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 .
This is a first office action in response to the instant application for letters patent filed on 17 February 2026. Claims 1-20 are presented for examination.
Claim Objections
Claims 1 and 16 are objected to because of the following informalities: after “determine an input prompt from the request email comprising” please delete “;” and substitute -- : --.. Appropriate correction is required.
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.
Claim(s) 1-3, 5-18, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tsvetkov et al. hereinafter Tsvetkov US PUB Number 20250133042A1.
As per claim 1, Tsvetkov teaches a method comprising: receiving, via an email server, a request email from an email sender address (see par 0020, a user (e.g., email sender) selects the email template from within the email application, such as by selecting one of multiple pre-created templates from a template library or by selecting an email that the sender previously drafted and sent to another recipient that includes some relevant content); determining whether the email sender address corresponds to a registered user account (see par 0018, LLM to draft a customized email that is (1) personalized to the identity, needs, and previous interactions of the email recipient; and (2) stylized according to the personalized drafting style of the person sending the email; see par 0037, the context data 128 comprises profile information 126 such as profile information stored in association with the email accounts (e.g., contact card information) of the recipient ID and/or sender ID); if the email sender address corresponds to a registered user account: determining an input prompt from the request email comprising (see par 0042, relevant contextual information 124 is provided as input to an LLM prompt generator 134 along with “a template body,” which refers to either the PII-filtered template 132 or the template 104 (e.g., in use cases that do not include PII filtering) along with metadata of the template, if any exists): providing the input prompt from the request email as input to a trained model, wherein the trained model is trained to generate a response based on the input (see abstract, providing the LLM prompt as input to a trained large language model (LLM)); receiving an output from the trained model based on the input prompt (see abstract, receiving the customized email as an output from the LLM); and transmitting, via the email server, a reply email comprising the output received from the trained model based on the input prompt (see abstract, returning the customized email to the email application for display within a user interface).
As per claim 2, Tsvetkov teaches the method of claim 1, wherein determining whether the email sender address corresponds to a registered user account further comprises determining whether the registered user account associated with the email sender address has sufficient credits to use the trained model (see par 0017, Model training is time-consuming, processor intensive, and may have to be frequently repeated (depending upon the implementation) to ensure that the training dataset encompasses sufficient details about the evolving customer base and/or alterations to product and service offerings).
As per claim 3, Tsvetkov teaches the method of claim 1, wherein determining the input prompt from the request email further comprises determining timing data from the request email and scheduling a delay for providing the input prompt to the trained model based on a queue of pending requests (see par 0038, the context mining tool 114 may analyze timestamps of updates to the different profiles and select add the most-recently updated profile to the contextual data).
As per claim 5, Tsvetkov teaches the method of claim 1, wherein if the email sender address does not correspond to one of a plurality of registered user accounts, the method further comprises transmitting to the email sender address, via the email server, an initial email comprising an invitation to register a new user account (see par 0029, making initial contact with a prospective customer, follow-up contact with an existing customer, providing information about specific different products or services, providing information about resources that can guide the customer to better utilize products or services that the customer is already subscribed to, etc; par 0023, a user provides input to an email application 102 to initiate generation of a customized email).
As per claim 6, Tsvetkov teaches the method of claim 1, wherein determining the input prompt from the request email comprises determining the input prompt based on the subject of the request email and the body of the request email (see fig 1, par 0042, relevant contextual information 124 is provided as input to an LLM prompt generator 134 along with “a template body,” which refers to either the PII-filtered template 132 or the template 104 (e.g., in use cases that do not include PII filtering) along with metadata of the template).
As per claim 7, Tsvetkov teaches the method of claim 1, wherein determining an input prompt from the request email comprises determining an input prompt based on a forwarded email included in the request email (see fig 1, par 0042 and 0091).
As per claim 8, Tsvetkov teaches the method of claim 1, wherein determining an input prompt from the request email comprises determining an input prompt based on an attached image or a combination of the body and the attached image of the request email (see fig 1, par 0042, relevant contextual information 124 is provided as input to an LLM prompt generator 134 along with “a template body,” which refers to either the PII-filtered template 132 or the template 104 (e.g., in use cases that do not include PII filtering) along with metadata of the template).
As per claim 9, Tsvetkov teaches the method of claim 1, wherein determining an input prompt from the request email comprises determining an input prompt based on a body of the request email and an attachment of the request email, wherein the attachment is a document (see fig 1, par 0042).
As per claim 10, Tsvetkov teaches the method of claim 1, wherein determining an input prompt from the request email comprises determining an input prompt based on an email address to which the request email is addressed (see fig 1, par 0042).
As per claim 11, Tsvetkov teaches the method of claim 10, wherein the email address to which the request email is sent is a dynamic email address (see fig 1 in regard to dynamic email address).
As per claim 12, Tsvetkov inherently teaches the method of claim 1, wherein the reply email comprises a link to open a draft email comprising the output received from the trained model based on the input prompt and a recipient address corresponding to an email address in the request email (see par 0018 and 0044; due to the drafting instructing to compose the email based on the relevant contextual information 124).
As per claim 13, Tsvetkov teaches the method of claim 1, wherein the output received from the trained model based on the input prompt is one or more selected from a list consisting of text, image, video, audio, and data (see par 0036, recorded calls, voice conversation, relevant keywords, meeting transcripts, Microsoft Teams, Web-based calls etc).
As per claim 14, Tsvetkov teaches the method of claim 1, wherein the trained model is a private model trained with private data or fine-tuned with private data provided by an entity associated with the email sender address (see par 0081).
As per claim 15, Tsvetkov teaches the method of claim 1, wherein transmitting, via the email server, a reply email comprising the output received from the trained model based on the input prompt further comprises sending the reply email to a second email address based on information from one or more selected from a list consisting of: an addressee of the request email, a subject of the request email, a body of the request email, an attachment of the request email, and settings for the user account associated with the email sender address (see par 0042-0044; 0027-0028, and 0030).
As per claim 16, it is a system of the method claim 1 discussed above. Therefore, it is rejected under the same rationale. Furthermore, Tsvetkov teaches a system comprising: memory, control circuitry (see fig 5, elements 504, 502 …).
As per claim 17, it is rejected under the same rationale as claim 2.
As per claim 18, it is rejected under the same rationale as claim 3.
As per claim 20, it is rejected under the same rationale 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 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.
Claim(s) 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Tsvetkov and Gardner et al., hereinafter Gardner US PUB Number 20250061290 A1.
As per claims 4 and 19, Tsvetkov does not discuss reordering the queue of pending requests based on a determined priority for the request email, wherein the priority is based on one or more of the email sender address, the subject of the request email, the body of the request email, or an attachment of the request email. Gardner teaches requests that are queued and prioritized which are called VIP requests (see Gardner par 0313-0316). It would be obvious to one of ordinary skill in the art before the effective date of the invention as claimed to incorporate Gardner’s request queueing and prioritization in order to facilitate and process requests quickly.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANTZ B JEAN whose telephone number is (571)272-3937. The examiner can normally be reached 8-5 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Glenton B. Burgess can be reached at 5712723949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FRANTZ B JEAN/Primary Examiner, Art Unit 2454
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