DETAILED ACTION
This action is responsive to the application filed 8/22/2024.
Claims 1-20 are pending.
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)(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, 2, 4, 6, 8-13, 15, 17, 19 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Han, et al., U.S. PGPUB No. 2024/0086648 (“Han”).
With regard to Claim 1, Han teaches a method comprising:
receiving, via a user interface of a device, an element of an email ([0115]-[0116] and Fig. 6A describe that a user inputs several email elements, including a target audience, goal, theme, product name, and description into an interface);
generating a prompt as an input to a machine learning model based on the element of the email ([0117] describes that the input parameters are submitted as input to process 500, where [0101] describes that the process generates an email sequence using a machine learning model);
generating, using the machine learning model, formatted content based on the prompt, the formatted content corresponding to a brand associated with the email; generating the email based on the formatted content; and causing display of the email on the user interface of the device ([0117]-[0118] describes that an email sequence is generated from the prompt, where the content is generated in email format and displayed in the interface as emails, as shown in Figs. 6A and 6B).
Claim 12 recites a system comprising: one or more hardware processors; and a non-transitory machine-readable medium for storing instructions (Fig. 2) that, when executed, carry out the method of Claim 1, and is similarly rejected. Claim 20 recites a medium storing instructions (Fig. 2) which execute to carry out the method of Claim 1, and is likewise rejected.
With regard to Claim 2, Han teaches that the machine learning model comprises a large language model, and wherein the element of the email comprises one or more of a campaign type, a campaign description, an email layout, a text description of email content, a personalization element, an email template, a Uniform Resource Identifier (URI) associated with a brand asset. [0098] describes a generated prompt is input to a model, which can be a large language model such as GPT-3. Fig. 6A shows that the email elements can be campaign goal, a pr4oduct, a target audience, as well as a text description used for email content generation.
Claim 13 recites a system which carries out the method of Claim 2, and the claim is similarly rejected.
With regard to Claim 4, Han teaches causing display of a user interface on the device, the user interface including a plurality of input fields to receive a plurality of elements of the email, the plurality elements including the element of the email; and receiving, via the user interface, the element of the email via an input field from the plurality of input fields. Fig. 6A and [0116] describe the user inputting elements into a plurality of input fields and being submitted therefrom for content generation.
Claim 15 recites a system which carries out the method of Claim 4, and the claim is similarly rejected.
With regard to Claim 6, Han teaches accessing historical data associated with a brand, the historical data comprising a plurality of emails associated with the brand; generating, using the machine learning model, a plurality of elements associated with the brand based on the historical data associated with the brand; and causing display of the plurality of elements associated with the brand on the device for user selection.
Han at [0055]-[0059] describes that previous email sequences can be used as training data for the model, where features including a particular company can be identified therein. A GPT model can then be trained with this data. [0124]-[0125] describe that a user can identify a block and ask for alternative suggestions to be generated, causing the model trained on the previous email data to present multiple alternatives from which a user can select for inclusion in the email.
Claim 17 recites a system which carries out the method of Claim 6, and the claim is similarly rejected.
With regard to Claim 8, Han teaches using the machine learning model to generate a plurality of formatted content based on the prompt; generating a plurality of emails based on the plurality of formatted content; and providing the plurality of emails for user selection. [0118] describes that a sequence of a plurality of emails is generated and displayed in an interface, where [0123] describes that each of the content blocks in each of the displayed plurality of emails is selectable by the user.
Claim 19 recites a system which carries out the method of Claim 8, and the claim is similarly rejected.
With regard to Claim 9, Han teaches using the machine learning model to generate a portion of the email specified by a user of the device. [0123] describes that blocks of email content are selectable to be regenerated by the model.
With regard to Claim 10, Han teaches receiving a request, via the device, to update a portion of the email; using the machine learning model to generate content for the portion of the email; and causing display of the portion of the email on the device. [0123] describes that a user can selectively regenerate content for content blocks of generated emails, where [0125] describes that alternative texts can be generated, and a selected alternative inserted into the email in the interface.
With regard to Claim 11, Han teaches that device is an administrative device, comprising: causing display of the email on a user interface of a recipient device. [0128]-[0129] describes that a generated email sequence is subsequently saved, and the user who created the emails as part of the campaign can initiate the campaign by sending the first email and initiating the follow-up processes as specified.
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.
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 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Han, in view of Agrawal, et al., U.S. PGPUB No. 2024/0312087 (“Agrawal”).
With regard to Claim 3, Agrawal teaches that the brand asset comprises one or more of a brand image and a brand logo. [0054]-[0055] describe that a system can access image content at a website, to select a product image for inclusion in an email generated using a content generation process, also described at [0022]. [0029] describes that the content is generated using machine learning, and [0060] describes that image selection receives a URL at which the image can be accessed and selected.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Agrawal with Han. One of skill in the art would have sought the combination, to improve system function by including the ability to select and insert images in generated email content, thereby increasing the effectiveness of emails in marketing products.
Claim 14 recites a system which carries out the method of Claim 3, and the claim is similarly rejected.
Claims 5, 7, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Han, in view of Pal, et al., U.S. PGPUB No. 2019/0034403 (“Pal”).
With regard to Claim 5, Han, in view of Pal teaches generating an email template based on the formatted content generated by the machine learning model; and storing the email template along with a plurality of existing email templates for user selection.
Han teaches at [0102] that a user can input a template for the email message or sequence as a parameter for generating the emails. Pal teaches at [0018]-[0020] that when a user creates an email, natural language processing and modeling can be used to create a template from the email. Claim 11 describes that stored templates can subsequently be selected from the store.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Pal with Han. One of skill in the art would have sought the combination, to improve user experience by including the ability for users to exert greater control over email generation by being able to specify particular templates that generate content in a particularly desired manner.
Claim 16 recites a system which carries out the method of Claim 5, and the claim is similarly rejected.
With regard to Claim 7, Han, in view of Pal teaches receiving a user input that modifies the email generated by the machine learning model; and storing the modified email as an email template. Han at [0123] describes that content for blocks can be regenerated. Pal teaches at [0059] that a user editing or otherwise changing a generated email causes the system to learn and update the template generation process.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Pal with Han. One of skill in the art would have sought the combination, to improve user experience by including the ability for users to exert greater control over email generation by being able to specify particular templates that generate content in a particularly desired manner.
Claim 18 recites a system which carries out the method of Claim 7, and the claim is similarly rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEITH D BLOOMQUIST whose telephone number is (571)270-7718. The examiner can normally be reached M-F, 8:30-5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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, Kieu Vu can be reached at 571-272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KEITH D BLOOMQUIST/Primary Examiner, Art Unit 2171
5/29/2026
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