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 . Claims 1-20 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Final Office action. Applicant, on 05/29/2026, amended claims. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledge. The amended claims overcome the need for a 101 Rejection reciting additional elements
The amended claims overcome the 102 rejection by adding new limitations to the independent claims. However, a new 103 rejection is facilitated by the amendments.
Response to Arguments - 35 USC § 102/103
Applicant’s arguments with respect to the 35 USC 103 rejections have been fully considered, but they are not persuasive.
Applicant contends that Ryan fails to disclose an access control on one or more network devices…
Examiner finds the argument unpersuasive. The amended claim requires further search and consideration. The amended claim is taught by the combination of prior art with Sharma teaching the amended limitation.
Applicant contends that Ryan fails to teach large language models.
Examiner finds the argument unpersuasive. Examiner relies on Delaney to explicitly teach the use of large language models.
The 103 Rejection is updated and maintained.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 9-11, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. (US 20200228759 A1) in view of Sharma et al. (US 8107396 B1).
Regarding Claim(s) 1, 9, and 17, Ryan teaches: A computer-implemented method comprising:/ An apparatus comprising: a network interface that enables network communication a memory;/ One or more non-transitory computer readable storage media encoded with instructions that, when executed by a processor, cause the processor to: (Ryan, [123]; The remote computer system can thus implement the foregoing methods and techniques, and Ryan, [127]; The systems and methods described herein can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a user computer or mobile device, wristband, smartphone, or any suitable combination thereof).
using one or more sensors positioned to capture activity in a workspace, detecting, via a person detection model that operates on output from the one or more sensors, that a person has occupied a workspace; (Ryan, [08]; As shown in FIGS. 1 and 4, a method S100 for monitoring occupancy in a work area includes: at a sensor block, transitioning from an inactive state into an active state in Block S110 in response to an output of a motion sensor, integrated into the sensor block, indicating motion in a field of view of an optical sensor integrated into the sensor block, the field of view encompassing the work area; during a first scan cycle in the active state, recording a first image through the optical sensor at a first time in Block S120; detecting a first set of humans in the first image in Block S130 and Ryan, [40]; For example, the sensor block can store and implement one or more artificial intelligence models—trained on labeled past images of the same work environment and/or work environments in other facilities—to interpret states of rooms, humans, and objects in an image recorded by the sensor block during a scan cycle).).
determining, with the one or more sensors, that the workspace is no longer occupied by the person; (Ryan, [09]; recording a second image through the optical sensor at a second time in Block S120 and detecting absence of humans in the second image in Block S130; and Ryan, [48]; For example, for the sensor block installed in a conference room, the sensor block can thus determine that the conference room is vacant, offload both confirmation of vacancy in the conference room and data extracted from images recorded during a preceding meeting in the conference room to the remote computer system (e.g., via a local gateway), and then return to the inactive state until an occupant enters the conference room at the start of a next meeting).
upon determining that the workspace is no longer occupied by the person, using the one or more sensors for detecting, via an object detection model, one or more objects brought by the person to the workspace; (Ryan, [22-23]; the system can, in response to failure to identify an object as a “human effect,” a mobile asset, or a fixed asset, classify objects as “human effects” based on the presence of these objects on a desk, in a conference room, or proximal to another object classified as a “human effect” without specifically classifying the object as one of the categories of “human effects” described above. Thus, the system can classify an unidentifiable object as a “human effect” based only on the object's location relative to other detected objects or features within an image of an agile work environment or a conference room… In another example, the system can detect or identify “proximity” of a human or a human effect in response to the human or the human effect being located within a predefined area delimited by a five-foot by eight-foot region overlapping the front of the desk. In yet another example, the system can detect or identify “proximity” of a human or human effect to a desk within a work environment in response to the human or the human effect being located within a statistically relevant area around the desk as indicated by machine learning models utilized to detect occupancy at the desk and Ryan, [37]; As shown in FIGS. 1, 3B, and 4, to execute a scan cycle in the active state, the sensor block can: record a first image through the optical sensor at a first time in Block S120; detect a first set of humans in the first image in Block S130; detect a second set of human effects (e.g., a computer, a coffee cup, a coat, a notebook, etc.) in the first image and predict a second set of humans occupying but absent the work area based on the second set of human effects in Block S132 and further see Ryan, [Fig. 1]; specifically element 132 wherein the Laptop, mug -> occupant not present).
upon detecting the one or more objects while the workspace remains unoccupied, by the person, recording a first timestamp; comparing a duration of unoccupancy of the workspace by the person since the first timestamp to a maximum unoccupancy duration; and (Ryan, [117]; these predefined rules can specify a maximum time limit of two hours for occupation of an agile desk while a human is not present. In this example, if the sensor block detects objects (e.g., human effects and/or mobile assets) that suggest occupancy at one agile desk but fails to detect a human occupying this agile desk (e.g., within a threshold distance of the agile desk) during a series of scan cycles within a two-hour period).
performing one or more network management actions or one or more workspace management actions in response to the duration of unoccupancy exceeding the maximum unoccupancy duration. (Ryan, [117]; these predefined rules can specify a maximum time limit of two hours for occupation of an agile desk while a human is not present. In this example, if the sensor block detects objects (e.g., human effects and/or mobile assets) that suggest occupancy at one agile desk but fails to detect a human occupying this agile desk (e.g., within a threshold distance of the agile desk) during a series of scan cycles within a two-hour period, the sensor block can: connect to the local gateway; wirelessly transmit desk occupancy data—including a flag for abandonment of this agile desk—to the local gateway; and then return to the low-power mode. If other agile desks in the agile work environment are at or near capacity (e.g., more than 85% of these agile desks are occupied) at this time, the remote computer system can then: serve a prompt to the administrator of the agile work environment to collect and store human effects remaining at this agile desk, such as in a lost-and-found bin; and update the agile desk manager to indicate that this agile desk is available). Examiner interprets the flag for abandonment and updating the agile desk availability as network actions, Examiner relies on the prior art below to teach the specific network management actions.
While Ryan teaches performing network actions in response to a duration exceeding a maximum unoccupancy duration, workspaces, and devices, Ryan does not appear to teach the further network actions. However, Ryan in view of the analogous art of Sharma (i.e. device management) does teach: wherein the one or more network management actions include initiating an access control action on one or more network devices or one or more user devices in the workspace to prevent the person from accessing the one or more network devices or the one or more user devices in the workspace. (Sharma, [co. 1, li. 11-17]; One goal of network security is to prevent network access by unauthorized end host machines. To meet this goal, network access devices (NADs)--such as switches, routers, and wireless access points--dynamically grant access privileges to end user hosts upon request and revoke the hosts' access rights almost immediately should they get disconnected or become inactive).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Ryan including performing network actions in response to a duration exceeding a maximum unoccupancy duration, workspaces, and devices, with the teachings of Sharma including access controls to prevent a person from accessing network devices or user devices in order to prevent unauthorized user. (Sharma, [co. 1, li, 11-20]; One goal of network security is to prevent network access by unauthorized end host machines. To meet this goal, network access devices (NADs)--such as switches, routers, and wireless access points--dynamically grant access privileges to end user hosts upon request and revoke the hosts' access rights almost immediately should they get disconnected or become inactive. This security measure helps to prevent a malicious host from using the access privileges granted to an authorized host who is no longer connected to the network).
Regarding Claim(s) 2, 10, and 18, While Ryan teaches performing network actions in response to a duration exceeding a maximum unoccupancy duration, workspaces, and devices, Ryan does not appear to teach the further network actions. However, Ryan/Sharma teaches: The computer-implemented method of claim 1, wherein the one or more network management actions further include one or more of: changing a user permission of the pe1rson to the one or more network devices or the one or more user devices, preventing the one or more network devices or the one or more user devices in the workspace from receiving one or more network packets, and altering a network resource allocation scheme for data sent to or from the workspace. (Sharma, [co. 7, li. 7-15]; At step 220, the ARP request/reply probing mechanism to track the liveliness of connected hosts should be repeated at regular intervals, as previously described. Regular monitoring may permit the NAD 104 to revoke access privileges shortly after an end host machine has been disconnected or has remained inactive. For some embodiments, a default probing interval may be set to 30 seconds. Resending an ARP request to an unresponsive host may occur on a shorter interval when repeated following step 216.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Ryan including performing network actions in response to a duration exceeding a maximum unoccupancy duration, workspaces, and devices, with the teachings of Sharma including access controls to prevent a person from accessing network devices or user devices in order to prevent unauthorized user. (Sharma, [co. 1, li, 11-20]; One goal of network security is to prevent network access by unauthorized end host machines. To meet this goal, network access devices (NADs)--such as switches, routers, and wireless access points--dynamically grant access privileges to end user hosts upon request and revoke the hosts' access rights almost immediately should they get disconnected or become inactive. This security measure helps to prevent a malicious host from using the access privileges granted to an authorized host who is no longer connected to the network).
Regarding Claim(s) 3, 11, and 19, Ryan/Sharma teaches: The computer-implemented method of claim 1, further comprising: performing one or more workspace management actions in response to the duration of unoccupancy exceeding the maximum unoccupancy duration, wherein the one or more workspace management actions include one or more of: sending to the person or to a workspace administrator an alert communication to retrieve the one or more objects, sending to the workspace administrator a communication to reassign the workspace, and changing an occupancy state indicator to indicate the workspace is available. (Ryan, [117]; these predefined rules can specify a maximum time limit of two hours for occupation of an agile desk while a human is not present. In this example, if the sensor block detects objects (e.g., human effects and/or mobile assets) that suggest occupancy at one agile desk but fails to detect a human occupying this agile desk (e.g., within a threshold distance of the agile desk) during a series of scan cycles within a two-hour period, the sensor block can: connect to the local gateway; wirelessly transmit desk occupancy data—including a flag for abandonment of this agile desk—to the local gateway; and then return to the low-power mode. If other agile desks in the agile work environment are at or near capacity (e.g., more than 85% of these agile desks are occupied) at this time, the remote computer system can then: serve a prompt to the administrator of the agile work environment to collect and store human effects remaining at this agile desk, such as in a lost-and-found bin; and update the agile desk manager to indicate that this agile desk is available).
Claim(s) 4-6 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. (US 20200228759 A1) in view of Sharma et al. (US 8107396 B1), Yoshizawa et al. (US 20230133242 A1), and Delaney et al. (US 20260030575 A1).
Regarding Claim(s) 4 and 12, Ryan teaches: The computer-implemented method of claim 1, wherein detecting, via the person detection model, that the person has occupied the workspace comprises: capturing with at least one… camera a first image of the workspace when the workspace is unoccupied; and (Ryan, [25]; The optical sensor can include: a color camera configured to record and output 2D color images; and/or a depth camera configured to record and output 2D depth images or 3D point clouds and Ryan, [73]; in response to detecting absence of humans and/or absence of human effects in an image recorded during a current scan cycle and prior to transitioning from the active state back into the inactive state, the sensor block can transmit confirmation of vacancy in the conference room to the remote computer system).
generating, via the person detection model, one or more classifications based on the first image, wherein the one or more classifications include the person. (Ryan, [90]; The sensor block can therefore locally implement a contextual model to extract contextual understanding of present and absent occupancy of a desk from both human and non-human features detected near this desk in an image recorded by the sensor block during a scan cycle). Examiner notes the system of Ryan classifies objects as human or non-human.
While Ryan teaches a camera to take images, Ryan does not explicitly teach a video camera. However Ryan in view of the analogous art of Yoshizawa does teach a video camera. (Yoshizawa, [47]; When the sensors 11_1 and 11_2 are cameras, the acquisition unit 121 may acquire the information about the work areas based on results of analyzing videos captured by the cameras. In the example of FIG. 3, the acquisition unit 121 acquires information about work areas WA1 to WA4 in which places where work assignments are performed are surrounded by frames. The work area WA3 includes work areas WA3a and WA3b).
One of ordinary skill in the art would have recognized that applying the known technique of video cameras would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of video cameras as taught by Yoshizawa to the teachings of cameras as taught by Ryan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate video cameras in similar systems. Further, using video cameras to monitor workspaces and persons would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and improved management.
Regarding Claim(s) 5 and 13, Ryan/Yoshizawa teaches: The computer-implemented method of claim 4, wherein determining that the workspace is no longer occupied by the person comprises: capturing with the at least one video camera a second image of the workspace; (Ryan, [08]; recording a first image through the optical sensor at a first time in Block S120; detecting a first set of humans in the first image in Block S130 and Ryan, [09]; in response to lack of outputs from the motion sensor indicating motion in the conference room for more than a threshold duration and detecting absence of humans in the second image, transmitting confirmation of vacancy in the conference room… during a second scan cycle succeeding the first scan cycle in the active state, recording a second image through the optical sensor at a second time in Block S120 and detecting absence of humans in the second image in Block S130). Examiner notes that Yoshizawa is relied upon to teach the use of a video camera.
comparing the first image and the second image to determine one or more deviations; and (Ryan, [82]; the sensor block executes a first scan cycle upon entering the active state—including recording a first image at a first time and detecting a first distribution of objects in a first image recorded during this first scan cycle. During a second scan cycle at the conclusion of the current active state period, the sensor block: records a second image at a second time; detects a second distribution of objects in the second image; calculates an object entropy in the work area based on a difference between the first distribution of objects and the second distribution of objects).
determining that the workspace is no longer occupied by the person based on the one or more deviations. (Ryan, [09]; during a first scan cycle in the active state, recording a first image through an optical sensor—integrated into the sensor block and defining a field of view intersecting the conference room—at a first time in Block S120 and detecting a first set of humans in the first image in Block S130; transmitting the first total occupancy to a remote computer system for update of a scheduler for the conference room in Block S150; during a second scan cycle succeeding the first scan cycle in the active state, recording a second image through the optical sensor at a second time in Block S120 and detecting absence of humans in the second image in Block S130; and, in response to lack of outputs from the motion sensor indicating motion in the conference room for more than a threshold duration and detecting absence of humans in the second image, transmitting confirmation of vacancy in the conference room to the remote computer system).
Regarding Claim(s) 6 and 14, Ryan teaches: The computer-implemented method of claim 4, wherein detecting, via the object detection model, the one or more objects brought by the person to the workspace comprises: capturing with the at least one video camera a second image of the workspace upon determining that the workspace is no longer occupied by the person; (Ryan, [08]; recording a first image through the optical sensor at a first time in Block S120; detecting a first set of humans in the first image in Block S130 and Ryan, [09]; in response to lack of outputs from the motion sensor indicating motion in the conference room for more than a threshold duration and detecting absence of humans in the second image, transmitting confirmation of vacancy in the conference room… during a second scan cycle succeeding the first scan cycle in the active state, recording a second image through the optical sensor at a second time in Block S120 and detecting absence of humans in the second image in Block S130).
comparing the first image and the second image to determine one or more deviations; and (Ryan, [82]; see claim 4 for full citation).
detecting, via the object detection model, the one or more objects brought by the person based on the one or more deviations. (Ryan, [22-23, 37, and Fig. 1]; see claim 1 for full citation).
While Ryan/Sharma teach object detection models and the use of artificial intelligence (Ryan, [13]), neither appear to teach the use of LLMs. However, Ryan/Sharma in view of the analogous art of Delaney (i.e. workspace monitoring) does teach: wherein the object detection model includes a second large language model. (Delaney, [36]; Since the work space availability system is used in conjunction with identifying an availability status of a work space, some models that may be utilized by the system are image analysis models, sensor information analysis models, similarity identification models, language models, large language models, filtering models, classification models, and/or the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Ryan/Sharma teach object detection models and the use of artificial intelligence with the teachings of Delaney including large language models in order to have a model that is improved over time through further training (Delaney, [36]; The model may be trained using one or more training datasets. Additionally, as the model is deployed, it may receive feedback to become more accurate over time. The feedback may be automatically ingested by the model as it is deployed. For example, as the model is used to perform the described method, if a user modifies predictions that were made by the model, provides feedback regarding a prediction, or otherwise provides some indication that the predictions or selections made by the model may be incorrect, the model ingests this feedback to refine the model).
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. (US 20200228759 A1) in view of Sharma et al. (US 8107396 B1), and Yoshizawa et al. (US 20230133242 A1).
Regarding Claim(s) 7 and 15, While Ryan teaches determining workspaces are unoccupied, occupancy state indicators, person detection models, and proximity pairing; Ryan does not appear to explicitly teach a person moving to another workspace. However, Ryan in view of the analogous art of Yoshizawa (i.e. workspace monitoring) does teach the entirety of the limitation: The computer-implemented method of claim 1, wherein determining that the workspace is no longer occupied by the person comprises: determining that the person has occupied another workspace based on one or more of: an occupancy state indicator, a device pairing status, device usage information, one or more outputs generated by the person detection model or a face recognition algorithm, or proximity pairing information. (Yoshizawa, [61]; the monitoring target TG performs a work in the work area WA2 (time t11 to time t12). After that, the monitoring target TG performs a work in the work area WA3 (time t13 to time t14). More specifically, the monitoring target TG first performs a work in the work area WA3a of the work area WA3 (time t13a to time t13b), and then performs a work in the work area WA3b (time t14a to time t14b).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Ryan including determining workspaces are unoccupied, occupancy state indicators, person detection models, and proximity pairing; Ryan does not appear to explicitly teach a person moving to another workspace with the teachings of Yoshizawa including determining a person has occupied another workspace in order to determine what person is performing what work and at what location (Yoshizawa, [61]; the monitoring target TG performs a work in the work area WA2 (time t11 to time t12). After that, the monitoring target TG performs a work in the work area WA3 (time t13 to time t14). More specifically, the monitoring target TG first performs a work in the work area WA3a of the work area WA3 (time t13a to time t13b), and then performs a work in the work area WA3b (time t14a to time t14b).
Claim(s) 8, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. (US 20200228759 A1) in view of Sharma et al. (US 8107396 B1).
Regarding Claim(s) 8, 16, and 20, Ryan teaches: The computer-implemented method of claim 1, wherein the maximum unoccupancy duration is determined based on an occupancy policy associated with the workspace, and (Ryan, [117]; these predefined rules can specify a maximum time limit of two hours for occupation of an agile desk while a human is not present. In this example, if the sensor block detects objects (e.g., human effects and/or mobile assets) that suggest occupancy at one agile desk but fails to detect a human occupying this agile desk (e.g., within a threshold distance of the agile desk) during a series of scan cycles within a two-hour period).
wherein the occupancy policy is based on a workspace type of the workspace determined via a workspace classification model configured to classify the workspace based on one or more of: object detection, room layout analysis, and device usage information, and (Ryan, [111-112]; this implementation described above in which the remote computer system returns a desk map or desk boundaries for agile desks in the field of view of the optical sensor to the sensor block, the remote computer system can also return a unique identifier for each of these desk locations or desk boundaries to the sensor block. In this implementation, the sensor block can thus return a unique identifier and an occupancy state for each desk in its field of view to the remote computer system to conclude a scan cycle in Block S150… reference locations of these agile desks to a map of known agile desk locations in the agile work environment to determine a unique identifier of each of these agile desks; and update the agile desk manager to reflect these current occupancy states based on these unique identifiers in Block S160).
While Ryan/Sharma teach workspace classification models and the use of artificial intelligence (Ryan, [13]), neither appear to teach the use of LLMs. However, Ryan/Sharma in view of the analogous art of Delaney (i.e. workspace monitoring) does teach: wherein the workspace classification model includes a large language model (Delaney, [36]; Since the work space availability system is used in conjunction with identifying an availability status of a work space, some models that may be utilized by the system are image analysis models, sensor information analysis models, similarity identification models, language models, large language models, filtering models, classification models, and/or the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Ryan/Sharma teach object detection models and the use of artificial intelligence with the teachings of Delaney including large language models in order to have a model that is improved over time through further training (Delaney, [36]; The model may be trained using one or more training datasets. Additionally, as the model is deployed, it may receive feedback to become more accurate over time. The feedback may be automatically ingested by the model as it is deployed. For example, as the model is used to perform the described method, if a user modifies predictions that were made by the model, provides feedback regarding a prediction, or otherwise provides some indication that the predictions or selections made by the model may be incorrect, the model ingests this feedback to refine the model).
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 JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
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/JEREMY L GUNN/Primary Examiner, Art Unit 3624
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