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 .
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show system 300 of Fig. 3 as described in the specification in paras. [00032],[00034],[00042], and [00043]. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 1 is objected to because of the following informalities: the claim limitation recites “the similar threshold” when it should recite “the similarity threshold.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 13-20 recites the limitation “the media.” There is insufficient antecedent basis for this limitation in the claim.
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.
Claim 1 partly recites the following limitations:
...the predicted set comprising a first subset of sequential network actions corresponding to a first predicted instance of the user and a second subset of sequential network actions corresponding to a second predicted instance of the user...the first feature embedding representing the first predicted instance of the user, the second feature embedding representing the second predicted instance of the user; querying a feature embedding space based on the first and second embeddings to obtain a third feature embedding that satisfies a similarity threshold with the first and second embeddings, wherein the similarity threshold comprises a cosine distance that is greater than a threshold cosine distance; and generating a network content recommendation for the user based on the third feature embedding that satisfies the similar threshold with the first and second embeddings.
These limitations, as drafted, are a machine under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements:
one or more processors programmed with instructions that, when executed by the one or more processors, cause operations comprising
generating, via a machine learning model, based on prior sequential network actions of a user, a predicted set of future network actions of the user
generating a first feature embedding based on the first subset of sequential network actions and a second feature embedding based on the second subset of sequential network actions
The additional claim elements of one or more processors programmed with instructions that, when executed by the one or more processors, cause operations comprising are recited at a high-level of generality using generic computer components (i.e., using a generic processor
to perform generic computer functions) such that it does not amount to a particular machine.
The additional claim elements of generating, via a machine learning model, based on prior sequential network actions of a user, a predicted set of future network actions of the user; generating a first feature embedding based on the first subset of sequential network actions and a second feature embedding based on the second subset of sequential network actions recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model used and the type of training and/or finetuning procedure used by the model on the data to generate the first and second feature embeddings.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above, one or more processors programmed with instructions that, when executed by the one or more processors, cause operations comprising are recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine and the additional claim elements of generating, via a machine learning model, based on prior sequential network actions of a user, a predicted set of future network actions of the user; generating a first feature embedding based on the first subset of sequential network actions and a second feature embedding based on the second subset of sequential network actions only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Accordingly, claim 1 is not patent eligible.
Claim 2 partly recites the following limitations:
...the set of future actions comprising a first subset of actions corresponding to a first instance of the user and a second subset of actions corresponding to a second instance of the user; obtaining a first feature embedding based on the first subset of actions and a second feature embedding based on the second subset of actions; and generating one or more recommendations for the user based on the first and second embeddings.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements
obtaining a machine learning model that has been trained on prior sequential user actions to generate probabilities of future user actions;
predicting, via the machine learning model, based on prior sequential actions of a user, a set of future actions of the user
The additional claim elements of obtaining a machine learning model that has been trained on prior sequential user actions to generate probabilities of future user actions; predicting, via the machine learning model, based on prior sequential actions of a user, a set of future actions of the user recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model used to generate future user actions.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above obtaining a machine learning model that has been trained on prior sequential user actions to generate probabilities of future user actions; predicting, via the machine learning model, based on prior sequential actions of a user, a set of future actions of the user only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Accordingly, claim 2 is not patent eligible.
Claim 3 partly recites the following limitations:
The method of claim 2, further comprising...the set of instances comprising the first instance of the user, the second instance of the user, and a third instance of the user; determining that the third instance of the user fails to satisfy a quality threshold; removing the third instance from the set of instances based on the third instance failing to satisfy the quality threshold, wherein generating the one or more recommendations comprises, subsequent to the removal, using the set of instances to generate the one or more recommendations for the user.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements
generating a set of instances of the user, wherein each instance of the set of instances corresponds to a different set of actions predicted via the machine learning model
The additional claim elements of generating a set of instances of the user, wherein each instance of the set of instances corresponds to a different set of actions predicted via the machine learning model recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model used and the type of training and/or finetuning procedure used by the model on the data to generate a set of instances of the user.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above generating a set of instances of the user, wherein each instance of the set of instances corresponds to a different set of actions predicted via the machine learning model only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Accordingly, claim 3 is not patent eligible.
Claim 4 partly recites the following limitations:
The method of claim 3, wherein the quality threshold is related to a probability of a cybersecurity incident associated with the user.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 4 is not patent eligible.
Claim 5 partly recites the following limitations:
The method of claim 2, further comprising: obtaining one or more other feature embeddings that satisfies a similarity threshold with the first or second embedding, wherein generating the one or more recommendations comprises generating the one or more recommendations for the user based on the one or more other feature embeddings.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 5 is not patent eligible.
Claim 6 partly recites the following limitations:
The method of claim 5, wherein the first feature embedding represents the first instance of the user, the second feature embedding represents the second instance of the user, and the one or more other feature embeddings represent one or more other users.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 6 is not patent eligible.
Claim 7 partly recites the following limitations:
The method of claim 2....
Claim 7 is a process under Step 1 and recites the judicial exceptions of claim 2 limitations. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements
wherein the machine learning model comprises a decoder of a transformer, and wherein an action node of the machine learning model comprises a key vector and a query vector used to generate a second action that follows a first action
The additional claim elements of wherein the machine learning model comprises a decoder of a transformer, and wherein an action node of the machine learning model comprises a key vector and a query vector used to generate a second action that follows a first action
recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as the type of training and/or finetuning procedure used by the model on the data to generate a second action that follows a first action.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above wherein the machine learning model comprises a decoder of a transformer, and wherein an action node of the machine learning model comprises a key vector and a query vector used to generate a second action that follows a first action only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Accordingly, claim 7 is not patent eligible.
Claim 8 partly recites the following limitations:
The method of claim 2, further comprising: determining a cluster of users based on the first or second embedding, wherein generating the one or more recommendations comprises generating the one or more recommendations for the user based on the cluster of users.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 7 is not patent eligible.
Claim 9 partly recites the following limitations:
The method of claim 8, further comprising: determining, based on the cluster of users, a set of items obtained by users of the cluster, determining that the user has not obtained a first item of the set of items obtained by the users of the cluster, wherein generating the one or more recommendations comprises generating a recommendation of the first item for the user based on the determination that the user has not obtained the first item of the set of items obtained by the users of the cluster.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 9 is not patent eligible.
Claim 10 partly recites the following limitations:
The method of claim 2, wherein predicting the set of future actions of the user comprises, for the first subset of actions corresponding to the first instance of the user...appending the first future action of the user to the sequential action set such that the first future action follows the prior sequential actions of the user in the sequential action set; subsequent to the appending of the first future action of the user...wherein obtaining the first feature embedding comprises obtaining the first feature embedding based on the first subset of actions comprising the first and second future actions of the user.
These limitations, as drafted, are a process under Step 1 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements
inputting a sequential action set, comprising the prior sequential actions of the user, into the machine learning model to predict a first future action of the user;
inputting the sequential action set to the machine learning model to predict a second future action of the user
The additional claim elements of inputting a sequential action set, comprising the prior sequential actions of the user, into the machine learning model to predict a first future action of the user; inputting the sequential action set to the machine learning model to predict a second future action of the user amounts to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, inputting and/or outputting of data (i.e., inputting data into the machine learning model).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above inputting a sequential action set, comprising the prior sequential actions of the user, into the machine learning model to predict a first future action of the user; inputting the sequential action set to the machine learning model to predict a second future action of the user are well-understood, routine, conventional activity that court decisions, such as Versata Dev. Group, Inc., and Bancorp Services cited in MPEP 2106.05(d)(II) have indicated that the mere storing and retrieving information to do repetitive calculations are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here).
Accordingly, claim 10 is not patent eligible.
Claim 11 partly recites the following limitations:
The method of claim 10, wherein obtaining the first feature embedding comprises generating a first sequential action set in which the second future action follows the first future action based on the prediction of the second future action being derived from the prediction of the first future action.
These limitations, as drafted, are a process under Step 1 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 11 is not patent eligible.
Claim 12 partly recites the following limitations:
...the predicted set comprising a first subset of actions corresponding to a first version of the user and a second subset of actions corresponding to a second version of the user; obtaining a first feature embedding based on the first subset of actions and a second feature embedding based on the second subset of actions; and generating one or more recommendations for the user based on the first and second embeddings.
These limitations, as drafted, are a manufacture under Step 1 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements
instructions that when executed by one or more processors, cause operations
obtaining a machine learning model that has been trained to generate probabilities of future user actions
predicting, via the machine learning model, based on prior actions of a user, a set of actions of the user
The additional claim elements of instructions that when executed by one or more processors, cause operations are recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine.
The additional claim elements of obtaining a machine learning model that has been trained to generate probabilities of future user actions; predicting, via the machine learning model, based on prior actions of a user, a set of actions of the user recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model used and the type of finetuning procedure used by the model on the data to generate user actions.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above, instructions that when executed by one or more processors, cause operations are recited at a high-level of generality using generic computer components (i.e., using a generic processor to perform generic computer functions) such that it does not amount to a particular machine and the additional claim elements of obtaining a machine learning model that has been trained to generate probabilities of future user actions; predicting, via the machine learning model, based on prior actions of a user, a set of actions of the user only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Accordingly, claim 12 is not patent eligible.
Claim 13 partly recites the following limitations:
The media of claim 12, the operations further comprising...the set of versions comprising the first version of the user, the second version of the user, and a third version of the user; determining that the third version of the user fails to satisfy a quality threshold; removing the third version from the set of versions based on the third version failing to satisfy the quality threshold, wherein generating the one or more recommendations comprises, subsequent to the removal, using the set of versions to generate the one or more recommendations for the user.
These limitations, as drafted, are a manufacture under Step 11 that under its broadest reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements
generating a set of versions of the user, wherein each version of the set of versions corresponds to a different set of actions predicted via the machine learning model
The additional claim elements of generating a set of versions of the user, wherein each version of the set of versions corresponds to a different set of actions predicted via the machine learning model recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model used and the type of training and/or finetuning procedure used by the model on the data to generate user actions.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above, generating a set of versions of the user, wherein each version of the set of versions corresponds to a different set of actions predicted via the machine learning model only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Accordingly, claim 13 is not patent eligible.
Claim 14 partly recites the following limitations:
The media of claim 12, the operations further comprising: obtaining one or more other feature embeddings that satisfies a similarity threshold with the first or second embedding, wherein generating the one or more recommendations comprises generating the one or more recommendations for the user based on the one or more other feature embeddings.
These limitations, as drafted, are a manufacture under Step 12 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 14 is not patent eligible.
Claim 15 partly recites the following limitations:
The media of claim 14, wherein the first feature embedding represents the first version of the user, the second feature embedding represents the second version of the user, and the one or more other feature embeddings represent one or more other users.
These limitations, as drafted, are a manufacture under Step 13 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 15 is not patent eligible.
Claim 16 partly recites the following limitations:
The media of claim 12, the operations further comprising: determining a cluster of users based on the first or second embedding, wherein generating the one or more recommendations comprises generating the one or more recommendations for the user based on the cluster of users.
These limitations, as drafted, are a manufacture under Step 14 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 16 is not patent eligible.
Claim 17 partly recites the following limitations:
The media of claim 16, the operations further comprising: determining, based on the cluster of users, a set of items obtained by users of the cluster, determining that the user has not obtained a first item of the set of items obtained by the users of the cluster, wherein generating the one or more recommendations comprises generating a recommendation of the first item for the user based on the determination that the user has not obtained the first item of the set of items obtained by the users of the cluster.
These limitations, as drafted, are a manufacture under Step 15 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 17 is not patent eligible.
Claim 18 partly recites the following limitations:
The media of claim 12, wherein predicting the set of actions of the user comprises, for the first subset of actions corresponding to the first version of the user...appending the first future action of the user to the sequential action set such that the first future action follows the prior sequential actions of the user in the sequential action set; subsequent to the appending of the first future action of the user...wherein obtaining the first feature embedding comprises obtaining the first feature embedding based on the first subset of actions comprising the first and second future actions of the user.
These limitations, as drafted, are a manufacture under Step 16 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements
inputting a sequential action set, comprising the prior sequential actions of the user, into the machine learning model to predict a first future action of the user;
inputting the sequential action set to the machine learning model to predict a second future action of the user
The additional claim elements of inputting a sequential action set, comprising the prior sequential actions of the user, into the machine learning model to predict a first future action of the user; inputting the sequential action set to the machine learning model to predict a second future action of the user amounts to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation, inputting and/or outputting of data (i.e., inputting data into the machine learning model).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above inputting a sequential action set, comprising the prior sequential actions of the user, into the machine learning model to predict a first future action of the user; inputting the sequential action set to the machine learning model to predict a second future action of the user are well-understood, routine, conventional activity that court decisions, such as Versata Dev. Group, Inc., and Bancorp Services cited in MPEP 2106.05(d)(II) have indicated that the mere storing and retrieving information to do repetitive calculations are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here).
Accordingly, claim 18 is not patent eligible.
Claim 19 partly recites the following limitations:
The media of claim 18, wherein obtaining the first feature embedding comprises generating a first sequential action set in which the second future action follows the first future action based on the prediction of the second future action being derived from the prediction of the first future action.
These limitations, as drafted, are a manufacture under Step 17 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A, Prong
Two because there are no additional elements recited in the claim beyond the judicial exception.
And the claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception under Step 2B because there are no additional elements recited
in the claim beyond the judicial exception.
Accordingly, claim 19 is not patent eligible.
Claim 20 partly recites the following limitations:
The media of claim 18, wherein predicting the first future action of the user comprises... the first probabilities comprising a first probability of the user performing the first future action; and randomly selecting, from the first candidate actions, the first future action via a random selection proportional to the first probabilities of the first candidate actions.
These limitations, as drafted, are a manufacture under Step 18 that under its broadest
reasonable interpretation can be performed in the human mind through the use of observations, evaluations, judgements and opinion and falls under the mental process grouping. Thus, the claim recites a mental process under Step 2A, Prong One.
This judicial exception is not integrated into a practical application under Step 2A,
Prong Two because the claim recites the following additional elements
obtaining, via the machine learning model, first probabilities of first candidate actions of the user occurring
The additional claim elements of obtaining, via the machine learning model, first probabilities of first candidate actions of the user occurring recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model used and the type of training and/or finetuning procedure used by the model on the data to generate actions.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as discussed above, obtaining, via the machine learning model, first probabilities of first candidate actions of the user occurring only recites the idea of solution and fails to recite the details of how the solution is accomplished and amounts to no more than mere recitations of “apply it.”
Accordingly, claim 20 is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claims 1-2 and 5-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhai et al., US 2023/0252269-Al(“Zhai”) in view of Li, Haoyang, et al. "Intention-aware sequential recommendation with structured intent transition." IEEE Transactions on Knowledge and Data Engineering 34.11 (2021)(“Li”).
Regarding claim 1, Zhai teaches a system for facilitating network content recommendations for a user by generating predicted feature embeddings representing different instances of the user, the system comprising:
one or more processors programmed with instructions that, when executed by the one or more processors, cause operations(Zhai, para. [0020], see also fig., 1A, “As shown in FIG. 1A, computing environment 100 may include one or more client devices 110 (e.g., client
device 110-1, 110-2, through 110-N), also referred to as user devices, for connecting over network 150 to access computing resources 120. Client devices 110 may include any type of computing device, such as a smartphone, tablet, laptop computer, desktop computer, wearable, etc., and network 150 may include any wired or wireless network (e.g., the Internet, cellular, satellite, Bluetooth, Wi-Fi, etc.) that can facilitate communications between client devices 110 and computing resources 120.”) comprising:
generating, via a machine learning model, based on prior sequential network actions of a user, a predicted set of future network actions of the user, the predicted set comprising a first subset of sequential network actions corresponding to a first predicted instance of the user and a second subset of sequential network actions corresponding to a second predicted instance of the user(Zhai, paras. [0034-0041], see also Figs. 2A & 2B, “As illustrated in FIG. 2A, sequential trained machine learning model 202 may be provided user actions 212-1, 212-2, 212-3, 212-4, through 212-N as a sequence of user actions in connection with user timeline 210[generating, via a machine learning model, based on prior sequential network actions of a user]... [a]s illustrated in FIG. 2A, [the] sequential trained machine learning model 202 may generate a USER EMBEDDING output that is representative of the user and is configured to predict a set of user actions for the user over a future time period[a predicted set of future network actions of the user,]... [a]s shown in FIG. 2B, sequential trained machine learning model 202 may generate a new user embedding based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2. User actions 212-1 through 212-N-X may correspond to a subset of the sequence of user actions that were used in generating a previous user embedding[the predicted set comprising a first subset of sequential network actions corresponding to a first predicted instance of the user]... sequential trained machine learning model 202 may generate NEW EMBEDDING based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2 as the newly generated embedding for the user based on the newly acquired user actions[and a second subset of sequential network actions corresponding to a second predicted instance of the user].”);
generating a first feature embedding based on the first subset of sequential network actions and a second feature embedding based on the second subset of sequential network actions, the first feature embedding representing the first predicted instance of the user, the second feature embedding representing the second predicted instance of the user(Zhai, paras. [0034-0041], see also Figs. 2A & 2B, “As illustrated in FIG. 2A, sequential trained machine learning model 202 may be provided user actions 212-1, 212-2, 212-3, 212-4, through 212-N as a sequence of user actions in connection with user timeline 210... [a]s illustrated in FIG. 2A, [the] sequential trained machine learning model 202 may generate a USER EMBEDDING output that is representative of the user and is configured to predict a set of user actions for the user over a future time period... [a]s shown in FIG. 2B, sequential trained machine learning model 202 may generate a new user embedding based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2. User actions 212-1 through 212-N-X may correspond to a subset of the sequence of user actions that were used in generating a previous user embedding[generating a first feature embedding based on the first subset of sequential network actions; the first feature embedding representing the first predicted instance of the user]... sequential trained machine learning model 202 may generate NEW EMBEDDING based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2 as the newly generated embedding for the user based on the newly acquired user actions[and a second feature embedding based on the second subset of sequential network actions; the second feature embedding representing the second predicted instance of the user].”);
querying a feature embedding space based on the first and second embeddings to obtain a third feature embedding that satisfies a similarity threshold with the first and second embeddings, wherein the similarity threshold comprises a cosine distance [that is greater than a threshold cosine distance]( Zhai, paras. [0034-0041], see also Figs. 2A & 2B, “ As illustrated in FIG. 2A, [the] sequential trained machine learning model 202 may generate a USER EMBEDDING output that is representative of the user and is configured to predict a set of user actions for the user over a future time period... sequential trained machine learning model 202 may generate NEW EMBEDDING based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2 as the newly generated embedding for the user based on the newly acquired user actions[based on the first and second embeddings].” & Zhai, paras. [0048-0049], see also Fig. 3, “[T]he context aware updated embeddings may be determined in real-time as contextual information...e.g., a received query...is received... recommend content for the user, provide content items and/or search results...responsive to queries the content items may be identified, for example, based on distance to the user embeddings employing similarity, clustering, and/or search techniques, such as cosine similarity[querying a feature embedding space; obtain a third feature embedding that satisfies a similarity threshold with the first and second embeddings, wherein the similarity threshold comprises a cosine distance]....”);9
and generating a network content recommendation for the user based on the third feature embedding that satisfies the similar threshold with the first and second embeddings(Zhai, paras. [0034-0041], see also Figs. 2A & 2B, “ As illustrated in FIG. 2A, [the] sequential trained machine learning model 202 may generate a USER EMBEDDING output that is representative of the user and is configured to predict a set of user actions for the user over a future time period... sequential trained machine learning model 202 may generate NEW EMBEDDING based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2 as the newly generated embedding for the user based on the newly acquired user actions.” & Zhai, paras. [0048-0049], see also Fig. 3, “[T]he context aware updated embeddings may be determined in real-time as contextual information... recommend content for the user, provide content items and/or search results...responsive to queries the content items may be identified, for example, based on distance to the user embeddings employing similarity, clustering, and/or search techniques, such as cosine similarity[and generating a network content recommendation for the user based on the third feature embedding that satisfies the similar threshold with the first and second embeddings]....”).
Zhai does not teach: that is greater than a threshold cosine distance
However, Li teaches:
that is greater than a threshold cosine distance(Li, pgs. 4-5, see also fig. 1, “[W]e aim to infer an intention vector
m
t
...
m
t
can be drawn from the following categorical distribution:
m
t
~
C
a
t
e
g
o
r
i
c
a
l
(
S
o
f
t
m
a
x
s
t
,
1
,
s
t
,
2
,
…
,
s
t
,
K
)
... we adopt the cosine similarity between two vectors, i.e.,
s
t
,
k
=
x
t
⋅
c
k
x
t
2
c
k
2
... we obtain the new intent vector
m
t
+
1
by considering the norm of the corresponding intent feature vector, i.e.,
m
t
+
1
,
k
=
1
if and only if
z
t
+
1
,
k
2
>
γ
where
γ
is a threshold[that is greater than a threshold cosine distance].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhai with the teachings of Li the motivation to do so would be to incorporate user intentions behind user actions to make better sequential recommendations(Li, pgs., 1-2, “In practice, user behavior patterns in recommendation systems are highly driven by their intentions behind. To provide better recommendations, it is important to capture user intentions besides their historic interactions... To solve these challenges, in this paper, we proposed ISRec1, a structured intention-aware model for sequential recommendation.”).
Regarding claim 2, Zhai teaches a method comprising:
obtaining a machine learning model that has been trained on prior sequential user actions [to generate probabilities of future user actions](Zhai, para. [0040], see also fig. 2A, “[A] first user embedding may be generated by sequential trained machine learning model 202 using a sequence of user actions 212-1 through 212-N, as illustrated in FIG. 2A[obtaining a machine learning model that has been trained on prior sequential user actions].”);10
predicting, via the machine learning model, based on prior sequential actions of a user, a set of future actions of the user, the set of future actions comprising a first subset of actions corresponding to a first instance of the user and a second subset of actions corresponding to a second instance of the user(Zhai, paras. [0034-0041], see also Figs. 2A & 2B, “As illustrated in FIG. 2A, sequential trained machine learning model 202 may be provided user actions 212-1, 212-2, 212-3, 212-4, through 212-N as a sequence of user actions in connection with user timeline 210... [a]s illustrated in FIG. 2A, [the] sequential trained machine learning model 202 may generate a USER EMBEDDING output that is representative of the user and is configured to predict a set of user actions for the user over a future time period[predicting, via the machine learning model, based on prior sequential actions of a user, a set of future actions of the user]... [a]s shown in FIG. 2B, sequential trained machine learning model 202 may generate a new user embedding based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2. User actions 212-1 through 212-N-X may correspond to a subset of the sequence of user actions that were used in generating a previous user embedding[the set of future actions comprising a first subset of actions corresponding to a first instance of the user]... sequential trained machine learning model 202 may generate NEW EMBEDDING based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2 as the newly generated embedding for the user based on the newly acquired user actions[and a second subset of actions corresponding to a second instance of the user].”);
obtaining a first feature embedding based on the first subset of actions and a second feature embedding based on the second subset of actions(Zhai, paras. [0034-0041], see also Figs. 2A & 2B, “As illustrated in FIG. 2A, sequential trained machine learning model 202 may be provided user actions 212-1, 212-2, 212-3, 212-4, through 212-N as a sequence of user actions in connection with user timeline 210... [a]s illustrated in FIG. 2A, [the] sequential trained machine learning model 202 may generate a USER EMBEDDING output that is representative of the user and is configured to predict a set of user actions for the user over a future time period... [a]s shown in FIG. 2B, sequential trained machine learning model 202 may generate a new user embedding based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2. User actions 212-1 through 212-N-X may correspond to a subset of the sequence of user actions that were used in generating a previous user embedding... sequential trained machine learning model 202 may generate NEW EMBEDDING based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2 as the newly generated embedding for the user based on the newly acquired user actions[obtaining a first feature embedding based on the first subset of actions and a second feature embedding based on the second subset of actions].”);
and generating one or more recommendations for the user based on the first and second embeddings(Zhai, paras. [0032-0041], see also Figs. 2A & 2B, “The exemplary sequential trained machine learning models 202 illustrated in FIGS. 2A and 2B may, for example, be implemented by an online service, such as...a recommendation service, and the like, so as to generate embeddings representative of users of the online service, so that the online service can identify and provide more relevant content to users of the online service[and generating one or more recommendations for the user based on the first and second embeddings].”).
Zhai does not teach: to generate probabilities of future user actions
However, Li teaches:
to generate probabilities of future user actions(Li, pgs., 3-5, “For each user
u
∈
U
, the interaction sequence sorted in the chronological order is denoted as
S
u
=
[
v
1
u
,
v
2
u
,
…
,
v
S
u
|
(
u
)
]
, in which
v
t
(
u
)
∈
V
is the item that user u interacted at time index t...we calculate the similarity of the sequence representation with the item embedding vector to obtain a recommendation probability:
p
v
t
+
1
v
1
,
v
2
,
…
,
v
t
=
S
o
f
t
m
a
x
(
x
t
+
1
V
T
)
[ to generate probabilities of future user actions]”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhai with the teachings of Li the motivation to do so would be to incorporate user intentions behind user actions to make better sequential recommendations(Li, pgs., 1-2, “In practice, user behavior patterns in recommendation systems are highly driven by their intentions behind. To provide better recommendations, it is important to capture user intentions besides their historic interactions... To solve these challenges, in this paper, we proposed ISRec1, a structured intention-aware model for sequential recommendation.”).
Regarding claim 5, Zhai in view of Li teaches the method of claim 2, further comprising: obtaining one or more other feature embeddings that satisfies a similarity threshold with the first or second embedding, wherein generating the one or more recommendations comprises generating the one or more recommendations for the user based on the one or more other feature embeddings(Zhai, paras. [0046-0049], “[A] USER EMBEDDING output that is representative of the user and is configured to predict a set of user actions for the user over a future time period. The generated user embedding and certain contextual information may be processed by trained machine learning model 332 to generate a context aware updated user embedding in connection with the user... [t]he context aware updated user embedding may also be used by the online service to, for example...recommend content for the user...the content items may be identified, for example, based on distance to the user embeddings employing similarity, clustering, and/or search techniques, such as cosine similarity, nearest neighbor techniques, and the like.”).11
Regarding claim 6, Zhai in view of Li teaches the method of claim 5, wherein the first feature embedding represents the first instance of the user, the second feature embedding represents the second instance of the user, and the one or more other feature embeddings represent one or more other users(Zhai, paras. [0052-0056], see also fig. 4, “For example, user
actions 412-1, 412-2, 412-3, 412-4, through 412-N may be processed by block 420, which may generate embedding
e
1
424-1, which corresponds to user action 412-1[wherein the first feature embedding represents the first instance of the user], embedding
e
2
424-2, which corresponds to user action 412-2[the second feature embedding represents the second instance of the user,]... in connection with training the sequential machine learning model to learn the user embeddings[and the one or more other feature embeddings represent one or more other users]....”).
Regarding claim 7, Zhai in view of Li teaches the method of claim 2, wherein the machine learning model comprises a decoder of a transformer(Zhai, paras. [0052-0053], see also fig. 4, “[B]lock 420 may employ one or more transformers... the transformers may be comprised of alternating feedforward network (FFN) and multi-head self attention (MI-ISA)
blocks....”),
and wherein an action node of the machine learning model comprises a key vector and a query vector used to generate a second action that follows a first action(Li, pgs., 3-4, see also fig. 1 and table 1, “
x
t
∈
R
d
representation of the behavior sequence...[o]ne layer in the self-attention submodule can be formulated as follows:[as detailed by equations 3 and 4]... are parameters for queries, keys[and wherein an action node of the machine learning model comprises a key vector and a query vector]...[w]e denote the outputs of L such layers as
X
=
[
x
1
,
…
,
x
T
]
[ used to generate a second action that follows a first action]”).12
Regarding claim 8, Zhai in view of Li teaches the method of claim 2, further comprising: determining a cluster of users based on the first or second embedding, wherein generating the one or more recommendations comprises generating the one or more recommendations for the user based on the cluster of users(Zhai, paras. [0047-0049], “[T]he context aware user embedding may be a representation of the user in view of the contextual information... recommend content for the user...the content items may be identified, for example, based on distance to the user embeddings employing...clustering....”).13
Regarding claim 9, Zhai in view of Li teaches the method of claim 8, further comprising:
determining, based on the cluster of users, a set of items obtained by users of the cluster(Zhai, paras. [0047-0049], “[T]he context aware user embedding may be a representation of the user in view of the contextual information... recommend content for the user...the content items may be identified, for example, based on distance to the user embeddings employing...clustering[determining, based on the cluster of users, a set of items obtained by users of the cluster]....”),
determining that the user has not obtained a first item of the set of items obtained by the users of the cluster, wherein generating the one or more recommendations comprises generating a recommendation of the first item for the user based on the determination that the user has not obtained the first item of the set of items obtained by the users of the cluster(Zhai, paras. [0054-0056], “[N]egative examples 418 may include, for example, randomly sampled content items from a corpus of content items ( e.g.,content items 132, etc.) with which the respective user has not engaged and/or otherwise interacted, content items with which a user other than the respective user has engaged and/or otherwise interacted, and the like[determining that the user has not obtained a first item of the set of items obtained by the users of the cluster]... negative examples 418 may be provided to MLP 422 as labeled negative training data and the output of MLP 422 may be provided to dense layer 440 in training the sequential machine learning model to generate embeddings configured to predict a set of user actions for a defined timeframe and/or period of time[wherein generating the one or more recommendations comprises generating a recommendation of the first item for the user based on the determination that the user has not obtained the first item of the set of items obtained by the users of the cluster].”).
Regarding claim 10, Zhai in view of Li teaches the method of claim 2, wherein predicting the set of future actions of the user comprises,
for the first subset of actions corresponding to the first instance of the user: inputting a sequential action set, comprising the prior sequential actions of the user, into the machine learning model to predict a first future action of the user(Zhai, paras. [0040-0042], see also figs. 2a and 2b, “user embeddings are generated from user actions recorded over a period of ten days, user actions 212-1 through 212-N may correspond to user actions recorded on days 1 through 10 and the first user embedding may have been generated on day 10[for the first subset of actions corresponding to the first instance of the user: inputting a sequential action set, comprising the prior sequential actions of the user, into the machine learning model to predict a first future action of the user].”);
appending the first future action of the user to the sequential action set such that the first future action follows the prior sequential actions of the user in the sequential action set; subsequent to the appending of the first future action of the user, inputting the sequential action set to the machine learning model to predict a second future action of the user, wherein obtaining the first feature embedding comprises obtaining the first feature embedding based on the first subset of actions comprising the first and second future actions of the user (Zhai, paras. [0040-0042], see also figs. 2a and 2b, “[U]ser actions 212-N-X through 212-N may correspond to user actions recorded on day 1, and user actions 212-N-X through 212-1 may correspond to user actions recorded on days 2 through 10. The new user embedding may then be generated on day 11 based on user actions 212-1 through 212-N-X and 214-1 and 214-2, which correspond to user actions recorded on days 2 through 11.”).
Regarding claim 11, Zhai in view of Li teaches the method of claim 10, wherein obtaining the first feature embedding comprises
generating a first sequential action set in which the second future action follows the first future action based on the prediction of the second future action being derived from the prediction of the first future action(Zhai, para. [0052], see also fig. 4, “[U]ser actions 412-1, 412-2, 412-3, 412-4, through 412-N may be processed by block 420, which may generate embedding
e
1
424-1, which corresponds to user action 412-1, embedding
e
2
424-2, which corresponds to user action 412-2...”).
Regarding claim 12, Zhai teaches a non-transitory, computer-readable medium comprising instructions that when executed by one or more processors, cause operations(Zhai, para. [0020], see also fig., 1A, “As shown in FIG. 1A, computing environment 100 may include one or more client devices 110 (e.g., client device 110-1, 110-2, through 110-N), also referred to as user devices, for connecting over network 150 to access computing resources 120. Client devices 110 may include any type of computing device, such as a smartphone, tablet, laptop computer, desktop computer, wearable, etc., and network 150 may include any wired or wireless network (e.g., the Internet, cellular, satellite, Bluetooth, Wi-Fi, etc.) that can facilitate communications between client devices 110 and computing resources 120.”) and for all other claim limitations they are rejected on the same basis as independent claim 2 since they are analogous claims.
Referring to dependent claims 13-19, they are rejected on the same basis as
dependent claims 3, 5-6, and 8-11 since they are analogous claims.
Regarding claim 20, Zhai in view of Li teaches the media of claim 18, wherein predicting the first future action of the user comprises: obtaining, via the machine learning model, first probabilities of first candidate actions of the user occurring, the first probabilities comprising a first probability of the user performing the first future action(Li, pgs., 3-5, “Here we explicitly extract explainable user intents from the encoded sequence hidden representations X[obtaining, via the machine learning model, first probabilities of first candidate actions of the user occurring,]...[w]e adopt the similarity between the sequence representation and concept embeddings as the probability of activating the concepts. Then,
m
t
can be drawn from the following categorical distribution:
m
t
~
C
a
t
e
g
o
r
i
c
a
l
(
S
o
f
t
m
a
x
s
t
,
1
,
s
t
,
2
,
…
,
s
t
,
K
)
[ the first probabilities comprising a first probability of the user performing the first future action]”);
and randomly selecting, from the first candidate actions, the first future action via a random selection proportional to the first probabilities of the first candidate actions(Li, pgs., 3-5, “For each user
u
∈
U
, the interaction sequence sorted in the chronological order is denoted as
S
u
=
[
v
1
u
,
v
2
u
,
…
,
v
S
u
|
(
u
)
]
, in which
v
t
(
u
)
∈
V
is the item that user u interacted at time index t...we calculate the similarity of the sequence representation with the item embedding vector to obtain a recommendation probability:
p
v
t
+
1
v
1
,
v
2
,
…
,
v
t
=
S
o
f
t
m
a
x
(
x
t
+
1
V
T
)
[ and randomly selecting, from the first candidate actions, the first future action via a random selection proportional to the first probabilities of the first candidate actions]”).14
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Zhai et al., US 2023/0252269-Al(“Zhai”) in view of Li, Haoyang, et al. "Intention-aware sequential recommendation with structured intent transition." IEEE Transactions on Knowledge and Data Engineering 34.11 (2021)(“Li”) and in view of Foster et al., US 2019/0065748 Al(“Foster”)
Regarding claim 3, Zhai in view of Li teaches the method of claim 2, further comprising:
generating a set of instances of the user, wherein each instance of the set of instances corresponds to a different set of actions predicted via the machine learning model, the set of instances comprising the first instance of the user, the second instance of the user, and a third instance of the user(Zhai, para. [0034-0042], see also Fig. 2A, “For example, in an exemplary implementation where user embeddings are generated from user actions recorded over a period of ten days, user actions 212-1 through 212-N may correspond to user actions recorded on days 1 through 10 and the first user embedding may have been generated on day 10. Continuing the example implementation, user actions 214 may correspond to user actions
recorded on day 11... [t]he new user embedding may then be generated on day 11 based on user actions 212-1 through 212-N-X and 214-1 and 214-2, which correspond to user actions recorded on days 2 through 11... sequential trained machine learning model 202 may generate NEW EMBEDDING based on the sequence of user actions 212-1 through 212-N-X and 214-1 and 214-2 as the newly generated embedding for the user based on the newly acquired user actions.”);
wherein generating the one or more recommendations comprises[subsequent to the removal] using the set of instances to generate the one or more recommendations for the user(Zhai, paras. [0032-0041], see also Figs. 2A & 2B, “The exemplary sequential trained machine learning models 202 illustrated in FIGS. 2A and 2B may, for example, be implemented by an online service, such as...a recommendation service, and the like, so as to generate embeddings representative of users of the online service, so that the online service can identify and provide more relevant content to users of the online service.”).15
While Zhai in view of Li does teach the third instance, Zhai in view of Li do not teach: determining that the third instance of the user fails to satisfy a quality threshold; removing the third instance from the set of instances based on the third instance failing to satisfy the quality threshold; subsequent to the removal
However, Foster teaches:
determining that the third instance of the user fails to satisfy a quality threshold; removing the third instance from the set of instances based on the third instance failing to satisfy the quality threshold(Foster, para. [0141], “For example, data that is posted to the protected social entity's page, or sent to the protected social entity as a message may
be analyzed to determine the risk to the protected social entity. The analysis of the data may involve the comparison to the one or more user selected risk thresholds. The servers at the social threat protection tool may submit a request to the hosting social network to remove the data that is determined as a risk to the protected social entity.”);
subsequent to the removal(Foster, para. [0141], “For example, data that is posted to the protected social entity's page, or sent to the protected social entity as a message may be analyzed to determine the risk to the protected social entity. The analysis of the data may involve the comparison to the one or more user selected risk thresholds. The servers at the social threat protection tool may submit a request to the hosting social network to remove the data that is determined as a risk to the protected social entity.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Zhai in view of Li with the teachings of Foster the motivation to do so would be to identify actions by an imposter user that is actually part of a cyber-attack operation(Foster, paras. [0003-0004], “Modem cyber threats, however, evolve alongside computer technology, and attackers can be expected to leverage whatever means are available in order compromise or bypass traditional defenses... [t]hese risks include...fraud, impersonations, and social engineering... determining, based on the rating assigned to at least one of the one or more social entities that the at least one social entity is a security risk to the protected social entity....”).
Regarding claim 4, Zhai in view of Li and Foster teaches the method of claim 3, wherein the quality threshold is related to a probability of a cybersecurity incident associated with the user(Foster, para. [0141], “For example, data that is posted to the protected social entity's page, or sent to the protected social entity as a message may be analyzed to determine the risk to the protected social entity. The analysis of the data may involve the comparison to the one or more user selected risk thresholds. The servers at the social threat protection tool may submit a request to the hosting social network to remove the data that is determined as a risk to the protected social entity.”).16
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mosby et al., US 2021/0279337 A1(details a scoring device to determine user behavior that violates a security policy)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM C STANDKE whose telephone number is (571)270-1806. The examiner can normally be reached Gen. M-F 9-9PM EST.
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, Michael J Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Adam C Standke/
Primary Examiner
Art Unit 2129
1 Examiner Notes: Examiner has rejected the claim due to lack of antecedent basis due to “media.” Accordingly, Examiner is interpreting “media” as “medium” for purposes of 101.
2 Examiner Notes: Examiner has rejected the claim due to lack of antecedent basis due to “media.” Accordingly, Examiner is interpreting “media” as “medium” for purposes of 101.
3 Examiner Notes: Examiner has rejected the claim due to lack of antecedent basis due to “media.” Accordingly, Examiner is interpreting “media” as “medium” for purposes of 101.
4 Examiner Notes: Examiner has rejected the claim due to lack of antecedent basis due to “media.” Accordingly, Examiner is interpreting “media” as “medium” for purposes of 101.
5 Examiner Notes: Examiner has rejected the claim due to lack of antecedent basis due to “media.” Accordingly, Examiner is interpreting “media” as “medium” for purposes of 101.
6 Examiner Notes: Examiner has rejected the claim due to lack of antecedent basis due to “media.” Accordingly, Examiner is interpreting “media” as “medium” for purposes of 101.
7 Examiner Notes: Examiner has rejected the claim due to lack of antecedent basis due to “media.” Accordingly, Examiner is interpreting “media” as “medium” for purposes of 101.
8 Examiner Notes: Examiner has rejected the claim due to lack of antecedent basis due to “media.” Accordingly, Examiner is interpreting “media” as “medium” for purposes of 101.
9 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Zhai.
10 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Zhai.
11 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
12 It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the teachings of Zhai with the above teachings of Li for the same rationale stated at Claim 2.
13 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
14 It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the teachings of Zhai with the above teachings of Li for the same rationale stated at Claim 12___.
15 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Zhai in view of Li
16 It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the teachings of Zhai in view of Li with the above teachings of Foster for the same rationale stated at Claim 3.
Prosecution Insights