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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
Claims 1-10, 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sanders et al [US 2022/0197317]
Claim 1. (Original) A method, comprising: receiving, by one or more processors (the control processor 220, see Fig. 2, para [0069]), a prompt comprising natural language data regarding a rule associated with operation of an item of equipment of a building (the command languages and/or instructions (rules) by the processor 220 to cause the predictive maintenance system to receive the sensed signal, determine changes in the condition of the at least one component, and predict a maintenance requirement of the at least one component based on predetermined data. The network power quality includes a phase balance, an inrush current, a power factor, or combinations thereof., see Figs. 1, 22, para [0005, 0099]);
providing, by the one or more processors, the prompt as input to a machine learning model to cause the machine learning model to generate a representation of the rule (the machine learning model 300 including processor and command languages/instructions as rules, see abstract, para [0005, 0025, 0044, 0073, 0099-0101]); and
activating, by the one or more processors, the rule for the item of equipment (the CNN relates to applying matrix processing operations includes activation function layers. The term “controller” may be used to indicate a device that controls the transfer of data from a computer or computing device to a peripheral or separate device and vice versa, and/or a mechanical and/or electromechanical device (e.g., a lever, knob, etc.) that mechanically operates and/or actuates a peripheral or separate device, see para [0073, 0091]).
Claim 2. (Original) The method of claim 1, further comprising: triggering, by the one or more processors, an alert responsive to sensor data from at least one sensor for the item of equipment meeting an alert condition represented by the representation of the rule (the sensors 102 indicate or notify of failure or abnormal operation, see Fig. 8, para [0004-0011, 0035, 0059, 0065]).
Claim 3. (Original) The method of claim 1, further comprising operating a fault detection
and diagnostics (FDD) system using the rule (see para [0058-0060, 0102]).
Claim 4. (Currently Amended) The method of claim 1, further comprising generating, by
the one or more processors using the machine learning model, the representation of the rule to include one or more input data elements for the rule to receive, one or more operations for the rule to perform on the one or more input data elements, and one or more responses to initiate according to a processing of the one or more input data elements, the one or more responses including at least one of an alarm, an alert, or an actuation of an item of equipment (as cited in respect to claims 1 and 2 above, see Figs. 1, 4A and 4B).
Claim 5. (Original) The method of claim 1, further comprising providing, by the one or
more processors, a knowledge data base associated with the item of equipment as input to the machine learning model for the machine learning model to generate the rule.
Claim 6. (Original) The method of claim 1, wherein the machine learning model comprises at least one of a generative artificial intelligence model, a large language model, or a neural network comprising a transformer (read upon the storage and/or memory 230 and database in the machine learning model 300 and transformer, see Fig. 2, para [0025, 0069, 0070, 0081, 0098, 0099]).
Claim 7. (Original) The method of claim 1, wherein the machine learning model comprises a rule generation mode and an evaluation mode (read upon the commands/instructions and analysis/diagnostic cited in respect to claims 1 and 3 above, see para [0087]).
Claim 8. (Original) The method of claim 1, further comprising retrieving, by the machine
learning model to generate the rule, at least one of standards data, previous rule data, or historical data (see para [0065, 0087]).
Claim 9. Original) The method of claim 1, wherein the representation of the rule that is
generated is a draft rule in one of programming language or rule engine language (the command languages and programmed languages, see para [0099]).
Claim 10. (Original) The method of claim 9, further comprising: displaying the draft rule to a user (the user display interface, see para [0008, 0031]);
receiving a modification of the draft rule and modifying the draft rule based on the modification, wherein the rule is activated subsequent to modifying the draft rule (read upon the machine leaning ML model may receive signals of abnormal operation may come from increases in energy required to move the irrigation system, determine changes (modifies) in the condition of the at least one component, such as changes in speed of the system, or changes in sequence of the towers moving, endgun turn frequency, or power quality metrics such as phase balance, inrush current, power factor, THD, see para [0005, 0100]).
Claim 17. (Original) One or more non-transitory storage media storing instructions (see para [0024]) thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving a prompt comprising natural language data regarding a rule associated with operation of an item of equipment of a building; providing the prompt as input to a machine learning model to cause the machine learning model to generate a representation of the rule; and activating the rule for the item of equipment (as cited in respect to claim 1 above).
Claim 18. (Original) The one or more non-transitory storage media of claim 17, wherein the operations further comprise: triggering an alert responsive to sensor data from at least one sensor for the item of equipment meeting an alert condition represented by the representation of the rule (as cited in respect to claim 2 above).
Claim 19. (Original) The one or more non-transitory storage media of claim 17, wherein the operations further comprise: providing a knowledge data base associated with the item of equipment as input to the machine learning model for the machine learning model to generate the rule (as cited in respect to claim 5 above).
Claim 20. (Original) The one or more non-transitory storage media of claim 17, wherein the representation of the rule that is generated is a draft rule in one of programming language or rule engine language, and wherein the operations further comprise:
displaying the draft rule to a user; receiving a modification of the draft rule; and
modifying the draft rule based on the modification, wherein the rule is activated subsequent to modifying the draft rule (as cited in respect to claim 10 above).
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.
Claims 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over Sanders et al [US 2022/0197317] in view of Zhang et al [US 2023/0053431]
Claim 11. (Original) A building management system, comprising: one or more processing circuits having one or more processors and one or more memories, the one or more memories having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to: receive a prompt comprising natural language data regarding a rule associated with operation of an item of equipment of a building; provide the prompt as input to a machine learning model to cause the machine learning model to generate a representation of the rule; and
activate the rule for the item of equipment (as cited in respect to claim 1 above). But,
Sanders et al fails to disclose the building management system. However,
Sanders et al discloses the machine learning based predictive maintenance system includes an irrigation system having a plurality of components and configured to irrigate a farming area. The maintenance system includes a sensor disposed at a center pivot of the irrigation system or at a main disconnect of a utility 22, see abstract, Fig. 1, para [0005, 0025, 0087]).
Zhang et al suggests that the housing configured to be installed in at least one of an electrical wall box or a load center, a conductive path, a switch configured to selectively interrupt the conductive path, at least one sensor in electrical communication with the conductive path and configured to measure at least one electrical characteristic of the conductive path to provide a plurality of sensor measurements, a memory, and a controller. The memory is configured to store an arc detection program which implements a machine learning model and includes a field-updatable program portion configured to be field-updatable and a non-field-up datable program portion. The field-up datable program portion includes a plurality of program parameters to be used by the non-field-updatable program portion for deciding between presence of an arc event or absence of an arc event. The arc detection program, when executed by the controller, causes the controller to perform an operation that includes computing input data for the machine learning model based on the plurality of sensor measurements, deciding between presence of an arc event or absence of an arc event, based on the input data, to provide a decision, and causing the switch to interrupt the conductive path when the decision indicates presence of an arc event (see abstract, Figs. 1-3, 10, para [0009]).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to use or implement the programmed machine learning for monitoring and controlling of electrical arc event in a building or home of Zhang et al to the programmed machine learning used in irrigation system of Sanders et al for extending applications and uses of the programmed machine learning since the irrigation system use the electric utility, which like the electric meter and the main electrical panel in a building or home.
Claim 12. (Original) The building management system of claim 11, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: trigger an alert responsive to sensor data from at least one sensor for the item of equipment meeting an alert condition represented by the representation of the rule (as cited in respect to claim 2 above).
Claim 13. (Original) The building management system of claim 11, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: provide a knowledge data base associated with the item of equipment as input to the machine learning model for the machine learning model to generate the rule (as cited in respect to claim 5 above).
Claim 14. (Original) The building management system of claim 11, wherein the machine learning model comprises at least one of a generative artificial intelligence model, a large language model, or a neural network comprising a transformer (the machine learning model 300 including of artificial neural networks CNN being programming languages including metalanguages and transformers, see Figs. 15-22, para [0073, 0081, 0099])
Claim 15. (Original) The building management system of claim 11, wherein the
representation of the rule that is generated is a draft rule in one of programming language or rule engine language (as cited in respect to claim 9 above).
Claim 16. (Original) The building management system of claim 15, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: display the draft rule to a user; receive a modification of the draft rule; and modify the draft rule based on the modification, wherein the rule is activated subsequent to modifying the draft rule (as cited in respect to claim 10 above).
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new references of Sanders et al to make the rejection smoother as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Palshikar et al discloses the orchestration engine provides a technical output across multiple programmable objects such as electronic devices, virtual objects and cloud based services in response to user specified logic. The orchestration engine may be deployed on a mobile computer, a tablet computer, a laptop computer, a desktop computer, a wired or wireless electronic device in the system or on a server computer connected via internet. The orchestration engine is capable of supporting extensibility in order to expand support for similar common interaction methods to newer electronic devices via a plug-in framework by specifying the communication protocol of the new element and its capabilities in a descriptive way via a markup language. The orchestration engine is provided along with a library of drag and drop Visual Programming Language steps required for providing executable computer program steps for specifying a user specified logic by computer language illiterate person.
[US 2019/0227776]
Sanders et al discloses the machine learning based predictive maintenance system that includes an irrigation system configured to irrigate a farming area, a plurality of components, a sensor disposed at a center pivot of the irrigation system or at a main disconnect of a utility. The sensor is configured to generate a signal indicative of a condition of at least one component of the plurality of components of the irrigation system. The system further includes a processor, and a memory. The memory includes instructions, which when executed by the processor, cause the predictive maintenance system to receive the sensed signal, determine abnormal operation of the at least one component, and predict, by a machine learning model, a maintenance requirement of the at least one component based the determined abnormal operation.
[US 2025/0028316]
Any inquiry concerning this communication or earlier communications from examiner should be directed to primary examiner craft is Van Trieu whose telephone number is (571) 2722972. The examiner can normally be reached on Mon-Fri from 8:00 AM to 3:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Wang Quan-Zhen can be reached on (571) 272-3114.
Examiner interviews are available via telephone, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only.
For more information about the PAIR system, see http://pair- direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786- 9199 (IN USA OR CANADA) or 571-272-1000.
/VAN T TRIEU/
Primary Examiner, Art Unit 2685
03/23/2026