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 .
DETAILED ACTION
Election/Restrictions
Applicant’s election of Species 1, Fig.1C in the reply filed on04/13/2026 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)). The restriction is made final. Claims 8-13 are drawn to non-elected species and withdrawn from consideration.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2-3, 16, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 2, 16, and 20 recite “obtaining a display template corresponding to the first data based on the first data, or obtaining a display template corresponding to the second data based on the second data … using the display template”. It is unclear which display template, “the display template,” refers to. Further, the meanings of “corresponding to the first/second data” and “based on the first/second data” are the same. It is unclear whether these limitations are redundant. For examining purposes, the limitations are interpreted to be “obtaining a display template corresponding to the first data, or obtaining a display template corresponding to the second data … using the display template corresponding to the first data or the display template corresponding to the second data.” Appropriate correction is required. Dependent claim 3 fails to cure the deficiency and is rejected for the same reason.
Claim 3 further recites “obtaining the display template corresponding to the first data based on the first data, or obtaining the display template corresponding to the second data based on the second data comprises: obtaining, based on the recommendation reason field, a display template corresponding to the recommendation reason field.” It is unclear whether “a display template corresponding to the recommendation reason field” is a different template or the same template being obtained. Further, the meanings of “corresponding to the first/second data” and “based on the first/second data” are the same. In addition, the meanings of “based on the recommendation reason field” and “corresponding to the recommendation reason field” are the same. Thus, it is unclear whether these limitations are redundant. For examining purposes, the limitations are interpreted to be “obtaining the display template corresponding to the first data, or obtaining the display template corresponding to the second data comprises: obtaining, based on the recommendation reason field, the display template corresponding to the first data or the display template corresponding to the second data.” Appropriate correction is required.
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 1-7 and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Exemplary claim 15 is directed to a method/process.
Step 2A: Prong 1:
The following claim limitations are Certain Methods of Organizing Human Activity, specifically commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions):
“determining, … based on the user behavior information, target article information and first data corresponding to the target article information, wherein the first data comprises a recommendation reason field, and the first data is related to the target article information and the user behavior information;
generating … a first message based on the target article information and the first data;
receiving … the first message …, wherein the first message comprises the target article information and the first data;
displaying … a first interface, wherein the first interface comprises a first recommendation, the first recommendation comprises first content and recommendation information corresponding to the first data, and the first content is obtained based on the target article information;
receiving … a first operation on the first recommendation;
sending … a first request to the server in response to the first operation, wherein the first request comprises an identifier of the first message, the first request requests … to obtain content of a target article and second data based on the identifier of the first message, and the second data is related to the first data;
obtaining … the content of the target article and the second data based on the first request; sending … the target article and second data to the terminal;
receiving … the content of the target article and the second data that are sent …; and
displaying … a second interface, wherein the content of the target article and recommendation information corresponding to the second data are displayed on the second interface.”
Moreover, the following claim limitations are Mental Processes (observation, evaluation, judgment, and/or opinion):
“determining … based on the user behavior information, target article information and first data corresponding to the target article information, wherein the first data comprises a recommendation reason field, and the first data is related to the target article information and the user behavior information.”
Therefore, the claim recites an abstract idea.
Step 2A, Prong 2: the additional elements individually or as a whole do not integrate the judicial exception into a practical application.
The additional elements, “applied to a communication system, wherein the communication system comprises a terminal and a server,” “by the terminal,” and “by the server” are implementing an abstract idea on a computer, or merely uses a computer as a tool to perform/apply an abstract idea. It invokes a generic computer merely as a tool to perform the judicial exception or an existing process by using of a computer or other machinery in its ordinary capacity. (i.e., “apply it”, MPEP 2106.05(f)).
The additional elements, “sending, by the terminal, user behavior information of a user to the server,” are merely data gathering recited at a high level of generality and insignificant extra-solution activity (pre-solution activity) (MPEP 2106.05 (g)).
The additional elements, “target article” and “recommendation” are generally linking the use of the judicial exception to a particular technological environment or field of use (e.g., web article recommendation) (MPEP 2106.05(h)).
When considered a whole, the claimed invention fails to recite any improvement in any technology or technical field (MPEP 2106.05(a)) or recite any meaningful limitations (MPEP 2106.05(e)). The limitations are no more than mere automation of determining a user’s interest and providing a recommended article along with a reason for the recommendation using a generic computer as a tool. Specifically, a marketing personnel can collect behavior information of a user, identify the user’s interest in articles, and present a recommended article and a reason for the recommendation to the user. Upon the user accepts/selects the recommended article, the marketing personnel can further present the full article and detailed recommendation information (statistics of the user’s interests) to the user.
Further, although ¶ [0003] of the specification of the instant application recites “inaccurate messages pushed to a user may cause antipathy of the user and reduce user experience,” however improving a user’s experience while using a computer (e.g., a terminal and a server) is not, without more, sufficient to render the claims directed to an improvement in computer functionality. The claim focuses on solving a business problem using generic computer technology as a tool, rather than on improving computer capabilities. Providing an article and other recommendation information based on a user’s behavior are longstanding methods of human activity. Improvement, directed to determining what recommended/targeted information is presented/displayed to a user, is an improvement to the abstract idea itself and is not an improvement to the functioning of the computer itself or any other technology or technical field.
Even when considered in combination, these additional elements do no more than automate marketing/advertising/business relations and/or personal behavior/relationships that a business personnel used to perform using the computer components as a tool. Therefore, the additional elements represent mere instructions to apply a judicial exception/abstract idea, which do not integrate the judicial exception into a practical application.
Step 2B: the claim does not recite additional elements that are sufficient to amount to significantly more than the abstract idea when considered both individually and as a whole.
under Step 2B, besides the additional elements already analyzed under MPEP 2106.05(f), 2106.05(g), and 2106.05(h) (the same analysis as that of Step 2B, Prong 2), additional element(s)/limitation(s) that are insignificant extra-solution activity (MPEP 2106.05(g)) in step 2A, Prong 2, are re-evaluated in Step 2B as the following to determine whether the additional element(s)/limitation(s) are well-understood, routine, conventional activities.
Specifically, the claim limitations, “sending, by the terminal, user behavior information of a user to the server,” are merely receiving or transmitting data over a network, which are mere judicial-recognized well-understood, routine, conventional activity, and remains insignificant extra-solution activity even upon reconsideration. (MPEP 2106.05(d)(II).
When considered as a whole, these additional elements represent mere instructions to apply a judicial exception and insignificant extra-solution activities, which do not provide an inventive concept, and, in other words, amount to significantly more.
Therefore, independent claim 15 is not eligible.
Independent claims 1 and 19 recite similar claim limitations as claim 15. Compared to claim 15, claim 19 recites additional limitations, “A terminal, comprising: one or more processors; and a memory, storing code, wherein when the code is executed by the processor, the terminal is enabled to perform operations.” However, these limitations are additional elements directed to implementing an abstract idea on a computer, or merely uses a computer as a tool to perform/apply an abstract idea. It invokes a generic computer merely as a tool to perform the judicial exception or an existing process by using of a computer or other machinery in its ordinary capacity. (i.e., “apply it”, MPEP 2106.05(f)). Therefore, independent claims 1 and 19 are not eligible.
Dependent claims 2-7, 14, 16-18, and 20 fail to recite additional elements that could integrate the judicial exception into a practical application or amount to significantly more than the abstract idea. Specifically:
Dependent claims 2, 16, and 20 recite an abstract idea directed to Certain Methods of Organizing Human Activity: “obtaining a display template corresponding to the first data based on the first data, or obtaining a display template corresponding to the second data based on the second data; and wherein displaying the second interface comprises: displaying the second interface, wherein the content of the target article and the recommendation information that corresponds to the second data and that is displayed by using the display template are displayed on the second interface.” Moreover, claims 2, 16, and 20 recite an abstract idea directed to Mental Processes: “obtaining a display template corresponding to the first data based on the first data, or obtaining a display template corresponding to the second data based on the second data” (The claimed “obtaining” is interpreted as “determining.”).
Dependent claim 3 recites an abstract idea directed to Certain Methods of Organizing Human Activity and Mental Processes: “wherein obtaining the display template corresponding to the first data based on the first data, or obtaining the display template corresponding to the second data based on the second data comprises: obtaining, based on the recommendation reason field, a display template corresponding to the recommendation reason field.” (The claimed “obtaining” is interpreted as “determining.”).
Dependent claims 4 and 17 recites an abstract idea directed to Certain Methods of Organizing Human Activity: “wherein second content is further displayed on the second interface, and the second content is obtained based on the related data of the category to which the target article belongs.”
Moreover, claims 4 and 17 recite additional elements: “wherein before displaying the second interface, the method further comprises: receiving related data that is of a category to which the target article belongs and that is pushed by the server.” These additional elements are directed to insignificant extra-solution activity (pre-solution activity) (MPEP 2106.05 (g)) and judicial-recognized well-understood, routine, conventional activity (receiving or transmitting data over a network, MPEP 2106.05(d)(II)).
Dependent claims 5 and 18 recite additional elements: “wherein the user behavior information comprises any one of the following: geographical location information, subscription dynamic category information, social dynamic category information, or user interest category information.” These additional elements directed to linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
Dependent claim 6 recites additional elements: “wherein the user behavior information comprises the user interest category information; and wherein: the target article is determined by the server based on the interest category information, and the first data is related to the interest category information; or the first data is related to the target article information and the interest category information.” These additional elements directed to linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
Dependent claim 7 recites additional elements: “wherein the second data displayed on the second interface comprises: the second data comprises a ranking list of user interests.” These additional elements directed to linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
Dependent claim 14 recites an abstract idea directed to Mathematical Concepts (mathematical relationships; mathematical formulas or equations; mathematical calculations): “the user interest category information comprises an interest vector weight of each article category, and the interest vector weight of each article category indicates a preference degree of the user for each category of articles … determining an interest vector weight of each article category based on the article categories of the n historical articles and a tap frequency, a time attenuation factor, and a weight of each article category” (see the mathematical concept in ¶ [0146] of the specification of the instant application). Moreover, claim 14 recites an abstract idea directed to Certain Methods of Organizing Human Activity: “wherein before sending the user behavior information of the user, the method further comprises: obtaining n historical articles browsed by the user, wherein n is a positive integer greater than 1; extracting article categories of the n historical articles browsed by the user; and determining an interest vector weight of each article category based on the article categories of the n historical articles and a tap frequency, a time attenuation factor, and a weight of each article category.” Claim 14 recites an abstract idea directed to Mental Processes: “wherein before sending the user behavior information of the user, the method further comprises: obtaining n historical articles browsed by the user, wherein n is a positive integer greater than 1; extracting article categories of the n historical articles browsed by the user; and determining an interest vector weight of each article category based on the article categories of the n historical articles and a tap frequency, a time attenuation factor, and a weight of each article category” (The claimed “obtaining” is interpreted as “determining.” The calculation is simply enough to be practically performed by the human mind). Further, claim 14 recites additional elements: “wherein the user behavior information comprises the user interest category information.” These additional elements directed to linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)).
Therefore, dependent claims 2-7, 14, 16-18, and 20 are not eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bitran (US 2017/0039327 A1), in view of Moon (US 2013/0132401 A1), hereinafter Bitran-Moon.
Per claims 1 and 19, Bitran teaches “A terminal, (Fig.1; ¶ [0021], computing system 10 includes a client computing device 12, which, for example, may take the form of a smart phone or tablet computing device) comprising: one or more processors; and a memory, storing code, wherein when the code is executed by the processor, (Fig. 14; ¶ [0003], a client computing device comprises a processor and an electronic personal assistant application program executable by the processor; ¶ [0111], volatile memory 1403, and a non-volatile storage device 1404) the terminal is enabled to perform operations comprising: sending user behavior information of a user to a server; (Fig.1; ¶ [0022-0024], Client computing device 12 is configured to execute an electronic personal assistant application program 24 … the electronic personal assistant program is configured to passively monitor various user data 26 on the client computing device 12 and other client computing devices 16, such as location data, search history, download history, browsing history, contacts, social network data, calendar data, biometric data, medical device data, purchase history, etc. In some examples, user data 26 may be generated by or otherwise associated with user activities across a plurality of programs … User data 26 is transmitted from the electronic personal assistant application program 24 to the personal assistant user data interpretation engine 28 executed on server system 14; ¶ [0099], the suggestion engine 74 may learn behaviors of the user by analyzing the user's behavior over time) obtaining a first message sent by the server, wherein the first message comprises target article information and first data, the first data comprises a recommendation reason field, the target article information is determined by the server based on the user behavior information, and the first data is related to the target article information and the user behavior information; displaying a first interface, wherein the first interface comprises a first recommendation, the first recommendation comprises first content and recommendation information corresponding to the first data, and the first content is obtained based on the target article information; (Fig. 7, a push notification/article 710 and a recommendation information/reason 720 (corresponding to the claimed first data): “Here is a local event for your favorite workout activity that fits your fitness level. Your weekend is free.”; Fig. 9, a push notification/article 910 and a recommendation information/reason: “Here is some news about one of your favorite workout activities.” Fig.10, a push notification/article 1010/1020 and a recommendation information/reason: “People who suffer from similar conditions like you read these.” Fig.11, a push notification/article 1110/1120/1130/1140 and a recommendation information/reason: “Consider the following insights regarding health advisories”; ¶ [0092-0094], the personal assistant user data interpretation engine 28 may infer from calendar data that in one month the user is traveling to India for a three-month visit. The suggestion engine 74 may identify a vaccine alert for India in the alerts 60 … A notification agent 64 within server system 14 may instruct the alert notification engine 68 of the electronic personal assistant application server 66 to send a message 70 in the form of a push notification featuring the content of the India vaccine alert 60 to the electronic personal assistant application program 24 executed on the client device 12 … the alert notification engine 68 may transmit to the electronic personal assistant application program 24 a link 1110 to the identified alert regarding India vaccines. The client computing device 12 may then display the link 1110 in the GUI 510 … personal assistant application program 24 may generate for the user and display a customized title 1120 for the alert that is based on data received from the suggestion engine 74 and/or other data from the user personal assistant knowledge base 30. In this example, the customized title 1110 makes reference to the user's upcoming trip to India, to thereby make the article more relevant to the user and increase the chances of the user clicking on the link 1110 to the alert; ¶ [0081-0083], With reference now to FIG. 9 and in another example, the electronic personal assistant application program 24 may provide a health-related suggestion in the form of a proactive push notification of a news article in which the user may be interested … The suggestion engine 74 may identify a news article that contains content about an amateur golfer. Based on the user's golfing ability, the personal assistant user data interpretation engine 28 may infer that the user may enjoy reading the article. Accordingly, the suggestion engine 74 may transmit to the electronic personal assistant application program 24 a link 910 to the identified news article for display in the GUI 510; ¶ [0084-0085], With reference now to FIG. 10 and in another example, the electronic personal assistant application program 24 may provide a health-related suggestion in the form of a proactive push notification of a news article, publication or other digital content in which the user may be interested … In the course of such searching the suggestion engine 74 may determine that the user suffers from migraine headaches ... the suggestion engine 74 may identify two articles related to migraine headaches. Accordingly, the suggestion engine 74 may transmit to the electronic personal assistant application program 24 two links 1010 and 1020 to the identified articles for display in the GUI 510; ¶ [0088], the suggestion engine 74 may locate the article indicated by link 1010 that suggests that migraine intensity can be reduced by ingesting bananas. The suggestion engine 74 may also analyze the current location of the user to determine that the user is at home. The suggestion engine may further determine from the user's purchasing history in the user personal assistant knowledge base 30 that the user regularly purchases bananas. Accordingly, the suggestion engine 74 may correlate information gleaned from the migraine-related article with the user biometric data, and in some examples the current location of the user, to determine that providing the health-related suggestion of the article to the user may help the user quickly address and perhaps alleviate the migraine headache. In response to such correlation, the suggestion engine may transmit to the electronic personal assistant application program 24 the link 1010 to the identified article discussing possibly reducing migraine intensity with bananas. The client computing device 12 may then display the link 1010 in the GUI 510; ¶ [0074], The suggestion engine 74 also may analyze calendar data of the user to determine that the user is available during the time and date of the bike ride event 710. Based on the user's calendar data, the user's location, the user's preference for bike riding, and the user's fitness level, the suggestion engine 74 may provide a health-related suggestion in the form of the bike ride event 710 to the electronic personal assistant application program 24 for display in the GUI 510. In some examples the electronic personal assistant application program 24 may generate for the user and display a customized title 720 for the event that is based on data received from the suggestion engine 74 and/or other data from the user personal assistant knowledge base 30. The customized title 720 may communicate that the event relates to the user's favorite workout activity, fits the user's fitness level, and that the user is available for the event; also see ¶¶ [0068-0074] regarding Fig.7, ¶¶ [0081-0083] regarding Fig.9, ¶¶ [0084-0090] regrading Fig.10, ¶¶ [0091-0099] regarding Fig.11) receiving a first operation on the first recommendation;” (¶ [0094], make the article more relevant to the user and increase the chances of the user clicking on the link 1110 to the alert).
However, Bitran does not explicitly disclose the claim limitations regarding after clicking the recommendation (pushed recommendation/notification of an article), “sending a first request to the server in response to the first operation, wherein the first request comprises an identifier of the first message, the first request requests the server to obtain content of a target article and second data based on the identifier of the first message, and the second data is related to the first data; obtaining the content of the target article and the second data from the server; and displaying a second interface, wherein the content of the target article and recommendation information corresponding to the second data are displayed on the second interface.”
Moon teaches “sending a first request to the server in response to the first operation, wherein the first request comprises an identifier of the first message, the first request requests the server to obtain content of a target article and second data based on the identifier of the first message, and the second data is related to the first data; obtaining the content of the target article and the second data from the server; and displaying a second interface, wherein the content of the target article and recommendation information corresponding to the second data are displayed on the second interface.” (Figs.1, 2, 3; ¶ [0035-0036], each news result includes, at least, a title 204 and an abstract 206. Title 204 provides a short description of the content of the news article, and the abstract 206 provides a synopsis of the content of the news article. Once the user clicks on a news article, the Internet browser accesses the referenced webpage to present the news article. In one embodiment, the textual content of the news article is referred to herein as the body of the news article. In other embodiments, the news article include one or more of a title, a body, a summary, one or more pictures, a video clip, user comments, statistical information about the page, etc. On the right side of the page, an advertising area includes a list of advertisements, which are sometimes related to the topic of the search; ¶ [0038-0040], FIG. 3 shows a news article with links to related content, according to one embodiment. A typical news article includes, at least, a title 304 and a body 306. The title 304 provides a headline for the article, and the body 306 includes a textual description of the news article. In some cases, the news article may include one or more pictures, and one or more multimedia segments. Embodiments of the invention provide a related content section 302, which includes one or more links to related news articles … For a related article to be recommended, the related article should cover only a fraction of the content of the article be read, and include other content that is novel and news-worthy …; ¶ [0049], Given a seed article s, the goal of the method to generate post-click news recommendation is to identify a set of candidate article; also see ¶ [0028] for server-terminal relationship). [Comment: the related content section 302 (corresponding to second data) is related to the topic/reason of the article (corresponding to the first data).]
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine post-click operations including displaying a target article and recommendation information of Moon into displaying a recommendation of a target article and a recommendation reason of Bitran, such that a recommendation of a target article and a recommendation reason for the article would be displayed and, after clicking the recommendation, the article and another recommendation information related to the recommendation topic/reason would be displayed. One of ordinary skill in the art would have been motivated to do so because Bitran recognizes that it would have been advantageous to provide post-click recommendation information along with a target article (¶ [0005-0006], Recommending interesting news articles to users has become extremely important for internet providers looking to maintain users' interest … methods to engage users after their initial visit is largely under explored … This process is referred to herein as post-click news recommendation, which has the goal of promoting users' navigation to other web pages … Therefore, an effective post-click news recommendation is critical to online news websites). (KSR(G), TSM, MPEP 2143). Additionally, one of ordinary skill in the art would have been motivated to do so because this is combining prior art elements according to known methods to yield predictable results, specifically, combining known methods of recommendation for articles to yield predictable results (KSR(A), MPEP 2143).
Per claim 15, Bitran teaches “A method, applied to a communication system, wherein the communication system comprises a terminal and a server, (Fig.1; ¶ [0021], computing system 10 includes a client computing device 12, which, for example, may take the form of a smart phone or tablet computing device, configured to communicate via a computer network with a server system 14) and the method comprises: sending, by the terminal, user behavior information of a user to the server; (Fig.1; ¶ [0022-0024], Client computing device 12 is configured to execute an electronic personal assistant application program 24 … the electronic personal assistant program is configured to passively monitor various user data 26 on the client computing device 12 and other client computing devices 16, such as location data, search history, download history, browsing history, contacts, social network data, calendar data, biometric data, medical device data, purchase history, etc. In some examples, user data 26 may be generated by or otherwise associated with user activities across a plurality of programs … User data 26 is transmitted from the electronic personal assistant application program 24 to the personal assistant user data interpretation engine 28 executed on server system 14; ¶ [0099], the suggestion engine 74 may learn behaviors of the user by analyzing the user's behavior over time) determining, by the server based on the user behavior information, target article information and first data corresponding to the target article information, wherein the first data comprises a recommendation reason field, and the first data is related to the target article information and the user behavior information; generating, by the server, a first message based on the target article information and the first data; receiving, by the terminal, the first message sent by the server, wherein the first message comprises the target article information and the first data; displaying, by the terminal, a first interface, wherein the first interface comprises a first recommendation, the first recommendation comprises first content and recommendation information corresponding to the first data, and the first content is obtained based on the target article information; (Fig. 7, a push notification/article 710 and a recommendation information/reason 720 (corresponding to the claimed first data): “Here is a local event for your favorite workout activity that fits your fitness level. Your weekend is free.”; Fig. 9, a push notification/article 910 and a recommendation information/reason: “Here is some news about one of your favorite workout activities.” Fig.10, a push notification/article 1010/1020 and a recommendation information/reason: “People who suffer from similar conditions like you read these.” Fig.11, a push notification/article 1110/1120/1130/1140 and a recommendation information/reason: “Consider the following insights regarding health advisories”; ¶ [0092-0094], the personal assistant user data interpretation engine 28 may infer from calendar data that in one month the user is traveling to India for a three-month visit. The suggestion engine 74 may identify a vaccine alert for India in the alerts 60 … A notification agent 64 within server system 14 may instruct the alert notification engine 68 of the electronic personal assistant application server 66 to send a message 70 in the form of a push notification featuring the content of the India vaccine alert 60 to the electronic personal assistant application program 24 executed on the client device 12 … the alert notification engine 68 may transmit to the electronic personal assistant application program 24 a link 1110 to the identified alert regarding India vaccines. The client computing device 12 may then display the link 1110 in the GUI 510 … personal assistant application program 24 may generate for the user and display a customized title 1120 for the alert that is based on data received from the suggestion engine 74 and/or other data from the user personal assistant knowledge base 30. In this example, the customized title 1110 makes reference to the user's upcoming trip to India, to thereby make the article more relevant to the user and increase the chances of the user clicking on the link 1110 to the alert; ¶ [0081-0083], With reference now to FIG. 9 and in another example, the electronic personal assistant application program 24 may provide a health-related suggestion in the form of a proactive push notification of a news article in which the user may be interested … The suggestion engine 74 may identify a news article that contains content about an amateur golfer. Based on the user's golfing ability, the personal assistant user data interpretation engine 28 may infer that the user may enjoy reading the article. Accordingly, the suggestion engine 74 may transmit to the electronic personal assistant application program 24 a link 910 to the identified news article for display in the GUI 510; ¶ [0084-0085], With reference now to FIG. 10 and in another example, the electronic personal assistant application program 24 may provide a health-related suggestion in the form of a proactive push notification of a news article, publication or other digital content in which the user may be interested … In the course of such searching the suggestion engine 74 may determine that the user suffers from migraine headaches ... the suggestion engine 74 may identify two articles related to migraine headaches. Accordingly, the suggestion engine 74 may transmit to the electronic personal assistant application program 24 two links 1010 and 1020 to the identified articles for display in the GUI 510; ¶ [0088], the suggestion engine 74 may locate the article indicated by link 1010 that suggests that migraine intensity can be reduced by ingesting bananas. The suggestion engine 74 may also analyze the current location of the user to determine that the user is at home. The suggestion engine may further determine from the user's purchasing history in the user personal assistant knowledge base 30 that the user regularly purchases bananas. Accordingly, the suggestion engine 74 may correlate information gleaned from the migraine-related article with the user biometric data, and in some examples the current location of the user, to determine that providing the health-related suggestion of the article to the user may help the user quickly address and perhaps alleviate the migraine headache. In response to such correlation, the suggestion engine may transmit to the electronic personal assistant application program 24 the link 1010 to the identified article discussing possibly reducing migraine intensity with bananas. The client computing device 12 may then display the link 1010 in the GUI 510; ¶ [0074], The suggestion engine 74 also may analyze calendar data of the user to determine that the user is available during the time and date of the bike ride event 710. Based on the user's calendar data, the user's location, the user's preference for bike riding, and the user's fitness level, the suggestion engine 74 may provide a health-related suggestion in the form of the bike ride event 710 to the electronic personal assistant application program 24 for display in the GUI 510. In some examples the electronic personal assistant application program 24 may generate for the user and display a customized title 720 for the event that is based on data received from the suggestion engine 74 and/or other data from the user personal assistant knowledge base 30. The customized title 720 may communicate that the event relates to the user's favorite workout activity, fits the user's fitness level, and that the user is available for the event; also see ¶¶ [0068-0074] regarding Fig.7, ¶¶ [0081-0083] regarding Fig.9, ¶¶ [0084-0090] regrading Fig.10, ¶¶ [0091-0099] regarding Fig.11) receiving, by the terminal, a first operation on the first recommendation;” (¶ [0094], make the article more relevant to the user and increase the chances of the user clicking on the link 1110 to the alert).
However, Bitran does not explicitly disclose the claim limitations regarding after clicking the recommendation (pushed recommendation/notification of an article), “sending, by the terminal, a first request to the server in response to the first operation, wherein the first request comprises an identifier of the first message, the first request requests the server to obtain content of a target article and second data based on the identifier of the first message, and the second data is related to the first data; obtaining, by the server, the content of the target article and the second data based on the first request; sending, by the server, the target article and second data to the terminal; receiving, by the terminal, the content of the target article and the second data that are sent by the server; and displaying, by the terminal, a second interface, wherein the content of the target article and recommendation information corresponding to the second data are displayed on the second interface”
Moon teaches “sending, by the terminal, a first request to the server in response to the first operation, wherein the first request comprises an identifier of the first message, the first request requests the server to obtain content of a target article and second data based on the identifier of the first message, and the second data is related to the first data; obtaining, by the server, the content of the target article and the second data based on the first request; sending, by the server, the target article and second data to the terminal; receiving, by the terminal, the content of the target article and the second data that are sent by the server; and displaying, by the terminal, a second interface, wherein the content of the target article and recommendation information corresponding to the second data are displayed on the second interface” (Figs.1, 2, 3; ¶ [0035-0036], each news result includes, at least, a title 204 and an abstract 206. Title 204 provides a short description of the content of the news article, and the abstract 206 provides a synopsis of the content of the news article. Once the user clicks on a news article, the Internet browser accesses the referenced webpage to present the news article. In one embodiment, the textual content of the news article is referred to herein as the body of the news article. In other embodiments, the news article include one or more of a title, a body, a summary, one or more pictures, a video clip, user comments, statistical information about the page, etc. On the right side of the page, an advertising area includes a list of advertisements, which are sometimes related to the topic of the search; ¶ [0038-0040], FIG. 3 shows a news article with links to related content, according to one embodiment. A typical news article includes, at least, a title 304 and a body 306. The title 304 provides a headline for the article, and the body 306 includes a textual description of the news article. In some cases, the news article may include one or more pictures, and one or more multimedia segments. Embodiments of the invention provide a related content section 302, which includes one or more links to related news articles … For a related article to be recommended, the related article should cover only a fraction of the content of the article be read, and include other content that is novel and news-worthy …; ¶ [0049], Given a seed article s, the goal of the method to generate post-click news recommendation is to identify a set of candidate article; also see ¶ [0028] for server-terminal relationship). [Comment: the related content section 302 (corresponding to second data) is related to the topic/reason of the article (corresponding to the first data).]
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine post-click operations including displaying a target article and recommendation information of Moon into displaying a recommendation of a target article and a recommendation reason of Bitran, such that a recommendation of a target article and a recommendation reason for the article would be displayed and, after clicking the recommendation, the article and another recommendation information related to the recommendation topic/reason would be displayed. One of ordinary skill in the art would have been motivated to do so because Bitran recognizes that it would have been advantageous to provide post-click recommendation information along with a target article (¶ [0005-0006], Recommending interesting news articles to users has become extremely important for internet providers looking to maintain users' interest … methods to engage users after their initial visit is largely under explored … This process is referred to herein as post-click news recommendation, which has the goal of promoting users' navigation to other web pages … Therefore, an effective post-click news recommendation is critical to online news websites). (KSR(G), TSM, MPEP 2143). Additionally, one of ordinary skill in the art would have been motivated to do so because this is combining prior art elements according to known methods to yield predictable results, specifically, combining known methods of recommendation for articles to yield predictable results (KSR(A), MPEP 2143).
Per claims 2, 16, and 20, Bitran further teaches “obtaining a display template corresponding to the first data based on the first data.” (Figs. 7, 9, 10, 11 show a template of a pushed notification/article (e.g., 710) and a recommendation information/reason (e.g., 720))
Moon further teaches “obtaining a display template corresponding to the first data based on the first data, (Fig.2; [0035-0036], each news result includes, at least, a title 204 and an abstract 206. Title 204 provides a short description of the content of the news article, and the abstract 206 provides a synopsis of the content of the news article…) or obtaining a display template corresponding to the second data based on the second data; and wherein displaying the second interface comprises: displaying the second interface, wherein the content of the target article and the recommendation information that corresponds to the second data and that is displayed by using the display template are displayed on the second interface” (Fig.3., ¶ [0036], Once the user clicks on a news article, the Internet browser accesses the referenced webpage to present the news article. In one embodiment, the textual content of the news article is referred to herein as the body of the news article. In other embodiments, the news article include one or more of a title, a body, a summary, one or more pictures, a video clip, user comments, statistical information about the page, etc. On the right side of the page, an advertising area includes a list of advertisements, which are sometimes related to the topic of the search; ¶ [0038-0039], FIG. 3 shows a news article with links to related content, according to one embodiment. A typical news article includes, at least, a title 304 and a body 306. The title 304 provides a headline for the article, and the body 306 includes a textual description of the news article. In some cases, the news article may include one or more pictures, and one or more multimedia segments. Embodiments of the invention provide a related content section 302, which includes one or more links to related news articles … For a related article to be recommended, the related article should cover only a fraction of the content of the article be read, and include other content that is novel and news-worthy). [Comment: the combination/motivation is the same as that of independent claims.]
Per claim 3, Bitran further teaches “wherein obtaining the display template corresponding to the first data based on the first data, or obtaining the display template corresponding to the second data based on the second data comprises: obtaining, based on the recommendation reason field, a display template corresponding to the recommendation reason field” (Fig. 7, a push notification/article 720 and a recommendation information/reason 720 (corresponding to the claimed first data): “Here is a local event for your favorite workout activity that fits your fitness level. Your weekend is free.”; Fig. 9, a push notification/article 910 and a recommendation information/reason: “Here is some news about one of your favorite workout activities.” Fig.10, a push notification/article 1010/1020 and a recommendation information/reason: “People who suffer from similar conditions like you read these.” Fig.11, a push notification/article 1110/1120/1130/1140 and a recommendation information/reason: “Consider the following insights regarding health advisories”; ¶ [0094], the customized title 1110 makes reference to the user's upcoming trip to India, to thereby make the article more relevant to the user and increase the chances of the user clicking on the link 1110 to the alert; ¶ [0074], The customized title 720 may communicate that the event relates to the user's favorite workout activity, fits the user's fitness level, and that the user is available for the event).
Moon further teaches “wherein obtaining the display template corresponding to the first data based on the first data, or obtaining the display template corresponding to the second data based on the second data comprises: obtaining, based on the recommendation reason field, a display template corresponding to the recommendation reason field” (Figs 2, 3; ¶ [0038-0039], FIG. 3 shows a news article with links to related content … Embodiments of the invention provide a related content section 302, which includes one or more links to related news articles … For a related article to be recommended, the related article should cover only a fraction of the content of the article be read, and include other content that is novel and news-worthy; also see ¶ [0035-0036]). [Comment: the combination/motivation is the same as that of independent claims.]
Per claims 4 and 17, Bitran further teaches “wherein before displaying, by the terminal, the second interface, the method further comprises: receiving, by the terminal, related data that is of a category to which the target article belongs and that is pushed by the server” (Fig. 7, a push notification/article 720 and a recommendation information/reason 720 (corresponding to the claimed first data): “Here is a local event for your favorite workout activity that fits your fitness level. Your weekend is free.”; Fig. 9, a push notification/article 910 and a recommendation information/reason: “Here is some news about one of your favorite workout activities.” Fig.10, a push notification/article 1010/1020 and a recommendation information/reason: “People who suffer from similar conditions like you read these.” Fig.11, a push notification/article 1110/1120/1130/1140 and a recommendation information/reason: “Consider the following insights regarding health advisories”; ¶ [0094], the customized title 1110 makes reference to the user's upcoming trip to India, to thereby make the article more relevant to the user and increase the chances of the user clicking on the link 1110 to the alert; ¶ [0074], The customized title 720 may communicate that the event relates to the user's favorite workout activity, fits the user's fitness level, and that the user is available for the event).
Moon further teaches “wherein before displaying, by the terminal, the second interface, the method further comprises: receiving, by the terminal, related data that is of a category to which the target article belongs and that is pushed by the server, wherein second content is further displayed on the second interface, and the second content is obtained based on the related data of the category to which the target article belongs” (Fig.3; ¶ [0039-0040], Embodiments of the invention provide a related content section 302, which includes one or more links to related news articles. What makes a news article related to the current news article? First of all, two news articles should be contextually similar to each other. In this sense, similarity or relevance is an important indicator of relatedness … For a related article to be recommended, the related article should cover only a fraction of the content of the article be read, and include other content that is novel and news-worthy … In addition to the notions of similarity and novelty, two new factors for measuring relatedness are introduced below: connection clarity and transition smoothness; ¶ [0042], To find related news articles, there should be a clear connection between two documents to maintain topical continuity. In other words, the overlap between two documents should be comprised of some meaningful topical context; ¶ [0062], in order to make two documents related, the documents should share a clear story thread that makes them topically cohesive. This can often be achieved by repeating the same topic, or a similar topic, which forms topical links that connect two documents together and make them related.) [Comment: the combination/motivation is the same as that of independent claims.]
Per claims 5 and 18, Bitran further teaches “wherein the user behavior information comprises any one of the following: geographical location information, (¶ [0023], Specific examples of these various types of user data 26 will now be described. Location data may include for example, GPS coordinate data (latitude and longitude) obtained by a GPS receiver implemented on any client computing device, an identifier such as an IP address and/or Wi-Fi access point identifier that can be resolved to a generalized geographic location, a user check-in at a location via a social network program, etc; ¶ [0091-0093], With reference now to FIG. 11 and in another example, the electronic personal assistant application program 24 may provide a health-related suggestion in the form of a proactive push notification of health advisories that relate … geographic travel history of the user … the personal assistant user data interpretation engine 28 may infer from calendar data that in one month the user is traveling to India for a three-month visit … the alert may be targeted to users who the data interpretation engine 28 infers will soon travel to India, such as the user in the present example) subscription dynamic category information, social dynamic category information, (¶ [0023], The social network data may include a user's friends list, a list of social network entities “liked” by the user, check-ins made by the user at locations via a social network program, posts written by the user, etc.) or user interest category information” (¶ [0023], Specific examples of these various types of user data 26 will now be described … Each of these applications and files may have metadata associated with them, such as categories, genres, etc; ¶ [0068], a proactive push notification of a fitness event in which the user may be interested. In some examples the suggestion engine 74 may perform searches in one or more of the user personal assistant knowledge base 30 …; ¶ [0071], The personal assistant user data interpretation engine 28 may infer from the user's profile 32 that the user enjoys bike riding and has, for example, performed bike rides in the last month of 25, 30 and 40 miles … matches the user's fitness level; ¶ [0082], the suggestion engine 74 may determine from data in the user's profile 32 that the user has a preference for playing golf and that the user has a golf handicap of 7, which corresponds to a numerical measure of a golfer's playing ability … he suggestion engine 74 may search news aggregators and other online content sources for articles and other publications that would be of interest to a person having the user's golfing playing ability (i.e., an amateur golfer having a single-digit handicap); ¶ [0084], the electronic personal assistant application program 24 may provide a health-related suggestion in the form of a proactive push notification of a news article, publication or other digital content in which the user may be interested).
Per claim 6, Bitran further teaches “wherein the user behavior information comprises the user interest category information; and wherein: the target article is determined by the server based on the interest category information, and the first data is related to the interest category information; or the first data is related to the target article information and the interest category information” (Figs. 7, 9, 10, and 11; ¶ [0074], The suggestion engine 74 also may analyze calendar data of the user to determine that the user is available during the time and date of the bike ride event 710. Based on the user's calendar data, the user's location, the user's preference for bike riding, and the user's fitness level, the suggestion engine 74 may provide a health-related suggestion in the form of the bike ride event 710 to the electronic personal assistant application program 24 for display in the GUI 510. In some examples the electronic personal assistant application program 24 may generate for the user and display a customized title 720 for the event that is based on data received from the suggestion engine 74 and/or other data from the user personal assistant knowledge base 30. The customized title 720 may communicate that the event relates to the user's favorite workout activity, fits the user's fitness level, and that the user is available for the event; ¶ [0083], The suggestion engine 74 may identify a news article that contains content about an amateur golfer. Based on the user's golfing ability, the personal assistant user data interpretation engine 28 may infer that the user may enjoy reading the article. Accordingly, the suggestion engine 74 may transmit to the electronic personal assistant application program 24 a link 910 to the identified news article for display in the GUI 510; ¶ [0088], the suggestion engine 74 may correlate information gleaned from the migraine-related article with the user biometric data, and in some examples the current location of the user, to determine that providing the health-related suggestion of the article to the user may help the user quickly address and perhaps alleviate the migraine headache …; ¶ [0093-0094], the alert may be targeted to users who the data interpretation engine 28 infers will soon travel to India, such as the user in the present example. Accordingly, the alert notification engine 68 may transmit to the electronic personal assistant application program 24 a link 1110 to the identified alert regarding India vaccines. The client computing device 12 may then display the link 1110 in the GUI 510. In one example, personal assistant application program 24 may generate for the user and display a customized title 1120 for the alert that is based on data received from the suggestion engine 74 and/or other data from the user personal assistant knowledge base 30. In this example, the customized title 1110 makes reference to the user's upcoming trip to India, to thereby make the article more relevant to the user and increase the chances of the user clicking on the link 1110 to the alert).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bitran-Moon, in view of Immonen (US 2014/0059647 A1).
Per claim 7, Bitran-Moon does not teach “wherein the second data displayed on the second interface comprises: the second data comprises a ranking list of user interests.”
Immonen teaches displaying, on a user interface, a ranking/top list of a user’ interests based on monitoring behavior of the user to provide content interested by the user (Fig.5C; ¶ [0110-0112], The interests have been categorized into multiple categories such as Alcohol&Tobacco, Cars, Travel and Sports … This view 504 especially illustrates how the user may conveniently go into details of a higher level category, such as ‘Alcohol & Tobacco’ … FIG. 5 c is one more example of a browser-based UI and related view. The shown view 506 discloses statistical data such as ‘Top categories’ or ‘Interest graph’. The arrangement may be configured to create such data indicative of user interests based on at least one tracked element selected from the group consisting of: search term, keyword, web site (visited, input or otherwise indicated), web page, web page category, web site category, message header, message topic, and message payload; claim 1, wherein the personal media profile describes the user's interests … wherein the interests are represented on a number of levels including interest categories and further wherein the interests are at least partially determined based on monitoring the user behavior relative to the network; ¶ [0028-0029], establishing a user-adjustable personal media profile for a web user capable of accessing a network … wherein the interests are represented on a number of levels including interest categories, such as automotive or travel categories, and further wherein the interests are at least partially determined based on monitoring the user behavior relative to the network … wherein the user is provided with notifications indicative of content suggestions conforming to the personal media profile).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine displaying a ranking list of interests of Immonen into displaying a target article along with information related to a user’s interest(s) of Bitran-Moon, such that a target article along with a ranking list of a user’s interests would be displayed. One of ordinary skill in the art would have been motivated to do so because Immonen recognizes that it would have been advantageous to display a ranking list of interests for a user to access and control/alter the list to provide content interested by the user (¶ [0011], wherein the user is provided with a number of notifications indicative of content suggestions conforming to the personal media profile via the terminal, and wherein personal media profile data is selectively, preferably user-controllably; ¶ [0110-0111], The shown view 502 visualizes few controls for managing profile details such as adjusting interests information. The interests have been categorized into multiple categories such as Alcohol&Tobacco, Cars, Travel and Sports. The user is provided via the UI a possibility to alter the current profile … This view 504 especially illustrates how the user may conveniently go into details of a higher level category, such as ‘Alcohol & Tobacco’, and within the category independently set off/on, i.e. block/allow, lower level items such as brand names or item types … ) (KSR(G), TSM, MPEP 2143). Additionally, one of ordinary skill in the art would have been motivated to do so because this is combining prior art elements according to known methods to yield predictable results, specifically, combining known methods of displaying user interest-related information to yield predictable results, especially given that Bitran, Moon, and Immonen are in the same field of endeavor of providing content interested by a user (KSR(A), MPEP 2143).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Bitran-Moon, in view of Dolan (US 2012/0290522 A1).
Per claim 14, Bitran further teaches “wherein the user behavior information comprises the user interest category information” (¶ [0023], Specific examples of these various types of user data 26 will now be described … Each of these applications and files may have metadata associated with them, such as categories, genres, etc; ¶ [0068], a proactive push notification of a fitness event in which the user may be interested. In some examples the suggestion engine 74 may perform searches in one or more of the user personal assistant knowledge base 30 …; ¶ [0071], The personal assistant user data interpretation engine 28 may infer from the user's profile 32 that the user enjoys bike riding and has, for example, performed bike rides in the last month of 25, 30 and 40 miles … matches the user's fitness level; ¶ [0082], the suggestion engine 74 may determine from data in the user's profile 32 that the user has a preference for playing golf and that the user has a golf handicap of 7, which corresponds to a numerical measure of a golfer's playing ability … he suggestion engine 74 may search news aggregators and other online content sources for articles and other publications that would be of interest to a person having the user's golfing playing ability (i.e., an amateur golfer having a single-digit handicap); ¶ [0084], the electronic personal assistant application program 24 may provide a health-related suggestion in the form of a proactive push notification of a news article, publication or other digital content in which the user may be interested).
Moon further teaches “wherein the user behavior information comprises the user interest category information, the user interest category information comprises an interest vector weight of each article category, and the interest vector weight of each article category indicates a preference degree of the user for each category of articles” (Abstract, providing internet content, such as related news articles … scores are calculated for relevance, novelty, connection clarity, and transition smoothness … the score for transition smoothness measures the interest in reading each candidate; ¶ [0098], a plurality of candidates is defined based on the seed, such as a news article that has been selected by the user. Further, in operation 1004, scores are calculated for each candidate for relevance, novelty, connection clarity, and transition smoothness. The score for connection clarity is based on the relevance score of an intersection between the seed and each candidate, and the score for transition smoothness measures the interest in reading each candidate when transitioning from the seed to the candidate; ¶ [0079], Such a document-based context is represented as a vector of documents … The relevance score of document di with respect to “query” (s−d) is g(s−d, di). In one embodiment, the BM25 retrieval function is used to compute relevance scores. This implies that if a document is more relevant to (s−d), the document would play a more important role to determine the context of (s−d). Similarly, the context vector {right arrow over (C)}d−s is estimated for (d−s). In one embodiment, the transition smoothness score is computed using the cosine similarity between context vectors as follows … ; ¶ [0062-0063], in order to make two documents related, the documents should share a clear story thread that makes them topically cohesive. This can often be achieved by repeating the same topic, or a similar topic, which forms topical links that connect two documents together and make them related … the clearer the topical connection is, the more related the two documents will probably be … topics associated with each document are identified using probabilistic topic models). [Comment: the combination/motivation is the same as that of independent claims.]
However, Bitran-Moon does not teach “wherein before sending the user behavior information of the user (e.g., a user’s interests), the method further comprises: obtaining n historical articles browsed by the user, wherein n is a positive integer greater than 1; extracting article categories of the n historical articles browsed by the user; and determining an interest vector weight of each article category based on the article categories of the n historical articles and a tap frequency, a time attenuation factor, and a weight of each article category.”
Dolan teaches “wherein before sending the user behavior information of the user (e.g., a user’s interests), the method further comprises: obtaining n historical articles browsed by the user, wherein n is a positive integer greater than 1; extracting article categories of the n historical articles browsed by the user; and determining an interest vector weight of each article category based on the article categories of the n historical articles and a tap frequency, a time attenuation factor, and a weight of each article category” (Figs. 1, 2B, 2C, 2D, 2E, ¶ [0006], a system and method are provided to provide personalized news recommendations based on a prediction of a user's interests. The prediction of the user's interest is based on the user's selection history … This enables a user to receive news recommendations for news items in the categories the user has a strong interest; ¶ [0067-0068], A particular user's interest for each category (or probability of selecting media items belonging to each category c) during a time period t is determined using the particular user's click distribution (e.g., 130) … a particular user's interest in a respective category ci (or probability of selecting an item belonging to category c) at any given time t is represented by the equation … In Equation 1, the probability of a particular user clicking on a media item (e.g., 126, 129) of category ci at time t is represented by pt(category=ci|click). For example, a particular user may click on health related media items 13% of the time. In Equation 1, pt(category=ci|click) corresponds to the particular user's click distribution (e.g., 130) during time period t for the respective category In Equation 1, pt(category=ci) is the probability that a media item (e.g., 126, 129) belongs to category ci during time period t. For example, in the last month, there is a 13% chance a given media item belongs to sports. In Equation 1, pt(category=ci) corresponds to the proportion of media items published in category ci during time period t and to the trend of news during time period t … The probability of the particular user clicking on any article at time t is represented by pt(click) … To accurately predict a particular user's genuine interest in a category ci, the particular user's interest (pt(click|category=ci)) in multiple time periods is combined as follows … M represents the total number of time windows combined; ¶ [0059], the click distribution (e.g., 130 of the particular user determines the particular user's likelihood of selecting media items associated with a respective category. For example, if 20% of the particular user's clicks were for technology, the particular user is likely to selection technology items 20% of the time. In some embodiments, a combination of weighted click data is used to determine the particular user's likelihood of selection. For example, 5% of the particular user's clicks may have been for health items but the determined likelihood of the particular user selecting health items may be greater than 5% due to weighted click data. For example, search clicks may be given greater weight than browse clicks. Clicks with shorter selection duration may be given greater weight than clicks with longer selection duration; ¶ [0073], click rates (e.g., the total number of users who clicked on an item divided by the number of times the item was displayed to users); ¶ [0032], media items 129 including but not limited to news articles; ¶ [0041], the clicks 260 include the total number of clicks for the respective category 214 during a predefined period of time. For example, for the category of Sports, a user may have had a total of 25 clicks in the past week).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine determining a user’s interest of an article category based on historical articles, a tap frequency, a time attenuation factor, and a weight as taught by Dolan into determining a user’s interest of an article category as taught by Bitran-Moon. One of ordinary skill in the art would have been motivated to do so because Dolan recognizes that it would have been advantageous to consider a user’s selection history to determine the user’s interest in categories of news/articles and provide news recommendations accordingly (¶ [0006], a system and method are provided to provide personalized news recommendations based on a prediction of a user's interests. The prediction of the user's interest is based on the user's selection history and the selection history of a community of users or general population. This enables a user to receive news recommendations for news items in the categories the user has a strong interest in) (KSR(G), TSM, MPEP 2143). Additionally, this is combining prior art elements according to known methods to yield predictable results, specifically, combining known methods of how to determine a user’s interest in articles to yield predictable results, especially given that Bitran, Moon, and Dolan are in the same field of endeavor of providing recommended articles based on a user’s interest (KSR(A), MPEP 2143).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gudla (US 2016/0014057 A1) discloses delivering push messages based on user behavior and interest category.
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/HANNAH S WANG/Supervisory Patent Examiner, Art Unit 2631
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