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 .
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) 1-10 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gamer et al. (EP3309632A1 -hereinafter Gamer) in view of Acharya et al. (US20240273398A1 -hereinafter Acharya).
Regarding Claim 1, Gamer teaches a method, comprising:
detecting… an initialization condition of the device responsive to establishment of a communication link between the device and a building management system; (see [0048]; Gamer: “A new BACnet light actuator is added into the network: as it includes a "device package" (i.e., a package containing device information, location information, as well as a mapping to function block and semantic parameters) and thus, provides all required information based on the Unified Information Model (defining, for instance, the function block types of the building automation system) to the system and requesting other devices.”)
operating a scan agent… responsive to detecting the initialization condition, to retrieve a building data structure from the building management system via the communication link; (see [0008]; Gamer: “network scanning mechanisms can be used to obtain information about the connectivity and neighboring devices;”)
extracting… from the building data structure, a context characteristic of the device relative to one or more remote devices coupled with the building management system; (see [0041]; Gamer: “. Device packages could, for instance, be implemented as simple mappings in a structured configuration file (e.g., XML, JSON) or as programmatic mappings contained in the code (e.g., C# or Java Annotations).”)
applying… the context characteristic of the device and an identifier of the device …to determine configuration data for installation on the device corresponding to one or more functions for the device to perform, (see [0008]; Gamer: “For information or data about the context of their installation new upcoming mechanisms are stepwise enabling the automatic determination of the device context. Bluetooth beaconing techniques, for instance, can be used to determine the installation of a beacon equipped device on room level; network scanning mechanisms can be used to obtain information about the connectivity and neighboring devices; technologies like RFID can be used to identify a device, a location or the current user; digital building plans based on standards such as Building Information Modeling (BIM) offer machine-readable, technology- and vendor-agnostic information on device locations.”)
providing, by the one or more processors to an installation data repository, a request for the configuration data; (see [0019]; Gamer; “. Alternatively - i.e., if the technology is not able to provide all required information automatically - additional tools might be used to manually assign the required information (e.g., its location) to the installed device, either by adding it to the device’s configuration data that can be requested by the building automation system or by adding it to some external information source (i.e., the UIM as a logical model).”)
receiving, by the one or more processors, the configuration data; and (see [0030]; Gamer: “Based on the Unified Information Model, configuration details can now be automatically determined, (e.g., the device packages can provide technology or device specific rules or business logic that is used to configure the device based on the selected semantics or function set of the planned system setup)”)
configuring, by the one or more processors, the device based on the configuration data. (see [0031]; Gamer: “The resulting configuration details are finally downloaded to the devices. The finally downloaded to the devices might include a mapping of these configuration details that are based on the semantic abstraction towards technology or device specific parameters.”)
However, Gamer does not explicitly teach:
…by one or more processors of a device,
applying, as input to a machine learning model, by the one or more processors, …to cause the machine learning model to determine…, the machine learning model configured according to training data comprising examples of configuration data associated with at least one of examples of context characteristics or examples of device identifiers;
Acharya from the same or similar field of endeavor teaches:
…by one or more processors of a device, (see [0060]; Acharya: “Examples of an apparatus 200 may include, but is not limited to, one or more components of one or more operational systems 110, a machine learning platform system 140, an enterprise management system 120, data repositories 150, and/or user devices 160. The apparatus 200 includes processor 20.”)
applying, as input to a machine learning model, by the one or more processors (see [0050]; Acharya: “The machine learning platform system 140 may also be configured to receive operational system context data associated with the one or more operational systems 110.”), …to cause the machine learning model to determine… (see [0050]; Acharya: “the machine learning platform system 140 may be configured to receive and/or generate model configuration data.”), the machine learning model configured according to training data comprising examples of configuration data associated with at least one of examples of context characteristics or examples of device identifiers; (see [0051]; Acharya: “The machine learning platform system 140 may be configured to generate model association metadata based at least in part on the model configuration data and the operational system context data.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Gamer to include Acharya’s features of one or more processors of a device and applying, as input to a machine learning model, by the one or more processors, to cause the machine learning model to determine, the machine learning model configured according to training data comprising examples of configuration data associated with at least one of examples of context characteristics or examples of device identifiers. Doing so would optimize the assets' and systems' performance and/or fulfill other objectives of the enterprise. (Acharya, [0034])
Regarding Claim 2, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Gamer further teaches wherein extracting, by the one or more processors, the context characteristic of the device comprises processing the building data structure to identify at least one of a location of the device, a location of the one or more remote devices, or a device function for the device to perform with respect to the one or more remote devices. (see [0007]; Gamer: “Regarding aspect (2) above, the context of the installation of a device is often determined during the electrical installation of the device. Therefore such information is commonly not available in a machine-interpretable format (e.g., noted down on paper or some kind of excel sheet) or can only partially be extracted from multiple different tools such as planning (e.g., Autodesk REVIT based on Building Information Modeling (BIM)) or engineering tools (e.g. Engineering Tool Software providing a KNX building structure).”)
Regarding Claim 3, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Gamer further teaches wherein the identifier of the device comprises a hardware configuration of the device. (see [0008]; Gamer: “technologies like RFID can be used to identify a device”)
Regarding Claim 4, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Acharya further teaches wherein the building data structure comprises a graph indicating one or more logical or physical relationships between the one or more remote devices. (see [0047]; Acharya: “he operational system context data may comprise an extensible object model and/or an extensible graph-based object model associated with the operational system(s) 110. The operational system context data may comprise knowledge graphs that model assets and/or processes of and/or associated with the operational system(s) 110. In one example, knowledge graphs of the operational system context data may define a collection of nodes and links that describe or represent real-world connections between the operational system(s) 110 and/or assets thereof.”)
The same motivation to combine Gamer and Acharya a set forth for Claim 1 equally applies to Claim 4.
Regarding Claim 5, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Acharya further teaches wherein the machine learning model comprises at least one of a neural network comprising a transformer, an encoder, or a decoder. (see [0171]; Acharya: “Alternatively, or in addition, the program instructions can be encoded on an artificially-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information/data for transmission to suitable receiver apparatus for execution by an information/data processing apparatus.”)
The same motivation to combine Gamer and Acharya a set forth for Claim 1 equally applies to Claim 5.
Regarding Claim 6, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Gamer further teaches wherein the configuration data comprises an installation package (see [0027]; Gamer: “Device packages are implemented for instance as simple mapping in a structured configuration file (e.g., XML, JSON) or as programmatic mappings contained in code (e.g., C# or Java Annotations).”) and configuring the device comprises installing the installation package on the device. (see [0030]; Gamer: “Based on the Unified Information Model, configuration details can now be automatically determined, (e.g., the device packages can provide technology or device specific rules or business logic that is used to configure the device based on the selected semantics or function set of the planned system setup)”)
Regarding Claim 7, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Acharya further teaches wherein the context characteristic of the device indicates a role for the device to control operation of the one or more remote devices. (see 0035]; Acharya: “The monitoring system may also aggregate and/or maintain operational system context data defining various attributes (e.g., relationships, types, locations, roles) associated with the assets of the operational system.”)
The same motivation to combine Gamer and Acharya a set forth for Claim 1 equally applies to Claim 7.
Regarding Claim 8, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Acharya further teaches wherein the context characteristic of the device indicates a role for the device to process data outputted by the one or more remote devices. (see [0047]; Acharya: “The operational system context data may comprise knowledge graphs that model assets and/or processes of and/or associated with the operational system(s) 110.”)
The same motivation to combine Gamer and Acharya a set forth for Claim 1 equally applies to Claim 8.
Regarding Claim 9, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Acharya further teaches wherein the context characteristic of the device indicates a performance metric for the building management system. (see [0116]; Acharya: “For example, based at least in part on the training process 404, the machine learning model 402 may be configured to express a prediction using a function ƒ(x1, x2, . . . , xp), where x1, x2, . . . , xp are features, quantities, values, and/or metrics extracted, calculated, and/or determined from the training data 450.”)
The same motivation to combine Gamer and Acharya a set forth for Claim 1 equally applies to Claim 9.
Regarding Claim 10, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Gamer further teaches further comprising:
providing, by the one or more processors, an indication of at least one of the configuration data or the one or more functions for the device to perform to a user interface; (see [0050]; Gamer: “Semi-automatic configuration using a runtime configuration tool: In case, the building user wants to reconfigure the system (e.g., configure a newly added light switch at the far end of a big meeting room to act as second switch in a room) but the system doesn't have all information necessary to perform an automatic configuration (e.g., it is not known that the new light switch is installed in room 1, i.e., its location is unknown), the runtime configuration tool allows the user to simply drag-and-drop device functions (drag the new light switch's ON/OFF function to the light actuator's SET function. All the rest of the configuration is then again detected and implemented automatically.”)
receiving, by the one or more processors from the user interface, an input indicating approval of the indication; and (see [0034]; Gamer: “In case of partially missing information a semi-automatic configuration mechanism can be applied: missing information can, for instance, be determined automatically and shown to the system integrator or system operator through a runtime configuration tool in order to approve the suggested configuration.”)
configuring, by the one or more processors, the device based on the configuration data responsive to receiving the approval. (see [0034]; Gamer: “If the information base is sufficient that these configuration details can be automatically determined, mechanisms for automatic generation of the device configuration can be executed after approval.”)
Regarding Claim 12, the limitations in this claim is taught by the combination of Gamer and Acharya as discussed connection with claim 1.
Regarding Claim 13, the limitations in this claim is taught by the combination of Gamer and Acharya as discussed connection with claim 2.
Regarding Claim 14, the limitations in this claim is taught by the combination of Gamer and Acharya as discussed connection with claim 3.
Regarding Claim 15, the limitations in this claim is taught by the combination of Gamer and Acharya as discussed connection with claim 4.
Regarding Claim 16, the limitations in this claim is taught by the combination of Gamer and Acharya as discussed connection with claim 5.
Regarding Claim 17, the limitations in this claim is taught by the combination of Gamer and Acharya as discussed connection with claim 6.
Regarding Claim 18, Gamer teaches a system comprising…
operate a scan agent to perform a scan of a building management system with which a device is connected; (see [0008]; Gamer: “network scanning mechanisms can be used to obtain information about the connectivity and neighboring devices;”)
…to detect one or more functions for the device to perform according to the scan; (see [0024]; Gamer: “Function information respective device targeted functionalities, including intended functionalities of the planned building automation system is provided as a "semantic description" in a machine-readable format. This is done, e.g., as export from planning and engineering tools, if functional planning and engineering is supported.” See [0008]: “For information or data about the context of their installation new upcoming mechanisms are stepwise enabling the automatic determination of the device context. Bluetooth beaconing techniques, for instance, can be used to determine the installation of a beacon equipped device on room level; network scanning mechanisms can be used to obtain information about the connectivity and neighboring devices; technologies like RFID can be used to identify a device, a location or the current user; digital building plans based on standards such as Building Information Modeling (BIM) offer machine-readable, technology- and vendor-agnostic information on device locations.”))
retrieve a package from an installation repository according to the one or more functions; and (see [0029]; Gamer: “Based on the information about planned system setup as well as the actual system setup, which is available from the UIM (e.g., by self-describing devices or manually created device packages or API annotations), it is possible to determine (e.g., using a mapping from devices to their semantic abstraction that was used in the planned system setup or a solver matching devices' capabilities available in the Unified Information Model to the planned system setup) which devices have to interact and how these devices have to be configured accordingly.”)
install the package. (see [0031]; Gamer: “The resulting configuration details are finally downloaded to the devices. The finally downloaded to the devices might include a mapping of these configuration details that are based on the semantic abstraction towards technology or device specific parameters.”)
However, Gamer does not explicitly teach:
one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:
operate a machine learning model to detect…;
Acharya from the same or similar field of endeavor teaches:
one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to: (see [0060]; Acharya: “Examples of an apparatus 200 may include, but is not limited to, one or more components of one or more operational systems 110, a machine learning platform system 140, an enterprise management system 120, data repositories 150, and/or user devices 160. The apparatus 200 includes processor 20.”)
operate a machine learning model to detect…; (see [0050]; Acharya: “The machine learning platform system 140 may also be configured to receive operational system context data associated with the one or more operational systems 110.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Gamer to include Acharya’s features of one or more memory devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to: operating a machine learning model to detect. Doing so would optimize the assets' and systems' performance and/or fulfill other objectives of the enterprise. (Acharya, [0034])
Regarding Claim 19, the combination of Gamer and Acharya teaches all the limitations of claim 18 above, Gamer further teaches wherein the machine learning model is configured to detect the one or more functions based on at least one of a function for the device to control a remote device associated with the building management system or a function for the device to receive a command from the remote device. (see [0114]; Acharya: “The machine learning model 402 may comprise a data object created by using machine learning to learn to perform a given function (e.g., a prediction) through training with the training data set.” See [0101]: “the orchestration process 314 may be configured to trigger real time and/or batch execution of the given machine learning model by invoking the real time execution process 316 and/or the batch execution process 318 for a given trained machine learning model at periodic intervals determined based at least in part on the execution schedules provided in the model association metadata and/or trained model metadata for the given trained machine learning model.”)
The same motivation to combine Gamer and Acharya a set forth for Claim 18 equally applies to Claim 19.
Regarding Claim 20, the combination of Gamer and Acharya teaches all the limitations of claim 18 above, Gamer further teaches detect an initialization condition of the device responsive to establishment of a communication link between the device and the building management system. (see [0048]; Gamer: “A new BACnet light actuator is added into the network: as it includes a "device package" (i.e., a package containing device information, location information, as well as a mapping to function block and semantic parameters) and thus, provides all required information based on the Unified Information Model (defining, for instance, the function block types of the building automation system) to the system and requesting other devices.”)
Acharya further teaches wherein the instructions cause the one or more processors to: (see [0060]; Acharya: “Examples of an apparatus 200 may include, but is not limited to, one or more components of one or more operational systems 110, a machine learning platform system 140, an enterprise management system 120, data repositories 150, and/or user devices 160. The apparatus 200 includes processor 20.”)
The same motivation to combine Gamer and Acharya a set forth for Claim 18 equally applies to Claim 20.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gamer et al. (EP3309632A1 -hereinafter Gamer) in view of Acharya et al. (US20240273398A1 -hereinafter Acharya) in view of Durrant et al. (US20020138791A1 -hereinafter Durrant).
Regarding Claim 11, the combination of Gamer and Acharya teaches all the limitations of claim 1 above, Gamer further teaches further comprising:
receiving, by the one or more processors, from the building management system, one or more sets of information corresponding to a plurality of devices deployed across a network; (see [0004]; Gamer: “(1) Information or data about the devices themselves (e.g., device type, vendor, communication technologies, actual technology and device configuration, etc.)”)
identifying, by the one or more processors, using the one or more sets of information, a status for each device of the plurality of devices (see [0019]; Gamer: “The semantic description includes device information, i.e., the targeted device types (e.g., light actuator or temperature sensor), their (abstract) mode of operation (e.g., on/off switching or dimming), and an interaction concept based on semantic parameters (e.g., set point as an input, status as an output for light actuator). Context information of the device installation is made available in a machine-readable format.”), wherein the status indicates a type of software being installed; (see [0027] and [0042]; Gamer: “In addition, so called "drivers" can be used to provide the actual integration with the technical system by implementing the specific type model of a single technology (e.g., KNX or BACnet) in order to make it understandable to the system.”)
However, it does not explicitly teach: generating, by the one or more processors, responsive to identifying the status for each device of the plurality of devices, a report to indicate the status for each device of the plurality of devices.
Durrant from the same or similar field of endeavor teaches: generating, by the one or more processors, responsive to identifying the status for each device of the plurality of devices, a report to indicate the status for each device of the plurality of devices. (see Abstract; Durrant: “A device driver (GRAPHICS, NETWORK, H2IO, IO2L, SERIAL) for use in a computer system comprising a processor (P), memory (M) and a device (GRAPHICS, NETWORK, H2IO, IO2L, SERIAL) operatively coupled to the computer system the device driver being operable to control the device to monitor an operational status of the device, and consequent upon a change in the operational status to generate a fault report data indicating whether the change of status was caused internally within the device or externally by another connected device which caused the change of operational status to occur. The fault reports may also include an indication of the operational status of the device”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Gamer and Acharya to include Durrant’s features of generating, by the one or more processors, responsive to identifying the status for each device of the plurality of devices, a report to indicate the status for each device of the plurality of devices. Doing so would be affected quickly repair and reduce down time of the computer system. (Durrant, [0005])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sharma et al. (US20040111315A1) discloses Device Model Agent providing an embedded services platform for enabling system management applications and services.
O'Flaherty (US20220286371A1) discloses automatically generating a context for each support request, the context including historical resolution data for that local network, or device, or service.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VI N TRAN whose telephone number is (571)272-1108. The examiner can normally be reached Mon-Fri 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ROBERT FENNEMA can be reached at (571) 272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/V.N.T./Examiner, Art Unit 2117
/Christopher E. Everett/Primary Examiner, Art Unit 2117
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