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 .
Response to Amendment
This office action is written in response to an amendment filed on 6/12/2026. As directed by amendment: Claim 6 was amended. No new claims were added and no claims were cancelled. Thus, Claims 1-20 are presently pending in this application.
Response to Arguments
Applicant's arguments filed 6/12/2026 have been fully considered but they are not persuasive. Therefore, the rejection still stands.
Argument 1: Applicant respectfully submits that Korotkikh fails to teach or suggest at least the "determining" and "causing delivery" recitations of claim 1. Korotkikh is directed to determining a "spam prediction error parameter" used to retrain a spam detection algorithm. To that end, Korotkikh generates a "spam prediction parameter" indicating that an email "is one of a spam email and a non-spam email," clusters emails by similarity, determines a per-cluster "ground truth parameter" by analyzing previously collected user interactions, and generates an error parameter "based on a difference between the spam prediction parameter and the respective ground truth parameter." Korotkikh's analysis is therefore directed to classifying the message itself and auditing the accuracy of that classification.
Claim 1, by contrast, requires "determining, by the application, an action prediction by the user upon delivery of the message to an inbox, the action prediction being a type of interaction the user is predicted to perform on the message." What is determined is a prediction of the user's prospective conduct. Korotkikh determines no such prediction. Its only prediction is the spam prediction parameter, which characterizes the message as spam or non-spam rather than predicting any interaction the user will perform on it. Korotkikh's separate "user-interaction parameter" does not cure this deficiency, because that parameter is "indicative of whether an associated recipient ... agrees with the respective spam prediction parameter" and is collected from interactions the recipient has already performed, such as moving an email into a folder or clicking a button. It is a record of past conduct used to establish ground truth, not a determination of a type of interaction the user is predicted to perform upon delivery.
Examiner’s Response: Korotkikh teaches determining, by the application, an action prediction by the user upon delivery of the message to an inbox, the action prediction being a type of interaction the user is predicted to perform on the message (par 9; par 121-126);
The user may move an email to the spam folder if the prediction is incorrect. The action prediction is predicting whether the email is spam or not. The type of interaction is whether the user will classify the email as spam or not spam.
Paragraph 126 states “In another example, if a given email appears in the recipient's spam folder, and/or if the given email is flagged as being spam, the given email is associated with a spam prediction parameter indicative of that the given email is spam. The server 106 may be configured to analyze the user-interactivity data between the recipient and the given email for determining whether the recipient agrees with the spam prediction parameter. In this example, if user-interactivity data for that given email comprises an indication of that the recipient moved the given email from the spam folder to the inbox folder, and/or that the recipient clicked a “non-spam” button while selecting the given email, the server 106 may determine that the recipient does not agree with the respective spam prediction parameter for that given email. Otherwise, the server 106 may determine that the recipient agrees with the respective spam prediction parameter.”
The system tries to predict that the user will not mark the email as “not-spam” and agrees with the spam prediction parameter when the message is predicted to be spam.
Therefore, the Korotkikh still teaches the aforementioned limitations.
Argument 2 - Claim 1 further recites "causing delivery, by the application, of the message based on the determined action prediction, the caused delivery comprising causing the message to be routed to a specific portion of the inbox." Korotkikh provides no delivery operation performed "based on" a predicted user action, because it determines no such prediction in the first place. Moreover, the sequence in Korotkikh runs the opposite direction: the message is placed first, the recipient's interactions with the already-placed message are then collected, and those interactions are used downstream to compute the ground truth and error parameters and retrain the algorithm. Korotkikh's user-interaction data is thus an input to an after-the-fact accuracy audit, not a basis on which delivery is caused.
Examiner’s Response: Korotkikh teaches causing delivery, by the application, of the message based on the determined action prediction, the caused delivery comprising causing the message to be routed to a specific portion of the inbox (par 9; par 121-126).
The user may move an email to the spam folder if the prediction is incorrect. The action prediction is predicting whether the email is spam or not. The type of interaction is whether the user will classify the email as spam or not spam.
Paragraph 126 states “In another example, if a given email appears in the recipient's spam folder, and/or if the given email is flagged as being spam, the given email is associated with a spam prediction parameter indicative of that the given email is spam. The server 106 may be configured to analyze the user-interactivity data between the recipient and the given email for determining whether the recipient agrees with the spam prediction parameter. In this example, if user-interactivity data for that given email comprises an indication of that the recipient moved the given email from the spam folder to the inbox folder, and/or that the recipient clicked a “non-spam” button while selecting the given email, the server 106 may determine that the recipient does not agree with the respective spam prediction parameter for that given email. Otherwise, the server 106 may determine that the recipient agrees with the respective spam prediction parameter.”
The system tries to predict that the user will not mark the email as “not-spam” and agrees with the spam prediction parameter when the message is predicted to be spam.
Therefore, the Korotkikh still teaches the aforementioned limitations.
Argument 3 - Finally, even the classification-driven placement in Korotkikh does not route a message "to a specific portion of the inbox," because Korotkikh distinguishes only between separate folders, namely a distinct "inbox folder" and "spam folder," and at most places a message in one folder or the other based on the spam prediction parameter. Routing among separate folders is not causing a message to be routed to a specific portion within the inbox.
Examiner’s Response: The inbox comprises folders such as the inbox folder and the spam folder. The specific portion of the inbox is the spam folder or the inbox folder depending on whether the message is predicted to be spam or not.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 5, 7-11, 13, 15-16, 18, and 20 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Korotkikh et al (“Korotkikh”, US 20220109649).
Regarding Claim 1, Korotkikh teaches a method comprising:
identifying, by an application, a message addressed to a user (par 9; par 121-126);
analyzing, by the application, the message (par 9; par 121-126);
determining, by the application, an action prediction by the user upon delivery of the message to an inbox, the action prediction being a type of interaction the user is predicted to perform on the message (par 9; par 121-126; The user may move an email to the spam folder if the prediction is incorrect. The action prediction is predicting whether the email is spam or not. The type of interaction is whether the user will classify the email as spam or not spam.);
and causing delivery, by the application, of the message based on the determined action prediction, the caused delivery comprising causing the message to be routed to a specific portion of the inbox (par 9; par 121-126; The user may move an email to the spam folder if the prediction is incorrect. The action prediction is predicting whether the email is spam or not. The specific portion of the inbox is the spam folder.).
Regarding Claim 3, Korotkikh teaches the method of claim 1.
Korotkikh teaches further comprising the analysis of the message involving processing selected from a group consisting of:
feature engineering,
behavior analysis (par 9; par 121-126)
and contextual analysis (par 9; par 121-126).
Regarding Claim 5, Korotkikh teaches the method of claim 1.
Korotkikh teaches further comprising: further analyzing the message based on the action prediction determination (par 9; par 121-126; The user may move an email to the spam folder if the prediction is incorrect. The action prediction is predicting whether the email is spam or not. The specific portion of the inbox is the spam folder.);
determining a classification for the message (par 9; par 121-126; The user may move an email to the spam folder if the prediction is incorrect. The action prediction is predicting whether the email is spam or not. The specific portion of the inbox is the spam folder.);
and performing the delivery of the message based further on the classification (par 9; par 121-126; The user may move an email to the spam folder if the prediction is incorrect. The action prediction is predicting whether the email is spam or not. The specific portion of the inbox is the spam folder.).
Regarding Claim 7, Korotkikh teaches the method of claim 1.
Korotkikh teaches further comprising enabling modifications to the caused delivery upon completion of the delivery to enable modification of the classification (par 9; par 121-126; par 9; par 121-126; The user may move an email to the spam folder if the prediction is incorrect. The action prediction is predicting whether the email is spam or not. The modification is the user moving the email to the spam folder manually.).
Regarding Claim 8, Korotkikh teaches the method of claim 1.
Korotkikh teaches further comprising the application comprising functionality related to an artificial intelligence (AI) model, such that the AI model is trained based on information related to the caused delivery (par 96-99).
Regarding Claim 9, Korotkikh teaches the method of claim 1.
Korotkikh teaches further comprising the application being associated with a mail application executed by a user device (par 9; par 121-126).
Regarding Claim 10, Korotkikh teaches the method of claim 1.
Korotkikh teaches further comprising the application being executed by a network device that is an intermediary between the user and a sender of the message (Fig. 1, elements {101, 106, 150}, par 60-62; par 77-79).
Regarding Claim 11, Claim 11 is rejected with the same reasoning as Claim 1.
Regarding Claim 13, Claim 13 is rejected with the same reasoning as Claim 3.
Regarding Claim 15, Claim 15 is rejected with the same reasoning as Claim 5.
Regarding Claim 16, Claim 16 is rejected with the same reasoning as Claim 1.
Regarding Claim 18, Claim 18 is rejected with the same reasoning as Claim 3.
Regarding Claim 20, Claim 20 is rejected with the same reasoning as Claim 5.
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, 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 test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Claims 2, 4, 12, 14, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Korotkikh in view of Kerschhofer et al (“Kerschhofer”, US 20160226811).
Regarding Claim 2, Korotkikh teaches the method of claim 1.
Korotkikh does not explicitly teach the action prediction determination comprising: determining a score for the message based on the analysis; and comparing the score against a threshold, such that a priority determination for the message is based on the score satisfying the threshold.
Kerschhofer teaches the action prediction determination comprising:
determining a score for the message based on the analysis (par 29; par 45; par 64);
and comparing the score against a threshold, such that a priority determination for the message is based on the score satisfying the threshold (par 29; par 45; par 64).
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 invention of Korotkikh with the priority-based email categorization of Kerschhofer because it allows for important emails to be viewed first.
Regarding Claim 4, Korotkikh teaches the method of claim 1.
Korotkikh teaches further comprising the type of interaction being user engagement with the message (par 9; par 121-126; The user may move an email to the spam folder if the prediction is incorrect. The action prediction is predicting whether the email is spam or not. The type of interaction is whether the user will classify the email as spam or not spam.),
Korotkikh does not explicitly teach the caused delivery being a priority tab of the inbox.
Kerschhofer teaches the caused delivery being a priority tab of the inbox (Fig. 2, element 200, par 41-45).
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 invention of Korotkikh with the priority-based email categorization of Kerschhofer because it allows for important emails to be viewed first.
Regarding Claim 12, Claim 12 is rejected with the same reasoning as Claim 2.
Regarding Claim 14, Claim 14 is rejected with the same reasoning as Claim 4.
Regarding Claim 17, Claim 17 is rejected with the same reasoning as Claim 2.
Regarding Claim 19, Claim 19 is rejected with the same reasoning as Claim 4.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Korotkikh in view of Lazaridis et al (“Lazaridis”, US 20120235930).
Regarding Claim 6, Korotkikh teaches the method of claim 5.
Korotkikh further teaches further comprising the further analysis being performed by an application.
Korotkikh does not explicitly teach another application.
Lazaridis teaches another application (par 38).
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 invention of Korotkikh with the second application of Lazaridis because it allows for applications to be previewed without being opened up resulting in a faster process (Lazaridis; par 39).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Azab et al (US 20190236486), Abstract - One embodiment provides a method comprising extracting natural language content from a piece of communication for a user, generating a representation of the piece of communication based on the natural language content extracted, and utilizing a global deep learning model and a personalized learning model for the user to assign a priority label to the piece of communication based on the representation and user behavioral information associated with recent conversations of the user. Another embodiment provides a method comprising, for each piece of communication of a set of multiple pieces of communication for multiple users, extracting natural language content from the piece communication and generating a representation of the piece of communication based on the natural language extracted, and training a deep learning neural network to predict a degree of priority of a subsequent piece of communication based on each representation generated.
Faddoul et al (US 20130282627), Abstract - A multi-task machine learning method is performed to generate a multi-task (MT) predictor for a set of tasks including at least two tasks. The machine learning method includes: learning a multi-task decision tree (MT-DT) including learning decision rules for nodes of the MT-DT that optimize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks; and constructing the MT predictor based on the learned MT-DT. In some embodiments the aggregate IG is the largest single-task IG value of the single-task IG values. In some embodiments the machine learning method includes repeating the MT-DT learning operation for different subsets of a training set to generate a set of learned MT-DT's, and the constructing comprises constructing the MT predictor as a weighted combination of outputs of the set of MT-DT's.
Stuntebeck et al (US 20160323226), Abstract - Disclosed are various examples for communicating notifications for received email messages. A monitoring service monitors inboxes to which clients are subscribed. When a message arrives in a subscribed inbox, the monitoring service sends a notification to a notification brokering service. The notification brokering service then forwards the notification to the appropriate notification service for communication to a subscribed client.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQIUL AMIN CHOUDHURY whose telephone number is (571)272-2482. The examiner can normally be reached Monday-Friday 7:30 AM - 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Follansbee can be reached at 571-272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAQIUL A CHOUDHURY/Examiner, Art Unit 2444
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