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 .
DETAILED ACTION
This action is in response to application filed 01/22/2026.
Claims 1-20 are pending in this application.
Response to Arguments
Applicant's arguments filed have been fully considered but they are not persuasive. Applicant assert that the prior art of record fails to disclose “…training, by one or more processors, a machine-learning model to output one or more priority prediction weights based on a regression algorithm comprising user labels and a communication dataset comprising sent, received, saved, and drafted emails…” Upon further consideration, Examiner respectfully disagrees. Regarding the claim element that recites “…a communication dataset comprising sent, received, saved, and drafted emails…”, the term “saved” emails can be interpret as emails stored in the user’s email inbox or archive folder. In that case, Zang discloses calculating an activity intensity score for each of a plurality of the messages in the training data by analyzing user activity associated with each message. The activity intensity score may then be assigned as a label for the message in the user training data… partitioning the activity intensity scores into a number of groups, each group characterizing one priority rank, and assigning the priority rank to each message in the message data store based on the group into which the predicted intensity score the message falls ([0005]-[0006]). The user activity may describe user actions taken in response to receipt of the message, and the user activities may include at least one of opening the message, closing the message, reading the message (e.g. received email), forwarding the message (e.g. sending), drafting a reply to the message (drafting), marking the message read, marking the message unread, marking the message for follow-up (e.g. saved message), a length of a reply to the message, forwarding the message, or time the user spent composing a reply to message or a forwarding message ([0007]). Machine learning techniques are applied to user training data to generate an intensity score model, which is a user-specific classifier to process and score new messages received for the user. Generally, training data for a supervised machine learning (i.e. classification) algorithm consists of a target/outcome variable which is to be predicted from a given set of features. Examples of supervised learning which are suitable for use in the present technology Linear Regression, Logistic Regression, and Support Vector Machine (SVM), as well as numerous others ([0050]). Furthermore, Zang discloses once a user begins receiving message data, user activity on messages received by the user is gathered and the messages are labeled to create training data for use in creating a user-specific classifier (fig. 6, [0062]).
Therefore, based on the broadest reasonable interpretation, Zang discloses training a machine-learning model to output priority prediction weights (e.g. The predicted intensity score used in training data and assign priority rank based on the predicted intensity score) based on a regression algorithm (supervised learning include Linear Regression, Logistic Regression) comprising user labels and a communication dataset comprising sent, received, saved, and drafted emails (e.g. user activities and actions).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-7, 9, 11-12, 15-17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zang et al. (US 2018/0219817 A1) in view of Mehta et al. (US 2024/0314093 A1).
Regarding claim 1, Zhang discloses a computer-implemented method for utilizing a machine-learning model to determine an electronic communication priority ([0006]: assigning the priority rank to each message in the message data store based on the group into which the predicted intensity score the message fall), the computer-implemented method comprising:
training, by one or more processors, a machine-learning model to output one or more priority prediction weights based on a regression algorithm ([0050]: machine learning techniques are applied to user training data to generate an intensity score model, which is a user-specific classifier to process and score new messages received for the user… supervised learning which are suitable for use in the present technology include Logistic Regression, and Support Vector Machine (SVM), as well as numerous others) comprising user labels and a communication dataset comprising sent, received, saved, and drafted emails (fig. 6, [0062]: receiving message data, user activity on messages received by the user is gathered and the messages are labeled to create training data for use in creating a user-specific classifier. [0007]: The user activity include at least one of opening the message, closing the message, reading the message (e.g. received email), forwarding the message (e.g. sending), drafting a reply to the message (drafting), marking the message read, marking the message unread, marking the message for follow-up (e.g. saved message), a length of a reply to the message, forwarding the message, or time the user spent composing a reply to message or a forwarding message);
receiving, by the one or more processors, an electronic communication dataset reflecting an electronic communication inbox of a user from one or more databases ([0055]: FIG. 3. The inbox interface may be created by a messaging application (such as an email client) operating on a processing device or in a Web-browser process displaying a messaging application interface provided by a web application server. In the user interface 300, a messaging interface having a series of folders such as inbox 320, drafts, Sent items, Deleted items, Archive items and Spam (e.g. databases), wherein the electronic communication dataset includes a plurality of electronic communications, a plurality of attributes corresponding to the plurality of electronic communications, or a plurality of user interactions with the plurality of electronic communications ([0047]: Such features may include, but are not limited to, social features, content features, and message metadata. Social features include features such as who the sender of the message is, how many co-recipients (in addition to the user) are present. Content features include, for example, specific keywords found in the message and the length of the message. Metadata may include whether the message is forwarded from the sender to the recipient, whether the message is a reply from the sender to the recipient, and the date and time stamp of the message. Forwarded messages and replied messages may indicate a higher importance of the message in that a replied message, for example, indicates a response to a user-originated message while forwarded messages may indicate that the sending (forwarding) user considered the message of sufficient importance to pass the message to the recipient.;
utilizing, by the one or more processors, the trained machine-learning model to determine a priority for at least one of the plurality of electronic communications based on the electronic communication dataset, wherein the priority corresponds to an importance level of the at least one of the plurality of electronic communications ([0042]-[0043]: a user-specific classifier determines an intensity score and assigns a priority rank to the message. The user-specific classifier is a machine learning model trained on a user-specific set of user messages and the training is performed by the classifier maintenance application/service. The user-specific classifier then applies the machine learning model on the plurality of features from a message to calculate predicted intensity scores for newly received messages);
filtering, by the one or more processors, the electronic communication dataset according to the priority ([0088]: The priority rank can be displayed with a single message folder in a user interface for a message application. Processing on the messages, including determining which messages and which portions of which messages should be retrieved to a message client based on the priority ranking. The priority ranking can be used to automatically filter higher priority rank messages for immediate display or notification to the user).
However, Zang does not disclose receiving, by the one or more processors, a selection by the user to provide a priority indicator; and based on the selection, displaying, by the one or more processors, the filtered electronic communication dataset and an explanation for the priority via an electronic communication interface of a user device, wherein the electronic communication interface corresponds to an electronic communication application.
In an analogous art, Mehta discloses receiving, by the one or more processors, a selection by the user to provide a priority indicator ([0035]: The importance summary 212 includes a plurality of summaries 214, 216, and 218 each of which correspond to a different category of interest for user 130. The summaries 214-218 may be collapsed, and provided with expansion actuators 220, 222, and 224, respectively); and based on the selection, displaying, by the one or more processors, the filtered electronic communication dataset and an explanation for the priority via an electronic communication interface of a user device, wherein the electronic communication interface corresponds to an electronic communication application ([0036]: When the importance summary 214 is expanded, the other importance summaries 216 and 218 can be removed from the display 202, and the previews in preview pane 204, and the email messages shown in reading pane 206, are sorted so that the previews shown and email messages in panes 204 and 206 respectively, all relate to the category of interest corresponding to importance summary 214. [0037]: Each of the bullet points in summary 214 textually describes the relevance of the activity which occurred. The description describes why the corresponding activity item (e.g., the corresponding email message) is important to user 130).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Zang to comprise “receiving, by the one or more processors, a selection by the user to provide a priority indicator; and based on the selection, displaying, by the one or more processors, the filtered electronic communication dataset and an explanation for the priority via an electronic communication interface of a user device, wherein the electronic communication interface corresponds to an electronic communication application” taught by Mehta.
One of ordinary skilled in the art would have been motivated because it would have enabled to generate importance summaries that summarize the importance of a particular electronic mail message (Mehta, [0005]).
Regarding claim 2, Zang-Mehta discloses the computer-implemented method of claim 1, wherein the plurality of attributes include at least one of: a date, at least one sender, at least one receiver, a subject, a body, a tone, an electronic communication type, or a topic (Zang, [0003], [0047]: the features include at least a sender of the message, metadata identifying a characteristic of the message, or metadata regarding content of the message).
Regarding claim 5, Zang-Mehta discloses the computer-implemented method of claim 2, the computer-implemented method further comprising: analyzing, by the machine-learning model, the plurality of electronic communications to determine relationship data between the user and one or more contacts, wherein the one or more contacts include the at least one receiver or the at least one sender; and based on the analyzing, updating, by the one or more processors, the one or more databases with the relationship data (Zang, fig. 7A: Sender’s Social Relationship. [0047]: Social features include features such as who the sender of the message is, how many co-recipients (in addition to the user) are present in the “to” line of the message, the identities of the co-recipients, and any other factors which may identify a relationship between the recipient (the user for whom the classification is being made) and other recipients or the sender of the message. [0075]: a table 710 showing a set of features and associated messages. Illustrated therein is data for 4 messages (Message ID: 1-4) and associated predicted intensity scores and priority ranks based on the data in the message ).
Regarding claim 6, Zang-Mehta discloses the computer-implemented method of claim 5, wherein the relationship data includes at least one of: a contact, a number of sent electronic communications, a number of received electronic communications, a number of opened electronic communications, a number of starred electronic communications, or a number of forwarded electronic communications (Zang, [0047]: Social features include features such as who the sender of the message is, how many co-recipients (in addition to the user) are present in the “to” line of the message, the identities of the co-recipients, and any other factors which may identify a relationship between the recipient (the user for whom the classification is being made) and other recipients or the sender of the message).
Regarding claim 7, Zang-Mehta discloses the computer-implemented method of claim 1, wherein the trained machine-learning model was previously trained to determine the priority for the at least one of the plurality of electronic communications (Zang, [0008]: the classifier created from user training data which includes at least prior user messages and an activity intensity score associated with each of the prior user messages, each feature having an assigned value, the calculating summing weighted feature values for all features parsed from the at least one message; and assigning a priority rank to the message based on the predicted intensity score), wherein the training comprises:
receiving, by the machine-learning model, a training electronic communication dataset reflecting one or more electronic communication inboxes of one or more users from one or more databases (Zang, [0048], [0063]: Machine learning techniques are used to derive the weights for each of the features defined in the model and this process is called the training process. Actual intensity scores can be calculated with the observations of users' activities in handling the messages. User activities can include, for example, actions performed by the user related to the user's manipulation of messages in the user's inbox,);
receiving, by the machine-learning model, one or more rules from the one or more users or the one or more databases (Zang, [0063]: calculating the activity intensity score for each of a plurality of the messages in the training data by: analyzing user activity associated with the message, wherein user activity describes user activities taken in response to receipt of the message, the user activities comprising at least one of opening the message, closing the message, reading the message, forwarding the message, drafting a reply to the message, marking the message read, marking the message unread, marking the message for follow-up, a length of a reply to the message, forwarding the message, or time the user spent composing a reply to the message or a forwarding message);
applying, by the machine-learning model, one or more labels to the training electronic communication dataset to create training data, the one or more labels based on the one or more rules (Zang, [0005]: The activity intensity score may then be assigned as a label for the message in the user training data. [0036]: User training data 136 is labeled, user-specific data used by the classifier maintenance application/service 132 to create and update the user-specific classifier 180); inputting, by the machine-learning model, the training data and the training electronic communication dataset into a logistic regression algorithm (Zang, [0050]: machine learning techniques are applied to user training data to generate an intensity score model, which is a user-specific classifier to process and score new messages received for the user. Generally, training data for a supervised machine learning (i.e. classification) algorithm consists of a target/outcome variable which is to be predicted from a given set of features. Examples of supervised learning which are suitable for use in the present technology include Logistic Regression, and Support Vector Machine (SVM), as well as numerous others); and
in response to the inputting, receiving, by the machine-learning model, one or more prediction weights for predicting the priority of the electronic communication dataset from the logistic regression algorithm (Zang, [0053]: a message with a total predicted intensity score of 0-0.33 may result in a “1” priority rank, a message with a predicted intensity score of 0.34 to 0.66 may result in a “2” rank, and a message with a predicted intensity score of 0.67 or higher may result in a “3” rank).
Regarding claim 9, Zang-Mehta discloses the computer-implemented method of claim 7,wherein the one or more labels include an important label and an unimportant label (Zang, [0057]: the email client fetch instructions may determine only to retrieve those messages with a priority rank of “2” or “3” (e.g. important label), leaving those with a lower priority rank of “1”(e.g. unimportant label) to be retrieved at a later time).
Regarding claims 11 and 16; the claims are interpreted and rejected for the same reason as set forth in claim 1.
Regarding claim 12; the claim is interpreted and rejected for the same reason as set forth in claim 2.
Regarding claim 15; the claim is interpreted and rejected for the same reason as set forth in claim 5.
Regarding claim 17; the claim is interpreted and rejected for the same reason as set forth in claim 7.
Regarding claim 19; the claim is interpreted and rejected for the same reason as set forth in claim 9.
Claims 3-4, 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zang in view of Mehta, as applied to claim 2, in view of Gutierrez et al. (US 2022/0309037 A1).
Regarding claim 3, Zang-Mehta discloses the computer-implemented method of claim 2.
However, Zang-Mehta does not disclose wherein the tone includes at least one of: a persuasive tone, a friendly tone, a direct tone, an apologetic tone, a conciliatory tone, an encouraging tone, a respectful tone, an optimistic tone, a urgent tone, an informal tone, a business-like tone, an empathetic tone, a sincere tone, a formal tone, a neutral tone, or an official tone.
In an analogous art, Gutierrez discloses wherein the tone includes at least one of: a persuasive tone, a friendly tone, a direct tone, an apologetic tone, a conciliatory tone, an encouraging tone, a respectful tone, an optimistic tone, a urgent tone, an informal tone, a business-like tone, an empathetic tone, a sincere tone, a formal tone, a neutral tone, or an official tone ([0234]: Sentiment, as used herein, may include tone of the electronic communication event. [0236]: Similarly, the analytics server identifies that employee A's communications with employee B and D correspond to a much friendlier sentiment than his communications with employee C. Using the above described information, the analytics server may generate a higher score for employee C than employee B or D).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Zang-Mehta to comprise “wherein the tone includes at least one of: a persuasive tone, a friendly tone, a direct tone, an apologetic tone, a conciliatory tone, an encouraging tone, a respectful tone, an optimistic tone, a urgent tone, an informal tone, a business-like tone, an empathetic tone, a sincere tone, a formal tone, a neutral tone, or an official tone” taught by Gutierrez.
One of ordinary skilled in the art would have been motivated because it would have enabled to sort/filter messages based on various attributes received from the user (Gutierrez, [0320]).
Regarding claim 4, Zang-Mehta discloses the computer-implemented method of claim 2.
However, Zang-Mehta does not disclose the computer-implemented method further comprising: analyzing, by the machine-learning model, the plurality of electronic communications to determine the tone corresponding to each of the plurality of electronic communications; and storing, by the one or more processors, the tone for each of the plurality of electronic communications in the one or more databases.
In an analogous art, Gutierrez discloses the computer-implemented method further comprising: analyzing, by the machine-learning model, the plurality of electronic communications to determine the tone corresponding to each of the plurality of electronic communications ([0234]: Sentiment, as used herein, may include tone of the electronic communication event. [0236]: Similarly, the analytics server identifies that employee A's communications with employee B and D correspond to a much friendlier sentiment than his communications with employee C. Using the above described information, the analytics server may generate a higher score for employee C than employee B or D, and storing, by the one or more processors, the tone for each of the plurality of electronic communications in the one or more databases ([0237]: the analytics server may generate a second nodal data structure comprising a set of nodes where each node corresponds to an employee and their respective score. The analytics server may then arrange the nodal data structure according to each employee's score. In a non-limiting example, as depicted in FIG. 13B, the analytics server may arrange different employees based on their score).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Zang-Mehta to comprise “the computer-implemented method further comprising: analyzing, by the machine-learning model, the plurality of electronic communications to determine the tone corresponding to each of the plurality of electronic communications; and storing, by the one or more processors, the tone for each of the plurality of electronic communications in the one or more databases” taught by Gutierrez.
One of ordinary skilled in the art would have been motivated because it would have enabled to sort/filter messages based on various attributes received from the user (Gutierrez, [0320]).
Regarding claim 13; the claim is interpreted and rejected for the same reason as set forth in claim 3.
Regarding claim 14; the claim is interpreted and rejected for the same reason as set forth in claim 4.
Claims 8, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zang in view of Mehta, as applied to claim 7, in further view of Rogynskyy et al. (US 2019/0361937 A1).
Regarding claim 8, Zang-Mehta discloses the computer-implemented method of claim 7.
However, Zang-Mehta does not disclose wherein the machine-learning model includes a logic learning model (LLM).
In an analogous art, Rogynskyy discloses wherein the machine-learning model includes a logic learning model (LLM) ([0301]: The tagging engine 265 can then apply a rule, policy, logic, machine learning algorithm, or natural language processing techniques to assign one or more tags to the electronic activity based on the identified terms, text, content or other information in the body or metadata of the electronic activity).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Zang-Mehta to comprise “wherein the machine-learning model includes a logic learning model (LLM)” taught by Rogynskyy.
One of ordinary skilled in the art would have been motivated because it would have enabled a content filter tag to be use in order to perform content filtering (Rogynskyy, [0301]).
Regarding claim 18; the claim is interpreted and rejected for the same reason as set forth in claim 8.
Claims 10, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zang in view of Mehta, as applied to claim 1, in further view of Walters et al. (US 2022/0067663 A1).
Regarding claim 10, Zang-Mehta discloses the computer-implemented method of claim 1.
However, Zang-Mehta does not disclose the computer-implemented method further comprising: receiving, by the one or more processors, feedback from the user, wherein the feedback corresponds to an updated priority of at least one of the plurality of electronic communications; and retraining, by the one or more processors, the trained machine-learning model based on the feedback.
In an analogous art, Walters discloses the computer-implemented method further comprising: receiving, by the one or more processors, feedback from the user, wherein the feedback corresponds to an updated priority of at least one of the plurality of electronic communications ([0081]: the AI engine may query a user regarding the degree of importance the user assigns to an electronic message…the user response to such a query (e.g. feedback) may be used to train the predictive model or may be used to determine the accuracy of the predictive model. In some embodiments, the user interface may be configured to receive a response to the query via a client device. [0084]: a new degree of importance may be determined and compared with the received user determinations of importance); and retraining, by the one or more processors, the trained machine-learning model based on the feedback ([0084]: the AI engine may adjust the predictive model in response to the determined degree of accuracy. In some embodiments, the predictive model may be adjusted in an ongoing manner. By adjusting the predictive model as new or additional training data becomes available, some embodiments of the system may be continuously improved or be adjusted to adapt with the changing goals or priorities of a user or organization. In some embodiments, when the predictive model is adjusted, the revised predictive model may be applied to the extracted message information.
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Zang-Mehta to comprise “the computer-implemented method further comprising: receiving, by the one or more processors, feedback from the user, wherein the feedback corresponds to an updated priority of at least one of the plurality of electronic communications; and retraining, by the one or more processors, the trained machine-learning model based on the feedback” taught by Walters.
One of ordinary skilled in the art would have been motivated because it would have enabled to develop a more accurate predictive model over time (Walters, [0084]).
Regarding claim 20; the claim is interpreted and rejected for the same reason as set forth in claim 10.
Additional References
The prior art made of record and not relied upon is considered pertinent to applicants disclosure.
Gopathy et al., US 2025/0182056 A1: Utilizing User-Profile to Prioritize Processing of Messages.
Rafferty et al., US 2021/0297376 A1: Systems and Methods for Processing User Concentration Levels for Workflow Management.
Jung et al., US 2020/0120050 A1: Systems, Methods and Interfaces for Processing Message Data.
Conclusion
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.
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/J.C.T/Examiner, Art Unit 2446
/BRIAN J. GILLIS/Supervisory Patent Examiner, Art Unit 2446
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