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 .
Response to Election/Restrictions
1. Applicant’s amendments to the claims as filed on 05/07/2026 in view of the restriction requirement have been considered and are persuasive. In view of the amendments, claim election /restriction requirement has been withdrawn, and amended claims 1-20 have been analyzed in this office action.
Claim Rejections - 35 USC § 101
2. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The following reasons are provided to evaluate subject matter eligibility.
(1) Are the claims directed to a process, machine, manufacture or composition of matter;
(2A) Prong One: Are the claims directed to a judicially recognized exception, i.é., a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims are directed to a judicial exception under Prong One, then is the judicial exception integrated into a practical application;
(2B) If the claims are directed to a judicial exception and do not integrate the judicial exception, do the claims provide an inventive concept.
With regard to (1), the analysis is a ‘yes’, claim 1 recites a process, claim 8 recites a device, and claim 14 recites a manufacture.
With regard to (2A) Prong One, the analysis is a “yes”. Claims 1 and 14 recites “identifying, by the computer system, a surface temperature corresponding to a portion of the physical structure from the one or more images depicting the aerial view of the physical structure; determining, by the computer system based at least in part on the surface temperature and the one or more attributes associated with the physical structure, that a capability of a temperature control system associated with the physical structure is likely insufficient to meet a temperature control requirement associated with the physical structure; and executing, by the computing system, one or more operations based at least in part on determining that the capability of the temperature control system associated with the physical structure is likely insufficient to meet the temperature control requirement associated with the physical structure.” When viewed under the broadest most reasonable interpretation the claim recites an abstract idea of mental processes. The steps of “identifying”, “determining”, and “executing” steps can be performed mentally by a human or using a pen and paper is generically recited because there is no description of how this is accomplished. It can be interpreted as merely looking at the data, and evaluating the data in the mind. The concepts, as claimed, are observations and/or evaluations (“analyze...”), judgements (“identify...” and “compare...”), and opinions (“determine, obtaining...”). The claims do not explain how the temperature control system's insufficiency is calculated (e.g., missing specific algorithms or a structured, concrete data-processing technique). There is nothing in the claim that requires more than an operation that a human, armed with the appropriate apparatus, pen/paper, can perform. The computer is merely used as a generic tool to automate these steps faster. See MPEP 2106.04 and the 2019 PEG
Regarding claim 8, claim 8 is merely “collecting data, feeding it to a trained mathematical model to make a prediction, and using a generic computer to do it”. The claims do not explain how the temperature control system's insufficiency is calculated (e.g., missing specific algorithms or a structured, concrete data-processing technique). The claim describes what the machine learning model does (identifying system performance) but does not explain how the model is structurally improved or how the underlying computer technology functions better; and relies entirely on result-based, functional output (e.g., "executing one or more operations based on output") without specific algorithms or technical pre/post-processing steps.
With regard to (2A) Prong Two: the analysis is a “No”. Claims 1, 8 and 14 recites the additional elements of “obtaining, by a computer system, one or more images depicting an aerial view of a physical structure; obtaining, by the computer system, one or more attributes associated with the physical structure”; and these additional elements represents mere data gathering that is necessary for use of the recited abstract idea. Therefore, the limitation(s) is/are insignificant extra-solution activity, as transforming data into vector(s) is a generic operation. See MPEP 2106.05(1). The claim as a whole, looking at the additional elements individually and in combination, does not integrate the abstract idea into a practical application.
With regard to (2B): the pending claims do not show what is more than a routine in the art presented in the claims, i.e., the additional elements are nothing more than routine and well-known steps. The additional elements do not reflect an improvement to a technology or technical field, including the use of a particular machine or particular transformation. It has not been shown that the mental process allows the “technology” to do something that it previously was not able to do.
Claims 2-7, 9-13 and 15-20 are similarly rejected for the same reasons as claims 1 and 14. Dependent claims 2-7, 9-13 and 15-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are rejected for the same reasons and not repeated herewith.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. Claims 1-4, 7-14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Rezvani et al., U.S. Patent Publication No. 2022/00667337 A1, and further in view of Savelyev et al., 2008, “Surface Temperature Mapping of the University of Northern Iowa Campus Using High Resolution Thermal Infrared Aerial Imageries” (pp. 5055-5058).
Regarding claim 1,
Rezvani as cited teaches generating a thermal model of the property by combining temperature data with one or more attributes of the property such as a spatial model of the property, so that the thermal model provides a three-dimensional representation of heat accumulation within the property; and further discloses a drone to collect thermal data related to thermal monitoring while navigating throughout different locations of a property (paras 0026, 0029, 0036). Rezvani further teaches “The drone 120 can include one or more thermal cameras configured to measure surface temperatures in the property” (para 0041). Rezvani further teaches “the thermal model can include surface temperature measurements collected by the drone 120 when monitoring the property” (para 0101).
Rezvani further teaches the thermal model can include additional data collected by the drone 120, such as air flow detected by the drone 120 in each region of the property, features identified in each region of the property, among others (para 0100); further discloses “The drone 120 can store data describing attributes of the property in a spatial data 120A. The spatial data 120A can include a floorplan and/or a three-dimensional representation of the property that enables the drone 120 to navigate the property. During initial configuration, the drone 120 can receive the data describing attributes of a property, determine a frame of reference to the data (e.g., a home or reference location in the property), and navigate the property based on the frame of reference and the data describing attributes of a property. In some instances, the spatial data 120A includes information that enables the drone 120 to perform diagnostics related to thermal monitoring or regulation. For example, the spatial data 120A can identify locations of HVAC system 140 components (e.g., fans, vans) so that the drone 120 can use collected sensor data and the spatial data 120A to determine if the components are property functioning. As another example, the spatial data 120A can identify locations of heat-generating elements (e.g., appliances, windows) so that the drone 120 can use collected sensor data and the spatial data 120A to determine how to optimize HVAC operation (e.g., configuring the HVAC system 140 to cool and/or heat a property with the least amount of energy required) (para 0042)” – these corresponds to obtaining, by the computer system, one or more attributes associated with the physical structure);
Rezvani further discloses “The drone 120 also aggregates the region-specific temperature measurements to compute an overall temperature measurement for the property 200A. In the example depicted, the drone 120 computes an average of the region-specific temperature measurements and sets the computed average as the property temperature” (para 0067); and further discloses “because the thermal model 202 includes region-specific temperatures, hot or cold spots in the property 200A can be identified.”; and further discloses “the drone 120 may use one or more models that are trained to identify patterns within monitoring data that are indicative of specified conditions within the property. For example, the drone 120 can apply a machine learning model that is trained to identify a region of the property that may be experiencing overheating due to a poor vent performance (e.g., air flow through a vent may be limited) (para 0080); and further teaches “information specified in the thermal model can be used to determine whether a present configuration of the HVAC system should be adjusted. For instance, if an active cooling operation is not improving a high air temperature inside a region of the property” (para 0106) – where active cooling operation not improving is read to correspond to determining, that a capability of a temperature control system associated with the physical structure is likely insufficient to meet a temperature control requirement associated with the physical structure).
Rezvani further discloses “if an active cooling operation is not improving a high air temperature inside a region of the property, the drone 120 can prioritize air flow through a vent in that region of the property. In some implementations, the drone 120 can use the thermal model to perform various operations that improve thermoregulation within a property. For instance, in the example depicted in FIG. 3, information specified in the monitoring data 306 can be used to determine that a present configuration of the HVAC system 140 (e.g., active cooling operation) is not improving the air temperature and/or air flow within the sun room. In this example, the drone 120 uses this information to adjust the present configuration of the HVAC system 140 to terminate the cooling operation and instead prioritize air flow to the sun room through a specific vent in the sunroom. In this way, the drone 120 applies the thermal model to dynamically configure monitoring system component to, for instance, increase energy efficiency, improve heat dissipation in a specific region of the property, and/or reduce the possibility of performing extraneous operations” (para 0106), which corresponds to claim limitation “executing, by the computing system, one or more operations based at least in part on determining that the capability of the temperature control system associated with the physical structure is likely insufficient to meet the temperature control requirement associated with the physical structure”).
.
Rezvani as cited teaches flying a drone and navigating through different locations of a property, majority within the property; and measures surface temperature using thermal/infrared imaging, where flying drone images can be considered as aerial images. But keeping in view of the specification, which cites aerial images that are outside above the property, Rezvani does not explicitly teach of aerial images that are outside above the property to map the surface temperature of the property. However, Savelyev discloses High-resolution thermal infrared aerial images can be used to map the surface temperature of a property such as a building in the campus (Abstract – “the goal of this project was to map the surface temperature of the university of Northern Iowa campus using high-resolution thermal infrared aerial imageries…High-resolution thermal infrared imagery proved highly effective tool for precise heat anomaly detection on the campus, and it can be used by university facility services for effective further maintenance of buildings and grounds); further discloses on page 5056 – 1st para – “thermal sensors can be handheld or fixed on a platform such as an airplane, a satellite or a car. Each of these platforms has its own advantages and disadvantages”; further see 3rd para – “Satellite systems have a number of advantages: they offer vast coverage, collect imagery at regular intervals and at lower cost; further discloses “the UNI campus represents a wide variety of objects of interest, including flat rooftops, sloped rooftops, underground pipelines, heat exhausts and other infrastructure” (page 5057 – 1st para); further discloses Aerial data in section 2.2 that includes time series of imagery (page 5058 – 2nd para); further discloses on page 5062 - “’Section 4.1 – Thermal Map of the UNI Campus” – “Figure 5 shows the surface temperature variation map of the campus. This map clearly depicts the hot spots on the campus”; and further discloses on page 5063 – “Section 4.2 – On-Campus buildings” – “In general, rooftops of the on-campus buildings are in good condition with no major leaks visible. A number of buildings in this region have roof temperatures elevated above the average level, as well as other issues. For example, Maucker Union (Figure 6) has the warmest rooftop. Latham Hall (Figure 7) has a signature hotspot on its rooftop that can probably be attributed to the moisture, captured under the insulation material [ 4]. McColum Science Hall (Figure 8) hosts a number of chemistry and physics labs that are well-ventilated. The heat exhaust from the ventilation system is unmistakable, along with the overall warm rooftop of this hall.”
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the teachings of Savelyev of using aerial images for measuring surface temperature of buildings with the invention of Rezvani. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to combine the teachings of Savelyev of using aerial images for measuring surface temperature of buildings with the invention of Rezvani, because both references belong to the same field of endeavor of monitoring the structural temperatures; and Savelyev teaches high-resolution thermal infrared imagery proved to be a highly useful tool for heat anomalies detection (see page 5067 - section 5 – Conclusions and Future Directions – last few lines), which provides the advantage that only infrared imaging can be used for early or first detection of heat anomalies in structures; and Rezvani as cited can include Savelyev measurements aggregating the region-specific temperature measurements to compute an overall temperature measurement for the property (Rezvani - paras 0067 and 0080); and further adding to the thermal model, which as cited can be used to find the reasons of temperature anomalies.
Regarding claim 2, the combined invention of Rezvani and Savelyev discloses “The computer-implemented method of claim 1, wherein at least one of the one or more images is an infrared image that depicts a heat signature at one or more corresponding locations of the physical structure, and wherein identifying the surface temperature corresponding to at least the portion of the physical structure comprises identifying at least one of a color or an intensity of the color within the one or more images” (see the citations made in the rejection of claim 1, both Rezvani and Savelyev teach infrared image; and further see Savelyev- page 5063 – figures that identify the surface temperature corresponding to at least the portion of the physical structure with respect to one of a color or an intensity of the color within the one or more images).
Regarding claim 3, the combined invention of Rezvani and Savelyev discloses “The computer-implemented method of claim 1, wherein the one or more attributes associated with the physical structure comprises at least one of 1) operational data corresponding to one or more devices associated with powering or managing temperature within the physical structure, 2) location data identifying corresponding locations of the one or more devices associated with powering or managing the temperature within the physical structure, 3) environmental data indicating at least one or more ambient temperature values within the physical structure, or 4) a schematic of the physical structure” (see the citations made in the rejection of claim 1 with respect to attributes – Rezvani - the thermal model can include additional data collected by the drone 120, such as air flow detected by the drone 120 in each region of the property, features identified in each region of the property, among others (para 0100); further discloses “The drone 120 can store data describing attributes of the property in a spatial data 120A. The spatial data 120A can include a floorplan and/or a three-dimensional representation of the property that enables the drone 120 to navigate the property. During initial configuration, the drone 120 can receive the data describing attributes of a property, determine a frame of reference to the data (e.g., a home or reference location in the property), and navigate the property based on the frame of reference and the data describing attributes of a property. In some instances, the spatial data 120A includes information that enables the drone 120 to perform diagnostics related to thermal monitoring or regulation. For example, the spatial data 120A can identify locations of HVAC system 140 components (e.g., fans, vans) so that the drone 120 can use collected sensor data and the spatial data 120A to determine if the components are property functioning. As another example, the spatial data 120A can identify locations of heat-generating elements (e.g., appliances, windows) so that the drone 120 can use collected sensor data and the spatial data 120A to determine how to optimize HVAC operation (e.g., configuring the HVAC system 140 to cool and/or heat a property with the least amount of energy required) (para 0042)).
Regarding claim 4, the combined invention of Rezvani and Savelyev discloses “The computer-implemented method of claim 1, wherein obtaining the one or more attributes of the physical structure comprises identifying one or more temperature control systems from the one or more images based at least in part on an object detection algorithm (see Rezvani - para 0042 - the spatial data 120A includes information that enables the drone 120 to perform diagnostics related to thermal monitoring or regulation. For example, the spatial data 120A can identify locations of HVAC system 140 components (e.g., fans, vans) so that the drone 120 can use collected sensor data and the spatial data 120A to determine if the components are property functioning. As another example, the spatial data 120A can identify locations of heat-generating elements (e.g., appliances, windows) so that the drone 120 can use collected sensor data and the spatial data 120A to determine how to optimize HVAC operation (e.g., configuring the HVAC system 140 to cool and/or heat a property with the least amount of energy required)).
Regarding claim 7, the combined invention of Rezvani and Savelyev discloses “The computer-implemented method of claim 1, wherein determining that the capability of the temperature control system is likely insufficient to meet the temperature control requirement associated with the physical structure comprises comparing the surface temperature from the one or more images depicting the aerial view of the physical structure to at least one attribute of the one or more attributes associated with the physical structure” (see Savelyev – page 5066 – last para – “Temperature estimates produced by the model were compared with the ground measurements. The results of the validation are presented in Table 1. The mean temperature error is -0.25°C, standard deviation is 0.58°C. This makes the model accurate to within ±l .2° C.” - where comparison of temperature provides the accuracy of the measured temperatures inside/on the ground, further providing the high temperature accuracy further citing the capability of the temperature control system being likely insufficient to meet the temperature control requirement associated with the physical structure).
Regarding claim 8, claim 8 limitations have been similarly analyzed and rejected as per citations made in the rejection of claim 1. Further adding, claim 8 further recites a trained machine-learning model which analyzes the operational data and the infrared image (See Rezvani – paras 0078-0083 and 106 - At step “A,” the drone 120 obtains spatial data 302 and HVAC data 304. The spatial data 302 includes various types of spatial information of a property, such as a three-dimensional layout (e.g., in a spatial model) or an identification of components to be monitored for thermal activity (e.g., HVAC vents, windows, doors, walls, etc.). The HVAC data 304 includes monitored information associated with an HVAC system 140 located in a property, such as historical data of recently performed operations or an indication that a certain type of heating/cooling operation has been recently initiated. As discussed throughout, the drone 120 uses the spatial data 302 and the HVAC data 304 to determine how and when to adjust the configuration of an HVAC system 140 to improve efficiency and/or temperature settings within a property. At step “B,” the drone 120 navigates about a property and monitors thermal data associated with the property. For example, the drone 120 can perform one or more monitoring operations similar to the techniques shown in FIGS. 2A and 2B. The drone 120 generates monitoring data 306 based on the monitoring operations that were performed. The monitoring data 306 identifies three regions of the property (e.g., living room, sunroom, bedroom), and a respective ambient temperature and air flow detected by the drone 120 at each region of the property. The drone 120 evaluates information specified in the monitoring data 306 to determine if an adjustment to the present configuration of an HVAC system 140 may be beneficial. In some instances, the drone 120 may use one or more models that are trained to identify patterns within monitoring data that are indicative of specified conditions within the property. For example, the drone 120 can apply a machine learning model that is trained to identify a region of the property that may be experiencing overheating due to a poor vent performance (e.g., air flow through a vent may be limited…).
Regarding claim 9, claim 9 recites “The computing device of claim 8, wherein the machine-learning model is trained based at least in part on a supervised machine-learning algorithm and labeled data set, the labeled data set comprising example including an operational data instance, a corresponding infrared image, and a label indicating an operational status or attribute of one or more components associated with the physical structure”. As cited in the rejection of claims 1 and 8, Rezvani cites a trained machine learning model that relies on operational data, thermal data and other attributes related to physical structure, therefore the implicit teachings for training using the data it relies on are quite evident, and the indexing/labeling is inherent when used in machine learning as that’s what is being taught to the algorithm; and it has to be remembered by the algorithm. Rezvani nowhere cites self-learning machine learning model and supervised machine-learning algorithm is the default option as the start step is always supervised by default.
Regarding claim 10, claim 10 recites “The computing device of claim 9, wherein the labeled data set comprises historical data associated with the physical structure, the physical structure being a datacenter”. As cited in combined invention of Rezvani and Savelyev, Rezvani teaches using trained models to identify patterns in monitoring data and monitoring data includes historical spatial and HVAC data included in the structural property (paras 0078-0080); and Savelyev teach lab buildings and other buildings on university campus. Both Rezvani and Savelyev teach buildings/properties that have devices/appliances installed in them that output some data hence are kind of data centers. However, using the method of monitoring/controlling temperature of the building as used by combined invention of Rezvani and Savelyev on any kind of datacenter would simply be design choice here, as claim doesn’t provide any specifics on what a datacenter structure looks like.
Regarding claim 11, the combined invention of Rezvani and Savelyev discloses “The computing device of claim 8, wherein executing the computer-executable instructions further causes the one or more processors to: obtain environmental data associated with a geographical area in which the physical structure is located; and provide the environmental data as part of the input data to the machine-learning model” (see Rezvani – paras 0079-0080 - The drone 120 generates monitoring data 306 based on the monitoring operations that were performed. The monitoring data 306 identifies three regions of the property (e.g., living room, sunroom, bedroom), and a respective ambient temperature and air flow detected by the drone 120 at each region of the property. The drone 120 evaluates information specified in the monitoring data 306 to determine if an adjustment to the present configuration of an HVAC system 140 may be beneficial. In some instances, the drone 120 may use one or more models that are trained to identify patterns within monitoring data that are indicative of specified conditions within the property. For example, the drone 120 can apply a machine learning model that is trained to identify a region of the property that may be experiencing overheating due to a poor vent performance (e.g., air flow through a vent may be limited) – where temperature and air flow are environmental data associated with a geographical area).
Regarding claim 12, the combined invention of Rezvani and Savelyev discloses “The computing device of claim 8, wherein the one or more operations comprise providing a notification at a user interface” (see Rezvani - para 0041 – “Air quality measured by the drone 120 can then be used to provide environmental context for other measurements, such as temperature measurements and air flow measurements. Air quality problems identified by the drone 120 can be addressed with our without requiring user intervention depending on the severity of the identified problems. For example, if the drone 120 determines an air quality problem resulting from a property condition that requires repair (e.g., lead paint on walls), then an alert of the air quality issue may be provided to a user device due to additional repairs being necessary to address the issue”).
Regarding claim 13, the combined invention of Rezvani and Savelyev discloses “The computing device of claim 8, wherein the infrared image is obtained during a monitoring process in which a plurality of infrared images depicting the aerial view of the physical structure are obtained over a period of time” (see Savelyev – page 5056 – 1st para – “thermal sensors can be handheld or fixed on a platform such as an airplane, a satellite or a car. Each of these platforms has its own advantages and disadvantages”; further see 3rd para – “Satellite systems have a number of advantages: they offer vast coverage, collect imagery at regular intervals and at lower cost”; see Rezvani – para 0050 – “the drone 120 can be used to perform routine surveillance operations on a property. For instance, the drone 120 can be assigned to one or more particular properties within a geographic location and can routinely collect surveillance footage during specified time periods (e.g., after dark), which can then be transmitted to the server 160 for transmitting back to each particular property owner. In such implementations, the property owner can receive the surveillance footage over the network 105 as a part of a service provided by a service provider that operates the server 160. For example, thermal data collected by the drone 120 can be part of a property monitoring service package provided by a service provider”; para 0027).
Regarding claim 14, claim 14 has been analyzed and rejected as per citations made in the rejection of claim 1.
Regarding claim 16, the combined invention of Rezvani and Savelyev discloses “The non-transitory computer-readable medium of claim 14, wherein the first set of infrared images are obtained from one or more satellite imaging sources” (see Savelyev – page 5056 – 1st para – “thermal sensors can be handheld or fixed on a platform such as an airplane, a satellite or a car. Each of these platforms has its own advantages and disadvantages”; and further teaches in 3rd para that using “Satellite systems have a number of advantages: they offer vast coverage, collect imagery at regular intervals and at lower cost”).
7. The prior art(s) of record as cited do not teach the subject matter as recited in claims 5-6, 15 and 17-20 in combination with all of the limitations of the base claim.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Manav Seth whose telephone number is (571) 272-7456. The examiner can normally be reached on Monday to Friday from 8:30 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Sumati Lefkowitz, can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manav Seth/
Primary Examiner, Art Unit 2672
June 22, 2026
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