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 .
Response to Arguments
Applicant's arguments filed 04/20/26 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Nicastri et al US 20230038059.
Regarding claim 1, Applicant states that prior art of record fails to teach first plurality of segments that were selected by users (Applicants Remarks page 8). Examiner agrees with Applicant. Nicastri et al teaches An upper area 90 of display screen 56, shown in FIG. 4, includes a picture frame 92 and several tabs 94 (e.g., tabs 94a-h) (paragraph 0045). video surveillance personnel 42 (user) entering a chosen export time window (selecting a plurality of video segments) and processor 30 (computer device) receiving it. In response to that, processor 30 limits its range of batch exporting video clips 40 (plurality of video segments) to only those video clips falling within the specified export time window (paragraph 0047). This reads on a user selecting a plurality of video segments. Applicant states that prior art fails to teach of determining a quantity of occurrences of each of the one or more characteristics (Applicants Remarks pages 8-9). Examiner agrees with Applicant. Nicastri et al teaches block 66 represents processor 30 batch searching multiple video streams 32 for video frames 34 showing an image of an individual with characteristics matching those of designated individual 36 (characteristics). This may include submitting a group of search jobs for processing on processor 30 and whose results (quantity) are obtained at a later time. A block 68 represents processor 30 extracting video clips 40 (quantity of occurrences) that include video frames 34 showing an image of an individual with characteristics matching those of designated individual 36 (quantity of characteristics) (paragraph 0041) Note: the results of searching for characteristics 38 reads on determining quantity of characteristics of each of the characteristics, the characteristics being read as individual 36, which is a characteristic of the video clips 40
Applicant states that Henry fails to teach of sorting step (Applicants Remarks page 9-10). Examiner disagrees with Applicant. Henry teaches the video segment selection module 106 can be configured to facilitate selecting a subset of video segments out of the set of video segments. For instance, the video segment selection module 106 can be configured to determine and select those video segments that are more likely to be interesting, enticing, appealing, and/or otherwise relevant to an audience (sorting). These video segments can be included in the subset of video segments selected by the video segment selection module 106 (paragraph 0036). the subset of video segments (second plurality of video segments) during time periods (e.g., midnight to 6:00 AM) where many people are not expected to pass by the security cameras, the storage algorithm may compare adjacent frames (or frames occurring within a particular time interval) and discard (sorting) those frames that differ from the first frame by less than a predetermined amount (paragraph 0040) the sorted list shown in Fig. 4B. In other words, the subset videos are taken during certain time frames and are sorted by time (paragraph 0040) that would read on quantity of occurrences because certain times of the day may be better than others to get a video.
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.
Claim(s) 1-3, 6-9 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nicastri et al US 20230038059 in view of Henry US 20170357854.
Regarding claim 1, Nicastri et al teaches a method comprising:
receiving, by a computing device, a first plurality of video segments that were generated by a plurality of video cameras associated with a premises (a computer assisted method 62 for gathering video clips 40 (video segments) each containing an individual with characteristics matching one or more characteristics 38 of a designated individual 36 in facility 12, wherein video clips 40 are extracted from a plurality of video streams 32, each video stream 32 captured by a corresponding one of a plurality of video cameras 14 of video surveillance system 10 of facility 12 (premises). (paragraph 0041) and that were selected by one or more users (video surveillance personnel 42 (user) entering a chosen export time window (selecting a plurality of video segments) and processor 30 (computer device) receiving it. In response to that, processor 30 limits its range of batch exporting video clips 40 (plurality of video segments) to only those video clips falling within the specified export time window (paragraph 0047);
determining, characteristics associated with the first plurality of video segments (video clips 40 (video segments) each containing an individual with characteristics matching one or more characteristics 38 of a designated individual 36 in facility 12 (paragraph 0041), wherein the characteristics comprise:
one or more characteristics associated with selection of the first plurality of video segments, and one or more characteristics associated with content of the plurality of video segments (gathering (selection) video clips 40 (video segments) each containing an individual with characteristics matching one or more characteristics 38 of a designated individual 36 in facility 12 (one or more characteristics associated with selection of the first plurality of video segments), wherein video clips 40 are extracted from a plurality of video streams 32, each video stream 32 captured by a corresponding one of a plurality of video cameras 14 of video surveillance system 10 of facility 12 (content) (one or more characteristics associated with content of the plurality of video segments). (paragraph 0041);
determining a quantity of occurrences of each of the characteristics (processor 30 receiving at least one characteristic 38 of designated individual 36. A block 66 represents processor 30 batch searching multiple video streams 32 for video frames 34 showing an image of an individual with characteristics matching those of designated individual 36 (characteristics). This may include submitting a group of search jobs for processing on processor 30 and whose results (quantity) are obtained at a later time. A block 68 represents processor 30 extracting video clips 40 (quantity of occurrences) that include video frames 34 showing an image of an individual with characteristics matching those of designated individual 36 (quantity of characteristics) (paragraph 0041) Note: the results of searching for characteristics 38 reads on determining quantity of characteristics of each of the characteristics, the characteristics being read as individual 36, which is a characteristic of the video clips 40;
Nicastri et al fails to teach sorting, based on the determined quantities, a second plurality of video segments generated by one or more video cameras of the plurality of video cameras;
causing presentation of the sorted second plurality of video segments;
Henry teaches sorting, based on the determined quantities, a second plurality of video segments generated by one or more video cameras of the plurality of video cameras (the video segment selection module 106 can be configured to facilitate selecting a subset of video segments out of the set of video segments. For instance, the video segment selection module 106 can be configured to determine and select those video segments that are more likely to be interesting, enticing, appealing, and/or otherwise relevant to an audience. These video segments can be included in the subset of video segments selected by the video segment selection module 106 (paragraph 0036). the subset of video segments (second plurality of video segments) during time periods (e.g., midnight to 6:00 AM) where many people are not expected to pass by the security cameras, the storage algorithm may compare adjacent frames (or frames occurring within a particular time interval) and discard those frames that differ from the first frame by less than a predetermined amount (paragraph 0040) the sorted list shown in Fig. 4B; and
causing presentation of the sorted second plurality of video segments (the user can scroll or browse to a post or content item corresponding to the video, such that the post or content item is visible or viewable on the display element (e.g., display screen, touch display, etc.) of the user's computing device. (paragraph 0040).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Agrawal et al in view of Nicastri et al to include: sorting, based on the determined quantities, a second plurality of video segments generated by one or more video cameras of the plurality of video cameras; causing presentation of the sorted second plurality of video segments.
The reason of doing so would be to organize video segments based on user criteria.
Regarding claim 2, Nicastri et al teaches wherein the characteristics comprise one or more of: camera ID; part of day; categories of motion entities in the content; derivative categories of motion entities in the content; or audio categories (it should be recognized that the present disclosure can be applied to any suitable location, and may be used to search for an individual with any other suitable characteristic(s) such as height, weight, clothing type, clothing color, color and/or type of carried or towed bag, walking gate, race, age, gender, activity (sitting, walking, running, loitering) and/or any other suitable characteristic(s) (paragraph 0032) sequence of operation of system 10, as designated individual 36 enters and travels through facility 12. The location of play head 84 along timeline 80 identifies the progression of the individual's movements (paragraph 0048).
Regarding claim 3, Nicastri et al in view of Henry teaches wherein the causing presentation comprises causing arrangement of the second plurality of video segments based on the sorting (Henry: the object recognition module 204 can determine or recognize whether interesting entities, such as celebrities, are included in or depicted by the still frames for a given video segment. If the given video segments depicts (a face of) an entity that has at least a threshold likelihood of being interesting to a particular audience and/or in general, then the video segment selection module 202 can include the given video segment in the subset (paragraph 0044).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Agrawal et al in view of Nicastri et al to include: wherein the causing presentation comprises causing arrangement of the second plurality of video segments based on the sorting.
The reason of doing so would be to organize video segments based on user criteria.
Regarding claim 6, Nicastri et al in view of Henry teach wherein the sorting comprises: sorting the second plurality of video segments based on corresponding lengths of time, and wherein the method further comprises determining, for each characteristic of the one or more characteristics associated with the first plurality of video segments, a length of time of each video segment associated with the characteristic (Henry: the video playback module 110 can provide playback for each video segment in the subset in an order specified by the list. In this example, during playback, the video playback module 110 can cause the video segments in the subset to appear to be stitched or combined together based on the playback sequence (paragraph 0038). the subset of video segments, during time periods (e.g., midnight to 6:00 AM) where many people are not expected to pass by the security cameras, the storage algorithm may compare adjacent frames (or frames occurring within a particular time interval) and discard those frames that differ from the first frame by less than a predetermined amount (paragraph 0040)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Nicastri et al to include: wherein the sorting comprises: sorting the second plurality of video segments based on corresponding lengths of time, and wherein the method further comprises determining, for each characteristic of the one or more characteristics associated with the first plurality of video segments, a length of time of each video segment associated with the characteristic.
The reason of doing so would be to organize video segments based on user criteria.
Regarding claim 7, Nicastri et al in view of Henry teaches comprising: removing, from the second plurality of video segments and prior to the causing presentation, duplicate, previously viewed, and/or low-relevancy video segments (Henry: the subset of video segments (second plurality of video segments) during time periods (e.g., midnight to 6:00 AM) where many people are not expected to pass by the security cameras, the storage algorithm may compare adjacent frames (or frames occurring within a particular time interval) and discard those frames that differ from the first frame by less than a predetermined amount (paragraph 0040)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Nicastri et al to include: comprising: removing, from the second plurality of video segments and prior to the causing presentation, duplicate, previously viewed, and/or low-relevancy video segments.
The reason of doing so would be to organize video segments based on user criteria.
Regarding claim 8, Nicastri et al in view of Henry teaches wherein the second plurality of video segments comprises a video segment inventory, wherein the method further comprises sorting the video segment inventory, based on the first plurality of video segments (Henry: the subset of video segments (second plurality of video segments) during time periods (e.g., midnight to 6:00 AM) where many people are not expected to pass by the security cameras, the storage algorithm may compare adjacent frames (or frames occurring within a particular time interval) and the storage algorithm may compare adjacent frames (or frames occurring within a particular time interval) and discard those frames that differ from the first frame by less than a predetermined amount (paragraph 0040)
Regarding claim 9, Nicastri et al in view of Henry teaches wherein the plurality of video cameras comprise video cameras of a security system for the premises (Nicastri et al: a plurality of video cameras 14. Video cameras 14 are often used for monitoring and recording suspicious or otherwise noteworthy activity (e.g., crimes, accidents, disruptions, lost children, the movement or whereabouts of certain individuals, etc.) (paragraph 0033).
Regarding claim 17, Nicastri et al teaches A method comprising:
determining, by a computing device and based on user interactions with video segments of a plurality of video segments generated at a plurality of times by a plurality of cameras associated with a premises, relevancies of the plurality of video segments (a computer assisted method 62 for gathering video clips 40 (video segments) each containing an individual with characteristics matching one or more characteristics 38 of a designated individual 36 in facility 12, wherein video clips 40 are extracted from a plurality of video streams 32, each video stream 32 captured by a corresponding one of a plurality of video cameras 14 of video surveillance system 10 of facility 12 (premises). (paragraph 0041). (video surveillance personnel 42 (user) entering a chosen export time window (selecting a plurality of video segments) and processor 30 (computer device) receiving it. In response to that, processor 30 limits its range of batch exporting video clips 40 (plurality of video segments) to only those video clips falling within the specified export time window (paragraph 0047);
sorting, based on the determined one or more cameras and based on the determined one or more times, subsequent video segments generated by the plurality of cameras (a user of the security system 10 may select which cameras to use (paragraph 0042). TIME tab 94e schematically illustrates video surveillance personnel 42 entering a chosen search time window (sorting) and processor 30 receiving it. In response to that, processor 30 limits its search to videos recorded only during the specified search time window (paragraph 0047); and
Nicastri et al fails to teach determining, based on the relevancies, one or more cameras, of the plurality of cameras, and one or more times, of the plurality of times;
generating, based on the sorted subsequent video segments, a presentation of at least a portion of the subsequent video segments;
Henry teaches determining, based on the relevancies, one or more cameras, of the plurality of cameras, and one or more times, of the plurality of times (the subset of video segments (second plurality of video segments) during time periods (e.g., midnight to 6:00 AM) where many people are not expected to pass by the security cameras, the storage algorithm may compare adjacent frames (or frames occurring within a particular time interval) and discard those frames that differ from the first frame by less than a predetermined amount (paragraph 0040);
generating, based on the sorted subsequent video segments, a presentation of at least a portion of the subsequent video segments (the user can scroll or browse to a post or content item corresponding to the video, such that the post or content item is visible or viewable on the display element (e.g., display screen, touch display, etc.) of the user's computing device. (paragraph 0040).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Nicastri et al to include: determining, based on the relevancies, one or more cameras, of the plurality of cameras, and one or more times, of the plurality of times; generating, based on the sorted subsequent video segments, a presentation of at least a portion of the subsequent video segments;
The reason of doing so would be to organize video segments based on user criteria.
Regarding claim 18, Nicastri et al in view of Henry teaches comprising: determining, based on the relevancies, categories of motion entities in content of the video segments (Henry: the subset of video segments (second plurality of video segments) during time periods (e.g., midnight to 6:00 AM) where many people are not expected to pass by (categories of motion) the security cameras (paragraph 0040); and sorting, based on the determined categories of motion entities, the subsequent video segments (Henry: the storage algorithm may compare adjacent frames (or frames occurring within a particular time interval) and discard those frames that differ from the first frame by less than a predetermined amount (paragraph 0040).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Agrawal et al to include: comprising: determining, based on the relevancies, categories of motion entities in content of the video segments; and sorting, based on the determined categories of motion entities, the subsequent video segments.
The reason of doing so would be to organize video segments based on user criteria.
Claim(s) 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nicastri et al US 20230038059 in view of Henry US 20170357854 further in view of Agrawal et al US 20190342556
Regarding claim 19, Nicastri et al in view of Henry teaches all of the limitations of claim 17
Nicastri et al in view of Henry fails to teach receiving user feedback for the presentation; and re-sorting, based on the user feedback, the subsequent video segments
Agrawal et al teaches receiving user feedback for the presentation; and re-sorting, based on the user feedback, the subsequent video segments (then a user requests (feedback) to view a video segment the user sends a request. the computing device may receive a request for a video segment captured by the particular camera of the plurality of cameras. The request specifies the particular camera, a date, a start time, and a length of the video segment (or an end time). The computing device may identify, based on the date, the start time, the length, and the specified camera, stored data that is associated with the video segment and stored in a storage device. The computing device may retrieve the stored data from the storage device and determine that the stored data includes a subset of the video frames that were sent from the particular camera and excludes a remainder of the video frames (paragraph 0020-0021).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Nicastri et al in view of Henry to include: receiving user feedback for the presentation; and re-sorting, based on the user feedback, the subsequent video segments.
The reason of doing so would be to organize video segments based on user criteria.
Regarding claim 20, Nicastri et al in view of Henry teaches all of the limitations of claim 17
Nicastri et al in view of Henry fails to teach wherein the presentation is a single presentation for all the at least a portion of the subsequent video segments
Agrawal et al teaches wherein the presentation is a single presentation for all the at least a portion of the subsequent video segments (The computing device provides the reconstructed video segment (e.g., to a user that requested to view the video segment) (paragraph 0023).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Nicastri et al in view of Henry to include: wherein the presentation is a single presentation for all the at least a portion of the subsequent video segments.
The reason of doing so would be to display video segments based on user criteria.
Claim(s) 4, 10, 12, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nicastri et al US 20230038059 in view of Henry US 20170357854 further in view of Marlow et al US 20180359530.
Regarding claim 4, Nicastri et al in view of Henry teaches all of the limitations of claim 1,
Nicastri et al in view of Henry fails to teach wherein the sorting comprises: sorting the second plurality of video segments based on corresponding relevancy scores, and wherein the method further comprises: determining weights for the characteristics; and calculating, based on the weights and a scoring model, the corresponding relevancy scores.
Marlow et al teaches wherein the sorting comprises: sorting the second plurality of video segments based on corresponding relevancy scores, and wherein the method further comprises: determining weights for the characteristics; and calculating, based on the weights and a scoring model, the corresponding relevancy scores (Recorded video is analyzed and links are generated to access video segments based on the content's relevancy to a question in view of a confidence score. In an example implementation, the recorded video analysis includes locating questions in a message history from the recorded video and analyzing the video feed and/or audio feed to identify segments that include relevant responses from the presenter. The identified segments undergo further processing to develop an ordered list (e.g., ranking) based on a confidence score. The confidence score is weight according to context factors (paragraph 0030).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Nicastri et al in view of Henry to include: wherein the sorting comprises: sorting the second plurality of video segments based on corresponding relevancy scores, and wherein the method further comprises: determining weights for the characteristics; and calculating, based on the weights and a scoring model, the corresponding relevancy scores.
The reason of doing so would be to organize video segments based on importance to the user
Claim(s) 10, 12, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nicastri et al US 20230038059 in view of Henry US 20170357854 further in view of Marlow et al US 20180359530.
Regarding claim 10, Nicastri et al teaches A method comprising:
creating, by a computing device and for a plurality of video cameras associated with a premises, a video segment inventory, wherein the video segment inventory comprises video segments generated by the plurality of video cameras within one or more designated time frames (a computer assisted method 62 for gathering video clips 40 (video segments) each containing an individual with characteristics matching one or more characteristics 38 of a designated individual 36 in facility 12, wherein video clips 40 are extracted from a plurality of video streams 32, each video stream 32 captured by a corresponding one of a plurality of video cameras 14 of video surveillance system 10 of facility 12 (premises). (paragraph 0041) and that were selected by one or more users (video surveillance personnel 42 (user) entering a chosen export time window (selecting a plurality of video segments) and processor 30 (computer device) receiving it. In response to that, processor 30 limits its range of batch exporting video clips 40 (plurality of video segments) to only those video clips falling within the specified export time window (paragraph 0047);
Nicastri et al fails to teach determining one or more characteristics associated with each of the video segments, wherein the one or more characteristics are determined based at least on content of the video segments and how the video segments are selected by a user
Henry teaches determining one or more characteristics associated with each of the video segments, wherein the one or more characteristics are determined based at least on content of the video segments and how the video segments are selected by a user (the subset of video segments (second plurality of video segments) during time periods (e.g., midnight to 6:00 AM) where many people are not expected to pass by the security cameras, the storage algorithm may compare adjacent frames (or frames occurring within a particular time interval) and discard those frames that differ from the first frame by less than a predetermined amount (paragraph 0040). the user can scroll or browse to a post or content item corresponding to the video, such that the post or content item is visible or viewable on the display element (e.g., display screen, touch display, etc.) of the user's computing device. (paragraph 0040);
Therefore, it would have been obvious to one of ordinary skill in the art to modify Nicastri et al to include: determining one or more characteristics associated with each of the video segments, wherein the one or more characteristics are determined based at least on content of the video segments and how the video segments are selected by a user
The reason of doing so would be to organize video segments based on importance to the user
Nicastri et al in view of Henry fails to teach assigning, based on the determined characteristics, a relevancy score to each video segment; and sorting the video segment inventory for presentation based on assigned relevancy scores;
Marlow teaches assigning, based on the determined characteristics, a relevancy score to each video segment; and sorting the video segment inventory for presentation based on assigned relevancy scores (Recorded video is analyzed and links are generated to access video segments based on the content's relevancy to a question in view of a confidence score. In an example implementation, the recorded video analysis includes locating questions in a message history from the recorded video and analyzing the video feed and/or audio feed to identify segments that include relevant responses from the presenter. The identified segments undergo further processing to develop an ordered list (e.g., ranking) based on a confidence score. The confidence score is weight according to context factors (paragraph 0030).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Nicastri et al in view of Henry to include: assigning, based on the determined characteristics, a relevancy score to each video segment; and sorting the video segment inventory for presentation based on assigned relevancy scores
The reason of doing so would be to organize video segments based on importance to the user
Regarding claim 12, Nicastri et al in view of Henry further in view of Marlow et al teaches wherein the characteristics comprise one or more of: camera ID; part of day; categories of motion entities in the content; derivative categories of motion entities in the content; or audio categories (Nicastri et al: it should be recognized that the present disclosure can be applied to any suitable location, and may be used to search for an individual with any other suitable characteristic(s) such as height, weight, clothing type, clothing color, color and/or type of carried or towed bag, walking gate, race, age, gender, activity (sitting, walking, running, loitering) and/or any other suitable characteristic(s) (paragraph 0032) sequence of operation of system 10, as designated individual 36 enters and travels through facility 12. The location of play head 84 along timeline 80 identifies the progression of the individual's movements (paragraph 0048).
Regarding claim 15, Nicastri et al in view of Henry further in view of Marlow et al teaches causing presentation of at least a portion of the sorted video segment inventory, via a user interface, wherein the causing presentation comprises:
arranging, based on the relevancy scores assigned to a plurality of video segments of the at least the portion of the sorted video segment inventory, the plurality of video segments (Marlow et al: Recorded video is analyzed and links are generated to access video segments based on the content's relevancy to a question in view of a confidence score (paragraph 0030); and
adjusting, based on user input changing the arrangement, one or more of the relevancy scores assigned to the plurality of video segments (Marlow et al: The identified segments undergo further processing to develop an ordered list (e.g., ranking) based on a confidence score. The confidence score is weight according to context factors (paragraph 0030)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Nicastri et al in view of Henry to include: causing presentation of at least a portion of the sorted video segment inventory, via a user interface, wherein the causing presentation comprises:
arranging, based on the relevancy scores assigned to a plurality of video segments of the at least the portion of the sorted video segment inventory, the plurality of video segments; adjusting, based on user input changing the arrangement, one or more of the relevancy scores assigned to the plurality of video segments
The reason of doing so would be to organize video segments based on importance to the user
Regarding claim 16, Nicastri et al in view of Henry further in view of Marlow et al teaches wherein the plurality of video cameras comprise video cameras of a security system for the premises (Nicastri et al: a plurality of video cameras 14. Video cameras 14 are often used for monitoring and recording suspicious or otherwise noteworthy activity (e.g., crimes, accidents, disruptions, lost children, the movement or whereabouts of certain individuals, etc.) (paragraph 0033).
Allowable Subject Matter
Claims 5, 11, 13 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication should be directed to Michael Burleson whose telephone number is (571) 272-7460 and fax number is (571) 273-7460. The examiner can normally be reached Monday thru Friday from 8:00 a.m. – 4:30p.m. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached at (571) 270- 3438.
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Michael Burleson
Patent Examiner
Art Unit 2683
Michael Burleson
June 27, 2026
/MICHAEL BURLESON/
/AKWASI M SARPONG/SPE, Art Unit 2681 7/14/2026
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