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 2/4/2026 has been considered.
Rejection to Claims 1-20 under 35 USC 101 have not been overcome.
Claims 1-2, 8-9 are amended.
Claims 1-20 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 1-20 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 computing system for generating recommendations comprising:
a processor; and
a non-transitory computer-readable storage device storing computer-executable instructions, the instructions operable to cause the processor to perform operations comprising:
detecting a presence of a user on a content platform;
generating a recommendation for the user via a contextual bandit model and a non-linear machine learning model, the recommendation comprising one or more recommended items, the non-linear machine learning model operating as an oracle for the contextual bandit model and being trained to predict future rewards for the one or more recommended items; and
collecting a reward from an interaction between the user and the one or more recommended items to re-train the non-linear machine learning model;
assigning the collected reward to a vector associated with the user to generate a vector-reward pair; and
re-training, with the vector-reward pair, the non-linear machine learning model to predict future rewards for the one or more recommended items,
wherein the training utilizes a plurality of weights of the user features, item features, and context features in a hyperparameter search space...
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 a user interface is generated from the list and products are displayed on the user interface, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “generating a user interface from the second list” and “on the user interface” language, “receiving” and “show” 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 computing system for generating recommendations comprising:
a processor; and
a non-transitory computer-readable storage device storing computer-executable instructions, the instructions operable to cause the processor to perform operations comprising:
detecting a presence of a user on a content platform;
generating a recommendation for the user via a contextual bandit model and a non-linear machine learning model, the recommendation comprising one or more recommended items, the non-linear machine learning model operating as an oracle for the contextual bandit model and being trained to predict future rewards for the one or more recommended items; and
collecting a reward from an interaction between the user and the one or more recommended items to re-train the non-linear machine learning model;
assigning the collected reward to a vector associated with the user to generate a vector-reward pair; and
re-training, with the vector-reward pair, the non-linear machine learning model to predict future rewards for the one or more recommended items,
wherein the training utilizes a plurality of weights of the user features, item features, and context features in a hyperparameter search space.
These limitations (deemphasized) 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 an application or computer based browser on advanced communication devices 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 personal shopping carts are virtual, and the sharing is done in an online (digital) environment, 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 system 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 8 is a method reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claim 15 is a method comprising a computer readable storage medium reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claims 2-7, 9-14, 16-20 are dependencies of claims 1, 8 and 15. 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.
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 1-4, 7-11, 14-17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0029135 A1 to KATZ in view of U.S. Patent Application No. 2023/0394758 A1 to FU.
Regarding Claim 1, KATZ discloses a computing system for generating recommendations comprising: a processor; and a non-transitory computer-readable storage device storing computer-executable instructions, the instructions operable to cause the processor to perform operations comprising:
detecting a presence of a user on a content platform; ([0027] the system 100 is configured to account for such patterns when detected in the selection histories of users)
generating a recommendation for the user via a contextual bandit model and a non-linear machine learning model, the recommendation comprising one or more recommended items, the non-linear machine learning model operating as an oracle for the contextual bandit model and being trained to predict future rewards for the one or more recommended items; and ([0046] a selection recommendation of the item is provided to the user based on the generated prediction score during a current time period. In some examples, the current time period includes an item selection session (e.g., an online shopping session) in which the user is engaging. In some such examples, the recommendation of the item is displayed to the user via a webpage or other similar interface, providing the user a chance to select the recommended item for selection. [0016] The disclosure operates in an unconventional manner at least by using hypernetwork architecture and explicit timestamps of past selection data to train a model to predict next basket selections (where a basket is a group or set of items that are selected at substantially the same time or otherwise selected together during a selection session) based on a specific user's selection cycle (or buy cycle in examples where items are being bought) for individual items. [0015] The sets of filter weights are used with the user's selection history data to generate item selection cycle prediction scores for the user. Those scores can be used to make recommendations to the user, or they can be combined with other predictive data to provide improved recommendations. )
collecting a reward from an interaction between the user and the one or more recommended items to re-train the non-linear machine learning model. ([0044] a set of filter weights is generated for a user-item pair using a trained hypernetwork. In some examples, the generation of the set of filter weights includes generating sets of filter weights for each item that the user of the user-item pair has previously selected (e.g., see FIG. 8 below). Further, in some examples, the optimization of parameters of the trained hypernetwork and/or otherwise the training of the trained hypernetwork is done using machine learning techniques as previously described herein. Additionally, or alternatively, the generation of the set of filter weights further includes applying a function of the hypernetwork to a user vector and an item vector, as described herein (e.g., see FIG. 6 below).)
concatenating user features associated with the user, item features associated with the one or more recommended items, and context features to generate a vector; ([0041] The hypernetwork 210 function f.sup.θ.sup.f is implemented via a concatenation of the input vectors followed by a single Leaky-ReLU activated hidden layer, such that θ.sub.f consists of a weight matrix and a bias vector.)
generate a vector-reward pair; and ([0044] At 402, a set of filter weights is generated for a user-item pair using a trained hypernetwork. In some examples, the generation of the set of filter weights includes generating sets of filter weights for each item that the user of the user-item pair has previously selected (e.g., see FIG. 8 below). Further, in some examples, the optimization of parameters of the trained hypernetwork and/or otherwise the training of the trained hypernetwork is done using machine learning techniques as previously described herein. Additionally, or alternatively, the generation of the set of filter weights further includes applying a function of the hypernetwork to a user vector and an item vector, as described herein (e.g., see FIG. 6 below).))
But does not explicitly disclose assigning the collected reward to the vector to generate a vector-reward pair; assigning the collected reward to a vector associated with the user to generate a vector-reward pair; and re-training, with the vector-reward pair, the non-linear machine learning model to predict future rewards for the one or more recommended items, wherein the training utilizes a plurality of weights of the user features, item features, and context features in a hyperparameter search space..
FU, on the other hand, teaches assigning the collected reward to the vector to generate a vector-reward pair;. ([0104] each type feature vector may be taken as a key vector and a value vector in the key-value pair; and the training reward weight parameter is taken as a query vector for attention processing. Specifically, an inner product operation may be performed on the query vector and the key vector in the key-value pair, to obtain similarity between the query vector and the key vector … an inner product operation is further performed on the value vector in the key-value pair through the attention weight, to obtain a final attention calculation result, that is, the attention weight is determined based on the training reward weight parameter and the respective type feature vectors, and weighted summation is performed on the respective type feature vectors based on the attention weight, so as to fuse the type feature vectors, and obtain a fusion result, that is, a fusion feature vector obtained after weighted summation.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KATZ, the features as taught by FU, 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 KATZ, to include the teachings of FU, in order to train a deep reinforcement learning model (FU, [0005]).
KATZ, on the other hand, teaches assigning the collected reward to a vector associated with the user to generate a vector-reward pair; and re-training, with the vector-reward pair, the non-linear machine learning model to predict future rewards for the one or more recommended items, wherein the training utilizes a plurality of weights of the user features, item features, and context features in a hyperparameter search space. ([0081] wherein generating the set of filter weights for the user-item pair using the trained hypernetwork includes: generating a user vector of the user profile of the user-item pair and an item vector of the item of the user-item pair; applying a function of the trained hypernetwork to the user vector and the item vector; and generating the set of filter weights for the user-item pair based at least in part on a result of applying the function of the trained hypernetwork to the user vector and the item vector.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KATZ, the features as taught by KATZ, 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 KATZ, to include the teachings of KATZ, in order to generate recommendations using users’ item selections during a session (KATZ, [0001]).
Regarding Claim 2, KATZ, FU teaches the system of claim 1.
KATZ discloses concatenating user features associated with the user, item features associated with the one or more recommended items, and context features to generate the vector; generate a vector-reward pair; and re-training, with the vector-reward pair, the non-linear machine learning model to predict future rewards for the one or more recommended items, wherein the training utilizes a plurality of weights of the user features, item features, and context features in a hyperparameter search space. ([0041] In some examples, the parameters of the described models (e.g., the selection cycle hypernetwork 110, the selection cycle network 102, and/or the item correlation network 104) are optimized using machine learning techniques. For instance, in some examples, the mapping functions 320 and 322 (e.g., q.sup.θ.sup.q and k.sup.θ.sup.k) are configured as fully connected neural networks with a single Leaky Rectified Linear Unit (Leaky-ReLU) activated hidden layer, such that θ.sub.q and θ.sub.k consist of a weight matrix and a bias vector. The hypernetwork 210 function f.sup.θ.sup.f is implemented via a concatenation of the input vectors followed by a single Leaky-ReLU activated hidden layer, such that θ.sub.f consists of a weight matrix and a bias vector. To derive the parameters θ.sub.q, θ.sub.k, and θ.sub.f as well as the user vectors Ψ and the item vectors Φ, a multi label one-versus-all loss is optimized based on max-entropy between each basket or order in the training set and the model's prediction. An exemplary loss term is:)
But does not explicitly disclose assigning the collected reward to the vector to generate the vector-reward pair;.
FU, on the other hand, teaches assigning the collected reward to the vector to generate the vector-reward pair;. ([0104] each type feature vector may be taken as a key vector and a value vector in the key-value pair; and the training reward weight parameter is taken as a query vector for attention processing. Specifically, an inner product operation may be performed on the query vector and the key vector in the key-value pair, to obtain similarity between the query vector and the key vector … an inner product operation is further performed on the value vector in the key-value pair through the attention weight, to obtain a final attention calculation result, that is, the attention weight is determined based on the training reward weight parameter and the respective type feature vectors, and weighted summation is performed on the respective type feature vectors based on the attention weight, so as to fuse the type feature vectors, and obtain a fusion result, that is, a fusion feature vector obtained after weighted summation.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KATZ, the features as taught by FU, 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 KATZ, to include the teachings of FU, in order to train a deep reinforcement learning model (FU, [0005]).
Regarding Claim 7,KATZ and FU teaches the system of claim 1.
KATZ discloses wherein generating the recommendation for the user via the contextual bandit model is performed based on exploration or exploitation. ([0029] Further, some correlations do not stem from a specific need or intent of the user. Instead, some correlations are related to the context of the user's selection session or visit. For example, weekly selection sessions may include large coalitions of correlated items which are not related to a specific need or intent, such that these types of correlations can only be observed at the level of a single user. Additionally, or alternatively, in some examples, some pairs of items even exhibit negative correlations, such that the selection of one item makes it less likely that the other item will also be selected (e.g., similar products from different brands or the like). )
Regarding Claim 15,KATZ discloses a computer-implemented method, performed by at least one processor, for training a model to generate recommendations comprising:
collecting a reward from an interaction between a user and one or more recommended items, the one or more recommended items being generated by a contextual bandit model; ([0044] a set of filter weights is generated for a user-item pair using a trained hypernetwork. In some examples, the generation of the set of filter weights includes generating sets of filter weights for each item that the user of the user-item pair has previously selected (e.g., see FIG. 8 below). Further, in some examples, the optimization of parameters of the trained hypernetwork and/or otherwise the training of the trained hypernetwork is done using machine learning techniques as previously described herein. Additionally, or alternatively, the generation of the set of filter weights further includes applying a function of the hypernetwork to a user vector and an item vector, as described herein (e.g., see FIG. 6 below).)
concatenating user features associated with the user, item features associated with the one or more recommended items, and context features to generate a vector; ([0041] The hypernetwork 210 function f.sup.θ.sup.f is implemented via a concatenation of the input vectors followed by a single Leaky-ReLU activated hidden layer, such that θ.sub.f consists of a weight matrix and a bias vector.)
generate a vector-reward pair; and (([0044] At 402, a set of filter weights is generated for a user-item pair using a trained hypernetwork. In some examples, the generation of the set of filter weights includes generating sets of filter weights for each item that the user of the user-item pair has previously selected (e.g., see FIG. 8 below). Further, in some examples, the optimization of parameters of the trained hypernetwork and/or otherwise the training of the trained hypernetwork is done using machine learning techniques as previously described herein. Additionally, or alternatively, the generation of the set of filter weights further includes applying a function of the hypernetwork to a user vector and an item vector, as described herein (e.g., see FIG. 6 below).))
training, with the vector-reward pair, a non-linear machine learning model ([0025] exploring may be used to collect feedback-rewards for items that may not be available currently, so that when additional feedback is available, the contextual bandit ML model may be re-trained and individual ML models may be re-trained or developed based on the feedback-rewards. )
to predict future rewards for the one or more recommended items, wherein the training utilizes a plurality of weights of the user features, item features, and context features in a hyperparameter search space and the non-linear machine learning model operates as an oracle for the contextual bandit model. ([0081] wherein generating the set of filter weights for the user-item pair using the trained hypernetwork includes: generating a user vector of the user profile of the user-item pair and an item vector of the item of the user-item pair; applying a function of the trained hypernetwork to the user vector and the item vector; and generating the set of filter weights for the user-item pair based at least in part on a result of applying the function of the trained hypernetwork to the user vector and the item vector.)
But does not explicitly disclose assigning the collected reward to the vector to generate a vector-reward pair;.
FU, on the other hand, teaches assigning the collected reward to the vector to generate a vector-reward pair;. ([0104] each type feature vector may be taken as a key vector and a value vector in the key-value pair; and the training reward weight parameter is taken as a query vector for attention processing. Specifically, an inner product operation may be performed on the query vector and the key vector in the key-value pair, to obtain similarity between the query vector and the key vector … an inner product operation is further performed on the value vector in the key-value pair through the attention weight, to obtain a final attention calculation result, that is, the attention weight is determined based on the training reward weight parameter and the respective type feature vectors, and weighted summation is performed on the respective type feature vectors based on the attention weight, so as to fuse the type feature vectors, and obtain a fusion result, that is, a fusion feature vector obtained after weighted summation.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KATZ, the features as taught by FU, 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 KATZ, to include the teachings of FU, in order to train a deep reinforcement learning model (FU, [0005]).
Regarding Claim 16,KATZ and FU teaches the method of claim 15.
FU teaches wherein training the non-linear machine learning model comprises training a tree-based machine learning model or a deep learning model.. [0005] train a deep reinforcement learning model).
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KATZ, the features as taught by FU, 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 KATZ, to include the teachings of FU, in order to train a deep reinforcement learning model (FU, [0005]).
Regarding Claim 17,KATZ and FU teaches the method of claim 15.
FU teaches wherein collecting the reward comprises at least one of collecting a binary reward, a multi-class reward, or a continuous reward. [0064] the variety of weight values form a group of reward weight parameters. Where, types and quantities of reward items may be set according to actual application scenarios, and the above are only illustrative and not limitative.).
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KATZ, the features as taught by FU, 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 KATZ, to include the teachings of FU, in order to train a deep reinforcement learning model (FU, [0005]).
Claim 3 recites a system comprising substantially similar limitations as claim 16. The claim is rejected under substantially similar grounds as claim 16.
Claim 4 recites a system comprising substantially similar limitations as claim 17. The claim is rejected under substantially similar grounds as claim 17.
Claim 8 recites a method comprising substantially similar limitations as claim 1. The claim is rejected under substantially similar grounds as claim 1.
Claim 9 recites a method comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
Claim 10 recites a method comprising substantially similar limitations as claim 3. The claim is rejected under substantially similar grounds as claim 3.
Claim 11 recites a method comprising substantially similar limitations as claim 4. The claim is rejected under substantially similar grounds as claim 4.
Claim 14 recites a method comprising substantially similar limitations as claim 7. The claim is rejected under substantially similar grounds as claim 7.
Claim 20 recites a method comprising substantially similar limitations as claim 16. The claim is rejected under substantially similar grounds as claim 16.
Claims 5, 12, 18 are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0029135 A1 to KATZ in view of U.S. Patent Application No. 2023/0394758 A1 to FU in view of U.S. Patent Application No. 2020/0394677 A1 to LAL.
Regarding Claim 18,KATZ and FU teaches the method of claim 15.
However the combination of KATZ and FU does not explicitly teach Receive. The combination does teach wherein training the non-linear machine learning model comprises calculating, for each hyperparameter selection, an average reward uniqueness indicator (average RUI) value based on cross-validation data.
LAL, on the other hand, teaches wherein training the non-linear machine learning model comprises calculating, for each hyperparameter selection, an average reward uniqueness indicator (average RUI) value based on cross-validation data.. ([0056] In the example of FIG. 6 the displayed confirmation 600 can include an indication of the reward earned 602 for the completed transaction as well as a unique indicator 604 for identifying and tracking the details of the reward earning transaction. In certain embodiments, the unique indicator 604 may be provided as a link to the generated record or maintained registry entry for the transaction. The data or information provided as part of the confirmation 600 can vary depending on implementation.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KATZ, the features as taught by LAL, 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 KATZ, to include the teachings of LAL, in order to provide feedback regarding the reward process (LAL, [0056]).
Claim 5 recites a system comprising substantially similar limitations as claim 18. The claim is rejected under substantially similar grounds as claim 18.
Claim 12 recites a method comprising substantially similar limitations as claim 5. The claim is rejected under substantially similar grounds as claim 5.
Claims 6, 13, 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0029135 A1 to KATZ in view of U.S. Patent Application No. 2023/0394758 A1 to FU and in view of U.S. Patent Application No. 2020/0394677 A1 to LAL.
Regarding Claim 19,KATZ and FU teaches the method of claim 15.
However the combination of KATZ and FU and LAL does not explicitly teach Receive. The combination does teach wherein training the non-linear machine learning model comprises constraining each average RUI value with a predefined threshold..
LAL, on the other hand, teaches wherein training the non-linear machine learning model comprises constraining each average RUI value with a predefined threshold. ([0154] selecting the ML model further comprises: testing a first model with several hyperparameter configurations, the testing of the first model with one of the hyperparameter configurations comprising selecting values for one or more hyperparameters of the first model, training the first model with the selected values, and calculating an accuracy of the first model, using validation data, for the selected values for the one or more hyperparameters; and selecting the hyperparameter configuration with a highest accuracy. [0064] the system or the user may define rules for determining when an anomaly has taken place based on the variance between actual and forecasted. For example, a percentage threshold may be identified and if the actual differs from the forecast more than the percentage threshold (e.g., up, or down, or both, depending on the metric), then the anomaly is flagged in the form of an alert. In the illustrated example, the variance from forecast may be marked as an anomaly or not, depending on the configured threshold.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KATZ, the features as taught by LAL, 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 KATZ, to include the teachings of LAL, in order to provide feedback regarding the reward process (LAL, [0056]).
Claim 6 recites a system comprising substantially similar limitations as claim 19. The claim is rejected under substantially similar grounds as claim 19.
Claim 13 recites a method comprising substantially similar limitations as claim 6. The claim is rejected under substantially similar grounds as claim 6.
Response to Arguments
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 claims are directed to training a non-linear model as an oracle for a contextual bandit such that it can differentiate between item features, user features and context features to a technical problem (limitations in utilizing non-linear models as oracles in contextual bandits.) However, the steps of colleting rewards from an interaction, concatenating user features, item features and context features, assigning the reward to a vector to create a vector-reward pair are abstract ideas, and they are only generally linked to a particular technological environment (machine learning and generating recommendations over the internet). The steps of training a non-linear machine learning model are used to predict future rewards, but this only applies the abstract idea of predicting future rewards to the technological environment of machine learning. Training to predict future rewards does not solve a technological problem, but instead solves a business problem using a technological solution.
Applicant’s arguments with respect to rejection of the claim under 35 USC 103 have been fully considered.
Applicant argues that Sankaraman does not qualify as prior art under 35 USC 102.
Examiner agrees. However, examiner relies on the new combination of KATZ and FU to teach the claims.
Conclusion
This action is made NON-FINAL.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle T. Kringen whose telephone number is (571)270-0159. The examiner can normally be reached M-F: 11am-7pm.
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, Marissa Thein can be reached at (571)272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689
Prosecution Insights