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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 8/8/24, 1/6/26 and 3/10/26. 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 § 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, 6, 7, 9, 14, 15 17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lupien et al. (US Publication).
Regarding claims 1, 9 and 17, Lupien teaches a device, comprising: a data generation source; a communication device; and a processor (i.e. fig. 1 shows a environment controller (device) comprising a processor (110), memory (120) and transceiver (130) for executing programmed instructions; see paragraphs 38 - 42)
configured to process data from the data generation source to generate inputs communicated to a server via the communication device; (i.e. fig. 3b shows the environment controller may process data from source devices and transmit the data as inputs to a training server (400); see elements 570-> 550; see paragraphs 107 - 109)
wherein the device is configured to receive, from the server a predictive model trained based at least in part on prior inputs generated from prior data from the data generation source and transmitted to the server; (i.e. fig. 3b shows the environment controller (100) may receive from a training server (400), an update of a predictive model based upon inputs previously transmitted to the server; see elements 550 -> 555-> 560 -> 565 -> 570; see also paragraphs 95 - 107) and
wherein the processor is configured to process, using the predictive model,
current data from the data generation source to generate current inputs, and communicate using the communication device, the current inputs to the server. (i.e. fig. 3b shows the environment controller (100) utilizes the predictive model in order to generate the current inputs utilizing the updated weights, and report the inputs to the training server (400)) (see Also: figure 1 shows a environment controller (100) may receive data from multiple IoT type devices may package the data as inputs that are transmitted to a training server (400), the server uses the input data to create or update a predictive model which is transmitted to the environment controller for use in prediction analysis, this process is continuously repeated, ensuring an accurate prediction model; see paragraphs 37 – 40, 55, 56)
Regarding claims 6 and 14, Lupien teaches The device of claim 1, wherein the predictive model is trained to detect an object in the current data from the data generation source. (i.e. fig 1 shows the predictive model is trained and retrained to detect an as an example, environmental conditions, among other object types from the data sources (multiple sensors and input devices); see paragraphs 4, 23, 36)
Regarding claims 7 and 15, Lupien teaches the device of claim 6, wherein an output of the predictive model is configured to provide a classification. (i.e. fig 1 shows the predictive model is trained and retrained to detect an as an example, environmental conditions, among other object types from the data sources (multiple sensors and input devices), the output is a model of the current conditions; see paragraphs 4, 23, 36)
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.
Claims 2 – 5, 8, 10 – 13, 16, 18 - 20 are rejected under 35 U.S.C. 103 as being obvious over Lupien et al. (US Publication) in view of Han et al. (US Publication 2019/0294954).
Regarding claims 2, 10, Lupien discloses all the recited limitations of claim 1, 9 as described previously from which claims 2, 10 depends. Lupien does not teach wherein the data generation source is configured to generate video data.
However, Han teaches wherein the data generation source is configured to generate video data. (i.e. Han discloses a data generation source may include voice, image and video data; see paragraphs 159 - 162 )
It would have been obvious to a person with ordinary skill in the art before the time the invention was filed to use the video input data of Han into the predictive model updating of Lupien. Both Lupien and Han teach receiving sensor data from an external source and transmitting this input data to a server for a predictive model update. Further, to a POSITA, the device simply processes a different data type through the same predictive loop. The substitution yields predictable results without requiring any new, unconventional computing technique.
A person with ordinary skill in the art would have been motivated to make the modification to Lupien to improve the accuracy of the predictive model
by replacing or augmenting traditional sensors with a video camera represents the straightforward application of a known tool.
Regarding claims 3, 11, 18 Lupien discloses all the recited limitations of claim 2, 10 as described previously from which claims 3, 11, 18 depends. Lupien does not teach wherein the current inputs include frames of the video data selected using the predictive model.
However, Han teaches wherein the current inputs include frames of the video data selected using the predictive model. (i.e. Han discloses the input may process image frames of still pictures produced by video; see paragraph 107)
It would have been obvious to a person with ordinary skill in the art before the time the invention was filed to input video frames of Han into the predictive model updating of Lupien. Both Lupien and Han teach receiving sensor data from an external source and transmitting this input data to a server for a predictive model update. Further, to a POSITA, the device simply processes a different data type through the same predictive loop. The substitution yields predictable results without requiring any new, unconventional computing technique.
A person with ordinary skill in the art would have been motivated to make the modification to Lupien to improve the accuracy of the predictive model by replacing or augmenting traditional sensors with a video camera represents the straightforward application of a known tool.
Regarding claims 4, 12, Lupien discloses all the recited limitations of claim 1, 9 as described previously from which claims 4, 12 depends. Lupien does not teach wherein the data generation source is configured to generate image data.
However, Han teaches wherein the data generation source is configured to generate image data. (i.e. Han discloses a data generation source may include voice, image and video data; see paragraphs 159 - 162 )
It would have been obvious to a person with ordinary skill in the art before the time the invention was filed to use the video input data of Han into the predictive model updating of Lupien. Both Lupien and Han teach receiving sensor data from an external source and transmitting this input data to a server for a predictive model update. Further, to a POSITA, the device simply processes a different data type through the same predictive loop. The substitution yields predictable results without requiring any new, unconventional computing technique.
A person with ordinary skill in the art would have been motivated to make the modification to Lupien to improve the accuracy of the predictive model by replacing or augmenting traditional sensors with a video camera represents the straightforward application of a known tool.
Regarding claims 5, 13, 19 Lupien discloses all the recited limitations of claim 4, 12 as described previously from which claims 5, 13, 19 depends. Lupien does not wherein the current inputs include image portions cropped from the image data based on the predictive model.
However, Han teaches wherein the current inputs include image portions cropped from the image data based on the predictive model. (i.e. Han discloses the input may process image frames of still pictures produced by video; see paragraph 107)
It would have been obvious to a person with ordinary skill in the art before the time the invention was filed to input video frames of Han into the predictive model updating of Lupien. Both Lupien and Han teach receiving sensor data from an external source and transmitting this input data to a server for a predictive model update. Further, cropping an image or frame is a standard data normalization technique in computer vision and machine learning.
A person with ordinary skill in the art would have been motivated to make the modification to Lupien to improve the accuracy of the predictive model by replacing or augmenting traditional sensors with a video camera represents the straightforward application of a known tool.
Regarding claims 8, 16, 20 Lupien discloses all the recited limitations of claim 6, 14 20 as described previously from which claims 8, 16 depends. Lupien does not wherein an output of the predictive model is configured to provide coordinates of bounding boxes of objects in the current data from the data generation source.
However, Han teaches wherein an output of the predictive model is configured to provide coordinates of bounding boxes of objects in the current data from the data generation source.(i.e. Han discloses a photo sensor and an input wherein the output of the predictive model may calculate the coordinates of the physical object according to variation of light to thus obtain position information of the physical object.; see paragraph 122)
It would have been obvious to a person with ordinary skill in the art before the time the invention was filed to input image data of Han into the predictive model updating of Lupien. Both Lupien and Han teach receiving sensor data from an external source and transmitting this input data to a server for a predictive model update. Further, cropping an image or frame is a standard data normalization technique in computer vision and machine learning.
A person with ordinary skill in the art would have been motivated to make the modification to Lupien to improve the accuracy of the predictive model by replacing or augmenting traditional sensors with a video camera represents the straightforward application of a known tool.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT J LOPATA whose telephone number is (571)270-5158. The examiner can normally be reached Mon-Fri 10-7 EST.
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, Sujoy Kundu can be reached at (571)272-8586. 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.
ROBERT J. LOPATA
Primary Examiner
Art Unit 2471
/ROBERT J LOPATA/
July 8, 2026Primary Examiner, Art Unit 2471
Prosecution Insights