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 .
DETAILED ACTION
The action is in response to the amendments filled on 02/24/2026.
Claims 1-18 are pending, where claims 1 and 18 are independent.
Response to Arguments
Applicant’s arguments filed 02/24/2026, with respect to the rejection(s) of claim(s) 1-18 under 35 USC 102 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 Therefore, the rejection has been withdrawn.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 Morgan.
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.
Claims 1-18 are rejected under 35 USC 103 as being unpatentable over Saffre et al (USPGPUB 20190186770 A1) in view of Morgan et al (USPGPUB 20190008072 A1).
As to claim 1, Saffre discloses An information processing device comprising: a storage section storing an element model created for each of elements affecting an air environment of a space, the element model predicting an effect of each of the elements on the air environment of the space in a case where each of the elements is at a specified position ((para0082–para0086, para0102–para0105): “Sensors (S1-S5) are the simplest elements in the framework of this embodiment and only report their own measurements to the actuators (A1-A3).” (para0082) “The actuators (and the climate control appliances) are more complex and are capable of learning about the effect they are having on various sensors. It is their information processing, decision-making and actions that underpin the self-configuration of the virtual BMS.” (para0086) “The systems and methods of the above embodiments may be implemented in a computer system (in particular in computer hardware or in computer software) in addition to the structural components and user interactions described.” (para0102) “The methods of the above embodiments may be provided as computer programs or as computer program products or computer readable media carrying a computer program which is arranged, when run on a computer, to perform the method(s) described above.” (para0104));
a placement acquisition section acquiring information on placement of each of the elements provided in a predetermined subject space ((para0097–para0098, para0081): “Other ways of populating the candidate list could be to include all sensors in the system (which may be practical for smaller systems, but is less likely to be efficient in a larger system). In other alternatives, the candidate list is populated with a defined subset of all the sensors in the system, perhaps based on those sensors which are within wireless communication range of the actuator, or which are within a defined spatial area compared to the actuator.” (para0097) “In the embodiment shown, each actuator A1-A3 controls a single vent V1-V3, but the same principles could readily be applied in a situation where an actuator, or other controller, controls more than one climate control device.” (para0081));
and an estimation section estimating an air environment in the subject space using a superposition model created by superimposing, based on the information on the placement of each of the elements acquired by the placement acquisition section, the element model, which corresponds to each of the elements, stored in the storage section. ( (para0086–para0091, para0090–para0093): “The actuators (and the climate control appliances) are more complex and are capable of learning about the effect they are having on various sensors. It is their information processing, decision-making and actions that underpin the self-configuration of the virtual BMS.” (para0086) “When active/open, each vent monitors the values reported by all the sensors to which it is subscribed. At regular intervals (e.g. every second), each reading is paired with the time elapsed since the vent opened, then entered as a new data-point. At the end of a period of arbitrary duration, time series are analysed.” (para0089) “The statistics obtained are used by the actuator to rank the sensors to which it was subscribed during the latest period.” (para0090)).
Saffre (US 2019/0186770 A1) discloses a system for modeling and managing environmental conditions (such as cooling and airflow) in a data center, including the use of equipment and their positions within a floor plan. However, Saffre does not explicitly teach the use of a superposition model that combines the effects of multiple elements by superimposing their individual contributions.
Morgan (US 2019/0008072 A1), on the other hand, expressly teaches the use of a superposition approach for combining the effects of multiple equipment enclosures on cooling capacity and airflow within a modeled floor plan. Specifically, Morgan discloses:
Developing a floor plan model of a data center, indicating the location of each equipment enclosure ([0013], [00232], Fig. 11).
For each equipment enclosure, displaying on the floor plan the remaining cooling capacity, which includes the additional power that can be drawn based on the remaining cooling capacity.
Determining cooling capacity by calculating a predicted cooling capacity based on the floor plan model.
Using superposition to combine airflows from multiple enclosures ([0013], [00232], Fig. 11).
One of ordinary skill in the art would have been motivated to combine the floor plan-based environmental modeling of Saffre with the superposition modeling approach of Morgan to improve the accuracy and flexibility of environmental predictions. Superposition modeling is well-known for its ability to efficiently and accurately estimate aggregate effects in complex environments where multiple sources contribute to overall conditions (e.g., airflow, temperature). Applying Morgan’s superposition technique to Saffre’s environmental modeling system would predictably yield more accurate and computationally efficient estimations of environmental parameters—such as temperature or airflow—based on the placement and operation of multiple devices or equipment enclosures.
Therefore, it would have been obvious to one of ordinary skill in the art, at the time of the invention, to modify Saffre’s system to include the superposition modeling approach taught by Morgan, thereby arriving at the claimed invention which estimates environmental parameters by superimposing the effects of multiple elements based on their placement within a space.
As to claim 2, Saffre discloses The information processing device according to claim 1, wherein the element model is a model of an element affecting the air environment of the space ((para0081–para0086): “For ease of description, the embodiments of the present invention will be described with reference to the situation in which the system is operating a collection of air-conditioning vents and the sensors are thermometers (i.e. any kind of temperature sensor). However, it will be understood that the same processes could also be used in other contexts (e.g. for heating, for managing air humidity etc.).” (para0081) “The actuators (and the climate control appliances) are more complex and are capable of learning about the effect they are having on various sensors.” (para0086)).
As to claim 3, Saffre discloses The information processing device according to claim 1, wherein the air environment of the space includes at least one of a temperature, a humidity, an airflow, and a concentration of carbon dioxide in the space. ((para0001, para0081): “It is particularly, but not exclusively, concerned with methods and system for controlling climate control appliances such as air conditioning units used in buildings.” (para0001) “For ease of description, the embodiments of the present invention will be described with reference to the situation in which the system is operating a collection of air-conditioning vents and the sensors are thermometers (i.e. any kind of temperature sensor). However, it will be understood that the same processes could also be used in other contexts (e.g. for heating, for managing air humidity etc.).” (para0081)).
As to claim 4, Saffre discloses The information processing device according to claim 1, wherein the element model includes an air conditioner model for predicting an effect of an air conditioner, which is provided to the space, on the air environment of the space ((para0081–para0086, para0094): “For ease of description, the embodiments of the present invention will be described with reference to the situation in which the system is operating a collection of air-conditioning vents and the sensors are thermometers…” (para0081) “The actuators (and the climate control appliances) are more complex and are capable of learning about the effect they are having on various sensors.” (para0086) “In the current simulated implementation, the difference between target and measured temperatures, averaged over all triggers, is used to command the activation and de-activation of the climate-control appliance, but it could of course be replaced by a ‘largest’ or ‘smallest’ deviation rule.” (para0094)).
As to claim 5, Saffre discloses The information processing device according to claim 1, wherein the element model includes a heating element model for predicting an effect of a heating element, which is present in the space, on the air environment of the space ((para0081, para0034–para0035): “For ease of description, the embodiments of the present invention will be described with reference to the situation in which the system is operating a collection of air-conditioning vents …it will be understood that the same processes could also be used in other contexts (e.g. for heating, for managing air humidity etc.).” (para0081) “The method of the present aspect is particularly useful in controlling the heating or cooling of a building …” (para0034)).
As to claim 6, Saffre discloses The information processing device according to claim 5, wherein the effect of the heating element on the air environment of the space is an effect of heat generated by the heating element ((para0001, para0056, para0034): “It is particularly, but not exclusively, concerned with methods and system for controlling climate control appliances such as air conditioning units used in buildings.” (para0001) “At their broadest, systems of the present invention provide climate control systems in which a climate control device is controlled by determining a subset of available sensors which are influenced by the climate control device and controlling the climate control device based on information from those sensors.” (para0056) “The method of the present aspect is particularly useful in controlling the heating or cooling of a building …” (para0034)).
As to claim 7, Saffre discloses The information processing device according to claim 1, wherein the element model includes a non-heating element model for predicting an effect of a non-heating element, which is present in the space, on the air environment of the space ( (para0005, para0081): (“Another, closely related, problem … presents itself when the sensor is suitably located with respect to the climate control appliance it triggers, but near an independent source that is not controlled (or monitored) by the BMS (for example a heat or cold source, but also a source of humidity such as a kettle.” (para0005) “However, it will be understood that the same processes could also be used in other contexts (e.g. for heating, for managing air humidity etc.).” (para0081)).
As to claim 8, Saffre discloses The information processing device according to claim 1, wherein the element model includes an external environment model for predicting an effect of an external environment of the space on the air environment of the space ((para0099–para0101, para0035): “In order to resolve this issue, a further embodiment of the present invention involves communication between actuators. Whenever a vent opens or closes, it broadcasts this change of state so as to inform all others within communication range.” (para0099) “Simulation-based testing indicates that, statistically, the method of the embodiment described is capable of limiting how far actual temperatures are allowed to drift away from target while simultaneously reducing the time that vents remain open …” (para0101) “Compared to a conventional BMS a method according to the present aspect may provide for improved efficiency, reduced infrastructure costs (no dedicated control centre), the ability to seamlessly deploy across multiple sites (as opposed to a single building) and/or improved flexibility …” (para0035)).
As to claim 9, Saffre discloses The information processing device according to claim 8, wherein the element model further includes a first substance model for predicting an effect of the external environment on the air environment of the space exerted through a first substance constituting an outer surface of the space((“first substance model” or “first substance constituting an outer surface”; c): “This invention relates to methods and systems for controlling appliances, particularly climate control appliances such as air conditioning units used in buildings.” (Abstract) (“first substance model” or “outer surface” appears in the supplied description text.)).
As to claim 10, Saffre discloses The information processing device according to claim 9, wherein the element model further includes a second substance model for predicting an effect of a second substance on the air environment of the space, the second substance controlling the effect exerted by the external environment through the first substance(: “For example, an A/C vent may be opened in response to a temperature reading from a sensor that is not located in its optimal area of influence.” (para0004, para0021, para0022)).
As to claim 11, Saffre discloses The information processing device according to claim 8, wherein the element model further includes a medium model for predicting an effect of the external environment on the air environment of the space exerted by a medium directly flowing into the space (“For example, an A/C vent may be opened in response to a temperature reading from a sensor that is not located in its optimal area of influence.” (para0004) “The sensors are devices capable of: measuring an environmental variable (e.g. temperature); recording a time-series over a short period (so as to be able to detect variations and extrema); and communicating (e.g. by sending a reading when certain conditions are met).” (para0082)).
As to claim 12, Saffre discloses The information processing device according to claim 11, wherein the element model further includes a second substance model for predicting an effect of a second substance on the air environment of the space, the second substance controlling the effect of the external environment exerted by the medium (para 0021, 0022).
As to claim 13, Saffre discloses The information processing device according to claim 1, further comprising: a target acquisition section acquiring a target air environment of the space; and a control section controlling the element provided in the subject space to reduce a difference between the air environment in the subject space estimated by the estimation section and the target air environment of the space acquired by the target acquisition section ((para0088, para0094, para0044): “At deployment time, an actuator is subscribed to an arbitrarily chosen number of randomly selected sensors to “bootstrap” the learning process. It is also assigned target ‘activation’ and ‘de-activation’ temperatures. For instance it may be instructed to turn itself on when the difference between the measured and target temperature is +2 degrees, then off when it reaches −2 degrees.” (para0088) “In the current simulated implementation, the difference between target and measured temperatures, averaged over all triggers, is used to command the activation and de-activation of the climate-control appliance…” (para0094) “Preferably at least one of the sensors has a predetermined target value for the climate parameter and the detected values from said sensor or sensors are in the form of differences between the measured and target values of the climate parameter at said sensor.” (para0044)).
As to claim 14, Saffre discloses The information processing device according to claim 1, wherein the placement acquisition section acquires the information on the placement by accepting designation of a position of an object image corresponding to each of the elements on a screen (“The term ‘computer system’ includes the hardware, software and data storage devices for embodying a system or carrying out a method according to the above described embodiments. For example, a computer system may comprise a central processing unit (CPU), input means, output means and data storage.” (para0103)).
As to claim 15, Saffre discloses The information processing device according to claim 14, wherein the placement acquisition section accepts designation of orientation of each of the elements in the subject space on the screen ((the general computer system/software statements, display (para0102–para0105)).
As to claim 16, Saffre discloses The information processing device according to claim 1, wherein the placement acquisition section acquires the information on the placement including information on a position of each of the elements in at least one of a vertical direction and a horizontal direction in the subject space ( “Other ways of populating the candidate list could be … based on those sensors which are within wireless communication range of the actuator, or which are within a defined spatial area compared to the actuator.” (para0097)).
As to claim 17, Saffre discloses The information processing device according to claim 1, wherein the estimation section estimates the air environment in the subject space using a plurality of the element models taking into account mutual effects among a plurality of the elements ((para0099–para0101, para0031–para0034): “In order to resolve this issue, a further embodiment of the present invention involves communication between actuators. Whenever a vent opens or closes, it broadcasts this change of state so as to inform all others within communication range.” (para0099) “This is enough for every actuator to identify the one of its counterpart with which it has the longest activity overlap … every time that readings from trigger sensors would normally require its activation, the air-conditioning unit first checks whether its main ‘competitor’ is already in action.” (para0100) “The method can be arranged to progressively identifying suitable pairings of climate control devices and sensors, for example through unsupervised machine learning, and implementing a self-defining policy capable of taking into account individual preferences (if specified).” (para0032)).
As to claim 18, Saffre discloses “A non-transitory computer readable medium storing a program causing a computer to execute: a function of storing an element model created for each of elements affecting an air environment of a space, the element model predicting an effect of each of the elements on the air environment of the space in a case where each of the elements is at a specified position; a function of acquiring information on placement of each of the elements provided in a predetermined subject space; and a function of estimating an air environment in the subject space using a superposition model created by superimposing, based on the acquired information on the placement of each of the elements, the element model corresponding to each of the elements.( (para0102–para0105): “The systems and methods of the above embodiments may be implemented in a computer system (in particular in computer hardware or in computer software) in addition to the structural components and user interactions described.” (para0102) "The methods of the above embodiments as rejected in claim 1).
Saffre (US 2019/0186770 A1) discloses a system for modeling and managing environmental conditions (such as cooling and airflow) in a data center, including the use of equipment and their positions within a floor plan. However, Saffre does not explicitly teach the use of a superposition model that combines the effects of multiple elements by superimposing their individual contributions.
Morgan (US 2019/0008072 A1), on the other hand, expressly teaches the use of a superposition approach for combining the effects of multiple equipment enclosures on cooling capacity and airflow within a modeled floor plan. Specifically, Morgan discloses:
Developing a floor plan model of a data center, indicating the location of each equipment enclosure ([0013], [00232], Fig. 11).
For each equipment enclosure, displaying on the floor plan the remaining cooling capacity, which includes the additional power that can be drawn based on the remaining cooling capacity.
Determining cooling capacity by calculating a predicted cooling capacity based on the floor plan model.
Using superposition to combine airflows from multiple enclosures ([0013], [00232], Fig. 11).
One of ordinary skill in the art would have been motivated to combine the floor plan-based environmental modeling of Saffre with the superposition modeling approach of Morgan to improve the accuracy and flexibility of environmental predictions. Superposition modeling is well-known for its ability to efficiently and accurately estimate aggregate effects in complex environments where multiple sources contribute to overall conditions (e.g., airflow, temperature). Applying Morgan’s superposition technique to Saffre’s environmental modeling system would predictably yield more accurate and computationally efficient estimations of environmental parameters—such as temperature or airflow—based on the placement and operation of multiple devices or equipment enclosures.
Therefore, it would have been obvious to one of ordinary skill in the art, at the time of the invention, to modify Saffre’s system to include the superposition modeling approach taught by Morgan, thereby arriving at the claimed invention which estimates environmental parameters by superimposing the effects of multiple elements based on their placement within a space.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The BES+CFD (Fan et al.) paper provides substantial support for many functional limitations of : it discloses model-based prediction of indoor air effects (supporting claims about “element model”/models) via the use of TRNSYS and ANSYS/FLUENT: “TRNSYS was used as the BES software and ANSYS/FLUENT, which provides detailed three-dimensional information about indoor air flow and temperature distribution, was adopted as the CFD software” (Introduction), and it discloses using those models together to estimate the air environment by exchanging boundary data: “The automatic fully dynamic coupling simulations of BES and CFD were carried out on the basis of exchanging mutual complementary boundary conditions at each BES time step (1 h).” (2.2 BES–CFD integration method). The reference explicitly describes acquiring and using placement/layout information for elements (supply/return openings, ERV units, AC units and room geometry): “Fig. 1 illustrates the geometry and configuration of the office model.” and “Ten supply inlet openings and corresponding exhaust (return) outlet openings through 4 ERV units were arranged on the ceiling…” (2.1 Office model), and it models media flowing into the space (air via ERV/AC) with flow rates and boundary conditions: “Air supply from the air-conditioning system and ERV were assumed as constant airflow velocity and set in accordance with the measurement data of the manufacturer.” (2.4 CFD model). The paper addresses the air-environment parameters claimed (temperature and airflow) and measures/considers humidity in field work (but states humidity transport was not simulated): “CFD simulation can provide more detailed information such as air flow, temperature, contaminant distributions” (Introduction) and “Humidity transportation inside building materials and in indoor air was not considered in this coupled simulation. Therefore, this analysis focuses only on the sensible heat transfer; the effect of latent heat was disregarded.” (2.3 BES model). Control/target acquisition limitations are supported: the target zone temperature was set and a PID controller was used to drive supply temperature to meet it: “the target zone temperature … was set at a constant level of 28 °C” and “A proportional-integral-derivative (PID) controller (Type 23) was applied to minimize the temperature fluctuation … by adjusting the process supply air temperature.” (2.3 BES model). The reference also shows modeling of wall/substrate thermal properties used as boundary conditions (supporting outer-surface/first-substance concepts): “Table 3 shows the information of wall structure of the target office model. The outer walls were constructed of lightweight concrete…” (2.3 BES model) and states that wall surface temperature/heat flux were used in CFD boundary conditions. However, the paper does not disclose several claimed specifics: it contains no direct disclosure of a stored “storage section” or a GUI-based “placement acquisition section” that “accepts designation of a position of an object image … on a screen” or “designation of orientation … on the screen”; no explicit “first substance/second substance” paired-model architecture as phrased in the claims (I could find “Table 3” on wall materials but no “first/second substance model” wording); and no explicit “second substance model” that controls an effect exerted through another substance. For those items the reference either omits the claimed term(s) or provides only partial conceptual support (e.g., walls and envelope heat flux are modeled, but not as a named “first substance model” with a separate “second substance model” controller). In sum: strong, direct support in the paper for model-based estimation of temperature/airflow using BES and CFD with placement/layout and control via PID; partial support for humidity and outer-surface/material effects; and no direct disclosure of the GUI placement/orientation items or the specific “first/second substance” model constructs as claimed.
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.
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/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119