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 statement (IDS) submitted on July 18, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Election/Restrictions
Applicant’s election without traverse of claims 1 – 7, 11 – 15, 26 – 36 and 38 – 41 in the reply filed on 03/27/2026 is acknowledged.
Claims Group I: 1, 8-10, 16-19, 37 and 42-43 and Group II: 1 and 20-25, withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected inventions, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 03/27/2026.
Applicant stands correct that the present application is a 371 National Stage application and should have been evaluated under the “Lack of Unity” test. Examiner appreciates applicant’s reminder for this oversight. However, lack of unity exists and the groupings are consistent with the restriction groupings in the last Office Action as can been see below.
REQUIREMENT FOR UNITY OF INVENTION
As provided in 37 CFR 1.475(a), a national stage application shall relate to one invention only or to a group of inventions so linked as to form a single general inventive concept (“requirement of unity of invention”). Where a group of inventions is claimed in a national stage application, the requirement of unity of invention shall be fulfilled only when there is a technical relationship among those inventions involving one or more of the same or corresponding special technical features. The expression “special technical features” shall mean those technical features that define a contribution which each of the claimed inventions, considered as a whole, makes over the prior art.
The determination whether a group of inventions is so linked as to form a single general inventive concept shall be made without regard to whether the inventions are claimed in separate claims or as alternatives within a single claim. See 37 CFR 1.475(e).
When Claims Are Directed to Multiple Categories of Inventions:
As provided in 37 CFR 1.475 (b), a national stage application containing claims to different categories of invention will be considered to have unity of invention if the claims are drawn only to one of the following combinations of categories:
(1) A product and a process specially adapted for the manufacture of said product; or
(2) A product and a process of use of said product; or
(3) A product, a process specially adapted for the manufacture of the said product, and a use of the said product; or
(4) A process and an apparatus or means specifically designed for carrying out the said process; or
(5) A product, a process specially adapted for the manufacture of the said product, and an apparatus or means specifically designed for carrying out the said process.
Otherwise, unity of invention might not be present. See 37 CFR 1.475 (c).
Accordingly, restriction is required under 35 U.S.C. 121 and 372 as the groupings outlined below do not fall under the aforementioned categories 1-5.
This application contains the following inventions or groups of inventions which are not so linked as to form a single general inventive concept under PCT Rule 13.1.
Group I. Claims 1, 8-10, 16-19, 37 and 42-43, drawn to generic load balancing factor,
classified in H04N 21/23103 using load balancing strategies, e.g. by placing or distributing content on different disks, different memories or different servers (storage management).
Group II. Claims 1 and 20-25, drawn to Al architecture variation, classified in
G06N3/02 Neural networks.
Group III. Claims 1-7, 11-15, 26-36 and 38-41, drawn to different scene importance.
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)(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, 40 and 41 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by BJÖRGVINSDÓTTIR et al. US Patent Application Publication No. US-20230344961-A1 (hereinafter Hanna).
Regarding claim 1, Hanna discloses a method for performing a load balancing assignment of a set of cameras having different camera specifications to a set of recorders having different recorder specifications (Hanna in [0007] discloses, “there is provided a method for prioritization of video processing resources in a video processing and/or management system ... The method includes ranking each of the two or more cameras as either a higher-priority camera or a lower-priority camera”. Furthermore Hanna in [0008] discloses about lower and higher priority camera have different storage (different camera specifications to a set of recorders); “As used herein, the “storage” used to store a video stream from a higher-priority camera may not necessarily be a same storage used to store a video stream from a lower-priority camera, or not even a same storage used to store a result of a video content analysis performed for a video stream from a higher-priority camera”), the method comprising: determining a set of load balancing factors configured to match cameras in the set of cameras to recorders in the set of recorders responsive to scene analysis information, the set of load balancing factors including at least a camera scene importance (Hanna in [0007] discloses, “the ranking of a camera includes one of: i) obtaining an indication of a historical usability of video content captured by the camera ... ii) obtaining an indication of a historical cost in terms of processing resources needed for performing video content analysis of video content captured by the camera, and ranking the camera as a higher-priority camera if the historical cost is below a cost threshold and otherwise as a lower-priority camera, and iii) ranking the camera as a higher-priority camera if the historical usability outweighs the historical cost of the camera and otherwise as a lower-priority camera”); assigning one or more of the cameras in the set of cameras to record to one or more of the recorders in the set of recorders responsive to the set of load balancing factors and respective ones of the different recorder specifications (Hanna in [0054] discloses, “If the ranking module 240 determines that the second camera 110 b is a higher-priority camera, the second video stream 114 b may be routed via a second analysis module 122 b , and a second result 115 b of such video content analysis (including also e.g., the second video stream 114 b itself) may be routed to, and stored in, storage 130 . Likewise, if the ranking module 240 instead determines that the second camera 110 b is a lower-priority camera, the second video stream 114 b may be routed directly to, and stored in, storage 130 without first, or in connection with, performing the video content analysis” wherein the storage (recorder) in [0008] discloses about different storge assigned to different camera based on priority); and recording, by the one or more of the recorders, video information from a corresponding one of the one or more cameras based on the assigning of the one or more of the cameras to record to the one or more of the recorders (Hanna in [0045] discloses, “the video streams 114 c and 114 d from these cameras 110 c and 110 d are sent directly to storage 130”. Hanna in [0024] discloses, “store the first video stream in storage without first, or in connection therewith, performing such video content analysis”).
Summary of Citations (Hanna)
Paragraph [0007]; “there is provided a method for prioritization of video processing resources in a video processing and/or management system ... The method includes ranking each of the two or more cameras as either a higher-priority camera or a lower-priority camera. the ranking of a camera includes one of: i) obtaining an indication of a historical usability of video content captured by the camera ... ii) obtaining an indication of a historical cost in terms of processing resources needed for performing video content analysis of video content captured by the camera, and ranking the camera as a higher-priority camera if the historical cost is below a cost threshold and otherwise as a lower-priority camera, and iii) ranking the camera as a higher-priority camera if the historical usability outweighs the historical cost of the camera and otherwise as a lower-priority camera”.
Paragraph [0008]; “As used herein, the “storage” used to store a video stream from a higher-priority camera may not necessarily be a same storage used to store a video stream from a lower-priority camera, or not even a same storage used to store a result of a video content analysis performed for a video stream from a higher-priority camera”.
Paragraph [0024]; “store the first video stream in storage without first, or in connection therewith, performing such video content analysis”.
Paragraph [0045]; “the video streams 114 c and 114 d from these cameras 110 c and 110 d are sent directly to storage 130”.
Paragraph [0054]; “If the ranking module 240 determines that the second camera 110 b is a higher-priority camera, the second video stream 114 b may be routed via a second analysis module 122 b , and a second result 115 b of such video content analysis (including also e.g., the second video stream 114 b itself) may be routed to, and stored in, storage 130 . Likewise, if the ranking module 240 instead determines that the second camera 110 b is a lower-priority camera, the second video stream 114 b may be routed directly to, and stored in, storage 130 without first, or in connection with, performing the video content analysis”.
Regarding claim 40, Hanna discloses the method in accordance with claim 1, further comprising repeating assigning of at least some of the cameras in the set of cameras to at least some of the recorders in the set of recorders depending on at least one of: a time of day; a day of week; a holiday; a sale duration; a sale start time; and a sale end time (Hanna in [0072] discloses, “As used herein, a “historical time interval” for which the number (or frequency) of e.g., filed incident reports and/or object tracks are counted may e.g., correspond to one or more hours, days, weeks, months, or longer. The length of the historical time interval may be fixed, or be changed dynamically. The historical time interval may also not necessarily be continuous, but instead include e.g., only all same hours (e.g., only during 12:30-13:30) during the last days, all same days (e.g., only Mondays) during the last weeks, all same weeks (e.g., only weeks with number 37 ) during the last years, all same months (e.g., only Octobers) during the last years, or similar”).
Summary of Citations (Hanna)
Paragraph [0072]; “As used herein, a “historical time interval” for which the number (or frequency) of e.g., filed incident reports and/or object tracks are counted may e.g., correspond to one or more hours, days, weeks, months, or longer. The length of the historical time interval may be fixed, or be changed dynamically. The historical time interval may also not necessarily be continuous, but instead include e.g., only all same hours (e.g., only during 12:30-13:30) during the last days, all same days (e.g., only Mondays) during the last weeks, all same weeks (e.g., only weeks with number 37 ) during the last years, all same months (e.g., only Octobers) during the last years, or similar”.
Regarding claim 41, Hanna discloses the method in accordance with claim 1, further comprising repeating assigning of at least some of the cameras in the set of cameras to at least some of the recorders in the set of recorders responsive to an impending recorder failure (Hanna in [0019]; “the method may include “activating” the categorization of cameras into higher- and lower-priority cameras when it is determined that an available amount of video processing resources for video content analysis becomes (or risks becoming) sufficiently low, and e.g., goes below a resource threshold ... may include “deactivating” such categorization when it is determined that the available amount of video processing resources for video content analysis becomes (or appears becoming) sufficiently high for there not to be such a need”).
Summary of Citations (Hanna)
Paragraph [0019]; “In some embodiments of the method, the method may include “activating” the categorization of cameras into higher- and lower-priority cameras when it is determined that an available amount of video processing resources for video content analysis becomes (or risks becoming) sufficiently low, and e.g., goes below a resource threshold. Likewise, the method may include “deactivating” such categorization when it is determined that the available amount of video processing resources for video content analysis becomes (or appears becoming) sufficiently high for there not to be such a need”.
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 2 – 7, 11 – 15, 26, 27, 32 and 38 are rejected under 35 U.S.C 103 as being unpatentable over Hanna in view of Snyder US Patent Publication No. US-10185628-B1 (hereinafter Snyder).
Regarding claim 2, Hanna in discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Snyder discloses comprising configuring the set of load balancing factors to further include at least one of (Snyder in Column – 19, Line 19 – 22] discloses, “The priorities or rankings of the data 702 can be determined by a content item prioritization and ranking system 640 on camera system 120-1 based on inputs 642-664 as previously described”): bandwidth (Snyder in [Column – 18, Line 15 – 19] discloses, “The backup destination can be selected based on one or more factors, such as urgency (e.g., predicted amount of time until damage), network conditions (e.g., bandwidth, congestion, connectivity, latency, etc.)”); storage redundancy (Snyder in [Column – 18, Line 62 – 65] discloses, “ the camera system 120-1 can first backup the most urgent or critical data to a local destination that is further away from the risk, and then backup that same data to the cloud 102 for more permanent or secure storage” wherein backup of data to multiple destination equates to storage redundancy); camera scene activity level (Snyder in [Column – 15, Line 6 – 15] discloses, “identify items that have not been identified or detected in the content of the content items 602, 604, 606. For example, the tags 602A, 604A, 606A can indicate that no events, objects, activities, humans, etc., have been detected or identified in the content ... The lack of something in the content, such as a human or event, can also provide valuable information for prioritizing content”); and object proximity (Snyder in [Column – 21, Line 8 – 12] discloses, “location information (e.g., location of an object, proximity of a human to another human or object, proximity of an object such as a camera to an area such as an entrance, sound localization, etc.), etc.”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Snyder into the system of Hanna because it would allow the system can make more informed and more context sensitive camera to recorder assignment.
Summary of Citations (Snyder)
[Column – 19, Line 19 – 22]; “The priorities or rankings of the data 702 can be determined by a content item prioritization and ranking system 640 on camera system 120-1 based on inputs 642-664 as previously described”.
[Column – 18, Line 15 – 19]; “The backup destination can be selected based on one or more factors, such as urgency (e.g., predicted amount of time until damage), network conditions (e.g., bandwidth, congestion, connectivity, latency, etc.)”.
[Column – 18, Line 62 – 65]; “ the camera system 120-1 can first backup the most urgent or critical data to a local destination that is further away from the risk, and then backup that same data to the cloud 102 for more permanent or secure storage”.
[Column – 15, Line 6 – 15]; “identify items that have not been identified or detected in the content of the content items 602, 604, 606. For example, the tags 602A, 604A, 606A can indicate that no events, objects, activities, humans, etc., have been detected or identified in the content ... The lack of something in the content, such as a human or event, can also provide valuable information for prioritizing content”.
[Column – 21, Line 8 – 12]; “location information (e.g., location of an object, proximity of a human to another human or object, proximity of an object such as a camera to an area such as an entrance, sound localization, etc.), etc.”.
Regarding claim 3, Snyder in the combination discloses the method in accordance with claim 2, further comprising combining the set of load balancing factors into a final combined value, and using the final combined value to make camera-to-recorder assignments (Snyder in Column – 19, Line 19 – 22] discloses, “The priorities or rankings of the data 702 can be determined by a content item prioritization and ranking system 640 on camera system 120-1 based on inputs 642-664 as previously described”. Snyder in [Column – 16, Line 2 – 6] discloses about using combination of different factors, “The inputs 642-664 can include, for example, events 642, objects 644, humans 646, timestamps 648, witnesses 650, audio 652, relative locations 654, sensor inputs 656, content quality 658, activities 660, conditions 662, and remote inputs 664”. Lastly, Snyder in [Column – 19, Line 17 -19] discloses about camera to recorder assignment, “Higher priority or ranking data can be backed up before lower priority or ranking data”).
Summary of Citations (Snyder)
[Column – 19, Line 19 – 22]; “The priorities or rankings of the data 702 can be determined by a content item prioritization and ranking system 640 on camera system 120-1 based on inputs 642-664 as previously described”.
[Column – 16, Line 2 – 6]; “The inputs 642-664 can include, for example, events 642, objects 644, humans 646, timestamps 648, witnesses 650, audio 652, relative locations 654, sensor inputs 656, content quality 658, activities 660, conditions 662, and remote inputs 664”.
[Column – 19, Line 17 – 19]; “Higher priority or ranking data can be backed up before lower priority or ranking data”.
Regarding claim 4, Snyder in the combination discloses the method in accordance with claim 3, further comprising repeating at least some of the set of load balancing factors in different combinations and selecting the final combined value from the different combinations responsive to a camera scene context (Snyder in [Column – 5, Line 30 – 33] discloses about other rules-based prioritization procedures can also be implemented equates to set of load balancing factors in different combinations, “Other configurations and/or rules-based prioritization procedures can also be implemented for intelligently determining priorities of backup data”. Furthermore, Snyder in [Column – 13, Line 46 – 51] discloses camera scene context (event characteristics), “the camera system can store two video segments marked as high priority and associated with two different events. The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc”).
Summary of Citations (Snyder)
[Column – 5, Line 30 – 33]; “Other configurations and/or rules-based prioritization procedures can also be implemented for intelligently determining priorities of backup data”.
[Column – 13, Line 46 – 51]; “the camera system can store two video segments marked as high priority and associated with two different events. The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc”.
Regarding claim 5, Hanna in the combination discloses the method in accordance with claim 3, further comprising mapping the final combined value to predetermined values representative of different ones of the cameras (Hanna in [0066] discloses about final combined value, “usability-to-cost ratio R may e.g., be calculated as R=U/C” mapping (“determined by checking whether R exceeds a usability-to-cost threshold (value)”) to predefined value (“usability threshold”). And different camera having different priority levels equates to values representative of different ones of the cameras, “the particular camera can be ranked as a higher-priority camera if the historical usability outweighs the historical cost ... otherwise be ranked as a lower-priority camera”)
Summary of Citations (Hanna)
Paragraph [0066]; “the particular camera can be ranked as a higher-priority camera if the historical usability outweighs the historical cost of the camera by more than a certain amount, and otherwise be ranked as a lower-priority camera. If e.g., defining the historical usability as a parameter U, and the historical cost as a parameter C, a usability-to-cost ratio R may e.g., be calculated as R=U/C, R=U2 /C2 , or as any other suitable fraction of a function of the usability and a function of the cost. Whether the historical usability outweighs the historical cost by more than a certain amount may e.g., be determined by checking whether R exceeds a usability-to-cost threshold (value).
Regarding claim 6, Snyder in the combination discloses the method in accordance with claim 3, wherein assigning at least some of the cameras in the set of cameras to at least some of the recorders in the set of recorders comprises selectively using the final combined value or one or more of the set of load balancing factors separately (Snyder in [Column – 65, Line 65 – 67 & Column – 8, Line – 1 – 11] discloses, “The backup operations can include prioritizing data to be backed up from the storage unit 206, triggering the backup operations based on one or more factors (e.g., damage risk factors or levels), ... other factors to determine when to backup data, which data to backup, where to backup the data, how to prioritize the transmission of data, when to trigger a backup, how to deliver backup data”).
Summary of Citations (Snyder)
[Column – 65, Line 65 – 67 & Column – 8, Line – 1 – 11]; “The backup operations can include prioritizing data to be backed up from the storage unit 206, triggering the backup operations based on one or more factors (e.g., damage risk factors or levels), timing of the backup operations, selection of backup destinations, reduction of bandwidth use by backups, managing communications and backup operations, etc. The controller 212 can process the storage data, detected conditions, stored rules and settings, and other factors to determine when to backup data, which data to backup, where to back up the data, how to prioritize the transmission of data, when to trigger a backup, how to deliver backup data”.
Regarding claim 7, Hanna in the combination discloses the method in accordance with claim 3, further comprising combining the set of load balancing factors in different manners depending on which of a plurality of pre-known camera scene contexts matches a current camera scene context (Hanna in [0007] discloses, the ranking of a camera includes one of: i) obtaining an indication of a historical usability of video content captured by the camera ... ii) obtaining an indication of a historical cost in terms of processing resources needed for performing video content analysis of video content captured by the camera, and ranking the camera as a higher-priority camera if the historical cost is below a cost threshold and otherwise as a lower-priority camera, and iii) ranking the camera as a higher-priority camera if the historical usability outweighs the historical cost of the camera and otherwise as a lower-priority camera”. Furthermore, Hann in [0051] discloses about pre known camera scene, “Each camera ... capture a different part of a same scene ... one scene may be of an interior of a parking garage, another scene may be of an entrance/exit to/from the parking garage, while a third scene (if using more than two cameras) may be of a roof of the parking garage, or similar”).
Summary of Citations (Hanna)
Paragraph [0007]; “there is provided a method for prioritization of video processing resources in a video processing and/or management system ... The method includes ranking each of the two or more cameras as either a higher-priority camera or a lower-priority camera. the ranking of a camera includes one of: i) obtaining an indication of a historical usability of video content captured by the camera ... ii) obtaining an indication of a historical cost in terms of processing resources needed for performing video content analysis of video content captured by the camera, and ranking the camera as a higher-priority camera if the historical cost is below a cost threshold and otherwise as a lower-priority camera, and iii) ranking the camera as a higher-priority camera if the historical usability outweighs the historical cost of the camera and otherwise as a lower-priority camera”.
Paragraph [0051]; “Each camera 110 a and 110 b may for example be configured to capture a different part of a same scene, or the cameras 110 a and 110 b may e.g., be arranged such that they each capture different scenes. For example, one scene may be of an interior of a parking garage, another scene may be of an entrance/exit to/from the parking garage, while a third scene (if using more than two cameras) may be of a roof of the parking garage, or similar”.
Regarding claim 11, Hanna discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Snyder discloses configuring the set of load balancing factors to further include object proximity of objects in a camera scene (Snyder in [Column – 16, Line 9 – 12] discloses, “The objects 644 can also include characteristics of the associated objects, such as shape, size, position or location, motion, color, proximity to other things, etc.”), wherein the object proximity increases when objects of particular types become proximate to each other (Snyder in [Column – 11, Line 18 – 22] discloses, “Like frame 502A, frame 502B also captures the static objects 504. However, in addition, frame 502B also captures a human 518 and car 516. The human 518 and car 516 in frame 502B are moving towards each other”).
Summary of Citations (Snyder)
[Column – 16, Line 9 – 12]; “The objects 644 can also include characteristics of the associated objects, such as shape, size, position or location, motion, color, proximity to other things, etc.”.
[Column – 11, Line 18 – 22]; “Like frame 502A, frame 502B also captures the static objects 504. However, in addition, frame 502B also captures a human 518 and car 516. The human 518 and car 516 in frame 502B are moving towards each other”.
Regarding claim 12, Hanna discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Snyder discloses further comprising increasing the camera scene importance responsive to a time of day coinciding with business hours versus non-business hours (Snyder in [Column – 13, Line 48 – 51] discloses, “The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc” wherein changing the importance of scene based on time equates to scene importance responsive to a time of day coinciding with business hours versus non-business hours).
Summary of Citations (Snyder)
[Column – 13, Line 48 – 51]; “The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc”.
Regarding claim 13, Hanna discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Snyder discloses further comprising increasing the camera scene importance responsive to a time of day coinciding with school hours versus non-school hours (Snyder in [Column – 13, Line 48 – 51] discloses, “The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc”. Snyder in [Column – 5, Line 25 – 26] discloses, “Prioritization schemes for video files can vary based on the specific application or context”. Snyder discloses about prioritizing based on time and context of the video equates determining scene importance based on school hours versus non-school hours”).
Summary of Citations (Snyder)
[Column – 13, Line 48 – 51]; “The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc”.
[Column – 5, Line 25 – 26]; “Prioritization schemes for video files can vary based on the specific application or context”.
Regarding claim 14, Hanna in discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Snyder discloses further comprising increasing the camera scene importance responsive to a time of day coinciding with event hours versus non-event hours (Snyder in [Column – 13, Line 48 – 51] discloses, “The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc”).
Summary of Citations (Snyder)
[Column – 13, Line 48 – 51]; “The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc”.
Regarding claim 15, Hanna discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Snyder discloses further comprising increasing the camera scene importance relating to camera in trafficked areas versus storage areas of a business (Snyder in [Column – 15, Line 15 – 20] discloses, “a content item that does not capture any events or activity can be given a lower priority than other content items that detect events or activity based on an assumption that the lack of events or activity signify that the content item does not provide information of interest about particular events”. Snyder giving lower priority based on lack of events (storage area of business)).
Summary of Citations (Snyder)
[Column – 15, Line 15 – 20]; “a content item that does not capture any events or activity can be given a lower priority than other content items that detect events or activity based on an assumption that the lack of events or activity signify that the content item does not provide information of interest about particular events”.
Regarding claim 26, Hanna discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Snyder discloses further comprising determining the camera scene importance responsive to object detection and object importance (Snyder in [Column – 3, Line 52 – 55] discloses, “the camera can analyze tags associated with media content items and rank the media content items based on information provided by the tags, such as events, objects, conditions, attributes, etc”).
Summary of Citations (Snyder)
[Column – 3, Line 52 – 55]; “the camera can analyze tags associated with media content items and rank the media content items based on information provided by the tags, such as events, objects, conditions, attributes, etc”.
Regarding claim 27, Snyder in the combination discloses the method in accordance with claim 26, further comprising configuring the camera scene importance to increase with increasing objects detected in a camera scene (Snyder in [Column – 3, Line 52 – 55] discloses, “the camera can analyze tags associated with media content items and rank the media content items based on information provided by the tags, such as events, objects, conditions, attributes, etc”. Snyder in [Column – 15, Line 15 – 20] determining the scene importance based on lack of event or activity and in [Column – 3, Line 52 – 55] Snyder discloses about ranking media content based on identifying object implies to determining scene importance based on increasing number of objects).
Summary of Citations (Snyder)
[Column – 3, Line 52 – 55]; “the camera can analyze tags associated with media content items and rank the media content items based on information provided by the tags, such as events, objects, conditions, attributes, etc”.
[Column – 15, Line 15 – 20]; “a content item that does not capture any events or activity can be given a lower priority than other content items that detect events or activity based on an assumption that the lack of events or activity signify that the content item does not provide information of interest about particular events”.
Regarding claim 32, Snyder in the combination discloses the method in accordance with claim 26, further comprising configuring the object importance to increase with an increase in object interaction (Snyder in [Column – 12, Line 5 – 15] discloses, “the camera system may further prioritize the frames 502B-D based on additional factors or conditions associated with the activities or events captured by those frames ... capture the moments closest in time to when the human 518 and car 516 crossed each other, which would have the highest likelihood of capturing the interaction between the human 518 and car 516”).
Summary of Citations (Snyder)
[Column – 12, Line 5 – 15]; “the camera system may further prioritize the frames 502B-D based on additional factors or conditions associated with the activities or events captured by those frames ... capture the moments closest in time to when the human 518 and car 516 crossed each other, which would have the highest likelihood of capturing the interaction between the human 518 and car 516”.
Regarding claim 38, Hanna discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Snyder discloses further comprising partitioning a camera scene into respective regions, with each of the respective regions having its own set of load balancing factors for camera-to-recorder assignment (Snyder in [Column – 13, Line 46 – 51] discloses, “the camera system can store two video segments marked as high priority and associated with two different events. The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc”)).
Summary of Citations (Snyder)
[Column – 12, Line 44 – 49]; “instead of sending the entire frame 502C to a backup destination during an incident (e.g., an attack), the camera system can instead take a screenshot of frame 502C and backup the screenshot, or crop an image from frame 502C down to an area of interest, such as the area around the human 518 and car 516, and backup the cropped image”.
[Column – 13, Line 46 – 51]; “the camera system can store two video segments marked as high priority and associated with two different events. The camera system may further prioritize the two video segments to assign one segment a higher priority than the other based on one or more factors, such as event characteristics, quality, time of events, etc”.
[Column – 22, Line 43 – 50]; the camera system 120 can backup a copy or version of the recording that has been trimmed to exclude portions of less interest (e.g., portions having lower quality, portions that do not capture an event or object of interest, etc.), cropped to reduce an image or frame to an area of interest (e.g., an area capturing an object or event of interest, an area capturing a portion of a view or scene of interest, etc.)”.
Claims 28 – 31 are rejected under 35 U.S.C 103 as being unpatentable over Hanna in view of Snyder and further in view of Kansara US Patent Application Publication No. US-20230300392-A1 (hereinafter Kansara).
Regarding claim 28, Hanna in the combination discloses the method in accordance with claim 26.
Hanna and Snyder in the combination doesn’t disclose about the following limitation as further recited in the claim.
Kansara discloses further comprising configuring the camera scene importance to increase with increasing objects of a same type detected in a camera scene (Kansara in [0064] discloses about grouping same type of object, “similar algorithms may be used to classify objects within a video scene into semantic groups such as cats, dogs, humans, electronic devices, houses, trains, landscape features, etc.”. Furthermore, Kansara in [0069] discloses, “Another factor used by the importance determining module 411 may include the frequency of an object’s appearance in the video scene or in a series of video scenes”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Kansara into the system of Hanna in view of Snyder because it would allow the system to more accurately determine the scene importance by identifying the scene contain more number of same object. Since, higher number of similar object type indicate a scene with more activity or events.
Summary of Citations (Kansara)
Paragraph [0064]; “similar algorithms may be used to classify objects within a video scene into semantic groups such as cats, dogs, humans, electronic devices, houses, trains, landscape features, etc.”.
Paragraph [0069]; “Another factor used by the importance determining module 411 may include the frequency of an object’s appearance in the video scene or in a series of video scenes”.
Regarding claim 29, Hanna in the combination discloses the method in accordance with claim 26.
Hanna and Snyder in the combination doesn’t disclose about the following limitation as further recited in the claim.
Kansara discloses further comprising determining the object importance responsive to a number of objects of given types in a camera scene (Kansara in [0064] discloses about grouping same type of object, “similar algorithms may be used to classify objects within a video scene into semantic groups such as cats, dogs, humans, electronic devices, houses, trains, landscape features, etc.”. Furthermore, Kansara in [0069] discloses, “Another factor used by the importance determining module 411 may include the frequency of an object’s appearance in the video scene or in a series of video scenes”. Lastly, Kansara in [0068] disclose, “The importance determining module 411 may thus rank the identified objects on importance based on which type of object or class of objects they are...objects such as humans or animals may take precedence over inanimate objects”).
Summary of Citations (Kansara)
Paragraph [0064]; “similar algorithms may be used to classify objects within a video scene into semantic groups such as cats, dogs, humans, electronic devices, houses, trains, landscape features, etc.”.
Paragraph [0068]; “The importance determining module 411 may thus rank the identified objects on importance based on which type of object or class of objects they are. Thus, in some cases for example, objects such as humans or animals may take precedence over inanimate objects”.
Paragraph [0069]; “Another factor used by the importance determining module 411 may include the frequency of an object’s appearance in the video scene or in a series of video scenes”.
Regarding claim 30, Hanna in the combination discloses the method in accordance with claim 26.
Hanna and Snyder in the combination doesn’t disclose about the following limitation as further recited in the claim.
Kansara discloses comprising determining the object importance responsive to a number of objects of given types in a camera scene (Kansara in [0064] and [0068] discloses about identifying types of object. And [0068] discloses about frequency of the object’s appearance) and a proximity of the objects to each other (Kansara in [0065] discloses, “identify how long each object appears in the scene, identify the position of each object within the scene and its position in relation to other objects within the scene”).
Summary of Citations (Kansara)
Paragraph [0064]; “similar algorithms may be used to classify objects within a video scene into semantic groups such as cats, dogs, humans, electronic devices, houses, trains, landscape features, etc.”.
Paragraph [0065]; “identify how long each object appears in the scene, identify the position of each object within the scene and its position in relation to other objects within the scene”.
Paragraph [0068]; “The importance determining module 411 may thus rank the identified objects on importance based on which type of object or class of objects they are. Thus, in some cases for example, objects such as humans or animals may take precedence over inanimate objects”.
Paragraph [0069]; “Another factor used by the importance determining module 411 may include the frequency of an object’s appearance in the video scene or in a series of video scenes”.
Regarding claim 31, Hanna in the combination discloses the method in accordance with claim 26.
Hanna and Snyder in the combination doesn’t disclose about the following limitation as further recited in the claim.
Kansara discloses comprising determining the object importance responsive to matching one or more detected objects in a camera scene against a database of known objects (Kansara in [0060] discloses about scanning module used to identify object (modules are in memory disclosed in Fig. 1) matching to known object with semantic identity, “the scanning module 409 may incorporate neural networks such as convolutional neural networks (CNNs) or other types of neural networks to perform object identification. In such cases, the neural networks may determine the semantic identity of the object (e.g., dog, tree, automobile, beach, etc.)”) of varying degrees of assigned importance and calculating the camera scene importance responsive to the assigned importance of one or more matched ones the known objects (Kansara in [0068] disclose, “The importance determining module 411 may thus rank the identified objects on importance based on which type of object or class of objects they are...objects such as humans or animals may take precedence over inanimate objects”).
Summary of Citations (Kansara)
Paragraph [0060]; “scanning module used to identify object (modules are in memory disclosed in Fig. 1) matching to known object with semantic identity, “the scanning module 409 may incorporate neural networks such as convolutional neural networks (CNNs) or other types of neural networks to perform object identification. In such cases, the neural networks may determine the semantic identity of the object (e.g., dog, tree, automobile, beach, etc.)”
Paragraph [0068]; “The importance determining module 411 may thus rank the identified objects on importance based on which type of object or class of objects they are. Thus, in some cases for example, objects such as humans or animals may take precedence over inanimate objects”.
Claims 33 – 36 are rejected under 35 U.S.C 103 as being unpatentable over Hanna in view of Dawes Patent Application Publication No. WO-2019177585-A1 (hereinafter Dawes).
Regarding claim 33, Hanna discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Dawes discloses comprising determining the camera scene importance responsive to text importance of text detected in a camera scene (Dawes in [0053] discloses, “when the media guidance application determines captioning data includes words such as “penalty, ”“foul” around the time when the electronic message is received, the media guidance application may determine that an object indicating a “ball” shape has a high importance score, as the position and movement of the soccer ball within a scene of a “penalty” kick is important”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Dawes into the system of Hanna because it would allow the system to textual information in the scene such as warning, signs or instruction to decide how important the scene is for recording.
Summary of Citations (Dawes)
Paragraph [0053]; “when the media guidance application determines captioning data includes words such as “penalty,” “foul” around the time when the electronic message is received, the media guidance application may determine that an object indicating a “ball” shape has a high importance score, as the position and movement of the soccer ball within a scene of a “penalty” kick is important”.
Regarding claim 34, Dawes in the combination discloses the method in accordance with claim 33, further comprising determining the text importance of the text detected in the camera scene responsive to a relation of the text to a camera scene context using a table of expected text for various prestored camera scene contexts (Dawes in [0053] discloses, “when the media guidance application determines captioning data includes words such as“penalty,”“foul” around the time when the electronic message is received, the media guidance application may determine that an object indicating a “ball” shape has a high importance score, as the position and movement of the soccer ball within a scene of a“penalty” kick is important”).
Summary of Citations (Dawes)
Paragraph [0053]; “when the media guidance application determines captioning data includes words such as“penalty,”“foul” around the time when the electronic message is received, the media guidance application may determine that an object indicating a “ball” shape has a high importance score, as the position and movement of the soccer ball within a scene of a“penalty” kick is important”.
Regarding claim 35, Dawes in the combination discloses the method in accordance with claim 33, further comprising determining the camera scene importance responsive to the text importance of the text detected in the camera scene combined with a shape importance of any shapes detected in the camera scene (Dawes in [0053] discloses, “when the media guidance application determines captioning data includes words such as “penalty,” “foul” around the time when the electronic message is received, the media guidance application may determine that an object indicating a “ball” shape has a high importance score, as the position and movement of the soccer ball within a scene of a “penalty” kick is important”).
Summary of Citations (Dawes)
Paragraph [0053]; “when the media guidance application determines captioning data includes words such as “penalty,” “foul” around the time when the electronic message is received, the media guidance application may determine that an object indicating a “ball” shape has a high importance score, as the position and movement of the soccer ball within a scene of a “penalty” kick is important”.
Regarding claim 36, Dawes in the combination discloses the method in accordance with claim 33, further comprising determining the text importance of the text detected in the camera scene responsive to a relation of the text to a camera scene context using a table of expected texts for various prestored camera scene contexts (Dawes in [0053] discloses, “when the media guidance application determines captioning data includes words such as “penalty,” “foul” around the time when the electronic message is received, the media guidance application may determine that an object indicating a “ball” shape has a high importance score, as the position and movement of the soccer ball within a scene of a “penalty” kick is important”).
Summary of Citations (Dawes)
Paragraph [0053]; “when the media guidance application determines captioning data includes words such as “penalty,” “foul” around the time when the electronic message is received, the media guidance application may determine that an object indicating a “ball” shape has a high importance score, as the position and movement of the soccer ball within a scene of a “penalty” kick is important”.
Claim 39 is rejected under 35 U.S.C 103 as being unpatentable over Hanna in view of Kansara.
Regarding claim 39, Hanna discloses the method in accordance with claim 1.
Hanna doesn’t disclose about the following limitation as further recited in the claim.
Kansara discloses further comprising partitioning a camera scene responsive to objects occurring in the camera scene and their proximity to each other (Kansara in [0059] discloses, “The segmented video scenes 407 may be divided up into discrete portions”. Additionally, Kansara in [0065] discloses, “identify how long each object appears in the scene, identify the position of each object within the scene and its position in relation to other objects within the scene”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Kansara into the system of Hanna because it would allow the system to identify portions of the scene where objects are more concentrated or interacting.
Summary of Citations (Kansara)
Paragraph [0059]; “The segmented video scenes 407 may be divided up into discrete portions”.
Paragraph [0065]; “identify how long each object appears in the scene, identify the position of each object within the scene and its position in relation to other objects within the scene”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to ZAID MUHAMMAD SALEH whose telephone number is (703)756-1684.
The examiner can normally be reached M-F 8 am - 5 pm ET. 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, Vu Le can be
reached on (571)272-7332. 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.
/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
04/14/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
Prosecution Insights