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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-8, 12-20 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Likhomanov et al. (Likhomanov) US 2024/00056661.
In regard to claim 1, Likhomanov disclose A method comprising: ([0023]-[0029])
receiving a request to train a machine learning (ML) model and responsively prompting a user for a first input indicating a subject for detection within media; (Fig. 2, [0053]-[0066] Fig. 2 and Fig. 3 evaluate the input frames to determine to proceed with training a ML model and ask the user to input and indicating area-of-interest within a frame of image data with prompt)
receiving, via a user interface, the first input; (Fig. 2, 3, [0053]-[0066] receive the user input representing a selection a portion of the frame captured by a camera device with identified area-of-interest)
using at least the received first input as a basis to obtain a set of media related to the subject for detection; (Fig. 2, 3 [0053]-[0066] obtain number of images based on the first input related to the identified area-of-interest)
outputting, via the user interface, the obtained set of media and prompting the user for second input indicating subject identification information relating to the subject for detection within the obtained set of media; (Fig. 2, 3 [0053]-[0066] display the identified number of images based on the first input related to the identified area-of-interest)
receiving, via the user interface, the second input; (Fig. 2, 3, [0049]-[0066] receive a user defined action based on the state change)
using at least (i) the obtained set of media as training input data and (ii) the received second input as training output data, to train the ML model; (Fig. 2, 3, [0053]-[0066] using the number of images identified as training data and the defined action as the training output data to train the ML model) and
performing a set of operations to facilitate causing a computing system to run the trained ML model, wherein the computing system running the trained ML model comprises the computing system using at least the trained ML model and received runtime input data to generate and output corresponding runtime output data. (Fig. 2, 3, [0027]-[0029][0044]-[0066] [0081] adjust the weights to cause rapid increase or decrease to the output of the loss function to run the trained ML model to make it adaptive and running the trained ML model using the input data (identified images) to generate and output the output data (alert))
In regard to claim 2, Likhomanov disclose The method of claim 1,
Likhomanov disclose wherein the media is video, the set of media includes multiple images, and the subject identification information specifies whether, for each of the multiple images, the subject for detection is represented within that respective image. (Fig. 2, 3 [0026]-[0031] [0039] [0053]-[0066] the media could be video and number of images, the identified area-of-interest is within the respective image)
In regard to claim 3, Likhomanov disclose The method of claim 2,
Likhomanov disclose wherein the subject identification information further specifies, for each of the multiple images, where the subject for detection is represented within that respective image. (Fig. 2, 3 [0026]-[0031] [0039] [0053]-[0066] for each image, the identified area-of-interest is within the respective image frame)
In regard to claim 5, Likhomanov disclose The method of claim 1,
Likhomanov disclose wherein using at least the received first input as a basis to obtain the set of media related to the subject for detection comprises: using at least the received first input to search for media to include in the set of media. (Fig. 2, 3 [0025]-[0031] [0053]-[0066] obtain number of images based on the first input to look up the images to include)
In regard to claim 6, Likhomanov disclose The method of claim 1,
Likhomanov disclose further comprising: identifying a media-capturing device associated with the user; ([0025]-[0031][0042]-[0043] [0053]-[0066] a camera device)
wherein using at least the received first input as a basis to obtain the set of media related to the subject for detection comprises: using at least the received first input to search, within media captured by the identified media-capturing device, for media to include in the set of media. ([0025]-[0031] [0053]-[0066] obtain number of images based on the first input to look up the images captured by the camera device to include)
In regard to claim 7, Likhomanov disclose The method of claim 1,
Likhomanov disclose wherein using at least the received first input as a basis to obtain the set of media related to the subject for detection comprises:
using at least the received first input to search for and obtain example media representing the subject for detection; (Fig. 2, 3 [0025]-[0031] [0053]-[0066] based on the first input to look up the images to include, obtain images with identified area-of-interest)
identifying a media-capturing device associated with the user and obtaining media captured by the identified media-capturing device; ([0025]-[0031][0042]-[0043] [0053]-[0066] identify a camera device associated with the user and obtain images captured by the camera device to include)
using at least (i) the obtained example media representing the subject for detection and (ii) the obtained media captured by the identified media-capturing device, to synthetically generate media that includes (i) the obtained example media representing the subject for detection and (ii) the obtained media captured by the identified media-capturing device; and including the synthetically generated media in the obtained set of media. ([0025]-[0031][0042]-[0043] [0053]-[0066] [0072]-[0075] using the captured images representing the area-of-interest and obtain the images captured by the camera device to generate synthetic frames of image data with include identified area-of-interest and captured by the camera device and include the synthetic frames in the set of images)
In regard to claim 8, Likhomanov disclose The method of claim 7,
Likhomanov disclose wherein the media-capturing device is a camera. ([0025]-[0031][0042]-[0043] [0053]-[0066] a camera device)
In regard to claim 12, Likhomanov disclose The method of claim 11,
Likhomanov disclose wherein the computing system profile data indicates a geographic location of the computing system. ([0118]-[0123] attribute data associated with the user includes geographic location of the user device)
In regard to claim 13, Likhomanov disclose The method of claim 1,
Likhomanov disclose wherein performing the set of operations to facilitate causing the computing system to run the trained ML model comprises transmitting an instruction configured to cause a server to transmit the trained ML model to the computing system. ([0025]-[0031][0099] [0109]-[0119] the trained model may be looked up from storage (a database server) which maybe a server to the user device)
In regard to claim 14, Likhomanov disclose The method of claim 1,
Likhomanov disclose wherein performing the set of operations to facilitate causing the computing system to run the trained ML model comprises transmitting an instruction configured to cause the computing system to use at least received runtime input data and the trained ML model to generate and output corresponding runtime output data. ([0025]-[0031][0053]-[0066] [0099] [0109]-[0119] cause the user device to use the received input data and the trained model to generate and output data)
In regard to claim 15, Likhomanov disclose The method of claim 1,
Likhomanov disclose wherein the computing system is an Internet-of-Things (IoT) device. (Fig. 1, [0042]-[0049][0112]-[0121] the system is an internet connected device)
In regard to claim 16, Likhomanov disclose The method of claim 15,
Likhomanov disclose wherein the computing system is a camera. ([0025]-[0031][0042]-[0043] [0053]-[0066] a camera device)
In regard to claim 17, Likhomanov disclose The method of claim 15,
Likhomanov disclose wherein the computing system is a television or a set-top box. ([0113]-[0119] a set-top box)
In regard to claim 18, Likhomanov disclose The method of claim 1,
Likhomanov disclose wherein the computing system is server connected to an Internet-of-Things (IoT) device. (Fig. 1, [0042]-[0049][0112]-[0121] the system is an internet connected device with a server)
In regard to claim 19, claim 19 is a system claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claim 20, claim 20 is a medium claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
Claim Rejections - 35 USC § 103
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 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.
Claims 4, 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Likhomanov et al. (Likhomanov) US 2024/00056661 in view of Zass et al. (Zass) US 2020/0211530
In regard to claim 4, Likhomanov disclose The method of claim 1,
Likhomanov disclose wherein the media is audio, the set of media includes multiple audio clips, (Fig. 2, 3 [0026]-[0031] [0039] [0053]-[0066] [0100]-[0102] media is audio, audio clips)
But Likhomanov fail to explicitly disclose “and the subject identification information specifies whether, for each of the multiple audio clips, the subject for detection is represented within that respective audio clip.”
Zass disclose and the subject identification information specifies whether, for each of the multiple audio clips, the subject for detection is represented within that respective audio clip. ([0040][0109]-[0114] [0140]-[0142] [0194]-[0195] for each audio segment, the local language voice is identified in the audio segment)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Zass‘s training a ML model with media stream into Likhomanov’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Zass‘s training the ML model with voice stream would help to provide more voice training data into Likhomanov’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing voice training data to train the ML model would help to expand the ML model’s usability to more scenarios.
In regard to claim 9, Likhomanov disclose The method of claim 1,
Likhomanov disclose further comprising: obtaining user profile data associated with the user; ([0118]-[0123] obtain attribute data associated with the user)
But Likhomanov fail to explicitly disclose “wherein using at least the received first input as the basis to obtain the set of media related to the subject for detection comprises: using at least the received first input and the obtained user profile data as a basis to obtain the set of media related to the subject for detection.”
Zass disclose wherein using at least the received first input as the basis to obtain the set of media related to the subject for detection comprises: using at least the received first input and the obtained user profile data as a basis to obtain the set of media related to the subject for detection. ([0006]-[0035] based on the received input and obtained profile data to obtain set of media with identified objects, for example, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Zass‘s training a ML model with media stream into Likhomanov’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Zass‘s training the ML model with voice stream based on user profile data would help to provide more voice training data into Likhomanov’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing voice training data based on user profile data to train the ML model would help to expand the ML model’s usability to more scenarios.
In regard to claim 10, Likhomanov and Zass disclose The method of claim 9,
Likhomanov disclose wherein the user profile data indicates a geographic location of the user. ([0118]-[0123] attribute data associated with the user includes geographic location of the user)
In regard to claim 11, Likhomanov disclose The method of claim 1,
Likhomanov disclose further comprising: obtaining computing system profile data associated with the computing system; ([0118]-[0123] obtain attribute data associated with the user device includes geographic location of the user device)
But Likhomanov fail to explicitly disclose “wherein using at least the received first input as the basis to obtain the set of media related to the subject for detection comprises: using at least the received first input and the obtained computing system profile data as a basis to obtain the set of media related to the subject for detection.”
Zass disclose wherein using at least the received first input as the basis to obtain the set of media related to the subject for detection comprises: using at least the received first input and the obtained computing system profile data as a basis to obtain the set of media related to the subject for detection. ([0006]-[0035] [0166] based on the received input and obtained profile data (geographical location, etc.) to obtain set of media with identified objects, for example, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Zass‘s training a ML model with media stream into Likhomanov’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Zass‘s training the ML model with voice stream based on user profile data would help to provide more voice training data into Likhomanov’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing voice training data based on user profile data to train the ML model would help to expand the ML model’s usability to more scenarios.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 11769531 B1 2023-09-26 Lucas et al.
Content System With User-input Based Video Content Generation Feature
Lucas et al. disclose n one aspect, an example method includes (i) obtaining a first segment of video content; (ii) outputting for presentation, the obtained first segment; (iii) after outputting for presentation the obtained first segment, causing a user to be prompted for user-input data; (iv) receiving user-input data provided in response to the prompting; (v) using at least the received user-input data to synthetically generate a second segment of the video content, wherein the generated second segment is static, non-interactive content; and (vi) outputting for presentation, the generated second segment… see abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, 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, Jennifer Welch can be reached at 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights