DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-16 are pending in the application.
Examiner’s Note: The examiner has cited particular passages including column and line numbers, paragraphs as designated numerically and/or figures as designated numerically in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claims, other passages, paragraphs and figures of any and all cited prior art references may apply as well. It is respectfully requested from the applicant, in preparing an eventual response, to fully consider the context of the passages, paragraphs and figures as taught by the prior art and/or cited by the examiner while including in such consideration the cited prior art references in their entirety as potentially teaching all or part of the claimed invention. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/14/2023, 11/16/2023, 08/09/2024, 09/23/2024, 12/19/2024, 01/14/2025 was filed after the mailing date of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “monitoring system”, “a disturbance data provider”, “a control system”, “a control mechanism scheduler” in claim 1, “a pattern recognizing engine” in claim 2.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Applicant’s specification has failed to provide sufficient description for the structures of the recited limitation “a control mechanism scheduler” in claim 1. The specification describes the functionality at a high level (e.g., training, generating schedules, achieving objectives) but does not describe the structural or algorithmic features necessary to demonstrate possession of the claimed training-based scheduler.
Regarding claims 2-8, dependent claims inherit the deficiencies of their respective parent(s).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitation “a control mechanism scheduler” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The specification discloses, at most, a processor or computing system configured to perform scheduling and training operations. However, the specification fails to disclose:
A specific algorithm for “training to generate a schedule of setpoints” including the model structure, or objective function.
A specific algorithm for generating the schedule based on disturbance data and operating data.
A specific algorithm or logical procedure for retrieving and outputting the schedule in response to real time operational data.
The specification of a generic processor executing instruction, without an algorithmic description of how the recited functions are performed, constitutes a “black box”1 disclosure and does not provide sufficient corresponding structure for the entirely of the claimed function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Regarding claims 2-8, dependent claims inherit the deficiencies of their respective parent(s).
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 –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 9-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Carty et al. US Pub. No. 2012/0232701 (“Carty”).
Regarding claim 1, Carty discloses a system for controlling a water resource infrastructure (WRI), the system comprising:
the water resource infrastructure (WRI) comprising infrastructure components, wherein at least one of the infrastructure components is actuatable to cause a change to the WRI;
[0030] In addition to user interface 110, the building control systems 112 may additionally interact with the predictive model generated by analytical engine 108. In one embodiment, the analytical engine 108 may transmit control instructions to the building control systems 112. The analytical engine 108 may transmit such instructions using various protocols or interfaces as needed for various building subsystems (e.g., HVAC, lighting, water, etc.)2
a monitoring system that communicates with one or more sensors that collect operating data related to the infrastructure components, wherein the operating data comprises a state of the infrastructure components [real-time building data];
[0038] FIG. 3 presents a block diagram illustrating a forecasting and estimation engine according to one embodiment of the present invention. In the illustrated embodiment, engine 300 contains a data conditioner module 302. In the illustrated embodiment, the data conditioner 302 receives input data, such as data from storage modules 202-212. This data may comprise data relating to sensor or equipment readings within a building or campus of buildings.
a disturbance data provider that provides disturbance data that may be expected to have an impact on operational parameters of the infrastructure components [real time external data]
[0031] FIG. 2 presents a block diagram illustrating an analytical engine used for use in monitoring and communication with one or more building control systems to optimize the performance of building assets according to one embodiment of the present invention. In the illustrated embodiment, the analytical engine 200 includes a plurality of data stores 202-212 including real-time building data storage 202, real-time external data storage 204, historical data storage 206, on-site energy resources storage 208, real-time energy availability storage 210, and client energy approaches storage 212. Although illustrated as single storage modules, the storage modules 202-212 may comprise a plurality of components including equipment or sensors that generate data.
[0032] In the illustrated embodiment, real-time building data storage 202 stores various metrics relating to the current, or real-time, state of a given building, or campus of buildings. Real-time data may include such data such as supply air temperature data, outside air temperature data, water temperature data, heating & cooling medium (e.g., water, steam, etc.) pressure data, humidity data, air flow data, air pressure data, air quality data, CO.sub.2 levels, lighting usage data, fuel or electricity consumption data, and water usage data. Real-time external data storage 204 may contain data such as environmental temperature data, solar position and irradiance data, wind speed data, and other weather data, as well as fuel oil rate data, natural gas rate data, electricity rate data, and other energy rate data. In the illustrated embodiment, the real-time external data storage 204 may receive such data from external sources. Historical data storage 206 maintains historical data previously stored in real-time building data storage 202 and real-time external data storage 204. In the illustrated embodiment, historical data storage 206 may contain various historical data regarding the building or campus including, but not limited to building zone conditions (e.g., temperature, humidity, CO.sub.2), occupancy history, HVAC conditions (e.g., temperature, humidity, air flow), weather conditions (e.g., solar radiation, temperature, humidity, wind speed) and energy rates.
a control mechanism scheduler [108] that:
receives the disturbance data from the disturbance data provider;
receives the operating data from the monitoring system;
trains to generate a schedule of setpoints for a control system that controls the at least one infrastructure component that is actuatable [SEE par. 0050-0051, 0066], wherein the schedule of setpoints is in accordance with approaching a predetermined objective [SEE par. 0044, 0066, 0073 - predictive demand forecasts of water usage and pattern analysis to predict water usage and plan alternative strategies to minimize water usage contributing to a lower water footprint.];
[0028] FIG. 1 presents a block diagram illustrating a system 100 for monitoring one or more building control systems according to one embodiment of the present invention. According to the embodiment that FIG. 1 illustrates, an analytical engine 108 interacts with external data source(s) 102, real-time building data source(s) 104, and historical data source(s) 106 and transmits information to and from user interface 110 and building control systems 112. At a high level, analytical engine 108 receives a plurality of data inputs from sources 102, 104, and 106 and performs various statistical analyses on the incoming data inputs, as will be discussed further herein. In one embodiment, analytical engine 108 employs various machine-learning mechanisms to generate a predictive model based on the received data. Analytical engine 108 may further employ various optimization routines based on client-defined goals or constraints in order to optimize the generated predictive model.
[0044] In the illustrated embodiment, energy management strategies 410 may comprise various strategies that the building manager or owner may wish to employ when optimizing the models. For example, the building management may wish to achieve a specified energy cost reduction. Additionally, the building management may wish to reduce greenhouse gas emissions/carbon impact by a target amount and utilize as much on-site power as percent of total power used as possible. In conjunction with energy management strategies 410, constraints and objectives 412 may additionally be specified by the building management. For example, the building management may specify various occupant comfort constraints such as temperature, humidity, and ventilation requirements. Additionally, the management may set constraint that certain thresholds for various equipment not be exceeded or a general rule such as manufacturer-supplied input may create such a constraint.
[0045] Based on the constraints, strategies, and rules 406-412, the optimizer 404 optimizes the received models 402. In the illustrated embodiment, the optimizer may use various optimization techniques including, but not limited to, nonlinear programming techniques including, but limited to, non-linear programming techniques including Genetic Algorithms, Simulated Annealing, Artificial Neural Networks, or other techniques or linear approximation techniques including Tailor series expansions or artificial neural networks (ANN). The optimizer 404 may output the optimized models to a storage module (not shown) for subsequent retrieval and usage. Additionally, the optimizer 404 may output the optimized model to the forecasting and estimation engine as feedback for subsequent model generation. Further details regarding the optimization of un-optimized models are discussed further with respect to FIG. 8.
[0050] FIG. 6 presents a flow diagram illustrating a method for generating predictive building subsystem demand models according to embodiment of the present invention. According to the embodiment that FIG. 6 illustrates, a method 600 receives input values, step 602, and feedback from the optimizer, step 604. In the illustrated embodiment, input values may correspond to raw data from sensors, equipment, real-time external data, and other data sources as discussed previously. Additionally, the method 600 receives feedback from the optimizer in order to further refine the demand model forecasts based on the optimized models. The feedback from the optimizer (step 604) together with the updated input values (step 602) provide adaptive learning about the building to improve the accuracy of future demand forecast predictions.
[0051] After receiving the input and feedback, the method 600 determines modeling parameters, step 606, and builds and stores the demand models, step 608. In one embodiment of step 606, memory-based time-series regression analysis may employ analytical techniques such as ARIMA, ANN, SVM or other regression techniques to update the parameters of the demand model considering the history of the process, general energy rules (from knowledge base held in, for example, storage 408), a physical model of the subsystem (if available) and the new input values from 602. In the illustrated embodiment, the method 600 generates demand models for a plurality of discrete subsystems including, but not limited to ventilation, lighting, water, plug load, and data centers. In step 608, we use the model parameters from step 606 to forecast the demand for each subsystem (including but not limited to lighting, water, ventilation, plug load and data center) In this approach we build the demand forecast hierarchically going from the most granular to the aggregate model for each subsystem to produce the overall subsystem demand forecast for the entire building/building complex/campus. The method 600 determines the relevant parameters for each demand model. For example, the method 600 may generate parameters for heating/cooling (such as temperatures, humidity, heating or cooling load), ventilation (such as air changes, air flow, air quality), lighting (such as illumination, electricity), water (such as total water volume, potable water volume, domestic hot water (DHW) volume, make up water volume), plug load (such as electricity), and data centers (such as electricity).
[0066] If the method 800b does not receive new forecasting inputs, the method 800b translates the optimized demand models into an integrated energy management strategy and recommendations, step 806. In one embodiment, an integrated energy management strategy may include recommendations for the operation of target systems including set-points and schedules, maintenance activities to restore building systems to peak functionality, and programs to participate in (e.g., demand response or similar contract-based programs). In the illustrated embodiment, the integrated energy management strategy and recommendations may additionally be based on current conditions such that the integrated energy management strategy and recommendations allow the building or campus of building to take an optimized course of action based on client optimization priorities.
retrieves and outputs the schedule of setpoints in response to receiving real-time operational data [SEE par. 0068]; and
[0068] In the illustrated embodiment, the method 800b may generate complementary control instructions specific to each building or campus subsystem such that the method 800b may allow for real-time control of each subsystem. Additionally, the method 800b may provide non-real-time recommendations to a building operator. For example, the method 800b may provide recommendations to a GUI display or similar mechanism that enables an operator to view the recommendations and take appropriate action. In addition to generating complementary control instructions, the method 800b sends the control instructions to the building control systems, step 816. In the illustrated embodiment, sending control instructions to the building control systems may comprise transmitting the control instructions through interfaces such as BACnet, Modbus, and LonWorks, for example, and interfacing to proprietary architectures in areas for which no standards exist.
a control system [112] that:
receives the schedule of setpoints; and
controls the infrastructure components based on the received schedule of setpoints [SEE fig. 8B].
[0030] In addition to user interface 110, the building control systems 112 may additionally interact with the predictive model generated by analytical engine 108. In one embodiment, the analytical engine 108 may transmit control instructions to the building control systems 112. The analytical engine 108 may transmit such instructions using various protocols or interfaces as needed for various building subsystems (e.g., HVAC, lighting, water, etc.). In one embodiment, the analytical engine 108 may transmit these instructions automatically to the systems, thus automating the building systems based on predictions formed from the generated model(s).
Regarding claim 2, Carty discloses the disturbance data received by the control mechanism scheduler comprises historical disturbance data; the operating data received by the control mechanism scheduler comprises historical operating data from a defined time period; and a pattern recognizing engine generates unique classes corresponding to patterns recognized in the historical disturbance data [par. 0028 - an analytical engine 108 interacts with external data source(s) 102, real-time building data source(s) 104, and historical data source(s) 106 and transmits information to and from user interface 110 and building control systems 112. At a high level, analytical engine 108 receives a plurality of data inputs from sources 102, 104, and 106 and performs various statistical analyses on the incoming data inputs, as will be discussed further herein. In one embodiment, analytical engine 108 employs various machine-learning mechanisms to generate a predictive model based on the received data. Analytical engine 108 may further employ various optimization routines based on client-defined goals or constraints in order to optimize the generated predictive mode].
Regarding claim 3, Carty discloses the pattern recognizing engine generates the unique classes based on clustering [e.g., abnormal data from pattern recognition – SEE Fig. 5 and par. 0047-0049].
Regarding claim 4, Carty discloses the historical disturbance data and historical operating data are used for initial training of a prediction engine [par. 0028]; each infrastructure component is represented by a machine learning driven regression estimator that describes operating parameters of the infrastructure component; and the machine learning driven regression estimator is interconnected recursively in a directed graph of a hierarchical learning model [par. 0051- we use the model parameters from step 606 to forecast the demand for each subsystem (including but not limited to lighting, water, ventilation, plug load and data center) In this approach we build the demand forecast hierarchically going from the most granular to the aggregate model for each subsystem to produce the overall subsystem demand forecast for the entire building/building complex/campus; SEE further par. 0035, 0078].
Regarding claim 5, Carty discloses the control mechanism scheduler trains to generate the schedule of setpoints by: generating interim simulations of the water resource infrastructure; generating interim schedules of setpoints in accordance with approaching the predetermined objective; and iterating the generation of the interim schedules and generation of the interim simulations until the predetermined objective is reached within a predetermined threshold [SEE par. 0070-0072 - After receiving the modeled demand forecasts and client constraints/strategies, the method 900 simulates the building systems, step 906. In the illustrated embodiment, simulating the building systems may comprise varying specific parameters based on the type of simulation suggested and utilizing the demand forecasts to make predictions regarding the outcomes of such changes in variables. The method 900, after performing the simulation, compares the simulation outcomes, step 908, and generates recommendations based on the comparison, step 910].
Regarding claim 6, Carty discloses at least some of the disturbance data is expected to impact water demand in the water resource infrastructure [weather, temperature – SEE par. 0013, 0034, 0051].
Regarding claims 9-14, they are directed to the method of steps to implement the system as set forth in claims 1-6. Therefore, they are rejected on the same basis as set forth hereinabove.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carty as applied to claim 1 or 9 above.
Regarding claim 7, Carty teaches receives a new disturbance signal for which the schedule of setpoints has already been generated; retrieves the schedule of setpoints associated with the first unique class; and controls the infrastructure components based on the retrieved schedule of setpoints [SEE par. 0065-0066]. Carty does not expressly teach classifies the new disturbance signal into a first unique class of one or more unique classes. However, Carty specifically teaches, if the method 800b determines that new forecasting inputs have been received, the method 800b sends these data values to the forecasting and estimation model, step 804. In the illustrated embodiment, sending these data values to the forecasting and estimation model allows the method to continually adjust the demand forecasts based on received events and if the method 800b does not receive new forecasting inputs, the method 800b translates the optimized demand models into an integrated energy management strategy and recommendations, step 806. In one embodiment, an integrated energy management strategy may include recommendations for the operation of target systems including set-points and schedules, maintenance activities to restore building systems to peak functionality. Therefore, it is obvious to one of ordinary skill in the art that Carty teach classifies the new disturbance signal into a first unique class of one or more unique classes since the system would not able to retrieve a set-points without able to classify the new disturbance signal.
Regarding claim 15, See discussion in claim 7.
Allowable Subject Matter
Claim 8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office Action and including all of the limitations of the base claim and any intervening claims.
Claim 16 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Claims 8 and 16 are considered allowable since, when reading the claims in light of the specification, none of the references of record alone or in combination disclose or suggest the combination of subject matter specified in the dependent claim(s): generating a simulation of the water resource infrastructure by operating functional modules of a prediction engine, wherein: the prediction engine predicts: water demand; pump flow rates and response time; storage tank water levels; system pressure response to the storage tank water levels and other infrastructure components that affect pressure.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2016/0103452 to Kang teaches system for managing water in a water pipe network is disclosed. The system draws an optimal pump operation schedule and a result of hydraulic analysis, by performing an integrated simulation of hydraulic analysis and optimization based on the hydraulic analysis data for optimization calculation and demand amount data generated using an optimization setting parameter and history data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT HUY TRAN whose telephone number is (571)272-7210. The examiner can normally be reached M-F 7:00-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini S Shah can be reached at 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VINCENT H TRAN
Primary Examiner
Art Unit 2115
/VINCENT H TRAN/Primary Examiner, Art Unit 2115
1 See MPEP 2181 (B)
2 Therefore, Carty discloses infrastructure components is actuatable to cause a change to the WRI.