DETAILED ACTION
1. Claims 1-7 and 9-20 are pending in this application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Response to Amendment
3. This office action is in response to applicant’s amendment filed on 11/11/2025 in response to the non-final rejection mailed on 08/14/2025. Claims 1, 9, 17 and 19-20 have been amended. Claims 2, 4, 6-7, 10-12, and 18 have been previously presented. Claims 3, 5, 13-14 and 16 have been kept original. Claim 8 have been cancelled. Amendment has been entered.
Response to Arguments
4. Applicant's arguments, filed on 11/11/2025, with respect to the rejection of claims 1-7 and 9-20 under 35 U.S.C. §101 an abstract idea (mental process) (Applicant' s arguments, pages 7-10), have been fully considered but are not persuasive. Respectfully, the examiner disagrees, see the clarification below.
Applicant argues that claim 1 improves a user interface because it uses a long-term user interface-independent recommender system and short-term user interface-specific recommender system. However, simply use a system to calculate the probability that a recommendation does not show any improvement to a user interface. The systems in the claims are being used merely as tools to implement the abstract idea; see MPEP § 2106.05(f).
A human is able to calculate probabilities using his or her own mind. For example, a human might estimate a 70% chance of enjoying a song recommendation by subconsciously comparing its genre and "vibe" to their past listening habits and current mood.
The long-term user interface-independent recommender system and short-term user interface-specific recommender system, as generically recited, is merely a component used to implement the abstract idea using a computer device. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer, see MPEP § 2106.04(a)(2)(III).
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.
A human can mentally aggregate subjective scores—such as a song's genre fit, emotional resonance, and production quality—to predict the likelihood of playing that music again. For example, an example is a listener who mentally assigns a "7/10" for the melody and an "8/10" for the lyrics of a new song, then concludes there is a high probability they will play it again during their next workout.
Applicant argues that “There is no practical sense in which a human mind can combine scores from a "short-term user-interface-specific recommender system for the user interface of the media application on which the recommendation is to be displayed" with a "long-term user-interface-independent recommender system" and then provide the resulting recommendation on the user interface of the media application.” However, simply use a system to calculate the probability that a recommendation does not show any improvement to a user interface. The systems in the claims are being used merely as tools to implement the abstract idea; see MPEP § 2106.05(f). It is also noted that Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer, see MPEP § 2106.04(a)(2)(III).
Applicant argues that “As described in the specification, using two different recommender systems that are separately trained "allows for training on specific data that can improve accuracy, efficiency, and user satisfaction.” And cited specification paragraphs [0044], [0049] and [0063]. The claims do not reflect in any accuracy, efficiency, and user satisfaction improvements. The no all elements of specification paragraph [0044], [0049] and [0063] are reflecting in the claims. The claims must reflect the mentioned improvement and have a direct link to the parts of the specification where it is recited in detail, see MPEP 2106.05(a).
Applicant argues that “the independent claims as a whole integrate the judicial exception into a practical application for determining and displaying recommendations on a user interface of a media application using "a long-term user-interface-independent recommender system" and "a short-term user-interface-specific recommender system."” However, simply using a system as tool to predict data do not integrate the clams into a practical application, see, MPEP 2106.05(f) (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. Display information is nothing more than transmitting information. The courts have found that transmitting information is well understood routine and conventional, see MPEP 2106.05(d)(ii) “i. 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); … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);”
For the reasons above, the rejection under 35 U.S.C. § 101 for being an abstract idea (mental process) is upheld.
Applicant's arguments, filed on 11/11/2025, with respect to the rejection of claims 1-7 and 9-20 under 35 U.S.C. §103 (Applicant’s arguments, pages 10-12), have been fully considered and are but are moot because the independent claims are amended and introduce new limitations that were not previously presented newly found prior art has been applied.
Claim Rejections - 35 USC § 101
5. 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 9-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea (Mental Process) without significantly more. The claims similarly recite steps to providing content recommendations for long-term user reengagement.
The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category?
Claims 1-7 and 9-18 are directed to a method.
Claim 19 is directed to a system.
Claim 20 is directed to a non-transitory computer-readable storage medium.
Therefore, claims 1-7, and 9-20 fall into at least one of the four statutory categories.
Step 2A, Prong I: Judicial Exception Recited?
The examiner submits that the foregoing claim limitations constitute a “Mental Process”, as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation.
As per independent claims 1, 19 and 20, the claims similarly recite the limitations of: “using a long-term user-interface-independent recommender system, acquiring, by a computing device, a first score corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time;” A human evaluator can assess a content item and provide a score based on their judgment, using a predefined set of evaluation criteria. The long-term user-interface-independent recommender system, prediction of subsequent reengagement, user, and future window of time are a merely component used for the mental steps recited in claim. There is nothing so complex in the limitation that could not be doing in the human mind.
“using a short-term user-interface-specific recommender system for the user interface of the media application on which the recommendation is to be displayed, acquiring, by the computing device, a second score corresponding to a probability of a selection of the content item by the user in the user interface of the media application;” A human evaluator can assess a content item and provide a score based on their judgment, using a predefined set of evaluation criteria. The short-term user-interface-specific recommender system, and probability of a selection of the content item by the user are a merely component used for the mental steps recited in claim. There is nothing so complex in the limitation that could not be doing in the human mind.
“ranking the set of content items based on the respective combined scores;” A human can observe a set of content items and rank them based on judgments made using a predefined set of evaluation criteria. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 2, the claim recites the limitation of: “wherein first the score is calculated from a combination of a user vector for the user and a content item vector for the content item.” The combination of a user vector for the user and a content item vector for the content item recited above are merely components used for the mental steps recited in claim 1.
As per dependent claim 4, the claim recites the limitation of: “wherein the prediction of subsequent reengagement comprises a calculation of a number of times the user will reengage with the content item during a future time period.” The calculation of the number of times the user will reengage with the content item during the future time period recited above is merely component used for the mental steps recited in claim 1.
As per dependent claim 5, the claim recites the limitation of: “wherein reengaging with the content item comprises playing back content of the content item for at least a preset amount of time.” The playing back content of the content item for at least the preset amount of time recited above is merely component used for the mental steps recited in claim 1.
As per dependent claim 6, the claim recites the limitation of: “wherein the prediction of subsequent reengagement comprises a calculation of an amount of time the user will engage with the content item during a future time period.” The calculation of the amount of time the user will engage with the content item during the future time period recited above is merely component used for the mental steps recited in claim 1.
As per dependent claim 7, the claim recites the limitation of: “wherein the prediction of subsequent reengagement comprises a calculation of an amount of time that will elapse before the user reengages with the content item.” The calculation of the amount of time that will elapse before the user reengages with the content item recited above is merely component used for the mental steps recited in claim 1.
As per dependent claim 9, the claim recites the limitation of: “wherein the short-term user-specific recommender system is trained using data specific to the user interface of the media application.” The short-term user-specific recommender system is trained using data specific to the user interface of the media application is merely component used for the mental steps recited in claim.
As per dependent claim 10, the claim recites the limitation of: “wherein the second score for each content item is based on information about the user, information about the content item, and context information.” The information about the user, information about the content item, and context information recited above are merely components used for the mental steps recited in claim.
As per dependent claim 11, the claim recites the limitation of: “wherein the first score for each content item is based on (i) the prediction of subsequent reengagement by a user with the content item and (ii) a probability of a selection of the content item by the user.” The user with the content item and (ii) the probability of the selection of the content item by the user recited above are merely components used for the mental steps recited in claim.
As per dependent claim 12, the claim recites the limitation of: “wherein the second score for each content item is based on information about the user, information about the content item, context information, and information about types of short-term outcomes for content items.” The information about the user, information about the content item, context information, and information about types of short-term outcomes for content items recited above are merely components used for the mental steps recited in claim.
As per dependent claim 13, the claim recites the limitation of: “wherein the types of short-term outcomes include one or more of: the user selecting the content item to watch, the user watching the content item, and the user adding the content item to a favorites list.” A human can observe a collection of items, mentally select the ones that interest them, and create a list of the selected items. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 14, the claim recites the limitation of: “wherein the set of content items includes one or more of: a podcast series, a show series, an audiobook series, and a music playlist.” The podcast series, show series, audiobook series, and music playlist recited above are merely components used for the mental steps recited in claim.
As per dependent claim 15, the claim recites the limitation of: “wherein the first score for each content item corresponds to a likelihood of consumption of the content item affecting a habit of the user.” The likelihood of consumption of the content item affecting the habit of the user recited above are merely components used for the mental steps recited in claim.
As per dependent claim 16, the claim recites the limitation of: “wherein the set of content items comprises content items not previously engaged with by the user.” The content items not previously engaged with by the user recited above are merely components used for the mental steps recited in claim.
As per dependent claim 17, the claim recites the limitation of: “wherein the long-term user-interface-independent recommender system is trained using a reward function.” The recommender system is trained using the reward function recited above are merely components used for the mental steps recited in claim.
As per dependent claim 18, the claim recites the limitation of: “wherein the first score for the content item comprises a difference score for reengagement, and wherein the difference score corresponds to a difference between a first reengagement score without the user receiving a recommendation and a second reengagement score with the user receiving the recommendation.” The difference score for reengagement, difference between the first reengagement score without the user receiving the recommendation and the second reengagement score with the user receiving the recommendation recited above are merely components used for the mental steps recited in claim.
Accordingly, claims 1-7 and 9-20 recite at least one abstract idea.
Step 2A, Prong II: Integrated into a Practical Application?
The claims recite the following additional limitations/elements:
As per independent claims 1, 19 and 20, the claims recite the additional limitation of: “for each content item of a set of content items provided by a media-providing service associated with a media application, the media application including a user interface on which a recommendation is to be displayed:” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis. The set of content items provided is equivalent to the set of content items received.
“providing recommendation information to the user for one or more highest ranked content items in the set of content items, including displaying the one or more highest ranked content items in the user interface of the media application.” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis. The set of content items provided is equivalent to the set of content items received. Additionally, displaying data in a Graphical User Interface (GUI) is an example of collecting and transmitting data. The respective surface per specification is a type of GUI specification paragraph [0007].
As per dependent claim 3, the claim recites the additional limitation of: “further comprising receiving a request to provide one or more recommendations to the user, wherein the recommendation information is provided in response to receiving the request.” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis. The set of content items provided is equivalent to the set of content items received.
As per independent claim 19, the claim recites the additional elements of: “one or more processors, memory, and one or more programs.” This element is example of mere instruction to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
As per independent claim 20, the claim recites the additional elements of: “a non-transitory computer-readable storage medium, computing device, one or more processors, memory, and one or more programs.” This element is example of mere instruction to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
Therefore, claims 1-7 and 9-20 do not integrate the recited abstract ideas into a practical application.
Step 2B: Claim provides an Inventive Concept?
With respect to the limitations identified as insignificant extra-solution activity above the conclusions are carried over, and both the “providing …; displaying ….; and receiving …” are well-understood, routine, and conventional operations.
For support as being well-understood, routine, and conventional for “providing …; displaying ….; and receiving …” as noted by the courts is well understood routine and conventional, see MPEP 2106.05(d)(ii) “i. 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); … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);” and/or MPEP 2106.05(d)(ii) “iv. 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;”, and/or MPEP 2106.05(d)(II) “iii. Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);” Megannon et al. (US 20240249226 A1) paragraph [0281] – “BI dashboards would typically present new models that might be of interest to the miner as recommendations”; Imbert et al. (US 20240037678 A1) paragraph [0003] – “In order to try to maximize sales and/or conversion rates in transactional apps or websites used for online shopping and self-ordering terminals (or other self-ordering devices such as drive in ordering displays), it has become common practice for online retailers to provide product recommendations, where specific systems are used to determine the most likely relevant products for a user accessing the transactional app or website and to display the recommended product on the interface in order to provide purchase guidance or alternatives to the user and therefore improve its shopping experience.” and Bhattacharya et al. (US 20230388596 A1) paragraph [0024] – “The media titles associated with the highest predicted likelihoods are then displayed within a conventional GUI as personalized recommendations.”. See also see [Display Interface - an overview | ScienceDirect Topics, Introducing ASP.NET Web Pages - Displaying Data | Microsoft Docs, Execute DBCC PAGE command to Display Contents of Data Pages in SQL Server (kodyaz.com) and Load and display paged data | Android Developers].
Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible.
Therefore, the claims 1-7 and 9-20 are not patent eligible.
Claim Rejections - 35 USC § 103
6. 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.
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 pre-AIA 35 U.S.C. § 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claims 1-4, 9-10, 14-15 and 18-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Rao et al. (US 20190266185 A1) in view of Chung et al. (US 20070260624 A1) in further view of Jacobi et al. (US 20010021914 A1).
As per claim 1, Rao teaches a method (i.e. “Methods”; Abstract) of
providing content to users (i.e. “providing content items to users”; para. [0016]), the method comprising:
for each content item (i.e. “a content item”; para. [0016], [0071])
of a set of content items (i.e. “… films starring Robert DeNiro in action films, it may recommend similar actors who also star in action films.”; fig. 2A-B, [0071], [0079]; Examiner note: the set of content items is interpreted as the films starring Robert DeNiro in action films)
provided by a media-providing service associated with a media application (i.e. “FIG. 2B schematically depicts another example dialog process interaction 222 in a virtual agent dialog window 224 in an application environment 226 of a smart virtual agent application 227.”; figs. 2A-B, para. [0067], [0071]; figs. 2A-B, Examiner note: the media-providing service is interpreted as the virtual agent dialog window 224; the virtual agent dialog window is providing the recommendation item information to the user, see fig. 2B. The media application is interpreted as the smart virtual agent application):
the media application including a user interface on which a recommendation is to be displayed (i.e. “The dialog process interaction 222 proceeds between the dialog process 230 and the user 232 until a content item is suggested by the content recommendation model 106 and selected for presentation in the content viewing window 234 by the user 232.”; fig. 2b, para. [0067]; Examiner note: the user interface is interpreted as the content viewing window 234. The recommendation is to be displayed is interpreted as the content item is suggested by the content recommendation model 106 and selected for presentation in the content viewing window 234):
assigning, by the computing device, a combined score to the content item by aggregating the first score and the second score (i.e. “a combined score deviation of an item of digital content is determined as the difference between the maximum content score, and the minimum content score of the item of digital content.”; figs. 3-4, para. [0077], [0081]; Examiner note: the combined score to the content item is interpreted as the combined score deviation of an item of digital content. Further, i.e. “For example, a combined score deviation for Inception can be provided as 15 (e.g., the difference between the highest content score, and the lowest content score), and a combined content score deviation for Interstellar can be provided as 65 (e.g., the difference between the highest content score, and the lowest content score).”; para. [0039]);
ranking the set of content items based on the respective combined scores (i.e. “Consequently, the combined score deviation of content scores for User 1, 2, and 3 is lower for Inception than for Interstellar, and, between these examples, Inception is provided as the candidate content item to the group of User 1, User 2, and User 3, and/or is provided as a more highly ranked candidate content item than Interstellar.”; para. [0039]. Further, i.e. “Candidate item(s) of digital content are determined based on the combined score deviations (308). For example, five items that have the lowest combined score deviations among the items of digital content (e.g., content items 110) are determined as the candidate items.”; figs. 3-4, para. [0078]);
However, it is noted that the prior art of Rao does not explicitly teach “using a long-term user-interface-independent recommender system, acquiring, by a computing device, a first score corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time; using a short-term user-interface-specific recommender system for the user interface of the media application on which the recommendation is to be displayed, acquiring, by the computing device, a second score corresponding to a probability of a selection of the content item by the user in the user interface of the media application; providing recommendation information to the user for one or more highest ranked content items in the set of content items, including displaying the one or more highest ranked content items in the user interface of the media application.”
On the other hand, in the same field of endeavor, Chung teaches using a short-term user-interface-specific recommender system for the user interface of the media application on which the recommendation is to be displayed (i.e. “The short-term behavioral targeting system categorizes the events (FIG. 11, block 1120). A model, corresponding to the short-term direct marketing/user objective, is selected (FIG. 11, block 1130). In part, the model includes a plurality of weights for dimension processing (e.g., recency, intensity and frequency).”; fig. 11, para. [0112]; Examiner note: the short-term user-interface-specific recommender system is interpreted as the short-term behavioral targeting system; the short-term behavioral targeting system is used to calculate a plurality of weights/scores. Further, i.e. “The client system 1420 typically includes one or more user interface devices 22 (such as a keyboard, a mouse, a roller ball, a touch screen, a pen or the like) for interacting with a graphical user interface (GUI) of the web browser on a display (e.g., monitor screen, LCD display, etc.).”; para. [0160]),
acquiring, by the computing device (i.e. “The user scores are input to the ad server (850)”; figs. 8-12, para. [0074]),
a second score (i.e. “The behavioral targeting system 1200 also comprises real-time behavior targeting processing 1270 and user data store 1290 to generate short-term user interest scores.”; fig. 12, para. [0130]-[0131]; Examiner note: the acquiring a second score is interpreted as the generate short-term user interest scores)
corresponding to a probability of a selection of the content item by the user in the user interface of the media application (i.e. “the short-term user interest scores may be used to select ads for various user and marketing objectives.”; para. [0131]. Further, i.e. “In general, the click-through-rate (CTR) of a user for a category reflects the probability that the user will select ("click on") content (e.g., advertisement, link, etc.) associated with the category.”; para. [0136]; Examiner note: the probability of the selection of the content item by the user is interpreted as the probability that the user will select ("click on") content (e.g., advertisement, link, etc.) associated with the category);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Chung that teaches an online behavioral targeting system into Rao that teaches interactive content recommendation for selecting and providing customized content to users. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to record and analyze information about a user to determine behavioral patterns and interests, because it can be used to present specific content to a user, which can provide a more meaningful and rich experience on the internet (Chung, para. [0005]).
However, it is noted that the combination of the prior arts of Rao and Chung do not explicitly teach “using a long-term user-interface-independent recommender system, acquiring, by a computing device, a first score corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time; providing recommendation information to the user for one or more highest ranked content items in the set of content items, including displaying the one or more highest ranked content items in the user interface of the media application.”
On the other hand, in the same field of endeavor, Jacobi teaches using a long-term user-interface-independent recommender system (i.e. “FIG. 5 illustrates the sequence of steps that are performed by the Instant Recommendations service to generate personal recommendations. Steps 180-194 in FIG. 5 correspond, respectively, to steps 80-94 in FIG. 2. In step 180, the process 52 identifies all popular items that have been purchased by the user (from a particular shopping cart, if designated) or rated by the user, within the last six months. In step 182, the process retrieves the similar items lists 64 for these popular items from the similar items table 60.”; fig.5, para. [0093]; Examiner note: The long-term is interpreted as the last six months. The recommendation system is interpreted as the Instant Recommendations service),
acquiring, by a computing device, a first score (i.e. “the BookMatcher application 50 records the ratings within the user's items rating profile. For example, if a user of the BookMatcher service gives the book Into Thin Air a score of “5,” the BookMatcher application 50 would record the item (by ISBN or other identifier) and the score within the user's item ratings profile.”; fig. 1, para. [0049]; Examiner note: the acquiring is interpreted as the record. The computing device is interpreted as the BookMatcher application 50. The first score is interpreted as the score),
corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time (i.e. “in an Instant Recommendations implementation (FIGS. 5 and 6) of the service, the recommendations are generated and displayed in real-time (based on the user's purchase history and/or item ratings profile) in response to selection by the user”; fig. 2, para. [0063]; Examiner note: the prediction is interpreted as the recommendations are generated. The subsequent reengagement by a user is interpreted as the in response to selection by the user);
providing recommendation information to the user for one or more highest ranked content items in the set of content items, including displaying the one or more highest ranked content items in the user interface of the media application (i.e. “Finally, in step 294, the top M (e.g., 5) items of the list are returned as recommendations. The recommendations are preferably presented to the user on the same Web page (not shown) as the shopping cart contents.”; fig. 6,7, para. [0109]; Examiner note: the one or more highest ranked content items is interpreted as the top M (e.g., 5) items of the list).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Jacobi that teaches information filtering and recommendation systems into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, and Chung that teaches an online behavioral targeting system. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to have a have a recommendation system that generates real-time suggestions in response to user requests, as it is possible to compare a specific user's ratings profile to those of all other users (Jacobi, para. [0009]).
As per claim 3, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
Additionally, Rao teaches further comprising receiving a request to provide one or more recommendations to the user (i.e. “receiving a request for digital content to be provided from a plurality of items of digital content”; para. [0017]. Further, i.e. “a recommendation system can recommend content based on a content consumption history, and/or demographics of a user who is submitting a request”; para. [0018]),
wherein the recommendation information is provided in response to receiving the request (i.e. “request (which can be referred to as a "group request") for content recommendation for a group of users is received”; para. [0019]).
As per claim 4, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
Additionally, Rao teaches wherein the prediction of subsequent reengagement comprises a calculation of a number of times the user will reengage with the content item (i.e. “recommendation models 106 are used to determine…a history of the user's interaction with the content recommendation platform 122 (e.g., through the application 112). For example, the recommendation model 106 may associate a user with horror movies when the user watches horror movies on a regular basis (reengagement) (e.g., three nights a week)”; para. [0028]; Examiner note: the calculation of a number of times the user will reengage with the content item is interpreted as the three nights a week)
during a future time period (i.e. “The system tracks the user's watch pattern and determines that the user likes a particular genre… for example, a user watching romance movies three nights in a row” para. [0029]; Examiner note: the future time period is interpreted as the three nights in the row).
As per claim 9, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Rao and Jacobi do not explicitly teach “wherein the short-term user-interface-specific recommender system is trained using data specific to the user interface of the media application.”
On the other hand, in the same field of endeavor, Chung teaches wherein the short-term user-interface-specific recommender system is trained using data specific to the user interface of the media application (i.e. “each content recommendation model 106 is trained for a particular user on a user device 108”; para. [0060]. Further, i.e. “The short-term behavioral targeting system categorizes the events (FIG. 11, block 1120). A model, corresponding to the short-term direct marketing/user objective, is selected (FIG. 11, block 1130). In part, the model includes a plurality of weights for dimension processing (e.g., recency, intensity and frequency).”; fig. 11, para. [0112]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Chung that teaches an online behavioral targeting system into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users and Jacobi that teaches information filtering and recommendation systems. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to record and analyze information about a user to determine behavioral patterns and interests, because it can be used to present specific content to a user, which can provide a more meaningful and rich experience on the internet (Chung, para. [0005]).
As per claim 14, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
Additionally, Rao teaches wherein the set of content items includes one or more of: a podcast series, a show series, an audiobook series, and a music playlist (i.e. “receiving of data in an application environment 114 (e.g., a graphical user interface), including submitting content requests and receiving content items (e.g., movies, television shows, music, e-books, streaming content, or other media content) over the network 102”; para. [0023], [0055]-[0056]).
As per claim 15, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
Additionally, Rao teaches wherein the first score for each content item corresponds to a likelihood of consumption of the content item affecting a habit of the user (i.e. “The user may even want to hide some of their habits or interests from particular people in the group. In some situations, a user may want to share different interests with different groups of people (e.g., political views, romantic interests).”; para. [0018]. Further, i.e. “When Users 1 and 2 are among the group of users, for whom the content is to be recommended, the joint history of Users 1 and 2 can be used to filter the content items to what both Users 1 and 2 have previously shown interest in, or consumed.”; para. [0047]).
As per claim 18, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
Additionally, Rao teaches wherein the first score for the content item comprises a difference score for reengagement (i.e. “if a user had previously liked a first Tom Cruise movie, when the user provides a like feedback for a second Tom Cruise movie, the PSM detects a duplication and makes no changes to the content score of other Tom Cruise movies for the user. However, if the user provides a dislike for a third Tom Cruise movie, the PSM may detect the feedback as new feedback and updates the content scores”; para. [0037]. Examiner Note: The difference score for reengaging is that once the user dislikes the Tom Cruise movie score will be updated.), and
wherein the difference score corresponds to a difference between a first reengagement score without the user receiving a recommendation (i.e. “the web browser can enable a user to display and interact with text, images, videos”; para. [0023]; Examiner Note: The examiner interprets “without a recommendation” as when a user searches for the first time for what they want to watch or listen to on a platform. The reengagement is when the user focuses on searching one category more than once. Additionally, the examiner interprets the difference score for reengagement as corresponding to the difference between first engagement score without a recommendation. In other words, if the user dislikes a Tom Cruise movie as discussed above, the score would be updated to prevent future recommendations of that movie. The examiner interprets that difference score to correspond to the difference between a first engagement score without a recommendation, because a score for something the user dislikes would be similar or corresponding to something that was never recommended but the user searched for.) and
a second reengagement score with the user receiving the recommendation (i.e. “the user with the content recommendation platform”; para. [0036]. Further, i.e. “if the feedback had been previously provided for the same content attribute in the same content type. For example, each time a user likes a romance movie the PSM adds a score to the content score of every romance movie for the user”; para. [0037]).
As per claim 19, Rao teaches a computing device (i.e. “an electronic device capable of requesting and receiving content items over the network 102. Example user devices 108 include personal computers, mobile communication devices”; para. [0023]), comprising:
one or more processors (i.e. “one or more processors”; para. [0009]) ;
memory (i.e. “a memory”; para. [0086]); and
one or more programs stored in the memory (i.e. “one or more memory devices for storing instructions and data”: para. [0089]) and
configured for execution by the one or more processors (i.e. “a processor for performing instructions”; para. [0089]),
the one or more programs comprising instructions for (i.e. “program instructions”; para. [0089]):
for each content item of a set of content items (i.e. “calculating a combined score deviation for each item of digital content”; para. [0017], [0079])
provided by a media-providing service associated with a media application (i.e. “Example digital content includes, without limitation, movies, television shows, video clips, music content, games, online e-books, interactive media, web-based streaming content, virtual reality content, shopping items, trip arrangements, holiday planning, or other forms of consumable media content.”; fig. 3, para. [0016], [0079]; Examiner note: the media-providing service is interpreted as the digital content. Further, “an example dialog process interaction 202 in a virtual agent dialog window 204 in an application environment 206 of a smart virtual agent application 207.”; fig. 2A, para. [0063]-[0064]; Examiner note: the media application is interpreted as the smart virtual agent application),
the media application including a user interface on which a recommendation is to be displayed (i.e. “The dialog process interaction 222 proceeds between the dialog process 230 and the user 232 until a content item is suggested by the content recommendation model 106 and selected for presentation in the content viewing window 234 by the user 232.”; fig. 2b, para. [0067]; Examiner note: the user interface is interpreted as the content viewing window 234. The recommendation is to be displayed is interpreted as the content item is suggested by the content recommendation model 106 and selected for presentation in the content viewing window 234):
assigning, by the computing device, a combined score to the content item by aggregating the first score and the second score (i.e. “a combined score deviation of an item of digital content is determined as the difference between the maximum content score, and the minimum content score of the item of digital content.”; figs. 3-4, para. [0077], [0081]; Examiner note: the combined score to the content item is interpreted as the combined score deviation of an item of digital content. Further, i.e. “For example, a combined score deviation for Inception can be provided as 15 (e.g., the difference between the highest content score, and the lowest content score), and a combined content score deviation for Interstellar can be provided as 65 (e.g., the difference between the highest content score, and the lowest content score).”; para. [0039]);
ranking the set of content items based on the respective combined scores (i.e. “Consequently, the combined score deviation of content scores for User 1, 2, and 3 is lower for Inception than for Interstellar, and, between these examples, Inception is provided as the candidate content item to the group of User 1, User 2, and User 3, and/or is provided as a more highly ranked candidate content item than Interstellar.”; para. [0039]. Further, i.e. “Candidate item(s) of digital content are determined based on the combined score deviations (308). For example, five items that have the lowest combined score deviations among the items of digital content (e.g., content items 110) are determined as the candidate items.”; figs. 3-4, para. [0078]);
However, it is noted that the prior art of Rao does not explicitly teach “using a long-term user-interface-independent recommender system, acquiring, by a computing device, a first score corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time; using a short-term user-interface-specific recommender system for the user interface of the media application on which the recommendation is to be displayed, acquiring, by the computing device, a second score corresponding to a probability of a selection of the content item by the user in the user interface of the media application; providing recommendation information to the user for one or more highest ranked content items in the set of content items including displaying the one or more highest ranked content items in the user interface of the media application.”
On the other hand, in the same field of endeavor, Chung teaches using a short-term user-interface-specific recommender system for the user interface of the media application on which the recommendation is to be displayed (i.e. “The short-term behavioral targeting system categorizes the events (FIG. 11, block 1120). A model, corresponding to the short-term direct marketing/user objective, is selected (FIG. 11, block 1130). In part, the model includes a plurality of weights for dimension processing (e.g., recency, intensity and frequency).”; fig. 11, para. [0112]; Examiner note: the short-term user-interface-specific recommender system is interpreted as the short-term behavioral targeting system; the short-term behavioral targeting system is used to calculate a plurality of weights/scores. Further, i.e. “The client system 1420 typically includes one or more user interface devices 22 (such as a keyboard, a mouse, a roller ball, a touch screen, a pen or the like) for interacting with a graphical user interface (GUI) of the web browser on a display (e.g., monitor screen, LCD display, etc.).”; para. [0160]),
acquiring, by the computing device (i.e. “The user scores are input to the ad server (850)”; figs. 8-12, para. [0074]),
a second score (i.e. “The behavioral targeting system 1200 also comprises real-time behavior targeting processing 1270 and user data store 1290 to generate short-term user interest scores.”; fig. 12, para. [0130]-[0131]; Examiner note: the acquiring a second score is interpreted as the generate short-term user interest scores)
corresponding to a probability of a selection of the content item by the user in the user interface of the media application (i.e. “the short-term user interest scores may be used to select ads for various user and marketing objectives.”; para. [0131]. Further, i.e. “In general, the click-through-rate (CTR) of a user for a category reflects the probability that the user will select ("click on") content (e.g., advertisement, link, etc.) associated with the category.”; para. [0136]; Examiner note: the probability of the selection of the content item by the user is interpreted as the probability that the user will select ("click on") content (e.g., advertisement, link, etc.) associated with the category);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Chung that teaches an online behavioral targeting system into Rao that teaches interactive content recommendation for selecting and providing customized content to users. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to record and analyze information about a user to determine behavioral patterns and interests, because it can be used to present specific content to a user, which can provide a more meaningful and rich experience on the internet (Chung, para. [0005]).
However, it is noted that the combination of the prior arts of Rao and Chung do not explicitly teach “using a long-term user-interface-independent recommender system, acquiring, by a computing device, a first score corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time; providing recommendation information to the user for one or more highest ranked content items in the set of content items including displaying the one or more highest ranked content items in the user interface of the media application.”
On the other hand, in the same field of endeavor, Jacobi teaches using a long-term user-interface-independent recommender system (i.e. “FIG. 5 illustrates the sequence of steps that are performed by the Instant Recommendations service to generate personal recommendations. Steps 180-194 in FIG. 5 correspond, respectively, to steps 80-94 in FIG. 2. In step 180, the process 52 identifies all popular items that have been purchased by the user (from a particular shopping cart, if designated) or rated by the user, within the last six months. In step 182, the process retrieves the similar items lists 64 for these popular items from the similar items table 60.”; fig.5, para. [0093]; Examiner note: The long-term is interpreted as the last six months. The recommendation system is interpreted as the Instant Recommendations service),
acquiring, by a computing device, a first score (i.e. “the BookMatcher application 50 records the ratings within the user's items rating profile. For example, if a user of the BookMatcher service gives the book Into Thin Air a score of “5,” the BookMatcher application 50 would record the item (by ISBN or other identifier) and the score within the user's item ratings profile.”; fig. 1, para. [0049]; Examiner note: the acquiring is interpreted as the record. The computing device is interpreted as the BookMatcher application 50. The first score is interpreted as the score),
corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time (i.e. “in an Instant Recommendations implementation (FIGS. 5 and 6) of the service, the recommendations are generated and displayed in real-time (based on the user's purchase history and/or item ratings profile) in response to selection by the user”; fig. 2, para. [0063]; Examiner note: the prediction is interpreted as the recommendations are generated. The subsequent reengagement by a user is interpreted as the in response to selection by the user);
providing recommendation information to the user for one or more highest ranked content items in the set of content items including displaying the one or more highest ranked content items in the user interface of the media application (i.e. “Finally, in step 294, the top M (e.g., 5) items of the list are returned as recommendations. The recommendations are preferably presented to the user on the same Web page (not shown) as the shopping cart contents.”; fig. 6,7, para. [0109]; Examiner note: the one or more highest ranked content items is interpreted as the top M (e.g., 5) items of the list).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Jacobi that teaches information filtering and recommendation systems into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, and Chung that teaches an online behavioral targeting system. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to have a have a recommendation system that generates real-time suggestions in response to user requests, as it is possible to compare a specific user's ratings profile to those of all other users (Jacobi, para. [0009]).
As per claim 20, Rao teaches a non-transitory computer-readable storage medium storing one or more programs configured for execution by a computing device having one or more processors (i.e. “a computer-readable storage medium coupled to the one or more processors having instructions stored thereon”; para. [0009]) and
memory (i.e. “a memory”; para. [0086])
the one or more programs comprising instructions for (i.e. “program instructions”; para. [0089]):
for each content item of a set of content items (i.e. “calculating a combined score deviation for each item of digital content”; para. [0017], [0079])
provided by a media-providing service associated with a media application (i.e. “Example digital content includes, without limitation, movies, television shows, video clips, music content, games, online e-books, interactive media, web-based streaming content, virtual reality content, shopping items, trip arrangements, holiday planning, or other forms of consumable media content.”; fig. 3, para. [0016], [0079]; Examiner note: the media-providing service is interpreted as the digital content. Further, “an example dialog process interaction 202 in a virtual agent dialog window 204 in an application environment 206 of a smart virtual agent application 207.”; fig. 2A, para. [0063]-[0064]; Examiner note: the media application is interpreted as the smart virtual agent application),
the media application including a user interface on which a recommendation is to be displayed (i.e. “The dialog process interaction 222 proceeds between the dialog process 230 and the user 232 until a content item is suggested by the content recommendation model 106 and selected for presentation in the content viewing window 234 by the user 232.”; fig. 2b, para. [0067]; Examiner note: the user interface is interpreted as the content viewing window 234. The recommendation is to be displayed is interpreted as the content item is suggested by the content recommendation model 106 and selected for presentation in the content viewing window 234):
assigning, by the computing device, a combined score to the content item by aggregating the first score and the second score (i.e. “a combined score deviation of an item of digital content is determined as the difference between the maximum content score, and the minimum content score of the item of digital content.”; figs. 3-4, para. [0077], [0081]; Examiner note: the combined score to the content item is interpreted as the combined score deviation of an item of digital content. Further, i.e. “For example, a combined score deviation for Inception can be provided as 15 (e.g., the difference between the highest content score, and the lowest content score), and a combined content score deviation for Interstellar can be provided as 65 (e.g., the difference between the highest content score, and the lowest content score).”; para. [0039]);
ranking the set of content items based on the respective combined scores (i.e. “Consequently, the combined score deviation of content scores for User 1, 2, and 3 is lower for Inception than for Interstellar, and, between these examples, Inception is provided as the candidate content item to the group of User 1, User 2, and User 3, and/or is provided as a more highly ranked candidate content item than Interstellar.”; para. [0039]. Further, i.e. “Candidate item(s) of digital content are determined based on the combined score deviations (308). For example, five items that have the lowest combined score deviations among the items of digital content (e.g., content items 110) are determined as the candidate items.”; figs. 3-4, para. [0078]);
However, it is noted that the prior art of Rao does not explicitly teach “using a long-term user-interface-independent recommender system, acquiring, by a computing device, a first score corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time; using a short-term user-interface-specific recommender system for the user interface of the media application on which the recommendation is to be displayed, acquiring, by the computing device, a second score corresponding to a probability of a selection of the content item by the user in the user interface of the media application; providing recommendation information to the user for one or more highest ranked content items in the set of content items, including displaying the one or more highest ranked content items in the user interface of the media application.”
On the other hand, in the same field of endeavor, Chung teaches using a short-term user-interface-specific recommender system for the user interface of the media application on which the recommendation is to be displayed (i.e. “The short-term behavioral targeting system categorizes the events (FIG. 11, block 1120). A model, corresponding to the short-term direct marketing/user objective, is selected (FIG. 11, block 1130). In part, the model includes a plurality of weights for dimension processing (e.g., recency, intensity and frequency).”; fig. 11, para. [0112]; Examiner note: the short-term user-interface-specific recommender system is interpreted as the short-term behavioral targeting system; the short-term behavioral targeting system is used to calculate a plurality of weights/scores. Further, i.e. “The client system 1420 typically includes one or more user interface devices 22 (such as a keyboard, a mouse, a roller ball, a touch screen, a pen or the like) for interacting with a graphical user interface (GUI) of the web browser on a display (e.g., monitor screen, LCD display, etc.).”; para. [0160]),
acquiring, by the computing device (i.e. “The user scores are input to the ad server (850)”; figs. 8-12, para. [0074]),
a second score (i.e. “The behavioral targeting system 1200 also comprises real-time behavior targeting processing 1270 and user data store 1290 to generate short-term user interest scores.”; fig. 12, para. [0130]-[0131]; Examiner note: the acquiring a second score is interpreted as the generate short-term user interest scores)
corresponding to a probability of a selection of the content item by the user in the user interface of the media application (i.e. “the short-term user interest scores may be used to select ads for various user and marketing objectives.”; para. [0131]. Further, i.e. “In general, the click-through-rate (CTR) of a user for a category reflects the probability that the user will select ("click on") content (e.g., advertisement, link, etc.) associated with the category.”; para. [0136]; Examiner note: the probability of the selection of the content item by the user is interpreted as the probability that the user will select ("click on") content (e.g., advertisement, link, etc.) associated with the category);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Chung that teaches an online behavioral targeting system into Rao that teaches interactive content recommendation for selecting and providing customized content to users. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to record and analyze information about a user to determine behavioral patterns and interests, because it can be used to present specific content to a user, which can provide a more meaningful and rich experience on the internet (Chung, para. [0005]).
However, it is noted that the combination of the prior arts of Rao and Chung do not explicitly teach “using a long-term user-interface-independent recommender system, acquiring, by a computing device, a first score corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time; providing recommendation information to the user for one or more highest ranked content items in the set of content items, including displaying the one or more highest ranked content items in the user interface of the media application.”
On the other hand, in the same field of endeavor, Jacobi teaches using a long-term user-interface-independent recommender system (i.e. “FIG. 5 illustrates the sequence of steps that are performed by the Instant Recommendations service to generate personal recommendations. Steps 180-194 in FIG. 5 correspond, respectively, to steps 80-94 in FIG. 2. In step 180, the process 52 identifies all popular items that have been purchased by the user (from a particular shopping cart, if designated) or rated by the user, within the last six months. In step 182, the process retrieves the similar items lists 64 for these popular items from the similar items table 60.”; fig.5, para. [0093]; Examiner note: The long-term is interpreted as the last six months. The recommendation system is interpreted as the Instant Recommendations service),
acquiring, by a computing device, a first score (i.e. “the BookMatcher application 50 records the ratings within the user's items rating profile. For example, if a user of the BookMatcher service gives the book Into Thin Air a score of “5,” the BookMatcher application 50 would record the item (by ISBN or other identifier) and the score within the user's item ratings profile.”; fig. 1, para. [0049]; Examiner note: the acquiring is interpreted as the record. The computing device is interpreted as the BookMatcher application 50. The first score is interpreted as the score),
corresponding to a prediction of subsequent reengagement by a user with the content item within a future window of time (i.e. “in an Instant Recommendations implementation (FIGS. 5 and 6) of the service, the recommendations are generated and displayed in real-time (based on the user's purchase history and/or item ratings profile) in response to selection by the user”; fig. 2, para. [0063]; Examiner note: the prediction is interpreted as the recommendations are generated. The subsequent reengagement by a user is interpreted as the in response to selection by the user);
providing recommendation information to the user for one or more highest ranked content items in the set of content items, including displaying the one or more highest ranked content items in the user interface of the media application (i.e. “Finally, in step 294, the top M (e.g., 5) items of the list are returned as recommendations. The recommendations are preferably presented to the user on the same Web page (not shown) as the shopping cart contents.”; fig. 6,7, para. [0109]; Examiner note: the one or more highest ranked content items is interpreted as the top M (e.g., 5) items of the list).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Jacobi that teaches information filtering and recommendation systems into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, and Chung that teaches an online behavioral targeting system. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to have a have a recommendation system that generates real-time suggestions in response to user requests, as it is possible to compare a specific user's ratings profile to those of all other users (Jacobi, para. [0009]).
8. Claim 2 is rejected under 35 U.S.C. § 103 as being unpatentable over Rao et al. (US 20190266185 A1) in view of Chung et al. (US 20070260624 A1) in further view of Jacobi et al. (US 20010021914 A1) still in further view of Sokolov et al. (US 20200090247 A1).
As per claim 2, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Rao, Chung and Jacobi do not explicitly teach “wherein the first score is calculated from a combination of a user vector for the user and a content item vector for the content item.”
On the other hand, in the same field of endeavor, Sokolov teaches wherein the score is calculated from a combination of a user vector for the user and a content item vector for the content item (i.e. “the content item selection routine 302 may be configured to select one or more candidate content items which may be potentially relevant to the user 102 based on a user interest profile associated with the user 102, which may for example be a set of vectors representing the interests of the user 102.”; para. [0107], [0139]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Sokolov that teaches a method and a system for generating a digital content recommendation into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, Chung that teaches an online behavioral targeting system, and Jacobi that teaches information filtering and recommendation systems. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to leverage user feedback about content to continuously update the server, enabling the dynamic refinement of content recommendations presented to the user (Sokolov, para. [0008]).
9. Claims 5-6 are rejected under 35 U.S.C. § 103 as being unpatentable over Rao et al. (US 20190266185 A1) in view of Chung et al. (US 20070260624 A1) in further view of Jacobi et al. (US 20010021914 A1) still in further view of Neumann et al. (US 20160274744 A1).
As per claim 5, Rao, Chung and Jacobi teach all the limitations as discussed in claim 4 above.
However, it is noted that the combination of the prior arts of Rao, Chung and Jacobi do not explicitly teach “wherein reengaging with the content item comprises playing back content of the content item for at least a preset amount of time.”
On the other hand, in the same field of endeavor, Neumann teaches wherein reengaging with the content item comprises playing back content of the content item for at least a preset amount of time (i.e. “recommendations and personalization based on real-time usage data”; para. [0042]. Further, i.e. “Interactions between the users and their respective devices in the consumption of media content… opening program information, rewind, fast forward, replay (playback position)”; para. [0043]. Further, i.e. “For example, an accelerometer may be preset to send a signal that the user has stopped viewing a program if no vibrations above and/or satisfying a vibration threshold have been registered within a predetermined time period”; para. [0044]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Neumann that teaches content and service providers in a media delivery network may provide improved recommendations into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, Jacobi that teaches information filtering and recommendation systems, and Chung that teaches an online behavioral targeting system. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to generate content recommendations that are better tailored to the user's preferences, thereby improving the accuracy of recommendations and personalizing the user's experience based on their real-time activity, as well as the behavior of similar users (Neumann, para. [0005]).
As per claim 6, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Rao, Chung and Jacobi do not explicitly teach “wherein the prediction of subsequent reengagement comprises a calculation of an amount of time the user will engage with the content item during a future time period.”
On the other hand, in the same field of endeavor, Neumann teaches wherein the prediction of subsequent reengagement comprises a calculation of an amount of time the user will engage with the content item during a future time period (i.e. “a recommendation system may track usage information regarding (1) what the user is doing and/or (2) what content is being viewed by the user. Information regarding the user's control commands and time spent viewing and experiencing content may be collected in real-time and used to dynamically adjust models and recommendations for the user”; para. [0021], [0024]. Further, i.e. “Determine preferences and interests of the user based on the event and past events associated with the user”; para. [ 0081]. Further, i.e. “Users are very interested in the program based on analyzing the real-time usage information”; para. [0139]. Examiner Note: analyzing the user’s real-time usage information shows interest and the tracking is to allow for the recommendations to be based on the user’s past engagement and therefore determines the likelihood of reengagement).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Neumann that teaches content and service providers in a media delivery network may provide improved recommendations into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, Jacobi that teaches information filtering and recommendation systems, and Chung that teaches an online behavioral targeting system. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to generate content recommendations that are better tailored to the user's preferences, thereby improving the accuracy of recommendations and personalizing the user's experience based on their real-time activity, as well as the behavior of similar users (Neumann, para. [0005]).
10. Claim 7 is rejected under 35 U.S.C. § 103 as being unpatentable over Rao et al. (US 20190266185 A1) in view of Chung et al. (US 20070260624 A1) in further view of Jacobi et al. (US 20010021914 A1) still in further view of Demir et al. (US 20180103004 A1).
As per claim 7, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Rao, Chung and Jacobi do not explicitly teach “wherein the prediction of subsequent reengagement comprises a calculation of an amount of time that will elapse before the user reengages with the content item.”
On the other hand, in the same field of endeavor, Demir teaches wherein the prediction of subsequent reengagement comprises a calculation of an amount of time that will elapse before the user reengages with the content item (i.e. “By clicking the hyperlink, the user reengages with the merchant, and the merchant at least makes a discounted sale without having to spend significant time or resources to reengage the user.”; para. [0025], [0088], [0166]; Examiner note: the amount of time that will elapse before the user reengages with the content item is interpreted as the spend significant time or resources to reengage the user; the spend significant time is understood to be a calculated time frame).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Chung that teaches an online behavioral targeting system into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, and Jacobi that teaches information filtering and recommendation systems. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to tailor specific sections of content within a web page to individual website visitors, thereby enhancing the overall user experience and making the page more engaging and relevant to each visitor (Demir, para. [0002]-[0003]).
11. Claims 11-12 and 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Rao et al. (US 20190266185 A1) in view of Chung et al. (US 20070260624 A1) in further view of Jacobi et al. (US 20010021914 A1) still in further view of Roberts (US 20210097562 A1).
As per claim 11, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Rao, Chung and Jacobi do not explicitly teach “wherein the first score for each content item is based on (i) the prediction of subsequent reengagement by a user with the content item and (ii) a probability of a selection of the content item by the user.”
On the other hand, in the same field of endeavor, Roberts teaches wherein the score for each content item is based on (i) the prediction of subsequent reengagement by a user with the content item (i.e. “the boost monitor process 1051 monitors the behavior of the users”; para. [0139]. “The boost monitor process 1051 also includes a user loot scores database…how to boost a user's probabilities based on user's activities”; para. [0142]; and “adverAdvertisement’smmendation systems… reengage the user and encourage the user to reengage such as providing a promotion of a unique or rare loot item believed to be of interest to the user”; para. [0150]); and
(ii) a probability of a selection of the content item by the user (i.e. “an initial probability value may be determined for the user's purchase transaction by the new purchase process… selected multiple items for purchase, a separate initial probability value may be determined for each item. E.g., one probability value for all items in the user's checkout cart”; para. [0182]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Roberts that teaches digital data processing systems and methods for estimating and increasing user engagement in multiple platforms such as content platforms, gaming, retail, streaming video providers, social media, etc. into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, Jacobi that teaches information filtering and recommendation systems, and Chung that teaches an online behavioral targeting system. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to provide the cross-platform system with data in a confidential and anonymized manner, thereby improving metrics that support purposes such as targeted cross-platform advertising (Roberts, para. [0005]).
As per claim 12, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Rao, Chung and Jacobi do not explicitly teach “wherein the second score for each content item is based on information about the user, information about the content item, context information, and information about types of short-term outcomes for content items.”
On the other hand, in the same field of endeavor, Roberts teaches wherein the score for each content item is based on information about the user (i.e. “browser value (score related to stream depth”; Col. 6 lines 1-10. Further, i.e. “that measure local interactions between users and content streams”; Col. 6 lines 20-25. Further, i.e. “basic user information to the content portal 602 e.g., a search engine”; Col. 8 lines 34-46),
information about the content item (i.e. “content streams and interact with the content streams, for example, text, audio and image”; Col. 4 lines 1-11),
context information (i.e. “example, user's short-term on page behavior patterns or visit patterns may be obtained by the short-term behavior metrics measurement unit”; Col. 5 lines 1-5), and
information about types of short-term outcomes for content items (i.e. “the short-term behavior metrics measurement unit 202 in this example is configured to measure each of the short-term behavior metrics”; Col. 5 lines 9-15. Further, i.e. “Content streams 200 at least five days in one week may form a high frequency cohort, and users who clicked in the personalized content streams”; Col. 5 lines 20-25).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Roberts that teaches digital data processing systems and methods for estimating and increasing user engagement in multiple platforms such as content platforms, gaming, retail, streaming video providers, social media, etc. into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, Jacobi that teaches information filtering and recommendation systems, and Chung that teaches an online behavioral targeting system. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to provide the cross-platform system with data in a confidential and anonymized manner, thereby improving metrics that support purposes such as targeted cross-platform advertising (Roberts, para. [0005]).
As per claim 13, Rao, Chung and Jacobi teach all the limitations as discussed in claim 12 above.
Additionally, Rao teaches wherein the types of short-term outcomes include one or more of: the user selecting the content item to watch, the user watching the content item, and the user adding the content item to a favorites list (i.e. “For example, candidate content items can include content items having combined scores below a threshold score to provide a list of the top X (e.g., top 7) candidate content items.”; para. [0028], [0044], [0048]).
As per claim 17, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Rao, Chung and Jacobi do not explicitly teach “wherein the long-term user-interface-independent recommender system is trained using a reward function.”
On the other hand, in the same field of endeavor, Roberts teaches wherein the long-term user-interface-independent recommender system is trained using a reward function (i.e. “rewards points and associated loot score may be stored on a loot server 125. The potential for a reward increases the user's desire to interact and/or engage”; para. [0049]. Further, i.e. “Advertisement recommendation systems”; para. [0127]. Further, i.e. “The technique such as machine learning-based models (trained)”; para. [0150]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Roberts that teaches digital data processing systems and methods for estimating and increasing user engagement in multiple platforms such as content platforms, gaming, retail, streaming video providers, social media, etc. into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, Jacobi that teaches information filtering and recommendation systems, and Chung that teaches an online behavioral targeting system. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to provide the cross-platform system with data in a confidential and anonymized manner, thereby improving metrics that support purposes such as targeted cross-platform advertising (Roberts, para. [0005]).
12. Claim 16 is rejected under 35 U.S.C. § 103 as being unpatentable over Rao et al. (US 20190266185 A1) in view of Chung et al. (US 20070260624 A1) in further view of Jacobi et al. (US 20010021914 A1) still in further view of Liu et al. (US 10491694 B2).
As per claim 16, Rao, Chung and Jacobi teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Rao, Chung and Jacobi do not explicitly teach “wherein the set of content items comprises content items not previously engaged with by the user.”
On the other hand, in the same field of endeavor, Liu teaches wherein the set of content items comprises content items not previously engaged with by the user (i.e. “user engagement metrics, including click odds, skip odds (not previously engaged)”; Col. 22 lines 15-20. Further, i.e. “All the un-clicked content items above it in the stream are considered being skipped”; Col 24 lines 15-20).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Liu that teaches methods and systems for providing online content into the combination of the prior arts of Rao that teaches interactive content recommendation for selecting and providing customized content to users, Jacobi that teaches information filtering and recommendation systems, and Chung that teaches an online behavioral targeting system. Additionally, this can also improve the social experience by recommending content based on the preferences of similar users, creating a more community-driven experience.
The motivation for doing so is to enhance personalization because it can refining estimates of a user's interests based on a diverse range of user activities (Liu, Col. 6, lines 37-60).
Prior Art of Record
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Master et al. (US 8856148 B1), teaches determining underplayed or overplayed items.
LuVogt et al. (US 20130290339 A1), teaches content recommendation systems and methods.
LuVogt et al. (US 20130290905 A1), teaches facilitate determining relevance and recommending relevant content selected from different public and private data
Conclusion
14. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTONIO CAIA DO whose telephone number is (469)295-9251. The examiner can normally be reached on Monday - Friday / 06:30 to 16:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ng, Amy can be reached on (571) 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANTONIO J CAIA DO/
Examiner, Art Unit 2164
/MARK E HERSHLEY/Primary Examiner, Art Unit 2164