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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed 2/5/2026 has been entered.
Response to Arguments
Applicant’s arguments filed 2/5/2026 have been fully considered.
Applicant argues that Dunn fails to teach any of the features of the exemplary independent claim 1 because Dunn is structurally and functionally different from what is claimed. Applicant’s arguments are not persuasive because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant argues the prior art fails to teach or suggest: the “generating, via the computing device, without modifying the electronic message a presentation of the electronic message that is from the inbox, the electronic message’s presentation comprising at least some of the electronic message’s content…”; and “causing, … at least some of the electronic message’s content to be displayed in a user interface at a computing device of the recipient without displaying or modifying the electronic message that is in the inbox of the recipient”.
Applicant’s arguments are not persuasive because Jakobsson discloses the generation and presentation of the electronic message without modifying in at least col. 46:5-67, col. 47:5-55, and Fig. 14B where a presentation of at least some of the electronic message’s content is generated and presented without modifying the message.
Applicant’s arguments that the prior art fails to disclose the generation and presentation steps for an electronic message “that is from the inbox” or “that is in the inbox of the recipient” are not persuasive because Jakobsson discloses the generation and presentation via the message of fig. 14B which is sent while the corresponding email is in the inbox of the recipient as indicated in col. 46:55-67 and col. 47:5-55 where Jakobsson discloses the generation and presentation functions when the email “has already been delivered” and is in the recipient’s inbox (Jakobsson, col. 47:5-47).
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 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.
Claim 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over 11,477,222 to Dunn in view of US 11019076 to Jakobsson.
Regarding claim 1, Dunn teaches a method comprising:
obtaining, by a computing device, a corpus of electronic messages (abstract, obtaining email messages);
generating, by the computing device, training data comprising a set of training instances using the corpus of electronic messages, each training instance, of the set of training instances, comprising data extracted from a respective electronic message from the corpus of electronic messages (abstract, col. 1:55-67, generation of training data including data extracted from emails; col. 2:1-14) and labeling information indicating at least one type of information included in the respective electronic message (col. 1:55-67, col. 2:1-14, labeling type of information associated with normal behavior, risks, and/or threats);
training, by the computing device, an attribute generation model using the training data (abstract, col. 1:55-67, col. 2:1-14, fig. 5, training of model using data);
analyzing, by the computing device, an electronic message addressed to a recipient and generating model input based on the analysis, the electronic message being retrieved from an inbox of the recipient (col. 1:55-67, col 2:1-37, analysis of emails and generated model input based on analysis);
obtaining, by the computing device, model output from the attribute generation model based on the model input, the model output comprising, in connection with a respective type of information, a set of attribute values from the electronic message determined by the attribute generation model, to correspond to a set of attributes corresponding to the respective type of information (col. 14:1-65, output corresponding to analysis);
Dunn fails to explicitly teach, but Jakobsson teaches:
generating, via the computing device, without modifying the electronic message a presentation of the electronic message that is from the inbox, the electronic message’s presentation comprising at least some of the electronic message’s content using the set of attributes and the set of attribute values from the electronic message determined, by the attribute generation model, to correspond to the set of attributes (col. 46:5-67, col. 47:5-55, Fig. 14B, presentation via user interface; presentation of user interface to message recipient based on correspondence between attribute values); and
causing, via the computing device, the generated presentation of the electronic message that is from the inbox comprising at least some of the electronic message’s content to be displayed in a user interface at a computing device of the recipient without displaying or modifying the electronic message that is in the inbox of the recipient (col. 46:5-67, col. 47:5-55, Fig. 14B, generated presentation of electronic message content, while electronic message quarantined).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the teachings of Jakobsson. The motivation to do so is that the teachings of Jakobsson would have been advantageous in terms of facilitating security risk assessments via preventing a recipient from having full access to a message prior to verification of the recipient's knowledge about the sender of the message (Jakobsson, abstract, col. 45:12-40).
Regarding claim 2, 16,
Dunn teaches:
communicating, via the computing device and over a network, the presentation to the user computing device, the communicating causing the presentation to be displayed (col. 25:4-20, fig. 10, presentation on user interface).
Regarding claim 3, 17,
Dunn teaches:
the presentation is displayed in an electronic mail messaging user interface (col. 25:4-20, fig. 10).
Regarding claim 4, 18,
Dunn teaches:
the presentation comprising at least one item that is an aggregate of data extracted from multiple electronic mail messages using the attribute generation model (col. 25:4-20, fig. 10).
Regarding claim 5,
Dunn teaches:
the model output further comprising information identifying at least one relationship among the set of attributes (see fig. 1A, relationship between email and related data and cyber threat comparisons; see scoring of fig. 1B, fig. 3).
Regarding claim 6,
Dunn teaches:
the model output is represented using a scripting language to associate a respective attribute value with a corresponding attribute and the at least one relationship is indicated using attribute nesting (see col. 3:55-67 regarding the scripting languages implemented).
Regarding claim 7, 19,
Dunn teaches:
the model output further comprising information identifying the respective type of information that is determined by the attribute generation model using the model input (col. 4:47-67, col. 7:30-41, col. 8:50-67).
Regarding claim 8,
Dunn teaches:
generating training data further comprising: analyzing, by the computing device, the respective electronic message, from the corpus of electronic messages, to identify header and body elements of the electronic message; and generating, by the computing device, a corresponding training instance using information extracted from the header and body elements of the electronic message (col. 4:65-67, col. 5:12-41, col. 8:1-25, col. 7:35-40, analysis and model input/training including HTML elements and corresponding email attributes including body and header elements).
Regarding claim 9,
Dunn teaches:
analyzing the electronic message from a message folder further comprising: identifying, by the computing device, header and body elements of the analyzed electronic message; and generating, by the computing device, the model input using information extracted from the header and body elements of the analyzed electronic message (col. 4:65-67, col. 5:12-41, col. 8:1-25, col. 7:35-40, analysis and model input/training including HTML elements and corresponding email attributes including body and header elements).
Regarding claim 10,
Dunn teaches:
analyzing the electronic message from a message folder further comprising: analyzing, by the computing device, one or more HTML elements and corresponding attributes included in the body element of the analyzed electronic message, at least a portion of the model input being generated based on the analysis of the one or more HTML elements and corresponding attributes in the analyzed electronic message (col. 4:65-67, col. 5:12-41, col. 8:1-25, col. 7:35-40, analysis and model input including HTML elements and corresponding email attributes).
Regarding claim 11,
Dunn teaches:
analyzing one or more HTML elements further comprising: identifying, by the computing device, an image using the one or more HTML elements; identifying, by the computing device, a description of the identified image using at least one attribute corresponding the one or more HTML elements (col. 4:65-67, analysis and ID of elements, identification of email image processing and analysis related; HTML element such as links or URLS see col. 20:63-67); and generating, by the computing device, the at least a portion of the model input based on the identified image and the identified description (col. 5:12-41, generation of model input, col. 7:35-40, col. 4:65-67, email image processing and analysis related; HTML element such as links or URLS see col. 20:63-67).
Regarding claim 12,
Dunn teaches:
the attribute generation model being further trained to identify the respective type of information (col. 4:47-67, col. 7:30-41, col. 8:50-67).
Regarding claim 13,
Dunn teaches:
the attribute generation model comprising an information-type prediction component enabling the attribute generation model to identify the respective type of information (col. 4:47-67, col. 7:30-41, col. 8:50-67, identification of information types). .
Regarding claim 14,
Dunn teaches:
wherein the data extracted from the respective electronic message comprises subject, sender and body information, and the model input comprises subject, sender and body information (col. 8:1-25).
Claim 15, 20 are addressed by similar rationale as claim 1.
CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN J JAKOVAC whose telephone number is (571)270-5003. The examiner can normally be reached on 8-4 PM EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oscar A. Louie can be reached on 572-270-1684. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN J JAKOVAC/Primary Examiner, Art Unit 2445