendation label by basing it on actual browsing duration, viewing intensity, comments, likes, replies, and publisher/source activity.
Lehtiniemi specifically doesn’t disclose, determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information, however Jackson discloses, and the interaction data comprises comment content and/or the recommended content published by the publisher for the media content (Examiner interprets that the interaction data including comment content and published content because Jackson scores authors and posts based on comments, quality of comments, likes, replies, user interaction, and posts submitted by authors) (“The score for each individual author in the plurality is based on a score of one or more authors in the plurality that have requested to subscribe to a stream of posts that the individual author submits to the server system. A particular post submitted by a particular author in the plurality is received at the server system and from a computing device. A score is assigned to the particular post based on a score of the particular author. The particular post is transmitted from the server system to computing devices that are associated with authors who have requested to subscribe to posts by the particular author. (Col. 5 Ln. 35-65, Col. 6 Ln. 1-20);
and determining the at least one content consumption feature label associated with the publisher based on the browsing data and/or the interaction data (Examiner interprets that the determining a label/reason based on browsing/interaction data because Jackson identifies primary criteria for recommending a post and displays recommendation dialog boxes based on high post activity, likes, comments, @replies, same topic, same source, top poster, and past importance) (“a post is selected as a recommended post based on a high score, and in response to the selection of the post, an analysis is performed of the post (or the post's score) to identify the primary criteria that resulted in the high score. … The “Popular” recommendation dialog boxes 402 may appear with posts that are recommended based on a high amount of post activity. For example, most of the recommended dialog boxes are displayed to a post recipient when the post is associated with a significant number of likes, comments, and @replies. The post may be “popular” with people in general (as with dialog box 410), or popular specific to a particular type of additional criteria. The additional criteria may be that the activity is provided by subscribed users (as with dialog box 404). The additional criteria may be that the activity was popular with people having a similar interest (as with dialog box 406). The similar interest may be identified based on standing queries, which are discussed below in this document.”) (Col. 17 Ln. 1-65, Col. 18 Ln. 1-20).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, in response to a trigger operation performed by a user for any recommendation topic, displaying a content display page associated with the recommendation topic and displaying the at least one piece of recommended content related to the recommendation topic in the content display page, as taught by Jackson for the purpose for an interactive feed/page environment use a conventional user interface for interacting with recommended social/media content, including expanded displays, pop-ups, and recommendation reasons and presented in response to a user trigger operation within an interactive page, improving user control and understanding of recommended content.
As per claims 12, Lehtiniemi discloses, wherein determining the at least one content consumption feature label associated with the publisher based on the browsing data and/or the interaction data comprises: acquiring associated consumption data of a user for the media content, wherein the user is a user who performs a browsing operation on the at least one piece of recommended content (“the search/filter engine 220 may be configured to parse recommendation messages in the recommendation database 234 for a particular user and, for recommendation messages that are indicative of a recommendation that complies with the user's profile, provide information to the recommendation manager 218 regarding the corresponding recommendation (e.g., an indication of a recommendation) to enable the recommendation manager 218 to generate an indication of recommendation criteria (e.g., via a graphical symbol associated with the recommendation criteria) for the corresponding recommendation. However, in some cases, compliance with the user's profile may not be a factor. The indication may then be served to the user or posted generally in a manner accessible to the user. In other words, in some embodiments, the search/filter engine 220 may be configured to filter recommendations from other users or entities regarding recommendations, in some cases further based on a particular user's interests (e.g., as specified in the user profile database 230). In alternative embodiments, the search/filter engine 220 may be configured to search information available via a service and generate indications of recommendations for use by the recommendation manager 218 as described below”) (0041-0042).
Lehtiniemi specifically doesn’t disclose, determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information, however Xu discloses, determining a related browsing behavior between the user and the publisher based on the associated consumption data (“the recommendation system incrementally folds new user behavior data into a user history that summarizes all historical watching behavior in the form of a (user, video, intensity) tuple. In addition, the recommendation system distinguishes new information from old information, and only updates the video-pair-centric metrics 244 of video pairs that have new user activities … the video-centric CF metrics for a particular video are only updated in response to an new expression of interest in the particular video. Similarly, the video-pair-centric metrics for a particular video pair are only updated in response to a new expression of interest in at least one of the videos that belong to the video pair. Because many videos may experience long periods during which no user expresses an interest, the percentage of video-centric metrics that need to recalculated is reduced, thereby lowering the processing required to maintain the video-centric metrics 242 and video-pair-centric metrics 244 up to date in real time.”) (Col. 9 Ln. 15-52) and the browsing data and/or the interaction data (Examiner interprets) (“A normalization factor is another example of a model tuning parameter that may be used by some models. A normalization factor generally tries to normalize scores based on users' overall viewing behavior. For example, user A and user B both watched video pair (v1, v2) for 50 minutes. However, user A only watched videos v1 and v2, whereas user B watched hundreds of other videos. User B has more divided attention relative to the video pair. The endorsement from user A for the relatedness of video v1 and v2 should be more important than that from user B. This is in the same spirit of PageRank used in Web page ranking, where the weight given to an out-going link depends in part on the number of out-going links each web page may have.”) (Col. 14 Ln. 32-44).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, in response to a trigger operation performed by a user for any recommendation topic, displaying a content display page associated with the recommendation topic and displaying the at least one piece of recommended content related to the recommendation topic in the content display page, as taught by Xu for the purpose for the purpose to provide historical browsing/consumption data to improve the accuracy and transparency of the recommendation label by basing it on actual browsing duration, viewing intensity, comments, likes, replies, and publisher/source activity.
Lehtiniemi specifically doesn’t disclose, determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information, however Jackson discloses, and determining the at least one content consumption feature label associated with the publisher based on the related browsing behavior (Examiner interprets that the) (“a post is selected as a recommended post based on a high score, and in response to the selection of the post, an analysis is performed of the post (or the post's score) to identify the primary criteria that resulted in the high score. … The “Popular” recommendation dialog boxes 402 may appear with posts that are recommended based on a high amount of post activity. For example, most of the recommended dialog boxes are displayed to a post recipient when the post is associated with a significant number of likes, comments, and @replies. The post may be “popular” with people in general (as with dialog box 410), or popular specific to a particular type of additional criteria. The additional criteria may be that the activity is provided by subscribed users (as with dialog box 404). The additional criteria may be that the activity was popular with people having a similar interest (as with dialog box 406). The similar interest may be identified based on standing queries, which are discussed below in this document.”) (Col. 17 Ln. 1-65, Col. 18 Ln. 1-20).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, in response to a trigger operation performed by a user for any recommendation topic, displaying a content display page associated with the recommendation topic and displaying the at least one piece of recommended content related to the recommendation topic in the content display page, as taught by Jackson for the purpose for an interactive feed/page environment use a conventional user interface for interacting with recommended social/media content, including expanded displays, pop-ups, and recommendation reasons and presented in response to a user trigger operation within an interactive page, improving user control and understanding of recommended content.
As per claims 13, Lehtiniemi specifically doesn’t disclose, determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information, however Xu discloses, wherein determining the at least one content consumption feature label associated with the publisher based on the browsing data and/or the interaction data comprises: determining browsing feature information of the publisher for the media content based on the browsing data (“Unfortunately, there is no decay factor that yields optimal results in all situations. For example, using a high decay factor for videos from a publisher of highly-watched videos may yield CF scores 124 that accurately predict whether users that watched one video in a video pair may be interested in watching the other video in the video pair. Use of a high decay factor may be optimal for such types of videos because the quantity of recent viewing activity is sufficient to produce accurate predictions. Consequently, old behavior data can be discounted accordingly …”) (Col. 13 Ln. 1-45), wherein the browsing feature information comprises at least one media content category with a browsing duration of the publisher reaching a preset duration threshold and historical browsing age of the publisher (“On the other hand, two videos may be unrelated even though the statistics indicate that there is only a 45% chance that they co-occur independently in user logs. In this situation, use of a 40% odds-ratio cut off would have caused the model to predict that the videos are unrelated … A normalization factor is another example of a model tuning parameter that may be used by some models. A normalization factor generally tries to normalize scores based on users' overall viewing behavior. For example, user A and user B both watched video pair (v1, v2) for 50 minutes. However, user A only watched videos v1 and v2, whereas user B watched hundreds of other videos. User B has more divided attention relative to the video pair. The endorsement from user A for the relatedness of video v1 and v2 should be more important than that from user B. This is in the same spirit of PageRank used in Web page ranking, where the weight given to an out-going link depends in part on the number of out-going links each web page may have.”) (Col. 14 Ln. 1-65).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, in response to a trigger operation performed by a user for any recommendation topic, displaying a content display page associated with the recommendation topic and displaying the at least one piece of recommended content related to the recommendation topic in the content display page, as taught by Xu for the purpose for the purpose to provide historical browsing/consumption data to improve the accuracy and transparency of the recommendation label by basing it on actual browsing duration, viewing intensity, comments, likes, replies, and publisher/source activity.
Lehtiniemi specifically doesn’t disclose, determining the at least one target content consumption feature label in the at least one target content consumption feature dimension based on the feature information, however Jackson discloses, and determining the at least one content consumption feature label associated with the publisher based on the browsing feature information (Examiner interprets that the) (“The score for each individual author in the plurality is based on a score of one or more authors in the plurality that have requested to subscribe to a stream of posts that the individual author submits to the server system. A particular post submitted by a particular author in the plurality is received at the server system and from a computing device. A score is assigned to the particular post based on a score of the particular author. The particular post is transmitted from the server system to computing devices that are associated with authors who have requested to subscribe to posts by the particular author. (Col. 5 Ln. 35-65, Col. 6 Ln. 1-20);
and determining the at least one content consumption feature label associated with the publisher based on the browsing data and/or the interaction data (Examiner interprets that the) (“a post is selected as a recommended post based on a high score, and in response to the selection of the post, an analysis is performed of the post (or the post's score) to identify the primary criteria that resulted in the high score. … The “Popular” recommendation dialog boxes 402 may appear with posts that are recommended based on a high amount of post activity. For example, most of the recommended dialog boxes are displayed to a post recipient when the post is associated with a significant number of likes, comments, and @replies. The post may be “popular” with people in general (as with dialog box 410), or popular specific to a particular type of additional criteria. The additional criteria may be that the activity is provided by subscribed users (as with dialog box 404). The additional criteria may be that the activity was popular with people having a similar interest (as with dialog box 406). The similar interest may be identified based on standing queries, which are discussed below in this document.”) (Col. 17 Ln. 1-65, Col. 18 Ln. 1-20).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for displaying at least one piece of recommended content for recommending media content, wherein a publisher of the recommended content is associated with at least one content consumption feature label,, as taught by Lehtiniemi, in response to a trigger operation performed by a user for any recommendation topic, displaying a content display page associated with the recommendation topic and displaying the at least one piece of recommended content related to the recommendation topic in the content display page, as taught by Jackson for the purpose for an interactive feed/page environment use a conventional user interface for interacting with recommended social/media content, including expanded displays, pop-ups, and recommendation reasons and presented in response to a user trigger operation within an interactive page, improving user control and understanding of recommended content.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US. Pub. 20240012541 (“Liu”).
Liu outlines a display method and apparatus, once a user selects a first target book in a first book recommendation stream, a second book recommendation stream corresponding to a first target book and introduction information of a second book are displayed, and an inner book list stream i.e. the second book recommendation stream can be added to a page displaying book details, so that interaction costs of the user exiting the book detail page and reselecting other books so as to switch the book detail page are reduced, thereby improving the efficiency with which the user selects and views book information, increasing the book distribution and the abundance of recommendations, and taking user convenience into account while increasing creativity. The operation is convenient, and time and labor are reduced.
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/GAUTAM UBALE/
Primary Examiner, Art Unit 3689
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