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 Amendments and Remarks
Applicant's arguments filed 3/30/26 are moot due to the new ground of rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Use of indicates a limitation is not explicitly disclosed by the reference alone.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schmirler (US 2018/0130260) in view of Cardona (US 2024/0338904) and Santarone (US Patent 12,400,048)
Claim 1
Examiner’s Interpretation:
Synchronized Element:
Applicant’s specification does not explicitly describe, but refers to synchronized element as “graphical elements (e.g., virtual objects, visual elements, etc.) may be referred” (Specification, ¶ 133). “Synchronization manager 107 may synchronize the graphical elements (e.g., virtual objects, visual elements, augmented objects, mixed reality objects, etc.) between display areas associated with different enterprise applications presented in the user interfaces 160, 162.” (Specification, ¶ 133). “each user can view their own multi-dimensional virtual environment including one or more unique object models.” (Specification, ¶ 56).
The claim limitation synchronized element includes virtual objects and visual elements (which can range from UI elements to virtual representations of real objects) and do not have to be the same representation (“each user can view their own multi-dimensional virtual environment including one or more unique object models”).
Examiner therefore interprets the synchronization as synchronizing the underlying data when displaying individual virtual interfaces to more than one user.
limiting propagation of a second interaction:
The scope of the amended subject matter is unclear. Applicant’s specification does not explicitly use the phrase limiting propagation. Support from the amendment is found in paragraph 57:
In some embodiments, the interactions of the users can be in a virtual workspace which are synchronized or propagated to other user virtual workspaces, while other user interactions may not be synchronized or propagated. The artificial intelligence vision system may identify one or more characteristics of a user interaction and intelligently determine whether that user interaction should be reflected in the virtual workspaces of the other users. For example, a user rotating a view or perspective of a particular ship object model may automatically trigger rotation of that ship object model in one or more other user virtual workspaces. Similarly, a user selecting a particular ship object model may automatically trigger presentation of details of that ship object model (e.g., ship specifications) in that user's virtual workspace, and also automatically trigger presentation of some or all of those details in the other user virtual workspaces. Conversely, changing some parameters of the presentation (e.g., changing metric values to imperial values) may be limited to that user's virtual workspace without synchronization with the other user workspaces. Such restrictions on synchronization may be based on access control rules, among other things
The claim term is given its plain meaning in the context of paragraph 57.
Claim Mapping:
Schmirler discloses a computer-implemented artificial intelligence vision (Schmirler, ¶ 138: “For example, one or more of the video capture devices 1414 can support capture of infrared or ultraviolet data, and to provide this information in addition to or as an alternative to video data 1412. Monitoring component 316 can analyze this information to determine whether a temperature of a machine or a mechanical component is excessive. In another example, one or more of the video capture devices 1414 may be a time-of-flight (TOF) optical scanner or sensor, which generates distance information (e.g., point cloud or depth map information) for objects and surfaces within the scanner's field of view”) system method comprising:
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generating, by one or more processing devices, a virtual spatial environments, each of the plurality of virtual spatial environments (customized virtual environments for each user using the system; Schmirler, ¶ 52: “For users that are physically located on the plant floor, the VR and AR presentation system can provide automation system data, notifications, and proactive guidance to the user via modification of the user's view of his or her immediate surroundings. Such modifications can include, for example, superimposing data values or indicators on a user's view of a machine or automation system through the user's wearable computer (or other client device capable of rendering a substantially real-time view of the machine or system). The system can customize presentation of this information based on the user's role, location, line of sight, type of wearable device, and/or other contextual information.”) comprising one or more virtual objects (Schmirler, ¶ 50: “VR/AR presentations generated by the system can comprise three-dimensional (3D) holographic views of a plant facility or a location within a plant facility (e.g., a work area, a production line, etc.). The holographic views can be delivered to a wearable visualization computer, which renders the 3D view as a function of the user's current location and/or orientation”) depicting one or more enterprise assets associated with an enterprise machine-learning application (Schmirler, ¶ 73: “the presentation system 302 can also collect selected items of plant data from one or more devices or systems on office network 108, including but not limited to the MES system 526, ERP system 528, business intelligence systems, or other such assets”),
each of the plurality of virtual spatial environments rendered to one or more of a plurality of users based on one or more access control rules for the corresponding one or more users (Schmirler, ¶ 112: “Rendering component 308 can also filter the data presented to the user based on the user's identity or role, as defined by the user profiles 522. In this regard, user profiles 522 may define the set of information for each machine or device that the user is allowed to view, and rendering component 308 can limit the data that is accessible by the user to those defined sets of data. For example, for users having an “operator” role, rendering component 308 may only allow the user to view data relevant to operation of a machine or automation system (e.g., operating modes, alarm information, running speeds, product counts, etc.). For users having an “engineering” role, rendering component 308 may further allow the user to view firmware information for control devices, industrial control programming (e.g., ladder logic or other programming), network statistics, or other such engineering data.”), and wherein the (Schmirler, ¶ 78-79: “some embodiments of device interface component 314 can manage and deploy device-specific or platform-specific agents configured to extract and analyze information from specific types of devices or data platforms (e.g., controllers, HMIs, etc.). Some device-specific agents can be configured to locate application project files stored on particular device types (e.g., configuration and/or program files on an industrial controller, screen configuration files on an HMI, etc.), and extract relevant information about the devices based on analysis of data contained in these project files. By leveraging device-specific and platform-specific agents, embodiments of device interface component 314 can discover and retrieve data conforming to many different formats and platforms. In order to unify this disparate heterogeneous data under a common platform for collective searching, device interface component 314 (or the device-specific agents) can transform the collected data to a format understandable by the rendering component 308 to yield normalized plant data 610.”)
in response to receiving an input, causing, by the artificial intelligence vision system (Schmirler, ¶ 107: “In response to input or instructions received via the wearable appliance 206 from the user (e.g., a gesture or verbal command recognizable to the wearable appliance 206), presentation system 302 can remove layers from the presentation to expose interior views or layers of the cabinet 1102 or machine, such that interior components of the cabinet or machine are displayed. This interior presentation can include graphical representations of physical components or conditions within the cabinet or machine (e.g., jams within a machine, moving parts within the machine, non-moving parts, etc.) as well as data presentations (e.g., temperatures or temperature changes of interior machine or cabinet components, imminent overcurrent indications, etc.)”),
presentation of one or more visual elements for an artificial intelligence insight associated with a particular virtual object, wherein the one or more visual elements indicate information regarding the corresponding enterprise asset of the particular virtual object (Schmirler, ¶ 101:
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“While in the first-person view, rendering component 308 can render subsets of plant data 610, calculated production or machine statistics, or alphanumeric message as overlaid information placed on or near the virtual assets (e.g., control cabinets such as control cabinet 1102, machines, control devices, motors drives, valves, tanks, etc.) to which the information relates. For example, while the user is viewing a virtual control cabinet (e.g., virtual control cabinet 1102) in the first-person view, rendering component 308 can render information windows 1104 that display relevant information about the automation system controlled by the cabinet 1102 (e.g., “Line 4” in the illustrated example), as well as the panel-mounted devices within the cabinet.”);
designating a virtual object of the one or more virtual objects as a synchronized element (e.g. rule or role based designations for collaboration and sharing of data between users. Schmirler, ¶ 55: “In response to various conditions, such as the user's determined role, location, line of sight, or other information, the system can generate and deliver augmented or virtual reality presentations to the user's wearable appliance 206. Data used to populate the presentations 204 can be obtained by the VR/AR presentation system from the relevant industrial devices and delivered as part of the VR/AR presentations 204.”); and
synchronizing a representation of the synchronized element across a plurality of display areas (Schmirler, ¶ 157: “In the case of collaborative action in which multiple users are addressing a detected issue, rendering component 308 can deliver workflow presentation data 1602 to each recipient's wearable appliance 206 to coordinate activity between the recipients… rendering component 308 will render, on each user's wearable appliance 206, the step of the workflow capable of being carried out by that user based on the user's location and line of sight. When a step is completed by one user, rendering component 308 will update the workflow presentations delivered to the other users to reflect completion of the step.”)
Schmirler references agents and business intelligence, but does not explicitly mention machine-learning application employs machine-learning to generate artificial intelligence.
Cardonoa discloses machine-learning application employs machine-learning to generate artificial intelligence (Cardona, ¶ 56: “Noted above, in some embodiments, a chatbot 150 or other computing device may be configured to implement ML, such that server 105 “learns” to analyze, organize, and/or process data without being explicitly programmed”)
Before the effective filing date of this application, it would have been obvious to one of ordinary skill in the art to use machine learning.
One of ordinary skill in the art would have motivation to use a large language model to allow for more sophisticated responses, ingestion of large data, and ability to provide relevant information without explicit programming. One of ordinary skill in the art would have had a reasonable expectation of success because Schmirler considers natural language processing queries in the context of providing business intelligence, and could be improved by incorporating more sophisticated data mining.
Schmirler as modified by Cardona does not explicitly disclose, but Santarone discloses wherein synchronizing includes propagating a first interaction of a first user in a first virtual workspace to a second virtual workspace of a second user and limiting propagation of a second interaction (Santarone, Fig. 11; Col. 60: “Action may relate to any action that a sensor, electronic device, or other apparatus connected to the database may take. For example, Action may include changing a temperature, measuring a temperature, turning off lights, activating an emergency sprinkler system, opening a door, etc. In some embodiments, prior to taking the Action, a password may be requested as part of the permission check.”)
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Before the effective filing date of this application, it would have been obvious to one of ordinary skill in the art to limit propagation of changes.
One of ordinary skill in the art would have motivation to consider permissions in order to limit changes to approved users. One of ordinary skill in the art would have had a reasonable expectation of success because Schmirler also considers a permission based approach.
Claim 2
Schmirler discloses wherein the one or more virtual objects rendered are based on one or more access control rules for a user (Schmirler, ¶ 119: “Based on the identity of the device or system that the user is requesting to view, as well as the identity or role of the user, VR/AR presentation system 302 can determine whether the user is authorized to receive a virtual or augmented reality interface for the device or system, as well as a degree of control privilege for which the user is authorized based on either the user's identity or the user's role. For example, depending on the user's identity or role, the user may be granted view-only privileges, or may alternatively be granted full or limited control privileges whereby the user is permitted to deliver control instructions 1206 to the device or system.”)
Claim 3
Schmirler discloses wherein the enterprise machine-learning application uses one or more computer-implemented models trained to generate the one or more artificial intelligence insights based on one or more enterprise workflows, preferences of the user, historical usage patterns of the user, or some combination thereof (¶ 4: “dentification of the maintenance issue, retrieve workflow data defining a workflow for correcting the maintenance issue, and generate augmented reality presentation data that renders, on the wearable appliance, an augmented reality presentation based on the workflow data, the identity data, the location data, and the orientation data… These presentations can include both data generated by the devices or systems being viewed, data generated by other devices or systems (e.g., analytic systems, maintenance history databases, etc.) that has been defined as having an association with the device or system being viewed, or other such information.”).
Claim 4
Schmirler discloses wherein the plurality of display areas associated with a plurality of interfaces concurrently presented in a display of a computing device (e.g. in the case of two users working side by side; Schmirler, ¶ 52: “For users that are physically located on the plant floor, the VR and AR presentation system can provide automation system data, notifications, and proactive guidance to the user via modification of the user's view of his or her immediate surroundings. Such modifications can include, for example, superimposing data values or indicators on a user's view of a machine or automation system through the user's wearable computer (or other client device capable of rendering a substantially real-time view of the machine or system). The system can customize presentation of this information based on the user's role, location, line of sight, type of wearable device, and/or other contextual information.”)
Claim 5
Schmirler discloses wherein synchronizing the representation of the synchronized element further comprises synchronizing, using the artificial intelligence vision system, one or more of the one or more visual elements within the virtual spatial environment presented via a plurality of computing devices associated with a plurality of users. (Schmirler, ¶ 52: “For users that are physically located on the plant floor, the VR and AR presentation system can provide automation system data, notifications, and proactive guidance to the user via modification of the user's view of his or her immediate surroundings. Such modifications can include, for example, superimposing data values or indicators on a user's view of a machine or automation system through the user's wearable computer (or other client device capable of rendering a substantially real-time view of the machine or system). The system can customize presentation of this information based on the user's role, location, line of sight, type of wearable device, and/or other contextual information.”)
Claim 6
Schmirler discloses further comprising overlaying the virtual spatial environment at least partially on a physical environment visible through a computing device used by the user (Schmirler, ¶ 108: “the rendering component 308 overlays virtual elements over the user's view of a real environment through the wearable appliance 206. In this way, as the user traverses the production area, the presentation system 302 can enhance the user's real-world view of the production area with data overlays comprising relevant subsets of collected plant data 610, as well as any relevant computed statistics generated by reporting component 310.”)
Claim 7
Schmirler discloses wherein the one or more artificial intelligence insights comprise statuses, alerts, messages, locations, parameters, values, vehicles, buildings, people, robots, machines, or some combination thereof (Schmirler, ¶ 89: “statistic icons 802 are rendered at a fixed location above the production area. These production statistic icons 802 display production statistics or key performance indicators (KPIs) for the rendered production line. The production statistics can be calculated by reporting component 310 based on selected subsets of the plant data 610. Example statistics that can be rendered via production statistic icons 802 include, but are not limited to, overall equipment effectiveness (OEE), performance or production efficiency, percentage of machine availability over time, product quality statistics, cycle times, overall downtime or runtime durations (e.g., accumulated or per work shift), or other such statistics.”)
Claim 8
Schmirler discloses further comprising:
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receiving, via the one or more input devices, a selection of at least one of the one or more visual elements (Schmirler, ¶ 114: “1204 using gesture interactions with the AR presentation or recognizable verbal commands.”); and transmitting, to one or more processing devices associated with the one or more physical objects, one or more control instructions (Schmirler, ¶ 114: “In this example embodiment, device communication component 406 of the wearable appliance 206 supports a communication stack 1210 that allows direct communication between the wearable appliance 206 and industrial controller 1204 (and other industrial devices) via an industrial network (a wired or wireless network) on which industrial controller 1204 resides. In an example implementation for use with CIP networks, the communication stack 1210 can support CIP protocol carried by EtherNet/IP. However, embodiments described herein are not limited to these protocols. Through this direct communication between the wearable appliance 206 and industrial controller 1204 (or other automation systems, machines, etc.) the user can send control information to the industrial controller 1204 using gesture interactions with the AR presentation or recognizable verbal commands.”) to cause one or more modifications to operation of the one or more physical objects (Schmirler, 121: “through the direct communication between the wearable appliance 206 and industrial devices 504 (or other automation systems, machines, etc.) the virtual control panel can receive and display status information for the devices, as well as send control information to the devices (e.g., start/stop commands, switch settings, setpoint adjustments, alarm reset commands, etc.).”).
Claim 9
Schmirler discloses wherein the physical environment comprises one or more buildings, vehicles, rooms, robots, machines, or some combination thereof (Schmirler, ¶ 56, 69, 105: “In an example scenario, as a user is viewing an automation system, machine, or industrial device through a wearable computer (or as a substantially real-time video image rendered on the user's client device)… scaled down views of a factory floor area as well as virtualized first-person views of the plant floor… the user may speak a request for a current status of a particular asset (e.g., an industrial robot, a production line, a motor, a stamping press, etc.), which is received by the user's wearable appliance 402 and relayed to the VR/AR presentation system 302)
Claim 10
Schmirler discloses wherein the input comprises a query (Schmirler, ¶ 105: “The presentation system 302 can translate the spoken request into a query for the desired information about the specified asset, retrieve the relevant subset of plant data 610, and render the requested information as an AR presentation on the user's wearable appliance 206.”), and the method further comprises:
based on the query, identifying, using one or more (¶ 105: “process natural language spoken queries requesting specified information about an industrial asset, regardless of whether the user is currently viewing the asset”); and
rendering the virtual spatial environment comprising at least a subset of the one or more virtual objects associated with the subset of the one or more artificial intelligence insights (Schmirler, ¶ 105: “The presentation system 302 can translate the spoken request into a query for the desired information about the specified asset, retrieve the relevant subset of plant data 610, and render the requested information as an AR presentation on the user's wearable appliance 206.”).
Schmirer does not explicitly disclose, but Cardona discloses a similar system including using one or more large language models, at least a subset of the one or more artificial intelligence insights (Cardona, ¶¶ 99-100: “Programmable chatbots, such the chatbot 150 and/or the ML chatbot 152 (e.g., ChatGPT), may provide tailored, conversational-like abilities relevant to recommending placement of new devices proximate a structure. The chatbot may be capable of understanding user requests/responses, providing relevant information, etc. Additionally, the chatbot may generate data from user interactions which the enterprise may use to personalize future support and/or improve the chatbot's functionality, e.g., when retraining and/or fine-tuning the chatbot. The ML chatbot may provide advanced features as compared to a non-ML chatbot, which may include and/or derive functionality from a large language model (LLM). The ML chatbot may be trained on a server, such as server 105, using large training datasets of text which may provide sophisticated capability for natural-language tasks, such as answering questions and/or holding conversations”)
Before the effective filing date of this application, it would have been obvious to one of ordinary skill in the art to use a large language model.
One of ordinary skill in the art would have motivation to use a large language model to allow for more sophisticated responses, ingestion of large data, and ability to provide relevant information. One of ordinary skill in the art would have had a reasonable expectation of success because Schmirler considers natural language processing queries in the context of providing business intelligence, and could be improved by incorporating more sophisticated language processing.
Claim 11
Schmirler discloses wherein the input comprises limb gesture, appendage gesture, eye gaze, eye movement, head movement, voice, touch, noise, or some combination thereof (Schmirler, ¶ 59, 95: “Interact with one or more external user interface devices, such as a keyboard, a mouse, a display monitor, a touchscreen, or other such interface devices…configured to recognize selection gestures performed by the wearer, and these selection gestures…Icons can also be selected using verbal commands in some embodiments.”)
Claim 12
Examiner’s Interpretation:
Applicant’s specification does not explicitly describe, but refers to synchronized element as “graphical elements (e.g., virtual objects, visual elements, etc.) may be referred” (Specification, ¶ 133). “Synchronization manager 107 may synchronize the graphical elements (e.g., virtual objects, visual elements, augmented objects, mixed reality objects, etc.) between display areas associated with different enterprise applications presented in the user interfaces 160, 162.” (Specification, ¶ 133). “each user can view their own multi-dimensional virtual environment including one or more unique object models.” (Specification, ¶ 56).
The claim limitation synchronized element includes virtual objects and visual elements (which can range from UI elements to virtual representations of real objects) and do not have to be the same representation (“each user can view their own multi-dimensional virtual environment including one or more unique object models”).
Examiner therefore interprets the synchronization as synchronizing the underlying data when displaying individual virtual interfaces to more than one user.
Scope of Machine Readable Media:
Machine readable media can encompass forms of signal transmission media that falls outside of the four statutory categories of invention. MPEP 2106; citing In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. MPEP 2106.
Claim 12 as drafted recites non-transitory, which explicitly excludes ineligible subject matter.
The broadest reasonable interpretation of the claimed medium in view of Applicant’s specification covers only eligible subject matter.
Claim Mapping:
Schmirler discloses a tangible, non-transitory computer-readable medium storing instructions that, when executed, cause one or more processing devices (Schmirler, ¶ 6: “Also, according to one or more embodiments, a non-transitory computer-readable medium is provided having stored thereon instructions”) to:
receive, at the one or more processing devices executing an artificial intelligence vision system, one or more enterprise insights, wherein the one or more enterprise insights are generated via one or more computer-implemented models (Schmirler, ¶ 78-79: “some embodiments of device interface component 314 can manage and deploy device-specific or platform-specific agents configured to extract and analyze information from specific types of devices or data platforms (e.g., controllers, HMIs, etc.). Some device-specific agents can be configured to locate application project files stored on particular device types (e.g., configuration and/or program files on an industrial controller, screen configuration files on an HMI, etc.), and extract relevant information about the devices based on analysis of data contained in these project files. By leveraging device-specific and platform-specific agents, embodiments of device interface component 314 can discover and retrieve data conforming to many different formats and platforms. In order to unify this disparate heterogeneous data under a common platform for collective searching, device interface component 314 (or the device-specific agents) can transform the collected data to a format understandable by the rendering component 308 to yield normalized plant data 610.”);
render, by the one or more processing devices, a multi-dimensional virtual environment comprising one or more virtual objects associated with the one or more enterprise insights (Schmirler, ¶ 50: “VR/AR presentations generated by the system can comprise three-dimensional (3D) holographic views of a plant facility or a location within a plant facility (e.g., a work area, a production line, etc.). The holographic views can be delivered to a wearable visualization computer, which renders the 3D view as a function of the user's current location and/or orientation”), wherein the multi-dimensional virtual environment is rendered based on a digital twin of a physical environment representing at least one of a current, historical, and projected state of one or more physical objects (Schmirler, ¶ 164: “The VR/AR representation of an industrial factory generated by VR/AR presentation system 302 can be used as the basis for a digital twin of the factory.”);
receive, at the one or more processing devices from one or more input devices, input from the user, wherein the input pertains to the one or more virtual objects (Schmirler, ¶ 107: “In response to input or instructions received via the wearable appliance 206 from the user (e.g., a gesture or verbal command recognizable to the wearable appliance 206), presentation system 302 can remove layers from the presentation to expose interior views or layers of the cabinet 1102 or machine, such that interior components of the cabinet or machine are displayed. This interior presentation can include graphical representations of physical components or conditions within the cabinet or machine (e.g., jams within a machine, moving parts within the machine, non-moving parts, etc.) as well as data presentations (e.g., temperatures or temperature changes of interior machine or cabinet components, imminent overcurrent indications, etc.)”); and
based on the input, cause, using the (Schmirler, ¶ 165: “This simulation technique can be used to test and debug control programs without putting field equipment and machinery at risk, to simulate modifications to plant or machine operations and estimation how such modifications affect certain performance or financial metrics, or to perform other analytics.”).
and determine, in response to a first user's action within the first multi-dimensional virtual spatial environment, whether to propagate the first user's action to a second virtual spatial environment corresponding to a second user (¶ 97, 112: “(Schmirler, ¶ 112: “Rendering component 308 can also filter the data presented to the user based on the user's identity or role, as defined by the user profiles 522. In this regard, user profiles 522 may define the set of information for each machine or device that the user is allowed to view, and rendering component 308 can limit the data that is accessible by the user to those defined sets of data. For example, for users having an “operator” role, rendering component 308 may only allow the user to view data relevant to operation of a machine or automation system (e.g., operating modes, alarm information, running speeds, product counts, etc.). For users having an “engineering” role, rendering component 308 may further allow the user to view firmware information for control devices, industrial control programming (e.g., ladder logic or other programming), network statistics, or other such engineering data….This allows the remote user to provide verbal instructions to selected personnel on the plant floor (e.g., guidance in connection with addressing an operational or maintenance issue), or to share visual information between the users”; Schmirler, claim 9: “viewing permission criterion for the alphanumeric message, and the rendering component is further configured to, in response to determining that a current location and a current orientation of a second wearable appliance causes the industrial asset to be within a field of view of the second wearable appliance, and that the second wearable appliance is associated with a user identity that satisfies the viewing permission criterion, render the alphanumeric message on the second wearable appliance).
designating a virtual object of the one or more virtual objects as a synchronized element (e.g. rule or role based designations for collaboration and sharing of data between users. Schmirler, ¶ 55: “In response to various conditions, such as the user's determined role, location, line of sight, or other information, the system can generate and deliver augmented or virtual reality presentations to the user's wearable appliance 206. Data used to populate the presentations 204 can be obtained by the VR/AR presentation system from the relevant industrial devices and delivered as part of the VR/AR presentations 204.”); and
synchronizing a representation of the synchronized element across a plurality of display areas (Schmirler, ¶ 157: “In the case of collaborative action in which multiple users are addressing a detected issue, rendering component 308 can deliver workflow presentation data 1602 to each recipient's wearable appliance 206 to coordinate activity between the recipients… rendering component 308 will render, on each user's wearable appliance 206, the step of the workflow capable of being carried out by that user based on the user's location and line of sight. When a step is completed by one user, rendering component 308 will update the workflow presentations delivered to the other users to reflect completion of the step.”)
Schmirler does not explicitly reference artificial intelligence.
Cardona discloses artificial intelligence (Cardona, ¶ 56: “Noted above, in some embodiments, a chatbot 150 or other computing device may be configured to implement ML, such that server 105 “learns” to analyze, organize, and/or process data without being explicitly programmed”)
Before the effective filing date of this application, it would have been obvious to one of ordinary skill in the art to use machine learning.
One of ordinary skill in the art would have motivation to use a large language model to allow for more sophisticated responses, ingestion of large data, and ability to provide relevant information without explicit programming. One of ordinary skill in the art would have had a reasonable expectation of success because Schmirler considers natural language processing queries in the context of providing business intelligence, and could be improved by incorporating more sophisticated data mining.
Schmirler as modified by Cardona does not explicitly disclose, but Santarone discloses wherein synchronizing includes propagating a first interaction of a first user in a first virtual workspace to a second virtual workspace of a second user and limiting propagation of a second interaction (Santarone, Fig. 11; Col. 60: “Action may relate to any action that a sensor, electronic device, or other apparatus connected to the database may take. For example, Action may include changing a temperature, measuring a temperature, turning off lights, activating an emergency sprinkler system, opening a door, etc. In some embodiments, prior to taking the Action, a password may be requested as part of the permission check.”)
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Before the effective filing date of this application, it would have been obvious to one of ordinary skill in the art to limit propagation of changes.
One of ordinary skill in the art would have motivation to consider permissions in order to limit changes to approved users. One of ordinary skill in the art would have had a reasonable expectation of success because Schmirler also considers a permission based approach.
Claim 13
The same teachings and rationales in claim 2 are applicable to claim 13.
Claim 14
The same teachings and rationales in claim 3 are applicable to claim 14.
Claim 15
The same teachings and rationales in claim 4 are applicable to claim 15.
Claim 16
The same teachings and rationales in claim 5 are applicable to claim 16.
Claim 17
The same teachings and rationales in claim 6 are applicable to claim 17.
Claim 18
The same teachings and rationales in claim 7 are applicable to claim 18.
Claim 19
The same teachings and rationales in claim 8 are applicable to claim 19.
Claim 20
The same teachings and rationales in claim 12 are applicable to claim 20, with Schmirler disclosing a system comprising:one or more memory devices storing instructions; and one or more processing devices communicatively coupled to the one or more memory devices, where the one or more processing devices execute the instructions (Fig. 18; 23)
Additional Prior Art
Additional prior art relevant to Applicant’s disclosure but not relied upon:
Pelski (US 2024/0283675) considers updates to a digital twin (see ¶¶ 239-241)
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN M GRAY whose telephone number is (571)272-4582. The examiner can normally be reached on Monday through Friday, 9:00am-5:30pm (EST).
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/RYAN M GRAY/Primary Examiner, Art Unit 2611
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