DETAILED ACTION
Claims 1 – 20, which are currently pending, are fully considered below.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 6, 2024, February 19, 2025, and September 18, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
35 USC § 101
NOTE: The instant claims have been evaluated with respect to 35 USC 101.
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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 – 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mark Lambert (U.S. Patent Publication 20250274407).
With respect to claims 1, 14, and 20, Lambert teaches:
detecting context information relating to one or more user interface elements displayed by an application executing on a computing device during a user interaction with the computing device (see paragraph [0032] and [0033], where the context of the email is detected, where the context may be the sender and the relational information associated with the sender);
identifying one or more candidate generative machine learning actions based at least on the context information relating to the one or more user interface elements displayed by the application (see paragraph [0031], where a generate custom response function button is identified, see Fig. 3A and Fig. 3B below);
outputting one or more identifiers of the one or more candidate generative machine learning actions (see paragraph [0032] and [0033], where the context of the email is detected, where the identifiers may be the sender);
receiving input identifying a selected identifier of a selected generative machine learning action (see paragraph [0031], where a user selects a generate custom response function button); and
triggering a generative machine learning model to perform the selected generative machine learning action based at least on the context information (see paragraph [0031], where the system is triggered to display a custom email response).
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With respect to claims 2, Lambert teaches,
identifying the one or more candidate generative machine learning actions by inputting the context information to a particular generative machine learning model (see paragraph [0031], where a generative AI model is used to identify generative actions).
With respect to claims 3, Lambert teaches,
the particular generative machine learning model being a decoder-based generative language model (see paragraph [0028], for decoder model).
With respect to claims 4, Lambert teaches:
information including constraint information describing one or more constraints associated with the one or more user interface elements, the particular generative machine learning model identifying the one or more candidate generative machine learning actions based at least on the constraint information (see paragraph [0032] and [0033], where the context of the email is detected, and constraints on using the generate a response email may be on the relationship of the sender).
With respect to claims 5, Lambert teaches:
the constraint information conveying a character limit of a text box (see paragraph [0038], where conditions, constraints, or rules may be established with respect to generating the responses using the generative AI model).
With respect to claims 6, Lambert teaches:
the constraint information conveying an attachment size limit for an email attachment (see paragraph [0038], where conditions, constraints, or rules may be established with respect to generating the responses using the generative AI model).
With respect to claims 7,Lambert teaches,
the context information including capability information describing capabilities of one or more available generative machine learning models (see paragraph [0031], [0032] and [0033], where the context may identify the capabilities of the AI model).
With respect to claims 8 and 15, Lambert teaches:
prompting the particular generative machine learning model to identify the one or more candidate generative machine learning actions based at least on the capability information describing the capabilities of one or more available generative machine learning models and the constraint information describing the one or more constraints
associated with the one or more user interface elements (see paragraph [0031], where the generative AI models is prompted to generate an email response and the system is triggered to display a custom email response).
With respect to claims 9, Lambert teaches:
context information including task information describing a task associated with
the user interaction, the particular generative machine learning model identifying the one or more candidate generative machine learning actions based at least on the task information (see paragraph [0023], for tasks).
With respect to claims 10, Lambert teaches:
the task information relating to text or images displayed on the computing device that are associated with the task (see paragraph [0023], for tasks).
With respect to claims 11, Lambert teaches:
caching the one or more candidate generative machine learning actions in a cache (see paragraph [0043], for cache);
detecting other context information relating to another user interaction with the computing device (see paragraph [0031] and [0032], for contextual direction); and
based at least on similarity of the other context information to the context information, retrieving the one or more candidate generative machine learning actions from the cache without invoking the particular generative machine learning model (see paragraph [0031] and [0032], for using contextual direction to apply a generative AI model).
With respect to claims 12, Lambert teaches:
identifying the one or more candidate generative machine learning
actions by applying one or more rules to the context information (see paragraph [0052], for rules).
With respect to claim 13, Lambert teaches:
tracking user feedback relating to individual candidate generative machine learning actions (see paragraph [0048], for user feedback); and
identifying subsequent candidate generative machine learning actions based at least on the user feedback (see paragraph [0048], for user feedback).
With respect to claims 16, Lambert teaches,
the one or more candidate generative machine learning actions including generating text by a generative language model based on the context information (see paragraph [0031], where the system is triggered to display a custom email response).
With respect to claims 17, Lambert teaches:
the one or more candidate generative machine learning actions including generating an image or video by a generative image model based on the context information (see paragraph [0052], for images or objects).
With respect to claims 18, Lambert teaches:
rank individual candidate machine learning actions relative to one another based at least on the context information (see paragraph [0031], where the system is triggered to display a custom email response); and
output the individual candidate machine learning actions in ranked
order (see paragraph [0031], where the system is triggered to display a custom email response).
With respect to claims 19, Lambert teaches:
trigger a particular generative machine learning model to perform a particular candidate generative machine learning action prior to receiving the input (see paragraph [0031], where the system is triggered to display a custom email response); and
output content generated via the particular candidate generative
machine learning action for selection by a user (see paragraph [0031], where the system is triggered to display a custom email response).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDRIA Y BROMELL whose telephone number is (571)270-3034. The examiner can normally be reached M-F 8-4.
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/ALEXANDRIA Y BROMELL/Primary Examiner, Art Unit 2156 June 19, 2026
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