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 12 November 2024. Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/12/24 and 05/21/2025 was filed before the mailing date of the first office action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
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(s) 1-5, 9-14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Garner et al. hereinafter Garner Pub Number 20250069360 A1.
As per claim 1, Garner teaches a method for natively generating a first protocol, the method comprising receiving, via a user device, a first transmission and first transmission data, wherein the first transmission is at least one of a call, a voicemail, a text message, an audio message, or a video message (par 0007, a user may be notified with a text message or an application push notification on a user device; see also par 0065, 0086-0087); determining, via a first trained machine learning model, the first protocol based on the first transmission and the first transmission data, the first trained machine learning model having been trained to determine one or more protocols using training transmissions and training transmission data (see par 0086, camera system 310 may may transmit one or more frames of the video … image compiler 350 may include robust machine learning model that may have been trained to identify more classes …; see par 0065 as well); generating, via the first trained machine learning model, a first responsive output based on the first protocol (see par 0071, camera system 310 sets a response command to perform a home automation action in response to detection of the feature by the machine learning model …); and transmitting, via the user device, the first responsive output to a device (see par 0071, camera system 310 may also transmit configuration instructions to a sprinkler management system to establish communications).
Garner does not specifically discuss that the sprinkler management system is a third-party device. However, one skill artisan at then effective of the invention would acknowledge that any means of communication device that is able to receive and send data response would be acceptable as a third-party device.
As per claim 2, Garner teaches the method of claim 1, wherein the first trained machine learning model has been trained by: receiving a plurality of transmissions and transmission data; receiving a plurality of user preference data (par 0005, selection process); receiving a plurality of classifications of transmissions and sub-classifications (par 0047, additional classification) of transmissions; receiving a plurality of user inputs associated with classification notifications; and training a machine learning model to generate a protocol based on at least one of a transmission, transmission data, user preference data, a classification of a transmission, a sub-classification of a transmission, or at least one user input associated with a classification notification (see par 0047, Garner discusses transmission of notification to user device, obtain classification, machine learning models trained …; see par 0089, notification includes classification label).
As per claim 3, Garner teaches the method of claim 1, wherein the transmission data includes at least one of call data, call metadata, voicemail data, voicemail metadata, text data, text metadata, audio data, audio metadata, video data, video metadata, static user data, static user metadata, dynamic user data, or dynamic user metadata (see par 0065, text message, audio and voice command and more).
As per claim 4, Garner teaches the method of claim 1, wherein the responsive output includes at least one of an output call, an output voicemail, an output text message, an output audio message, or an output video message (see par 0051, audio alarm, transmit a command; see also par 0011).
As per claim 5, Garner teaches the method of claim 1, further comprising: receiving, via the user device, user preference data; and determining, via the first trained machine learning model, the first protocol based on the first transmission, the first transmission data, and the user preference data (see par 0005, the camera system can download and install selected machine learning models, which may be configured to identify particular features. A guided setup process may be provided with the camera system and/or accessible via a user device to provide recommendations for machine learning models to install. The recommended machine learning models may be adapted to the camera system's environment).
As per claim 9, Garner teaches the method of claim 1, further comprising: receiving, via the user device, a second transmission and second transmission data in response to the first responsive output (see par ; based on the second transmission and the second transmission data, determining a second protocol via the first trained machine learning model; generating, via the first trained machine learning model, a second responsive output based on the second protocol; and transmitting, via the user device, the second responsive output to the third-party device (see par 0071 and 0056, it must be noted that first and any other transmission or transmission data are part of Garner system for data transfer and communication; see claim 1 reasoning in regard to third-party device)
As per claim 10, Garner teaches the method of claim 9, further comprising: determining, via a second trained machine learning model, a second classification or second sub-classification of the first transmission based on the first transmission, the first transmission data, the second transmission, and the second transmission data; and based on the second classification or the second sub-classification, determining the second protocol via the first trained machine learning model (see par 0065 and 0086-0087 which discuss transmission data, trained machine learning model and classification).
Claims 11-14 and 18-20 are system and non-statutory computer-readable medium of method claims 1-5 and 9-10 discussed above. They contain the similar limitations. Therefore, they are rejected under the same rationale.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Garner and Lee Pub Number 20180253659 A1.
As per claims 6, 15 Garner teaches the method of claim 1, further comprising: determining, via a second trained machine learning model, a first classification or a first sub-classification (par 0047, sub-classification is additional classification) of the first transmission, wherein: based on the determined first classification of the first sub-classification, determining the first protocol via the first trained machine learning model (see par 0047).
Garner does not discuss the first classification or the first sub-classification is at least one of junk, spam, scam, authentic, priority, or blocked, and the second trained machine learning model has been trained using training data to predict the first classification or the first sub-classification of the first transmission. However, Lee teaches (see par 0068, machine learning engine 112e may classify the messages using a machine learning model 112g trained to classify the messages into one or more discrete categorizations, such as “personal,” “important,” “junk,” and the like) and the steps of predicting (par 0088). It would be obvious to a skill artisan to at the effective filing time of the invention as claimed to incorporate Lee’s features to Garner’s system for task specific categorization.
Claims 7-8 and 16-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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