DETAILED ACTION
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 .
Status of Claims
Applicant's “Amendment” filed on 1/2/2026 has been considered.
Rejection to Claims 1-20 under 35 USC 101 have not been overcome.
Rejection to Claims 6 under 35 USC 112 have been overcome.
Claims 1, 6, 10, 19 are amended.
Claims 8, 17 are cancelled.
Claims 21-22 are added.
Claims 1-7, 9-16, 18-22 are currently pending and have been examined.
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, 9-16, 18-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), it is determined whether the claims are directed to a judicially recognized exception. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
A computer-implemented method for recommending content items to a user, comprising:
selecting, by at least one computer processor, a first set of content items to recommend to the user based at least on a first set of weights respectively associated with different user interests in a plurality of user interests;
transmitting information about the first set of content items to a media device for presentation to the user via a graphical user interface (GUI) displayed by the media device via a display device, thus causing the first set of content items to be presented to the user;
determining a measure of user interaction with the first set of content items via the media device based on one or more signals transmitted from the media device to the at least one computer processor;
providing the measure of user interaction with the first set of content items to a machine learning (ML) model, wherein the ML model comprises one of a multi-arm bandit (MAB) model, a contextual MAB (CMAB) model or a reinforcement learning (RL) model;
selecting, by the ML model and based at least on the measure of user interaction with the first set of content items, a second set of weights respectively associated with the different user interests;
selecting a second set of content items to recommend to the user based at least on the second set of weights, wherein the selecting the second set of content items comprises:
generating a first user embedding for the user with respect to content items in a category representative of a first user interest of the plurality of user interests;
generating a second user embedding for the user with respect to content items in a category representative of a second user interest of the plurality of user interests;
for each candidate content item in a first set of candidate content items associated with the first user interest, determining a respective similarity score based on a measure of similarity between an item embedding that represents the candidate content item and the first user embedding;
for each candidate content item in a second set of candidate content items associated with the first user interest, determining a respective similarity score based on a measure of similarity between an item embedding that represents the candidate content item and the second user embedding;
based on the similarity score for each candidate content item and the user interest associated with each candidate content item, identifying a set of top-ranked candidate content items for the first user interest and identifying a set of top-ranked candidate content items for the second user interest; and
selecting the second set of content items from among the set of top-ranked candidate content items for the first user interest and the set of top-ranked candidate content items for the second user interest based on the second set of weights; and
transmitting information about the second set of content items to the media device for presentation to the user via the GUI, displayed by the media device via display device, thus causing the second set of content items to be presented to the user..
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations as emphasized, are a process that, under its broadest reasonable interpretation, covers a commercial interaction. That is, other than reciting that the measure of user interaction is provided to a machine learning model and the selecting is by at least one computer processor, nothing in the claim element precludes the step from practically being performed by people. For example, “selecting, causing, determining, providing, selecting, selecting and causing” in the context of this claim encompasses advertising, and marketing or sales activities.
If a claim limitation, under its broadest reasonable interpretation, covers a commercial interaction but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
The claim recites additional elements beyond the judicial exception(s), including:
A computer-implemented method for recommending content items to a user, comprising:
selecting, by at least one computer processor, a first set of content items to recommend to the user based at least on a first set of weights respectively associated with different user interests in a plurality of user interests;
transmitting information about the first set of content items to a media device for presentation to the user via a graphical user interface (GUI) displayed by the media device via a display device, thus causing the first set of content items to be presented to the user;
determining a measure of user interaction with the first set of content items via the media device based on one or more signals transmitted from the media device to the at least one computer processor;
providing the measure of user interaction with the first set of content items to a machine learning (ML) model, wherein the ML model comprises one of a multi-arm bandit (MAB) model, a contextual MAB (CMAB) model or a reinforcement learning (RL) model;
selecting, by the ML model and based at least on the measure of user interaction with the first set of content items, a second set of weights respectively associated with the different user interests;
selecting a second set of content items to recommend to the user based at least on the second set of weights, wherein the selecting the second set of content items comprises:
generating a first user embedding for the user with respect to content items in a category representative of a first user interest of the plurality of user interests;
generating a second user embedding for the user with respect to content items in a category representative of a second user interest of the plurality of user interests;
for each candidate content item in a first set of candidate content items associated with the first user interest, determining a respective similarity score based on a measure of similarity between an item embedding that represents the candidate content item and the first user embedding;
for each candidate content item in a second set of candidate content items associated with the first user interest, determining a respective similarity score based on a measure of similarity between an item embedding that represents the candidate content item and the second user embedding;
based on the similarity score for each candidate content item and the user interest associated with each candidate content item, identifying a set of top-ranked candidate content items for the first user interest and identifying a set of top-ranked candidate content items for the second user interest; and
selecting the second set of content items from among the set of top-ranked candidate content items for the first user interest and the set of top-ranked candidate content items for the second user interest based on the second set of weights; and
transmitting information about the second set of content items to the media device for presentation to the user via the GUI, displayed by the media device via display device, thus causing the second set of content items to be presented to the user..
These limitations (deemphasized above) are not indicative of integration into a practical application because:
The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea.) Specifically, the additional element of at least one computer processor, and a machine learning model comprising a MAB, contextual MAB or RL model, is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of connecting to a platform on a network) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements to no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). For example, stating that the providing is to a particular kind of ML model, only generally links the commercial interactions and management of relationships or interactions between people to a computer environment. Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claim 1 does not provide an inventive concept and does not qualify as eligible subject matter.
Claim 10 is a system reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons. Examiner notes that Claim 1 additionally recites one or more memories, at least one processor each coupled to at least one of the memories. The use of one or more memories couples to at least one processor is recited at a high level of generality, and only generally links the abstract idea to a technological environment (computers).
Claim 19 is a non-transitory computer-readable medium reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claims 2-9, 11-18, 20 are dependencies of claims 1, 10 and 19. The dependent claims do not add “significantly more” to the abstract idea. They recite additional functions that describe the abstract idea and only generally link the abstract idea to a particular technological environment, including:
wherein the ML model comprises the CMAB model, the method further comprises providing context information to the ML model, and selecting the second set of weights respectively associated with the different user interests comprises: selecting, by the ML model and based at least on the context information and the measure of user interaction with the first set of content items, the second set of weights respectively associated with the different user interests.. (further limiting the model does not make the abstract idea less abstract)
wherein the ML model comprises the CMAB model, the operations further comprise providing context information to the ML model, and selecting the second set of weights respectively associated with the different user interests comprises: selecting, by the ML model and based at least on the context information and the measure of user interaction with the first set of content items, the second set of weights respectively associated with the different user interests.
wherein the ML model comprises the RL model, the operations further comprise providing state information to the ML model, and selecting the second set of weights respectively associated with the different user interests comprises: selecting, by the ML model and based at least on the state information and the measure of user interaction with the first set of content items, the second set of weights respectively associated with the different user interests.
Accordingly, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim Rejections - 35 USC § 103
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.
Claims 1-7, 9-16, 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 11,605,117 B1 to ZAVALETA in view of U.S. Patent Application No. 2018/0336645 A1 to PRICE in further view of US 20240256619 A1 to NOSKOV.
Regarding Claim 1, ZAVALETA discloses a computer-implemented method for recommending content items to a user, comprising:
selecting, by at least one computer processor, a first set of content items to recommend to the user based at least on a first set of weights respectively associated with different user interests in a plurality of user interests; ([Col 2 Ln 60-Col 3 Ln 5] A recommendation model and/or algorithm, such as a machine-learning model, can be used to determine the recommended or new media item. For example, the familiarity score and/or the user data can be used as inputs into a machine-learning model such as a multi-armed bandit model. Using the familiarity score and/or the user data, the machine-learning model can determine a set of probability distributions associated with a set of media items and identify a recommended media item for the user.)
transmitting information about the first set of content items to a media device for presentation to the user via a graphical user interface (GUI) displayed by the media device via a display device, thus causing the first set of content items to be presented to the user; ([Col 18 Ln 65-Col 19 Ln 2] At 814, the media item transmission component 330 can access the media item database 208 and transmit and/or cause the transmission of the recommended media item, identified by the recommendation model component 318, to a user interface and/or device associated with the user.)
determining a measure of user interaction with the first set of content items via the media device based on one or more signals transmitted from the media device to the at least one computer processor;; ([Col 2 Ln 60-Col 3 Ln 5] As the user consumes the media item, the user's current consumption (e.g., listening duration) and/or interactions (e.g., skip requests, like/dislike indications, etc.) with the media item can be used to filter and/or adjust additional recommendations made by the machine-learning model. [Col 19 Ln 1-10] At 816, the consumption extent component 334 can identify an extent to which the user has consumed the recommended media item. For example, the consumption extent component 334 can identify if the user requested a different media item, skipped the media item, and/or determine the extent of the media item consumption.)
providing the measure of user interaction with the first set of content items to a machine learning (ML) model, ([Col 19 Ln 9-15] At 818, based on the extent to which the user consumed the recommended media item, the updating component 336 can update the media consumption data associated with the user in the media consumption data database 206. [Col 16 Ln 1-15] The interaction time period component 338 can identify these media items and indicate to the recommendation model component 318 that the user 104 has not consumed these media items for a particular duration of time. The recommendation model component 318 can then determine that the media items align with the user's 104 preferences (e.g., based on the familiarity score and/or the user data) and provide the media items for the user 104 to consume.) wherein the ML model comprises one of a multi-arm bandit (MAB) model, a contextual MAB (CMAB) model or a reinforcement learning (RL) model; ([Col 18 Ln 60-65] the recommendation model component 318 can use a multi-armed bandit model to identify recommended media items.)
selecting, by the ML model and based at least on the measure of user interaction with the first set of content items, a second set of weights respectively associated with the different user interests; ([Col 13 Ln 1-10] . The contextual data component 322 can then identify that the user 104, based on the current location data, that the user is at the gym and indicate to the recommendation model component 318 that the user 104 may have a preference for fast-paced music. The recommendation model component 318 can then assign a weight and/or significance to this indication and provide a recommended media item to the user 104 while the user is at the gym.)
selecting a second content item to recommend to the user based at least on the second set of weights; and ([Col 13 Ln 1-10] . The contextual data component 322 can then identify that the user 104, based on the current location data, that the user is at the gym and indicate to the recommendation model component 318 that the user 104 may have a preference for fast-paced music. The recommendation model component 318 can then assign a weight and/or significance to this indication and provide a recommended media item to the user 104 while the user is at the gym.)
thus causing the second set of content items to be presented to the user. ([Col 14 Ln 50-60] the media item transmission component 330 can receive the media item. In some instances, the media item transmission component 330 can transmit instructions that cause the media item database 208 to transmit the media item to the user interface and/or device associated with the user 104.)
But does not explicitly disclose second set of content items; wherein the selecting the second set of content items comprises: generating a first user embedding for the user with respect to content items in a category representative of a first user interest of the plurality of user interests; generating a second user embedding for the user with respect to content items in a category representative of a second user interest of the plurality of user interests; for each candidate content item in a first set of candidate content items associated with the first user interest, determining a respective similarity score based on a measure of similarity between an item embedding that represents the candidate content item and the first user embedding; for each candidate content item in a second set of candidate content items associated with the first user interest, determining a respective similarity score based on a measure of similarity between an item embedding that represents the candidate content item and the second user embedding; based on the similarity score for each candidate content item and the user interest associated with each candidate content item, identifying a set of top-ranked candidate content items for the first user interest and identifying a set of top-ranked candidate content items for the second user interest; and selecting the second set of content items from among the set of top-ranked candidate content items for the first user interest and the set of top-ranked candidate content items for the second user interest based on the second set of weights; and transmitting information about the second set of content items to the media device for presentation to the user via the GUI, displayed by the media device via display device,. ZAVALETA does disclose filter and/or adjust additional recommendations made by the machine-learning model, and transmit media items to the user interface.
PRICE, on the other hand, teaches second set of content items. ([0067] In some implementations, subsequent to generating a training set and training machine learning model 160 using the training set, the machine learning model 160 may be further trained (e.g., additional data for a training set) or adjusted (e.g., adjusting weights associated with input data of the machine learning model 160, such as connection weights in a neural network) using a recommended live-stream media item (e.g., recommended using the trained or partially-trained machine learning model 160) and user interaction with the recommended live-stream media item. For example, after a training set is generated and machine learning model 160 is trained using the training set, the machine learning model 160 may be used to make a recommendation of a live-stream media item to a user of the content sharing platform 120. Subsequent to making the recommendation, the system 100 may receive an indication of consumption by the user of the recommended live-stream media item. For instance, the system 100 may receive an indication that the user consumed the recommended live-stream media item (e.g., watched the live-stream video item for a threshold amount of time) or an indication the user did not consume the recommended live-stream media item (e.g., did not select the recommended live-stream media item). Information regarding the recommended live-stream media item may be used as additional training inputs 230 or additional target outputs 240 to further train or adjust machine learning model 160. For example, contextual information of the user access and user information of the user associated with the recommended live-stream media item may be used as additional training inputs 230, and the recommended live-stream media item may be used as a target output 240. In still other examples, the indication of user consumption may be used to generate or adjust confidence data for the recommend live-stream media item, and the confidence data may be used to an additional target output 240.) .
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by ZAVALETA, the features as taught by PRICE, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify ZAVALETA, to include the teachings of PRICE, in order to improve the overall user experience (PRICE, [0030]).
NOSKOV, on the other hand, teaches wherein the selecting the second set of content items comprises: generating a first user embedding for the user with respect to content items in a category representative of a first user interest of the plurality of user interests; generating a second user embedding for the user with respect to content items in a category representative of a second user interest of the plurality of user interests; ([0034-0037] In one embodiment, a vector representation of an image and/or text may be generated for one or more of the specific person's interactions with social media using an embedding technique; For example, an embedding of text may be generated using a suitable NLP technique, or an image may be processed using a convolutional neural network or other image processing technique; The generated vector representation(s) for the specific person and other accounts in a network may be indexed and encoded to assist with efficient searching for “similar” posts, content, or images; The identified “similar” posts, content, or images may then be used as a “key” to identify accounts (and then groups and topics of interest) that are believed similar to, or representative of the specific person's interests; In one embodiment, a person's “followings” (the topics or accounts a particular person follows, as represented by a “followings” graph of nodes and edges) may be used as part of identifying a specific person's interests, with the application of one or more naming techniques to the nodes; )
for each candidate content item in a first set of candidate content items associated with the first user interest, determining a respective similarity score based on a measure of similarity between an item embedding that represents the candidate content item and the first user embedding; ([0130] The disclosed set of processing steps or stages in CRank may be used to “score” each of several (sub)networks/communities to identify those of potentially higher relevance or confidence that the set of nodes do form a group with a common interest. Human (or automated) review of the prioritized results and scores may be used to evaluate the results and select one or more communities that are believed to represent a valid interest of a person (who is represented by a node in the community network))
for each candidate content item in a second set of candidate content items associated with the first user interest, determining a respective similarity score based on a measure of similarity between an item embedding that represents the candidate content item and the second user embedding; ([0132] In one embodiment, a Word2vec model was constructed based on several million posts found in the Instagram social network. A 2-dimensional representation of the model was then built using the t-SNE algorithm.sup.8. The t-SNE algorithm was used to help define the most common interests, while Word2vec helped in building a hierarchy using the Word2vec distance between words. For analysis of images, in one embodiment, Retinanet.sup.9 was used to find custom audiences, by analyzing images and pictures from posts. .sup.8t-SNE (tsne) is an algorithm for dimensionality reduction that is well-suited to visualizing high-dimensional data. The name stands for t-distributed Stochastic Neighbor Embedding. The idea is to embed high-dimensional points in low dimensions in a way that respects similarities between points. Nearby points in the high-dimensional space correspond to nearby embedded low-dimensional points, and distant points in high-dimensional space correspond to distant embedded low-dimensional points. )
based on the similarity score for each candidate content item and the user interest associated with each candidate content item, identifying a set of top-ranked candidate content items for the first user interest and identifying a set of top-ranked candidate content items for the second user interest; and selecting the second set of content items from among the set of top-ranked candidate content items for the first user interest and the set of top-ranked candidate content items for the second user interest based on the second set of weights; and transmitting information about the second set of content items to the media device for presentation to the user via the GUI, displayed by the media device via display device,. . ([0033-0034] As described, an interest or topic may be associated with a group or cluster of accounts that belong to each social network—this information may be used to determine one or more likely interests of the specific person by identifying groups or clusters with which they are associated and/or to which they are recommended, and the topic or interest associated with that group or cluster; [0034] In one embodiment, a vector representation of an image and/or text may be generated for one or more of the specific person's interactions with social media using an embedding technique; For example, an embedding of text may be generated using a suitable NLP technique, or an image may be processed using a convolutional neural network or other image processing technique;) .
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by ZAVALETA, the features as taught by NOSKOV, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify ZAVALETA, to include the teachings of NOSKOV, in order to improve the overall user experience (NOSKOV, [0030]).
Regarding Claim 2, ZAVALETA in view of PRICE teaches the method of claim 1.
ZAVALETA discloses wherein determining the measure of user interaction with the first set of content items comprises determining a measure of one or more of: user selections of content items in the first set of content items; user launches of content items in the first set of content items for playback; or user playback durations of content items in the first set of content items.. ([Col 3 Ln 1-6] As the user consumes the media item, the user's current consumption (e.g., listening duration) and/or interactions (e.g., skip requests, like/dislike indications, etc.) with the media item can be used to filter and/or adjust additional recommendations made by the machine-learning model.)
Regarding Claim 3, ZAVALETA in view of PRICE teaches the method of claim 1.
ZAVALETA discloses wherein the ML model comprises the CMAB model, ([Col 12 Ln 30-42] As discussed above, in some instances, the recommendation model component 318 can use a machine-learning model such as a multi-armed bandit model to identify recommended media items, although other suitable algorithms, techniques, and machine-learning models are contemplated such as, for example, contextual multi-armed bandit models, binary multi-armed bandit models, Bernoulli multi-armed bandit models, Marvok machines, and/or the use of strategies such as an epsilon-greedy strategy, an epsilon-first strategy, an epsilon-decreasing strategy, an adaptive epsilon-greedy strategy based on value differences, and/or a contextual epsilon-greedy strategy.) the method further comprises providing context information to the ML model, and selecting the second set of weights respectively associated with the different user interests comprises: selecting, by the ML model and based at least on the context information and the measure of user interaction with the first set of content items, the second set of weights respectively associated with the different user interests. ([Col 19 Ln 60-Col 20 Ln 5] For purposes of illustration only, the user 104 can periodically travel to locations such as a home location, a work location, and an exercise location (e.g., a gym). The contextual data component 322 can determine that the user 104 typically consumes pop music while at home, classical music while at work, and disco music while at the gym. The familiarity score component 314 can determine familiarity scores associated with the different locations and the recommendation model component 318 can identify media items that correspond with the user's 104 location and the user's 104 familiarity preferences at the different locations.)
Regarding Claim 4, ZAVALETA in view of PRICE teaches the method of claim 3.
ZAVALETA discloses wherein providing the context information to the ML model comprises providing one or more of: a day of a week; a time of day; a date; a location; or a device type. ([Col 19 Ln 60-Col 20 Ln 5] For purposes of illustration only, the user 104 can periodically travel to locations such as a home location, a work location, and an exercise location (e.g., a gym). The contextual data component 322 can determine that the user 104 typically consumes pop music while at home, classical music while at work, and disco music while at the gym. The familiarity score component 314 can determine familiarity scores associated with the different locations and the recommendation model component 318 can identify media items that correspond with the user's 104 location and the user's 104 familiarity preferences at the different locations. [Claim 1] the current contextual data including a current time and a current location associated with the user, wherein the current time is based at least in part on current time data associated with one of a time of day, a day of week, or a time of year;)
Regarding Claim 5, ZAVALETA in view of PRICE teaches the method of claim 1.
ZAVALETA discloses wherein the ML model comprises the RL model, the method further comprises providing state information to the ML model, and selecting the second set of weights respectively associated with the different user interests comprises: selecting, by the ML model and based at least on the state information and the measure of user interaction with the first set of content items, the second set of weights respectively associated with the different user interests. [Col 19 Ln 60-Col 20 Ln 5] For purposes of illustration only, the user 104 can periodically travel to locations such as a home location, a work location, and an exercise location (e.g., a gym). The contextual data component 322 can determine that the user 104 typically consumes pop music while at home, classical music while at work, and disco music while at the gym. The familiarity score component 314 can determine familiarity scores associated with the different locations and the recommendation model component 318 can identify media items that correspond with the user's 104 location and the user's 104 familiarity preferences at the different locations. (context information is interpreted as state information))
Regarding Claim 6, ZAVALETA in view of PRICE teaches the method of claim 4.
ZAVALETA discloses wherein providing the state information to the ML model comprises providing one or more of: a retention rate associated with the user; a measure of activity of the user with respect to a media system; a measure of engagement by the user with content items per session; an indication of diversified items viewed by the user; exploration or collaborative filtering information associated with a user interest of the user; or context information.. [Col 19 Ln 60-Col 20 Ln 5] For purposes of illustration only, the user 104 can periodically travel to locations such as a home location, a work location, and an exercise location (e.g., a gym). The contextual data component 322 can determine that the user 104 typically consumes pop music while at home, classical music while at work, and disco music while at the gym. The familiarity score component 314 can determine familiarity scores associated with the different locations and the recommendation model component 318 can identify media items that correspond with the user's 104 location and the user's 104 familiarity preferences at the different locations. (context information is interpreted as state information))
Regarding Claim 7, ZAVALETA in view of PRICE teaches the method of claim 1.
ZAVALETA discloses wherein the plurality of user interests comprise: a plurality of genres; or a plurality of content item clusters. [Col 2 Ln 45-60] Using the user's user data, a user's preference for, for example, a genre or an artist can be determined. For example, the user data can indicate that the user frequently browses to websites associated with a particular artist. This can indicate that the user enjoys listening to music associated with the particular artist and recommendations can be generated based on this preference. Additionally, the user data can indicate a preference of the user at a particular location and/or at a particular time. For example, the user can go to a gym to exercise. Similarly, the user can exercise at various locations (e.g., go jogging outside) at a consistent time of day. While exercising, the user can have a preference for listening to a particular genre of music.)
Regarding Claim 8, ZAVALETA in view of PRICE teaches the method of claim 1.
ZAVALETA discloses wherein selecting the second set of content items to recommend to the user based at least on the second set of weights comprises: determining a similarity score for each candidate content item in a set of candidate content items based on a measure of similarity between an item embedding that represents the given candidate content item and a user embedding that represents the user with respect to a user interest associated with the given candidate content item; ([Col 13 Ln 45-55] genres of music can be mapped onto a spectrum where genres with more similarity are closer together than genres with less similarity. The diversity component 326 can identify genres with similarity to classical music and identify media items within that genre. Additionally, the diversity component 326 can identify a rate at which to introduce a diversity media item recommendation to the user 104.)
based on the similarity score for each candidate content item and the user interest associated with each candidate content item, identifying a set of top-ranked candidate content items for each user interest; and selecting the second set of content items from among the sets of top-ranked candidate content items based on the second set of weights. [Col 13 Ln 55-65] For purposes of illustration only, the diversity component 326 can use a rate of 1 media item recommendation per 100 media item recommendations to introduce a diversity media item recommendation. In some instances, the user 104 can interact with the diversity media item recommendation (e.g., consuming the entire diversity media item recommendation, skipping the diversity media item recommendation, requesting a different recommendation, etc.) and the diversity component 326 can adjust the rate of introducing diversity media item recommendations based on the user's 104 interaction.)
Regarding Claim 9, ZAVALETA in view of PRICE teaches the method of claim 1.
ZAVALETA discloses wherein selecting the second set of weights comprises: selecting the second set of weights based at least on the measure of user interaction with the first set of content items and historical trial information that specifies, for each of one or more prior trials, a set of weights selected by the ML model and a measure of user interaction with a set of content items identified based on the set of weights selected by the ML model and presented to the user. ([Col 3 Ln 30-40] A diversity recommendation can be generated that is based on the novel rate preference. In some instances, as discussed above, the user can consume the media item that is based on a diversity recommendation and the user's current consumption (e.g., listening duration) and/or interactions (e.g., skip requests, like/dislike indications, etc.) can be used to adjust the frequency of diversity recommendations. For example, a recommended media item consumption threshold can be used to indicate a minimum extent to which the user consumes a recommended media item. (meeting the consumption threshold is interpreted as a historical trial) [Col 2 Ln 10-15] As a user consumes media items, a history of the user's consumption can be stored in a media consumption database.)
Claim 10 recites a system comprising substantially similar limitations as claim 1. The claim is rejected under substantially similar grounds as claim 1.
Claim 11 recites a system comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
Claim 12 recites a system comprising substantially similar limitations as claim 3. The claim is rejected under substantially similar grounds as claim 3.
Claim 13 recites a system comprising substantially similar limitations as claim 4. The claim is rejected under substantially similar grounds as claim 4.
Claim 14 recites a system comprising substantially similar limitations as claim 5. The claim is rejected under substantially similar grounds as claim 5.
Claim 15 recites a system comprising substantially similar limitations as claim 6. The claim is rejected under substantially similar grounds as claim 6.
Claim 16 recites a system comprising substantially similar limitations as claim 7. The claim is rejected under substantially similar grounds as claim 7.
Claim 17 recites a system comprising substantially similar limitations as claim 8. The claim is rejected under substantially similar grounds as claim 8.
Claim 18 recites a system comprising substantially similar limitations as claim 9. The claim is rejected under substantially similar grounds as claim 9.
Claim 19 recites a non-transitory computer-readable medium comprising substantially similar limitations as claim 1. The claim is rejected under substantially similar grounds as claim 1.
Claim 20 recites a non-transitory computer-readable medium comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
Claim 21 recites a non-transitory computer-readable medium comprising substantially similar limitations as claim 3. The claim is rejected under substantially similar grounds as claim 3.
Claim 22 recites a non-transitory computer-readable medium comprising substantially similar limitations as claim 5. The claim is rejected under substantially similar grounds as claim 5.
Response to Arguments
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 112 have been fully considered.
The rejections under 35 USC 112 have been overcome by amendment to the claims.
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues that the application addresses technical problems inherent in the technical field of recommendation systems used in media content delivery systems such as for streaming television and movies, including the cold start problem, the multiple user variant, and the filter bubble problem. These problems did not exist prior to internet-based media content delivery systems and the specific technical solution is reflected in claim 1 as amended.
Examiner disagrees. The claims are not limited to streaming tv, movies or media, but rather are directed to recommending “content” for presentation to a user. While the problems outlined above may be technical in nature, it is not clear how generating embeddings, which are being interpreted as representations of objects in vector space, determining similarity of content items and user interests, and selecting top ranked content for user interests provides a technical solution to these problems. Rather, the steps seem to perform a specific way of utilizing technology to recommend items to users based on interests, which is a method of organizing human activity.
Applicant’s arguments with respect to rejection of the claim under 35 USC 103 have been considered but are moot in view of new grounds of rejection, necessitated by Applicant’s amendment.
Applicant argues that the use of multiple user interest specific embeddings to encode for different user interests when using an exploration feature ML model to generate content item recommendation features not taught or suggested in the cited art.
However, Zavaleta and Price are not relied upon to teach these limitations in the claims. Examiner directs Applicant’s attention to the office action, above.
Conclusion
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.
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689
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