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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/08/2025 has been entered.
Response to Amendment
Claims 1-20 are pending
Claims 1 and 11 have been amended
Specification
The amendment filed 12/08/2025 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows:
The disclosure fails to provide proper antecedent basis for the limitation “solar elements” For example, para [0055] discloses irradiance is applied to the elements. However, nothing in the written description have basis for the limitation “solar elements”. The meaning of every term used in any of the claims should be apparent from the descriptive portion of the specification with clear disclosure as to its import; and in mechanical cases, it should be identified in the descriptive portion of the specification by reference to the drawing, designating the part or parts therein to which the term applies”.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Rejections - 35 USC § 112
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 and 11 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.
Regarding Claim 1, the disclosure fails to comply with the written description of the limitation “solar elements”. For example, para [0055] discloses irradiance is applied to
the elements. However, nothing in the written description have basis for the limitation “solar elements”.
Regarding Claim 11, the disclosure fails to comply with the written description of the limitation “solar elements”. For example, para [0055] discloses irradiance is applied to
the elements. However, nothing in the written description have basis for the limitation “solar elements”.
MPEP 2173.05(i) reads:
“Any negative limitation or exclusionary proviso must have basis in the original disclosure. If alternative elements are positively recited in the specification, they may be explicitly excluded in the claims. See In re Johnson, 558 F.2d 1008, 1019, 194 USPQ 187, 196 (CCPA 1977) (“[the] specification, having described the whole, necessarily described the part remaining.”). See also Ex parte Grasselli, 231 USPQ 393 (Bd. App. 1983), aff’d mem., 738 F.2d 453 (Fed. Cir. 1984). The mere absence of a positive recitation is not basis for an exclusion. Any claim containing a negative limitation which does not have basis in the original disclosure should be rejected under 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement”.
Claims 2-10 are also rejected due to their dependence on claim 1.
Claims 12-20 are also rejected due to their dependence on claim 11.
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.
Claim(s) 1-9, are rejected under 35 U.S.C. 103 as being unpatentable over Loveland; Jim et al. (US application# US 20200293773 A1; previously cited; hereinafter Loveland; previously cited) in view of of Augenbraun; Joseph et al. (US publication # US 20120035887 A1; newly cited; hereinafter Augenbraun; previously cited) further in view of Ananthakrishnan; Vijay Anand et al. (US publication # US 20190303707 A1; hereinafter Ananthakrishnan; newly cited).
Regarding claim 1, Loveland teaches a system (abstract) to determine solar production (par.132 “the system will generate the watts produced 1405”) at a site (par.52 teaches at a site), comprising:
a network interface (par.95 teaches a network interface) to couple one or more (par.135 teaches using network to couple to associated computing systems, which are client devices) client computing devices (par.67 teaches plurality of client devices) over a communication network (par.101 teaches communication networks);
memory to store one or more of point cloud data and computer-readable instructions that when executed by a processor cause the system to perform operations (par.95 “The system may store the instructions in memory, and a processor may implement various modules to accomplish calculations and tasks performed by the system. The processor may be located on the UAV or may be remotely connected through a network and/or network interface. The network interface may enable communications of one or more operations to the UAV pre-flight, post-flight, and/or during flight.”); and
one or more processors to execute the computer-readable instructions to cause the system to perform operations (par.95 “The system may store the instructions in memory, and a processor may implement various modules to accomplish calculations and tasks performed by the system.”) to:
obtain data (par.100 “The estimator module may use historical data to estimate cloudy, rainy, and/or snowy lengths of time that occlude solar exposure.” Teaches obtaining cloud data) for a site (par.52 teaches a site);
identify, from the point cloud data, a surface of interest (par.33 “the system may identify and conceptually divide the roof surfaces”) at the site (par.100 “The estimator module may compute a heatmap of the solar irradiation on the roof or at the subject site.”);
identify from the point cloud data, one or more shading objects (par.89 “a user digitally places solar panels on a displayed model of the roof, the system may show the user how many kilowatts will be generated per hour, day, week, month, year, lifetime, etc. For example, a 300-watt solar panel in one location on the roof may be expected to collect 100 watts early in the morning, 300 watts in the afternoon, and be shaded in the later afternoon and evening.”) at the site (par.100 “The estimator module may compute a heatmap of the solar irradiation on the roof or at the subject site.”);
simulate a path of the sun (par.88 “the ray-path modeling may include ray paths between locations on the roof of the structure and the modeled location of the sun that are reflected off of the neighboring structure.”) with respect to the site to identify areas of the surface of interest that are shaded from the sun by the one or more shading objects (par.79 “the UAV solar irradiance system may generate a shaded map (e.g., a heat map or black body model) of the roof showing areas of the roof receiving varying levels of solar energy during a given time period.”);
one or more insolation values (par.69 “the system may utilize real-time solar irradiance values to ensure an accurate model for calculating temporally averaged solar irradiance values.”) determined for the surface of interest for a time period (par.79 “a given time period”), considering areas of the surface of interest that are shaded at one or more times within the time period (par.79 “the UAV solar irradiance system may generate a shaded map (e.g., a heat map or black body model) of the roof showing areas of the roof receiving varying levels of solar energy during a given time period.”); and
calculate solar production at the surface of interest (par.132 “the system will generate the watts produced 1405”), based on the one or more insolation values (par.134 “the system may provide total output values, payoff values, estimated costs, etc. as an operator virtually places solar panels on the roof with the overlaid irradiation values (e.g., via a drag and drop operation) at locations 1502 and 1504.”).
Loveland fails to teach obtain, via a server, data for a site based on stereoscopic images collected at the site; generate a shade map that identifies; generate an isolation map comprising; and display, using a client computing device located at the site, a proposed placement of one or more solar elements on a three-dimensional digital model of the site derived from the point cloud data, the proposed placement based on the calculated solar production.
Augenbraun does teach obtain, via a server (par.210 teaches server), data for a site based on stereoscopic images collected at the site (par.58-60 teach stereoscopic images; par.64 “a low-resolution digital terrain model generated from satellite imagery may be combined with a high resolution model generated through stereoscopic image pairs of a building site.”); generate a shade map that identifies (par.20 “This data may then be used to generate a shadow map. Statistical analysis may be performed on each spot to determine information regarding when the particular spot receives unobstructed sunlight and when it is shaded.”); generate an isolation map (par.167 “In the user interface, an insolation map of the roof may be overlaid on the 3-D model”) comprising; and display (par.137-139 teaches display), using a client computing device located at the site (par.213 “a device located at the site with data input and output capabilities (e.g., iPhone, Smartphone, e-reader, digital camera, cell phone, laptop, netbook, personal digital assistant (PDA), iPod Touch or custom electronic device designed for this purpose)”; Client devices are typically personal computing devices with network software applications installed that request and receive information over the network or Internet. Mobile devices like smartphones, tablets, iPads, laptops and also desktop computers can all function as clients.), a proposed placement (par.21 “The solar energy system may be designed algorithmically by determining the placement of solar components according to a set of rules and descriptions or models of solar energy system components.”) of one or more solar elements (par.167 “solar elements”) on a three-dimensional digital model (par.167 teaches a three-dimensional digital model) of the site derived (par.129 teaches data derivation) from the point cloud data, the proposed placement based on the calculated solar production (par.91 teaches placement based on calculated solar production).
It would have been prima facie obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have modified Loveland to include the teachings of Augenbraun; which would provide an improved method of performing shadow analysis using a software with a method for mapping shadows on a surface to determine the efficiency of a planned solar installation as disclosed by Augenbraun(par.7).
Loveland in view of Augenbraun fails to explicitly teach obtain point cloud data comprising a plurality of spatial points representing three-dimensional surfaces at.
Ananthakrishnan does teach obtain point cloud data comprising a plurality of spatial points representing three-dimensional surfaces at (par.48 “Viable solar area determination system 104 determines a point cloud by analyzing the data from multi-view imagery database 106 (e.g., using overlapping aerial photos to determine 3-dimensional coordinates associated with objects located at the input location. After determining the point cloud, the points of the point cloud are classified (e.g., as a structure, as ground, as vegetation, as distant terrain, etc.).”).
It would have been prima facie obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have modified Loveland in view of Augenbraun to include the teachings of Ananthakrishnan; which would provide a more rapid, accurate and refined solution for determination of an active or viable solar production area for a solar installation as disclosed by Ananthakrishnan(par.2).
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Loveland Figure 9
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Regarding claim 2, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the system of claim 1, Loveland further teaches wherein determining the one or more insolation values for the surface of interest for the time period, comprises:
masking surface of interest points of the point cloud (par.39 “Again, blocked ray paths correspond to shadows. For example, a relatively large chimney may cast very little or no shadow (i.e., block relatively few ray paths) on a rooftop when the sun is directly overhead.”);
determining a maximum available irradiance (par.33” a maximum possible solar irradiation level may be measured at the location.”) at each surface of interest point for one or more points in time during the time period (par.42 “the thousands of points on the surface of the roof may be identified, and the solar irradiance ray path tracing to various modeled locations of the sun at various time increments during a time period may be mapped”);
determining a shade metric (-par.148 “the graphical user interface generation subsystem 1692 may use the data from the ray-path modeling subsystem 1694 to display a heatmap that uses various shades of gray or different colors (e.g., black body temperature modeling) to illustrate the relative impact or effect of various obstacles and obstructions.”) at each surface of interest point for the one or more points in time during the time period (par.42 “the thousands of points on the surface of the roof may be identified, and the solar irradiance ray path tracing to various modeled locations of the sun at various time increments during a time period may be mapped”); and
determining the one or more insolation values based on the maximum available irradiances and shade metrics at each surface of interest point for the time period (par.52 “determines solar irradiance values at various locations at the time of the scan and for future time increments during future time periods.”).
Regarding claim 3, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the system of claim 1, Augenbraun teaches wherein determining the one or more insolation values for the surface of interest for the time period, comprises:
separating surface of interest points of the point cloud from non-surface of interest points (par.30 “allowing identification of separate geometric areas on the surface of interest.”; par.50 “The surface of interest may be split into a series of sections. In the limit, each and every point or combinations of points on the 3D model may be modeled as a separate section.”);
determining a maximum available irradiance at each surface of interest point of the surface of interest (par.230 “Alternatively, the system may generate in a step 720 power flux calculations. The power flux calculations may be used to design a solar energy system for the surface of interest.”);
determining a shade value (par.85 “A discrete value at the time the shading or power flux is calculated.”) at each surface of interest point of the surface of interest (par.24 “determine the shadow line on each surface”);
determining a shaded irradiance (par.20 “Statistical analysis may be performed on each spot to determine information regarding when the particular spot receives unobstructed sunlight and when it is shaded.”) at each surface of interest point of the surface of interest, the shaded irradiance based on the maximum available irradiance and the shade value at each point of the surface of interest (par.20 “Statistical analysis can be performed over some or all of the surface of interest to provide information for percentage shading. Integration of the ray trace data over the entire time period may further yield the power flux from light striking the rooftop. The power flux may also be calculated from the output of individual ray trace calculations.”); and
summing the shaded irradiances of the surface of interest points of the surface of interest (par.100 “This data may be combined with mathematical models of the solar collector (e.g., energy output derating versus temperature and/or energy output derating versus diffused light) to generate a weather-corrected expected energy output for the time of day and day of year, which may be summed to determine total energy collection far more accurately than current models.”).
Regarding claim 4, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the system of claim 1, Loveland further teaches wherein calculating solar production for the surface of interest comprises:
detecting placement obstacles on the surface of interest (par.33 “generate a three-dimensional model that includes the structure, obstacles on the structure, and obstacles proximate the structure.”);
determining measurements of the surface of interest (par.59 “a UAV may utilize a sensor to make at least two distance measurements from at least two different locations relative to the roof (e.g., two different elevations or horizontal positions) and calculate the pitch of the roof.”);
determining proposed placement of one or more solar panels (par.82 “a heatmap displayed for a user and/or used to calculate solar panel placement may graphically illustrate the optimal location for solar panel placement”) of a potential solar electrical generation system on the surface of interest, based on: the insolation values, the measurements, and the placement obstacles (par.82 “if energy demand is known to be higher in the summer between the hours of 12 pm and 5 pm, the expected irradiance values at various locations on the roof during these times may be weighted higher than the irradiance values at the same locations during winter months or off-peak hours.”); and
calculating solar production for the one or more solar panels (par.132 “the system will generate the watts produced 1405”), based on the one or more insolation values at panel points of the surface of interest points.
Regarding claim 5, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the system of claim 1, Augenbraun further teaches wherein identifying the surface of interest at the site comprises:
separating surrounding object points (par.31 “Automatic calculation of the dimensions of the outlined regions may be performed. These dimensions may be used to generate "keep out" regions for placement of solar energy system components. The "keep-out" regions may be defined by structural needs such as separation from obstructions such as vents, or regulatory requirements such as a setback from the edge of a roof.”) of the point cloud from surface of interest points (par.30 “allowing identification of separate geometric areas on the surface of interest.”; par.50 “The surface of interest may be split into a series of sections. In the limit, each and every point or combinations of points on the 3D model may be modeled as a separate section.”);
grouping surface of interest points of the point cloud into a grouping based on spatial position of the surface of interest points (par.91 “Energy collection predictions may account for tilt and inclination by calculating a projected area that is normal to the rays of sunlight for various times of day and days of year, and integrating a group of these calculations into total expected energy production over a time period.”);
identifying a representative surrounding boundary (par.141 “An operator may define areas to be analyzed. This may be accomplished by either manually drawing lines on the image through a user interface or simply choosing a desired surface which will be automatically bounded through, for example, machine-vision algorithms.”) of the grouping; and
estimating a face normal (par.131 “The normal to the solar panel may be determined.”) vector (par.120 “the polarization vector”) of the representative surrounding boundary (par.130 “Critical angle describes a boundary condition in optical phenomena.”).
Regarding claim 6, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the system of claim 1, Augenbraun further teaches wherein identifying the surface of interest at the site comprises:
identifying vertices of the surface of interest in one or more images of the site (par.24 “To determine the shadow line on each surface, the location, dimensions, and spectral data may be extracted from each surface within the photo.” Vertices can be identified from dimensions);
using a neural network (par.112 “the changes may include modification of parameters of a model or a weight within a neural network.”) to find a bounding box for the surface of interest (par.141 “An operator may define areas to be analyzed. This may be accomplished by either manually drawing lines on the image through a user interface or simply choosing a desired surface which will be automatically bounded through, for example, machine-vision algorithms.”);
algorithmically placing vertices inside of the bounding box (par.32 “Solar energy system components may be algorithmically placed according to a set of rules that includes provisions for these "keep-out" areas.”; solar rays are energy components that have vertices when they intersect); and
connecting vertices within each bounding box to identify a perimeter of the surface of interest (par.79 “This larger geographically oriented database can increase the accuracy of the ray trace by taking into account surface features that may not be available from a single source, such as large-scale terrain data, local high resolution imagery, and 3D CAD data from present or future buildings. Multiple 3D models of the same area may be combined to provide a 3D model that has a higher resolution than any of the individual input 3D models. The combination of such models may use data processing techniques known to those skilled in the art.”; Combining models containing light rays and dimensions will connect vertices).
Regarding claim 7, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the system of claim 1, Loveland further teaches wherein simulating a path of the sun with respect to the site to identify areas of the surface of interest that are shaded from the sun (par.88 “the ray-path modeling may include ray paths between locations on the roof of the structure and the modeled location of the sun that are reflected off of the neighboring structure.”) comprises:
obtaining sun positions for a plurality of points in time (abstract “The system maps ray paths between each of a plurality of locations on a roof of a structure and modeled locations of the sun at various time intervals during a selected time period to determine the solar irradiance at various locations.) that is representative of phases throughout a year (par.36 “The system may calculate the solar irradiance for different seasons, for each of a plurality of days, for only weekdays when a business is in operation, monthly, a yearly total, or an average over a time period.”) for a geolocation (par.60 “the site selection interface may identify geographic boundaries of an area, an address, and/or the GPS coordinates of a structure.”) of the site;
filtering the sun positions for the plurality of points in time to daytime sun positions (par.43 teaches filtering the sun positions for the plurality of points in time to daytime sun positions by using a heatmap that uses various shades(filters) to illustrate the relative impact or effect of various obstacles and obstructions.);
determining the daytime sun positions for at least two days of the year (par.38 “Effectively, the ray paths that are blocked correspond to the shadows that would be present for different locations of the sun at various times of the day and various days during the year.”); and
simulating the sun positions (par.39 “the sun may be modeled as being nearly directly overhead”) for the at least two days of the year (par.38 “Effectively, the ray paths that are blocked correspond to the shadows that would be present for different locations of the sun at various times of the day and various days during the year.”) to find shade on the surface of interest (par.43 “Darker grey shading or darker shades of blue may be used to show the impact or effect of shadows (ray-blocking objects) on the roof”).
Regarding claim 8, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the system of claim 1, Loveland further teaches wherein identifying from the point cloud data, one or more shading objects at the site comprises:
modeling the point cloud data to generate a site representation (par.149 “the graphical user interface may augment the three-dimensional model to show”) of the site that includes the surface of interest and one or more objects adjacent to the surface of interest (par.149 “the graphical user interface may augment the three-dimensional model to show tree growth, building construction.”).
Regarding claim 9, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the system of claim 1, Loveland further teaches wherein calculating solar production for the surface of interest is further based on equipment specifications (par.94 “User specification of available or acceptable angles may be used to update a heatmap and/or expected output of a specific solar panel layout.”).
Claim(s) 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Loveland in view of Augenbraun further in view of Ananthakrishnan.
Regarding claim 11, Loveland teaches a computer-implemented method (par.105 teaches computer-implemented method) to determine solar production (par.132 “the system will generate the watts produced 1405”), comprising:
obtaining data for a site (par.100 “The estimator module may use historical data to estimate cloudy, rainy, and/or snowy lengths of time that occlude solar exposure.” Teaches obtaining data);
identifying, from the point cloud data, a surface of interest at the site (par.33 “the system may identify and conceptually divide the roof surfaces”);
identifying from the point cloud data, one or more shading objects (par.89 “a user digitally places solar panels on a displayed model of the roof, the system may show the user how many kilowatts will be generated per hour, day, week, month, year, lifetime, etc. For example, a 300-watt solar panel in one location on the roof may be expected to collect 100 watts early in the morning, 300 watts in the afternoon, and be shaded in the later afternoon and evening.”) at the site (par.100 “The estimator module may compute a heatmap of the solar irradiation on the roof or at the subject site.”);
simulating a path of the sun (par.88 “the ray-path modeling may include ray paths between locations on the roof of the structure and the modeled location of the sun that are reflected off of the neighboring structure.”) with respect to the site to identify areas of the surface of interest that are shaded from the sun by the one or more shading objects (par.79 “the UAV solar irradiance system may generate a shaded map (e.g., a heat map or black body model) of the roof showing areas of the roof receiving varying levels of solar energy during a given time period.”);
determining one or more insolation values (par.69 “the system may utilize real-time solar irradiance values to ensure an accurate model for calculating temporally averaged solar irradiance values.”) for the surface of interest for a time period (par.79 “a given time period”), considering areas of the surface of interest that are shaded at one or more times within the time period (par.79 “the UAV solar irradiance system may generate a shaded map (e.g., a heat map or black body model) of the roof showing areas of the roof receiving varying levels of solar energy during a given time period.”); and
calculating solar production (par.132 “the system will generate the watts produced 1405”) at the surface of interest, based on the one or more insolation values (par.134 “the system may provide total output values, payoff values, estimated costs, etc. as an operator virtually places solar panels on the roof with the overlaid irradiation values (e.g., via a drag and drop operation) at locations 1502 and 1504.”).
Loveland fails to teach obtaining, via a server, data for a site based on stereoscopic images collected at the site, the point cloud data comprising a reference point and a plurality of spatial points each being located at a distance from the reference point and having a vector indication a direction and inclination relative to the reference point; generate a shade map that identifies; generating an isolation map comprising and displaying, using a client computing device located at the site, a proposed placement of one or more solar elements on a three-dimensional digital model of the site derived from the point cloud data, the proposed placement based on the calculated solar production.
Augenbraun does teach obtaining (par.14 “collecting shading data from one or two points”), via a server (par.210 teaches server), data for a site based on stereoscopic images collected at the site (par.58-60 teach stereoscopic images; par.64 “a low-resolution digital terrain model generated from satellite imagery may be combined with a high resolution model generated through stereoscopic image pairs of a building site.”); generate a shade map that identifies (par.20 “This data may then be used to generate a shadow map. Statistical analysis may be performed on each spot to determine information regarding when the particular spot receives unobstructed sunlight and when it is shaded.”); generating an isolation map (par.167 “In the user interface, an insolation map of the roof may be overlaid on the 3-D model”) comprising; and displaying (par.137-139 teaches display), using a client computing device located at the site (par.213 “a device located at the site with data input and output capabilities (e.g., iPhone, Smartphone, e-reader, digital camera, cell phone, laptop, netbook, personal digital assistant (PDA), iPod Touch or custom electronic device designed for this purpose)”; Client devices are typically personal computing devices with network software applications installed that request and receive information over the network or Internet. Mobile devices like smartphones, tablets, iPads, laptops and also desktop computers can all function as clients.), a proposed placement (par.21 “The solar energy system may be designed algorithmically by determining the placement of solar components according to a set of rules and descriptions or models of solar energy system components.”) of one or more solar elements (par.167 “solar elements”) on a three-dimensional digital model (par.167 teaches a three-dimensional digital model) of the site derived (par.129 teaches data derivation) from the point cloud data, the proposed placement based on the calculated solar production (par.91 teaches placement based on calculated solar production).
It would have been prima facie obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have modified Loveland to include the teachings of Augenbraun; which would provide an improved method of performing shadow analysis using a software with a method for mapping shadows on a surface to determine the efficiency of a planned solar installation as disclosed by Augenbraun(par.7).
Loveland in view of Augenbraun fails to teach the point cloud data comprising a reference point and a plurality of spatial points each being located at a distance from the reference point and having a vector indication a direction and inclination relative to the reference point.
Ananthakrishnan does teach the point cloud data comprising a reference point and a plurality of spatial points each being located at a distance from the reference point and having a vector indication a direction and inclination relative to the reference point (par.48 “Viable solar area determination system 104 determines a point cloud by analyzing the data from multi-view imagery database 106 (e.g., using overlapping aerial photos to determine 3-dimensional coordinates associated with objects located at the input location. After determining the point cloud, the points of the point cloud are classified (e.g., as a structure, as ground, as vegetation, as distant terrain, etc.).”; key components and characteristics of point cloud data include: Reference Point/Coordinate System: The data is usually organized within a 3D coordinate system (X, Y, Z), which acts as the reference frame. In many applications, this is a georeferenced system or an arbitrary local system, often with an "origin" (0,0,0) established by the scanner's position. Spatial Points: Each individual point in the cloud represents a precise location in 3D space. Vector/Position Representation: Each point
p
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is often treated as a vector from the reference origin. Direction and Inclination: While Cartesian coordinates (X, Y, Z) are standard, point cloud data can be interpreted as spherical coordinates (distance, azimuth, elevation/inclination) relative to the sensor, particularly in LiDAR, where the scanner measures distance and angles. These are features that must be present in point cloud data).
It would have been prima facie obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have modified Loveland in view of Augenbraun to include the teachings of Ananthakrishnan; which would provide a more rapid, accurate and refined solution for determination of an active or viable solar production area for a solar installation as disclosed by Ananthakrishnan(par.2).
Regarding claim 12, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the method of claim 11, wherein determining the one or more insolation values for the surface of interest for the time period, Loveland further teaches comprises:
masking surface of interest points of the point cloud (par.39 “Again, blocked ray paths correspond to shadows. For example, a relatively large chimney may cast very little or no shadow (i.e., block relatively few ray paths) on a rooftop when the sun is directly overhead.”);
determining a maximum available irradiance (par.33” a maximum possible solar irradiation level may be measured at the location.”) at each surface of interest point for one or more points in time during the time period (par.42 “the thousands of points on the surface of the roof may be identified, and the solar irradiance ray path tracing to various modeled locations of the sun at various time increments during a time period may be mapped”);
determining a shade metric (-par.148 “the graphical user interface generation subsystem 1692 may use the data from the ray-path modeling subsystem 1694 to display a heatmap that uses various shades of gray or different colors (e.g., black body temperature modeling) to illustrate the relative impact or effect of various obstacles and obstructions.”) at each surface of interest point for the one or more points in time during the time period (par.42 “the thousands of points on the surface of the roof may be identified, and the solar irradiance ray path tracing to various modeled locations of the sun at various time increments during a time period may be mapped”); and
determining the one or more insolation values based on the maximum available irradiances and shade metrics at each surface of interest point for the time period (par.52 “determines solar irradiance values at various locations at the time of the scan and for future time increments during future time periods.”).
Regarding claim 13, Loveland in view of Augenbraun further in view of Ananthakrishnan the method of claim 11, Augenbraun further teaches wherein determining the one or more insolation values for the surface of interest for the time period, comprises:
separating surface of interest points of the point cloud from non-surface of interest points (par.30 “allowing identification of separate geometric areas on the surface of interest.”; par.50 “The surface of interest may be split into a series of sections. In the limit, each and every point or combinations of points on the 3D model may be modeled as a separate section.”);
determining a maximum available irradiance at each surface of interest point of the surface of interest (par.230 “Alternatively, the system may generate in a step 720 power flux calculations. The power flux calculations may be used to design a solar energy system for the surface of interest.”);
determining a shade value (par.85 “A discrete value at the time the shading or power flux is calculated.”) at each surface of interest point of the surface of interest (par.24 “determine the shadow line on each surface”);
determining a shaded irradiance (par.20 “Statistical analysis may be performed on each spot to determine information regarding when the particular spot receives unobstructed sunlight and when it is shaded.”) at each surface of interest point of the surface of interest, the shaded irradiance based on the maximum available irradiance and the shade value at each point of the surface of interest (par.20 “Statistical analysis can be performed over some or all of the surface of interest to provide information for percentage shading. Integration of the ray trace data over the entire time period may further yield the power flux from light striking the rooftop. The power flux may also be calculated from the output of individual ray trace calculations.”); and
summing the shaded irradiances of the surface of interest points of the surface of interest (par.100 “This data may be combined with mathematical models of the solar collector (e.g., energy output derating versus temperature and/or energy output derating versus diffused light) to generate a weather-corrected expected energy output for the time of day and day of year, which may be summed to determine total energy collection far more accurately than current models.”).
Regarding claim 14, Loveland in view of Augenbraun further in view of Ananthakrishnan the method of claim 11, Augenbraun further teaches wherein calculating solar production for the surface of interest comprises:
detecting placement obstacles on the surface of interest (par.33 “generate a three-dimensional model that includes the structure, obstacles on the structure, and obstacles proximate the structure.”);
determining measurements of the surface of interest (par.59 “a UAV may utilize a sensor to make at least two distance measurements from at least two different locations relative to the roof (e.g., two different elevations or horizontal positions) and calculate the pitch of the roof.”);
determining proposed placement of one or more solar panels (par.82 “a heatmap displayed for a user and/or used to calculate solar panel placement may graphically illustrate the optimal location for solar panel placement”) of a potential solar electrical generation system on the surface of interest, based on: the insolation values, the measurements, and the placement obstacles (par.82 “if energy demand is known to be higher in the summer between the hours of 12 pm and 5 pm, the expected irradiance values at various locations on the roof during these times may be weighted higher than the irradiance values at the same locations during winter months or off-peak hours.”); and
calculating solar production for the one or more solar panels (par.132 “the system will generate the watts produced 1405”), based on the one or more insolation values at panel points of the surface of interest points.
Regarding claim 15, Loveland in view of Augenbraun the method of claim 11, Augenbraun further teaches wherein identifying the surface of interest at the site comprises:
separating surrounding object points (par.31 “Automatic calculation of the dimensions of the outlined regions may be performed. These dimensions may be used to generate "keep out" regions for placement of solar energy system components. The "keep-out" regions may be defined by structural needs such as separation from obstructions such as vents, or regulatory requirements such as a setback from the edge of a roof.”) of the point cloud from surface of interest points (par.30 “allowing identification of separate geometric areas on the surface of interest.”; par.50 “The surface of interest may be split into a series of sections. In the limit, each and every point or combinations of points on the 3D model may be modeled as a separate section.”);
grouping surface of interest points of the point cloud into a grouping based on spatial position of the surface of interest points (par.91 “Energy collection predictions may account for tilt and inclination by calculating a projected area that is normal to the rays of sunlight for various times of day and days of year, and integrating a group of these calculations into total expected energy production over a time period.”);
identifying a representative surrounding boundary (par.141 “An operator may define areas to be analyzed. This may be accomplished by either manually drawing lines on the image through a user interface or simply choosing a desired surface which will be automatically bounded through, for example, machine-vision algorithms.”) of the grouping; and
estimating a face normal (par.131 “The normal to the solar panel may be determined.”) vector (par.120 “the polarization vector”) of the representative surrounding boundary (par.130 “Critical angle describes a boundary condition in optical phenomena.”).
Regarding claim 16, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the method of claim 11, Augenbraun further teaches wherein identifying the surface of interest at the site comprises:
identifying vertices of the surface of interest in one or more images of the site (par.24 “To determine the shadow line on each surface, the location, dimensions, and spectral data may be extracted from each surface within the photo.” Vertices can be identified from dimensions);
using a neural network (par.112 “the changes may include modification of parameters of a model or a weight within a neural network.”) to find a bounding box for the surface of interest (par.141 “An operator may define areas to be analyzed. This may be accomplished by either manually drawing lines on the image through a user interface or simply choosing a desired surface which will be automatically bounded through, for example, machine-vision algorithms.”);
algorithmically placing vertices inside of the bounding box (par.32 “Solar energy system components may be algorithmically placed according to a set of rules that includes provisions for these "keep-out" areas.”; solar rays are energy components that have vertices when they intersect); and
connecting vertices within each bounding box to identify a perimeter of the surface of interest (par.79 “This larger geographically oriented database can increase the accuracy of the ray trace by taking into account surface features that may not be available from a single source, such as large-scale terrain data, local high resolution imagery, and 3D CAD data from present or future buildings. Multiple 3D models of the same area may be combined to provide a 3D model that has a higher resolution than any of the individual input 3D models. The combination of such models may use data processing techniques known to those skilled in the art.”; Combining models containing light rays and dimensions will connect vertices).
Regarding claim 17, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the method of claim 11, Loveland further teaches wherein simulating a path of the sun with respect to the site to identify areas of the surface of interest that are shaded from the sun (par.88 “the ray-path modeling may include ray paths between locations on the roof of the structure and the modeled location of the sun that are reflected off of the neighboring structure.”) comprises:
obtaining sun positions for a plurality of points in time (abstract “The system maps ray paths between each of a plurality of locations on a roof of a structure and modeled locations of the sun at various time intervals during a selected time period to determine the solar irradiance at various locations.) that is representative of phases throughout a year (par.36 “The system may calculate the solar irradiance for different seasons, for each of a plurality of days, for only weekdays when a business is in operation, monthly, a yearly total, or an average over a time period.”) for a geolocation (par.60 “the site selection interface may identify geographic boundaries of an area, an address, and/or the GPS coordinates of a structure.”) of the site;
filtering the sun positions for the plurality of points in time to daytime sun positions (par.43 teaches filtering the sun positions for the plurality of points in time to daytime sun positions by using a heatmap that uses various shades(filters) to illustrate the relative impact or effect of various obstacles and obstructions.);
determining the daytime sun positions for at least two days of the year (par.38 “Effectively, the ray paths that are blocked correspond to the shadows that would be present for different locations of the sun at various times of the day and various days during the year.”); and
simulating the sun positions (par.39 “the sun may be modeled as being nearly directly overhead”) for the at least two days of the year (par.38 “Effectively, the ray paths that are blocked correspond to the shadows that would be present for different locations of the sun at various times of the day and various days during the year.”) to find shade on the surface of interest (par.43 “Darker grey shading or darker shades of blue may be used to show the impact or effect of shadows (ray-blocking objects) on the roof”).
Regarding claim 18, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the method of claim 11, Loveland further teaches wherein identifying from the point cloud data, one or more shading objects at the site comprises:
modeling the point cloud data to generate a site representation (par.149 “the graphical user interface may augment the three-dimensional model to show”) of the site that includes the surface of interest and one or more objects adjacent to the surface of interest (par.149 “the graphical user interface may augment the three-dimensional model to show tree growth, building construction.”).
Regarding claim 19, Loveland in view of Augenbraun further in view of Ananthakrishnan teaches the method of claim 11, Loveland further teaches wherein calculating solar production for the surface of interest is further based on equipment specifications (par.94 “User specification of available or acceptable angles may be used to update a heatmap and/or expected output of a specific solar panel layout.”).
Allowable Subject Matter
Claims 10 and 20 are 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:
Regarding claim 10, the updated prior art does not teach or suggest, in combination with the rest of the limitation of claim 1,
“… for each point to be included in the IV, summing irradiation values of unshaded areas of the surface of interest to obtain a Total Irradiance Subset Where Shaded ("TISWS"); and calculating a Total Irradiance Subset ("TIS") minus TISWS, dividing by TIS, and multiplying by Total Yearly Irradiance ("TIY") (IV=(TIS-TISWS)/TIS)*TYI).”
Regarding claim 20, the updated prior art does not teach or suggest, in combination with the rest of the limitation of claim 11,
“… for each point to be included in the IV, summing irradiation values of unshaded areas of the surface of interest to obtain a Total Irradiance Subset Where Shaded ("TISWS"); and calculating a Total Irradiance Subset ("TIS") minus TISWS, dividing by TIS, and multiplying by Total Yearly Irradiance ("TIY") (IV=(TIS-TISWS)/TIS)*TYI).”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 20180350044 A1; Ponto; Kevin et al. is an embodiment for systems, methods, and media for hierarchical progressive point cloud rendering.
US 20190279420 A1; Moreno; Lennie et al. is an embodiment for automated roof surface measurement from combined aerial lidar data and imagery.
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/CARL F.R. TCHATCHOUANG/Examiner, Art Unit 2858
/HUY Q PHAN/Supervisory Patent Examiner, Art Unit 2858