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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/25/2025 has been entered.
Response to Arguments
Applicant’s arguments filed 11/25/2025 have been considered but are moot in view of a new ground of rejections.
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.
Claims 1-2 and 5-13 are rejected under 35 U.S.C. 103 as being unpatentable over Ross et al. (US 2015/0063776 A1 – hereinafter Ross), Kinoshita et al. (US 2020/0226898 A1), and Olson (US 2021/0101696 A1 – hereinafter Olson).
Regarding claim 1, Ross discloses an apparatus (Figs. 2a-2b) comprising: a memory ([0034]; [0041] – a circular buffer implemented using a volatile memory); a camera ([0041] – at least a camera unit); and a processor coupled to the memory and the camera (Fig. 1; [0033]-[0034] – processor 118 coupled to the volatile memory 120 and the camera, e.g. any of the cameras 102 and 104 shown in Fig. 1), wherein the processor is configured to: record video via the camera ([0041] – recording video captured by the camera); store a first portion of the video for a first particular time period in the memory ([0041] – storing a first portion of the video in the circular buffer for a first particular time period, which is determined by the length of the circular buffer and the rate at which encoded data is generated); and store a second portion of the video for a second particular time period responsive to a trigger ([0042]-[0043]; [0048] – responsive to a triggering signal, storing a second portion of the video for a second particular time period, which is equal to the first portion plus the portion following the event to a non-volatile memory 122).
However, Ross does not disclose the processor is configured to receive an artificial intelligence (AI) model trained remotely in a cloud device; store the AI model in the memory; input the video into the Al model, wherein the Al model identifies a trigger, wherein the trigger is only a specific user’s language or word choice.
Kinoshita discloses a processor is configured to receive an artificial intelligence (AI) model trained remotely in a cloud device ([0157]-[0158]; [0170] – a processor of a camera receiving an AI model trained remotely in a server device as further described in at least [0153]-[0154], which corresponds to a cloud device); store the AI model in a memory ([0198] – storing the AI model in a storage unit of the camera); record video via a camera (Fig. 11; [0087]; [0133] – recording audio and images via the camera); input the video into the Al model ([0135] – inputting the audio and images into the AI model), wherein the Al model identifies a trigger ([0135]-[0144]; [0290] – the AI model identifies an event, e.g. a harmful animal, a person, a voice of a target etc., that triggers an alarm).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Kinoshita into the apparatus taught by Ross because it would have been advantageous in updating the model to detect various types of triggering events (Kinoshita: [0213]).
Ross and Kinoshita do not disclose the trigger is only a specific user’s language or word choice.
Olson discloses a trigger is only a specific user’s language or word choice ([0022] – a sequence of numbers known only by the user).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Olson into the apparatus taught by Ross and Kinoshita to prevent unauthorized use of the device (Olson: [0022] – unauthorized persons do not know the sequence of numbers thus cannot use the device to trigger the recording).
Regarding claim 2, Ross also discloses the apparatus of claim 1, wherein the apparatus is a wearable device ([0006] – the apparatus is worn on the user’s body).
Regarding claim 5, Ross also discloses the apparatus of claim 1, wherein the second particular time period is longer than the first particular time period ([0048] – the second particular time period, which is equal to the first portion plus the portion following the event).
Regarding claim 6, Ross also discloses the apparatus of claim 1, further comprising a global position system (GPS) configured to determine a location of the apparatus ([0035] – comprising sensors include a global-positioning system (GPS) receiver for recording position data of the apparatus).
Regarding claim 7, Ross also discloses the apparatus of claim 6, wherein the memory is configured to store the location of the apparatus responsive to the trigger ([0041]; [0048] – responsive to the trigger, recording the video data combined with the sensor data including the GPS data as described in [0035]).
Regarding claim 8, Ross discloses an apparatus comprising: a camera ([0041] – at least a camera unit); a memory ([0034]; [0041] – a memory comprising a volatile memory 120 and a non-volatile memory 122); and a processor coupled to the memory and the camera (Fig. 1; [0033]-[0034] – processor 118 coupled to the volatile memory 120, the non-volatile memory 122, and the camera, e.g. any of the cameras 102 and 104 shown in Fig. 1), wherein the processor is configured to: record video via the camera ([0041] – recording video captured by the camera); store the video in the memory responsive to a trigger ([0041]-[0043]; [0048] – storing the video in the circular buffer implemented on the volatile memory); input the video into an event detection model ([0046] – the processor executing an event detection model to detect an event from the video inputted to the model); and delete a portion of the video responsive to an output of the model ([0048] – responsive to an detected event, triggering a recording, copying the contents of the volatile memory as new data is stored to the volatile memory, thus at least a portion of the oldest video in the volatile memory is deleted because the memory is implemented as a circular buffer as described at least in [0041]).
However, Ross does not disclose the processor is configured to receive an artificial intelligence (AI) model trained remotely in a cloud device; store the AI model in the memory; input the video into the Al model, wherein the Al model identifies a trigger, wherein the trigger is only a specific user’s language or word choice.
Kinoshita discloses a processor is configured to receive an artificial intelligence (AI) model trained remotely in a cloud device ( [0157]-[0158]; [0170] – a processor of a camera receiving an AI model trained remotely in a server device as further described in at least [0153]-[0154], which corresponds to a cloud device); store the AI model in a memory ([0198] – storing the AI model in a storage unit of the camera); record video via a camera (Fig. 11; [0087]; [0133] – recording audio and images via the camera); input the video into the Al model ([0135] – inputting the audio and images into the AI model), wherein the Al model identifies a trigger ([0135]-[0144]; [0290] – the AI model identifies an event, e.g. a harmful animal, a person, a voice of a target etc., that triggers an alarm).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Kinoshita into the apparatus taught by Ross because it would have been advantageous in updating the model to detect various types of triggering events (Kinoshita: [0213]).
Ross and Kinoshita do not disclose the trigger is only a specific user’s language or word choice.
Olson discloses a trigger is only a specific user’s language or word choice ([0022] – a sequence of numbers known only by the user).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Olson into the apparatus taught by Ross and Kinoshita to prevent unauthorized use of the device (Olson: [0022] – unauthorized persons do not know the sequence of numbers thus cannot use the device to trigger the recording).
Regarding claim 9, Ross also discloses the apparatus of claim 8, wherein the memory further comprises volatile memory ([0041] - volatile memory 120).
Regarding claim 10, Ross also discloses the apparatus of claim 9, wherein the processor is configured to store the video in the volatile memory ([0041] – storing the video in the circular buffer implemented on the volatile memory).
Regarding claim 11, Ross also discloses the apparatus of claim 8, wherein the processor is configured to store a different portion of the video responsive to the output of the AI model ([0048] – storing a portion following the event responsive to the output of the model, which is, in view of Kinoshita as discussed above, the AI model).
Regarding claim 12, Ross also discloses the apparatus of claim 11, wherein the memory further comprises non-volatile memory, and wherein the processor is configured to store the different portion of the video in the non-volatile memory ([0048] – storing the portion following the event onto the non-volatile memory 122).
Regarding claim 13, Ross also discloses the apparatus of claim 8, wherein the processor is configured to transmit a different portion of the video responsive to the output of the AI model ([0048] – transmitting the portion following the event onto the non-volatile memory 122 responsive to the output of the model, which is, in view of Kinoshita discussed above, the AI model).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Ross, Kinoshita, and Olson as applied to claims 1-2 and 5-13 above, and further in view of Wang (CN 110889354 A – hereinafter Wang, references to machine translated copy previously attached).
Regarding claim 3, the see teachings of Ross, Kinoshita, and Olson as discussed in claim 2 above. However, Ross, Kinoshita, and Olson do not disclose the wearable device is augmented reality (AR) glasses.
Wang discloses a wearable device is augmented reality (AR) glasses (page 2).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Wang into the apparatus taught by Ross, Kinoshita, and Olson to implement the apparatus in form of AR glasses which were known to have been advantageous due to their ability to assist patrol officers immediately identify suspects on the street, their ability to generate overlaid 3D maps on building floor plans and transport routes to enhance situational awareness, etc.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Ross, Kinoshita, and Olson as applied to claims 1-3 and 5-13 above, and further in view of Wiklof (US 2009/0324203 A1 – hereinafter Wiklof).
Regarding claim 14, see the teachings of Ross, Kinoshita, and Olson as discussed in claim 13 above. However, Ross, Kinoshita, and Olson do not disclose the processor is configured to transmit the different portion of the video to an external device.
Wiklof discloses a processor is configured to transmit a different portion of video to an external device ([0052]-[0053] – transmitting a portion of video to a remote storage device).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Wiklof into the apparatus taught by Ross, Kinoshita, and Olson to enhance storage scalability, i.e. increasing or decreasing the storage capacity without affecting the hardware configuration.
Regarding claim 15, see the teachings of Ross, Kinoshita, Olson, and Wiklof as discussed in claim 14 above, in which Wiklof also discloses the external device is a cloud device or computing device ([0042]; Fig. 4 – a server or a client computer).
The motivation for incorporating the teachings of Wiklof into the apparatus has been discussed in claim 14 above.
Claims 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kinoshita, Wang, and Seo (US 2023/0335129 A1 – hereinafter Seo).
Regarding claim 16, Wang discloses a method comprising: training an artificial intelligence (AI) model with a trigger (pages 5-6); recording video via a camera at a computing device (page 3 – collecting, i.e. recording, images via a camera, the images comprise a video as described on page 6); inputting the video into the AI model, wherein the Al model identifies a trigger (page 3 – inputting the collected images to the AI model to detect a triggering object); and storing a portion of the video responsive to an output of the AI model (page 3; page 6 – responsive to detecting a triggering object by the AI mode, storing a portion of the video corresponding to the detected object).
However, Wang does not disclose training the artificial intelligence (AI) model with a trigger remotely in a cloud device, wherein the trigger is only a specific user’s language or word choice; receiving the AI model at the computing device; and storing the AI model in a memory of the computing device.
Kinoshita discloses training an artificial intelligence (AI) model with a trigger remotely in a cloud device ([0153]-[0154] – training an AI model with a trigger remotely in a server device, which corresponds to a cloud device); storing the AI model at a computing device ([0198] – storing the AI model in a storage unit of a computing device comprising a camera); recording video via a camera of the computing device (Fig. 11; [0087]; [0133] – recording audio and images via the camera); input the video into the Al model at the computing device ([0135] – inputting the audio and images into the AI model at the computing device comprising the camera), wherein the Al model identifies a trigger ([0135]-[0144]; [0290] – the AI model identifies an event, e.g. a harmful animal, a person, a voice of a target etc., that triggers an alarm).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Kinoshita into the method taught by Wang because it would have been advantageous in updating the model to detect various types of triggering events (Kinoshita: [0213]).
However, Wang and Kinoshita do not disclose the trigger is only a specific user’s language or word choice.
Seo discloses a trigger is only a specific user’s language or word choice ([0050] – a trigger word is used to train an acoustic AI model, includes only one preset trigger word).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Seo into the method taught by Ross and Kinoshita to make the recording responding to the user’s command correctly.
Regarding claim 17, Wang in view of Kinoshita and Seo also discloses the method of claim 16, further comprising displaying a different video on a user interface (page 3 - according to automatically capture image playback command, playing active trigger captured unread image by the AR glass user, thus displaying the unread image, which comprises the video as described on page 8).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Wang, Kinoshita, and Seo as applied to claims 16-17 above, and further in view of Moon (US 2017/0289224 A1 – hereinafter Moon).
Regarding claim 18, see the teachings of Wang, Kinoshita, and Seo as discussed in claim 17 above. However, Wang, Kinoshita, and Seo do not disclose providing a selection to store or delete the different video to a user responsive to displaying the different video on the user interface.
Moon discloses providing a selection to store or delete a video to a user responsive to displaying the video on a user interface ([0034]).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Moon into the different video in the method taught by Wang, Kinoshita, and Seo to allow the user to correct any error made by the AI model by deleting unwanted videos.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Kinoshita, Seo, and Moon as applied to claims 16-18 above, and further in view of Ashlock et al. (US 2021/0350011 A1 – hereinafter Ashlock).
Regarding claim 19, see the teachings of Wang, Kinoshita, Seo, and Moon as discussed in claim 18 above. However, Wang, Kinoshita, Seo, and Moon do not disclose identifying characteristics of the different video.
Ashlock discloses identifying characteristics of a content ([0047] – identifying features of content that is not accepted).
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate the teachings of Ashlock into the different portion of video discussed in claim 18 above in order to re-train the AI model by providing the AI model with features identified as unwanted by the user, thus enhancing the accuracy of the model.
Regarding claim 20, see the teachings of Wang, Kinoshita, Seo, Moon, and Ashlock as discussed in claim 19 above, in which Ashlock in view of Wang also discloses training the AI model using the selection and the characteristics of the different video ([0047] - re-training a machine learning model with updated training sets to weaken associations between characteristics of the parent entity and the recommended feature responsive to the parent entity not accepting the recommendation).
The motivation for incorporating the teachings of Ashlock has been discussed in claim 19 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUNG Q DANG whose telephone number is (571)270-1116. The examiner can normally be reached IFT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thai Q Tran can be reached at 571-272-7382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUNG Q DANG/Primary Examiner, Art Unit 2484
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