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
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 1-10, 17, 18, 20, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Sanders et al [US 2022/0197317] in view of Garg et al [US 12,001,415]
Claim 1. (Currently Amended) 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]). But
Sanders et al fails to disclose outputting, by the machine learning model, a structure data comprising the rule. However,
Sanders et al teaches that the machine learning model 300 including processor and command languages/instructions. See abstract, para [0005, 0025, 0044, 0073, 0099-0101]). The command languages and/or instructions 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]);
Garg et al suggests that the network link 620 typically provides data communication through one or more networks to other data devices. For example, network link 620 may provide a connection through local network 622 to a host computer 624 or to data equipment operated by an Internet Service Provider (ISP) 626. (See Fig. 6, col. 20, lines 64-67, col. 21, lines 1-2).
The system may automatically modify one or more additional hierarchal data structures, without intervening user input, based on the output generated by the machine learning model. In an alternative embodiment in which the system trains a machine learning model to generate a set of rules for modifying hierarchal data structures, the system may apply the set of rules to a detected modification to a hierarchal data structure to identify one or more additional modifications to one or more additional hierarchal data structures. (See Fig. 3, 4, col. 12, lines 7-63).
Therefore, it would have been obvious to one skill in the art before the effective filing date of the invention to modify or implement the machine learning model to generate a set of rules for modifying hierarchal data structures to equipment of Garg et al to the command languages and/or instructions by the leaning machine processor of Sanders et al for convenience, faster and greater monitoring, tracking and modifying rule, instruction and/or guidance to activate and operate an equipment efficiency and up to date, since any particular equipment or machine has its own rules, instructions and procedures to activate and operate correctly to prevent damage of an equipment.
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. (Previously Presented) 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 the combination between Sanders et al and Garg et al in respect to claims 1 and 2 above, see Sanders et al, 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 (as the combination between Sanders et al and Garg et al in respect to claim 1 above).
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 (the combination between Sanders et al and Garg et al in respect to claim 1 above, and including the
commands or instructions and analysis/diagnostic cited in respect to claims 1 and 3 above. See Sanders et al, 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 (and the combination between Sanders et al and Garg et al in respect to claim 1 above, and wherein 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 Sanders et al, para [0005, 0100]).
Claim 17. (Currently Amended) 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; outputting, by the machine learning model, a structure data comprising the rule; outputting, by the machine learning model, a structure data comprising the rule; and activating the rule for the item of equipment (as the combination between Sanders et al and Garg et al 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 the combination between Sanders et al and Garg et al in respect to claim 2 above).
Claim 19. (Canceled)
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 the combination between Sanders et al and Garg et al in respect to claim 10 above, and including generating rule).
Claim 21. (New) The method of claim 1, wherein the rule includes one or more indications of one or more input data elements, one or more operations for processing the one or more input data elements, and one or more responses to initiate according to the processing of the one or more input data elements (as the combination between Sanders et al and Garg et al in respect to claim 10 above, and including the CNN typically includes convolution layers, activation function layers, and pooling (typically max pooling) layers to reduce dimensionality without losing too many features. Additional information may be included in the operations that generate these features. Providing unique information that yields features that give the neural networks information can be used to ultimately provide an aggregate way to differentiate between different data input to the neural networks. In aspects, the machine learning model 300 may include a combination of one or more deep learning networks (e.g., a CNN), and classical machine learning models (e.g., an SVM, a decision tree, etc.). For example, the machine learning model 300 may include two deep learning networks. See Sanders et al, Fig. 1, 3, para [0073]). And
In some aspects, the controller includes a display to send visual information to a user. In various aspects, the display is interactive (e.g., having a touch screen or a sensor such as a camera, a 3D sensor, a LiDAR, a radar, etc.) that can detect user interactions, gestures, or responses and the like. In some aspects, the display is a combination of devices such as those disclosed herein, (See Sanders et al, para [0093]).
Claims 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over
Sanders et al [US 2022/0197317] and Garg et al [US 12,001,415] and further in view of Zhang et al [US 2023/0053431]
Claim 11. (Currently Amended) 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; outputting, by the machine learning model, a structure data comprising 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 and Garg 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 Sanders et al, 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, see the amendment, filed 06/26/2026, with respect to the rejection(s) of claims 1-20 under Sanders et al have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Garg et al to make the rejection smoother based on the amendment subject matter.
Applicant’s arguments:
(A) Sanders does not teach or suggest that a machine learning model outputs the data structure to include a rule [associated with operation of an item of equipment of
a building].
(B) Sanders does not teach, or suggest "activating the rule for the item of
equipment," as recited in the claims. However, the activation layers of the machine learning model of Sanders are not "activating, by the one or more processors, the rule for the item of equipment," as recited in the claims.
(C) Zhang was not cited for and does not teach or suggest, alone or in combination with Sanders, at least the above-recited elements.
Response to the arguments:
(A) It is obvious to combine the modification or implementing the machine learning model to generate a set of rules for modifying hierarchal data structures to equipment of Garg et al to the command languages and/or instructions by the leaning machine processor of Sanders et al for convenience, faster and greater monitoring, tracking and modifying rule and/or instruction to activate and operate an equipment efficiency and up to date, since any particular equipment or machine has its own rules, instructions and procedures to activate and operate correctly to prevent a damage of that equipment.
(B) The equipment is activated and operated by one or more rules from the learning machine module between Sanders et al and Garg et al, as cited in respect to claim 1 above and in section (A).
(C) It is obvious to combine Zhang with Sanders et al and Garg et al according to claim 1 above.
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 99number 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
07/10/2026
Prosecution Insights