Response to Amendment
This action is responsive to applicant’s amendment and remarks received on 07/30/2026. Claims 1-20 have been presented for examination. Claims 1-6, 8-15 and 17-20 have been amended. Claims 1-20 have been examined.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Espel-Logan (Pub. No.: 2025/0238010 A1) in view of Kappi (Pub. No.: 2022/0201256 A1)
1) In regard to claim 1, Espel-Logan discloses the claimed computer-implemented method for potential hazard identification (fig. 4), comprising:
receiving, from one or more cameras, image data of a scene (fig. 4: 402 and ¶0127);
identifying, based on the image data and as identified activities, one or more activities occurring at the scene (¶0127 discloses detecting different hazard levels);
receiving contextual event data and one or more project documents associated with the scene (¶0084 discloses data curated by human labelers is used to help the model learn a supervised policy which may generate outputs from a selected list of prompts, and ¶0085 discloses the model may use data from documents to learn);
based on the identified activities, detecting a safety-relevant entity activity and zone at the scene (¶0127 discloses detecting different hazardous conditions);
analyzing the contextual event data and the one or more project documents using a fine-tuned large language model (LLM) to determine whether the detected safety-relevant entity corresponds to an approved project condition or a process deviation (¶0084 discloses using a fine-tuned language model to teach the model);
determining data is not required based on the detected safety-relevant entity activity corresponding to the approved project condition (¶0106 discloses the model may label items as “no hazardous condition present”; see also ¶0107);
then:
analyzing the image data to detect whether a hazard occurred (¶0130);
detecting that a hazard occurred (¶0130); and
communicating to a user device, a notification indicating that the hazard occurred and providing a mitigation action (¶0131 discloses transmitting a notification with a recommended action).
Espel-Logan do not explicitly disclose determining whether additional image data is required to detect whether a hazard occurred based on the detected safety-relevant entity activity and zone at the scene, and the additional image data is focused high spatial- resolution image data.
However, Kappi discloses it has been known for a computer-implemented method to determine whether additional image data is required to detect whether a condition occurred based on the detected activity and zone at the scene, and the additional image data is focused high spatial- resolution image data (¶0081 discloses it is known to recapture an image of a scene if a blur level is above a threshold).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the claimed invention was filed to allow the method of Espel-Logan to determine if additional image data is required, as taught by Kappi.
One skilled in the art would be motivated to modify Espel-Logan as described above in order to determine parameters that will result in predicting features at a target level of accuracy, as taught by Kappi (¶0043).
2) In regard to claim 2 (dependent on claim 1), Espel-Logan and Kappi further disclose the computer-implemented method of claim 1, further comprising:
determining that additional image data is required based on the detected safety-relevant entity activity corresponding to the process deviation; adapting at least one parameter of a camera to capture the additional image data by capturing focused high spatial-resolution image data of the safety-relevant entity activity and zone at the scene; receiving, by the camera, the focused high spatial-resolution image data of the safety- relevant entity activity and zone at the scene (Kappi ¶0081 and ¶0138);
analyzing the image data and the additional image data to detect whether a hazard occurred; detecting that a hazard has not occurred; and providing a notification indicating that a hazard has not occurred (Espel-Logan ¶0131).
3) In regard to claim 3 (dependent on claim 1), Espel-Logan and Kappi further disclose the computer-implemented method of claim 1, wherein receiving from one or more cameras, image data of a scene comprises receiving one or more panoramic images of the scene (Kappi fig. 1: 115 and 123).
4) In regard to claim 4 (dependent on claim 1), Espel-Logan and Kappi further disclose the computer-implemented method of claim 1, wherein identifying one or more activities occurring at the scene comprises utilizing one or more machine learning modules trained to identify activities (Kappi ¶0035).
5) In regard to claim 5 (dependent on claim 2), Espel-Logan and Kappi further disclose the computer-implemented method of claim 2, wherein adapting at least one parameter of a camera to capture the focused high spatial-resolution image data of the safety-relevant entity activity and zone at the scene comprises providing instructions to pan the camera, tilt the camera, zoom in, or zoom out (Kappi ¶0079 discloses spatial separation between images).
6) In regard to claim 6 (dependent on claim 2), Espel-Logan and Kappi further disclose the computer-implemented method of claim 2, wherein analyzing the image data and the focused high spatial-resolution image data to detect whether a hazard occurred comprises utilizing one or more machine learning modules trained to detect a hazard (Kappi ¶0035).
7) In regard to claim 7 (dependent on claim 1), Espel-Logan and Kappi further disclose the computer-implemented method of claim 1, further comprising:
receiving, at a first time, historical incident and hazard data from one or more different data sources, analyzing the historical incident and hazard data, generating, based on analyzing the historical incident and hazard data, a machine learning module that is trained to identify incidents and hazards, receiving, at a second time after the first time, additional historical incident and hazard data, and updating the machine learning module based on the additional historical incident and hazard data (Kappi ¶0055 disclose using historical data to determine a level of confidence or accuracy).
8) In regard to claim 8 (dependent on claim 1), Espel-Logan and Kappi further disclose the computer-implemented method of claim 1, further comprising:
receiving, at a first time, contextual event data from one or more sources; analyzing the contextual event data; generating, based on analyzing the contextual event data a machine learning module that is trained to identify one or more patterns within the contextual event data; and utilizing the machine learning module that is trained to identity one or more patterns within the contextual event data to generate a contextual model that represents a digital twin of the scene (Espel-Logan ¶0035).
9) In regard to claim 9 (dependent on claim 8), Espel-Logan and Kappi further disclose the computer-implemented method of claim 8, further comprising: utilizing the contextual model that represents a digital twin of the scene and the machine learning module that is trained to identify one or more patterns within the contextual event data to identify one or more activities occurring at the scene (Espel-Logan ¶0035);
based on identifying one or more activities occurring at the scene, determining whether additional image data is required to detect whether a hazard occurred at the scene; and based on determining that additional image data is required, adapting at least one parameter of a camera to capture focused high spatial-resolution image data of the scene (Kappi ¶0081).
10) In regard to claim 10, claim 10 is rejected and analyzed with respect to claim 1 and the references applied.
11) In regard to claim 11 (dependent on claim 10), claim x is rejected and analyzed with respect to claim 2 and the references applied.
12) In regard to claim 12 (dependent on claim 10), claim 12 is rejected and analyzed with respect to claim 3 and the references applied.
13) In regard to claim 13 (dependent on claim 10), claim 13 is rejected and analyzed with respect to claim 4 and the references applied.
14) In regard to claim 14 (dependent on claim 12), claim 14 is rejected and analyzed with respect to claim 5 and the references applied.
15) In regard to claim 15 (dependent on claim 12), claim 15 is rejected and analyzed with respect to claim 6 and the references applied.
16) In regard to claim 16 (dependent on claim 10), claim 16 is rejected and analyzed with respect to claim 7 and the references applied.
17) In regard to claim 17 (dependent on claim 10), claim 17 is rejected and analyzed with respect to claim 8 and the references applied.
18) In regard to claim 18 (dependent on claim 17), claim 18 is rejected and analyzed with respect to claim 9 and the references applied.
19) In regard to claim 19, claim 19 is rejected and analyzed with respect to claim 1 and the references applied.
20) In regard to claim 20 (dependent on claim 19), claim 20 is rejected and analyzed with respect to claim 2 and the references applied.
Response to Arguments
Applicant's arguments with respect to the amended claims, based solely on the amendments to the claims, have been considered but are moot because the prior art of record discloses the claimed limitation. See the above rejection for further details.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CURTIS J KING whose telephone number is (571)270-5160. The examiner can normally be reached Mon-Fri 6:00 - 2:00 EST.
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/CURTIS J KING/Primary Examiner, Art Unit 2685
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