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 .
Drawings
Drawings have been reviewed and accepted.
Specification
The specification filed on 01/08/24 has been entered. Specification has been reviewed and accepted.
Information Disclosure Statement
The information disclosure statement (IDS) submitted filed on 02/05/24 has been received. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim 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) 1-2, 4-5, 8-9, 11-12, 15-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hardcastle et al. (US20110224905A1, herein Hardcastle), in view of Ashdown et al. (US20190353985, herein Ashdown).
Regarding claim 1, Hardcastle teaches An artificial weathering system ([0018] an accelerated weathering test apparatus) comprising: a controller comprising a processor ([0025] a processor and a memory that stores programming instructions), configured to: analyse, … actual use data for an object in a non-artificial environment ([0053] The multi-variable micro-environment cycle may be selected from the group consisting of an outdoor micro-environment, an indoor micro-environment and a laboratory-generated micro-environment), wherein the actual use data comprises: measured ambient temperatures in the non-artificial environment at first different times over a defined period of time ([0029] operating parameters that are at least two selected from the group consisting of temperature, ultraviolet irradiance and moisture, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature, [[0025] adjusting the programming instructions so that the run-time variables reconcile to the operating parameters) ; and for each surface of a group of surfaces of the object, measured surface temperatures at the surface at the first different times ([0053] A micro-environment detector 10 may be installed in any imaginable location in order to record a desired or selected micro-environment cycle for a certain material, such as, for example only, on an exterior surface 22 of a building 20, such as a roof 24 or a wall 26, on an interior surface of the building 20, such as a frame of an exterior window 28 of the building 20, the interior walls or the floor, on continuously or non-continuously exposed components 30 of the building 22 structure, such as deck or porch railing, on an exterior surface 42 of a vehicle 40, on an interior surface 44 of the vehicle 40, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature); determine, …, simulation data for simulating the actual use data for the object in an artificial environment in a condensed period of time that is shorter than the defined period of time ([0044] a controller to ensure very accurate simulation of the end-use multi-variable micro-environment cycles inside the artificial accelerated weathering test apparatus so that the degradation of the test specimen exposed inside the artificial accelerated weathering test apparatus is accurately simulated, [0046] edit the operating or function parameters before reproduction of such cycles in the accelerated weathering test apparatus in order to achieve an accelerated test while still maintaining the basic micro-environment cycle characteristics for accurate simulation of material degradation in end-use), wherein the simulation data comprises: ambient temperatures in the artificial environment at second different times over the condensed period of time; for each surface of the group of surfaces of the object, surface temperatures at the surface at the second different times; and dynamic configurations of one or more radiation generators and one or more airflow generators over the condensed period of time to produce the ambient temperatures in the artificial environment and the respective surface temperatures of the surfaces of the object at the second different times ([0052] The operating or function parameters to remove non-critical time periods from such operating parameters, supplement the operating parameters for acceleration, adjust the operating parameters with respect to one of the group consisting of frequency, duration and sequence, average a plurality of micro-environment cycles, generate a statistically probable worst case scenario and interpolate the operating parameters to produce a generally smooth continuous rate of change, [0055] the temperature adjusting source 110 may include any combination of heating (e.g. direct or indirect heat source), cooling (e.g., direct or indirect cooling source) and air movement (e.g., fan, ducting, dampers, mixing devices or other air moving devices) components as commonly known as suitable for the intended application in the accelerated weathering art, so that the temperature exposure in the test chamber 104 may be adjusted up, down or the same based on the operating or function parameters under instruction from the controller 114, [0035] obtaining the same degradation results for identical materials or test specimens regardless if exposed to artificial or laboratory accelerated exposure cycles or natural end-use multi-variable micro-environment exposure cycles by generating exposures in the artificial accelerated weathering test apparatus that are an accurate simulation of the natural multi-variable micro-environment cycles (i.e., exposing the test specimen to the same operating or function parameters of a natural multi-variable micro-environment cycle within the accelerated weathering test apparatus)); and control …, the one or more radiation generators and the one or more airflow generators in the artificial environment according to the dynamic configurations over the condensed period of time to simulate the actual use data on the object ([0055] the controller 114. In one embodiment, the temperature adjusting source 110 may include any combination of heating (e.g. direct or indirect heat source), cooling (e.g., direct or indirect cooling source) and air movement (e.g., fan, ducting, dampers, mixing devices or other air moving devices) components as commonly known as suitable for the intended application in the accelerated weathering art, so that the temperature exposure in the test chamber 104 may be adjusted up, down or the same based on the operating or function parameters under instruction from the controller 114, [0025] a method for accurate service life prediction may include controlling an accelerated weathering test apparatus that may have a test chamber, a mount in the test chamber for a test specimen, an irradiance source in the test chamber, a temperature adjustment source in the accelerated weathering test apparatus, a moisture adjustment source in the accelerated weathering test apparatus).
Hardcastle does not teach using machine learning, using the machine learning
Ashdown teaches using machine learning, using the machine learning ([0011] accurately model the physical environment and simulate its performance. If the virtual simulation includes representations of the physical sensors and can reasonably approximate their outputs over time, then a spatially dense array of virtual sensors can be used to provide both supervised training data for the artificial intelligence engine, [0020] The system includes a trainable artificial intelligence (AI) based controller that is trained by the predicted environmental conditions and the output control signals)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hardcastle’s teaching of simulating data for service life prediction with Ashdown’s teaching of a simulation using trainable AI based controller training by predicted environment conditions . The combined teaching provides an expected result of simulating data using trainable AI based controller by predicting environment conditions for service life prediction. Therefore, one of ordinary skill in the art would be motivated to improve the accuracy of the predictions.
Regarding claim 2, the combination of Hardcastle and Ashdown teach The artificial weathering system of claim 1, wherein the simulation data further comprises respective locations of temperature measurement devices on the group of surfaces of the object in the artificial environment (Hardcastle, [0053] A micro-environment detector 10 may be installed in any imaginable location in order to record a desired or selected micro-environment cycle for a certain material, such as, for example only, on an exterior surface 22 of a building 20, such as a roof 24 or a wall 26, on an interior surface of the building 20, such as a frame of an exterior window 28 of the building 20, the interior walls or the floor, on continuously or non-continuously exposed components 30 of the building 22 structure, such as deck or porch railing, on an exterior surface 42 of a vehicle 40, on an interior surface 44 of the vehicle 40… the micro-environment detector 10 may be installed in an accelerated weathering test apparatus, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature, [0035] obtaining the same degradation results for identical materials or test specimens regardless if exposed to artificial or laboratory accelerated exposure cycles or natural end-use multi-variable micro-environment exposure cycles by generating exposures in the artificial accelerated weathering test apparatus that are an accurate simulation of the natural multi-variable micro-environment cycles (i.e., exposing the test specimen to the same operating or function parameters of a natural multi-variable micro-environment cycle within the accelerated weathering test apparatus)).
Regarding claim 4, the combination of Hardcastle and Ashdown teach The artificial weathering system of claim 1, wherein the determining the simulation data comprises at least one of interpolating or condensing the actual use data to determine the respective surface temperatures of the surfaces of the object at the second different times over the condensed period of time (Hardcastle, Fig. 2, Fig. 7, Fig. 9 [0030] editing the operating parameters may include removing non-critical time periods of the operating parameters from the multi-variable micro-environment cycle, adjusting the operating parameters with respect to one of the group consisting of frequency, duration and sequence, averaging a plurality of micro-environment cycles or generating a statistically probable worst case scenario, [0058] editing of the operating parameters and the generating of the function parameters, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature).
Regarding claim 5, the combination of Hardcastle and Ashdown teach The artificial weathering system of claim 1, wherein the determining the simulation data comprises dividing the defined period of time into segments, and for each segment of the segments, at least one of interpolating or condensing the actual use data in the segment to determine the respective surface temperatures of the surfaces of the object at the second different times in a corresponding segment of the condensed period of time (Hardcastle, Fig. 2, Fig. 7, Fig. 9 [0030] editing the operating parameters may include removing non-critical time periods of the operating parameters from the multi-variable micro-environment cycle, adjusting the operating parameters with respect to one of the group consisting of frequency, duration and sequence, averaging a plurality of micro-environment cycles or generating a statistically probable worst case scenario, [0058] editing of the operating parameters and the generating of the function parameters, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature).
Regarding claim 8, Hardcastle A method, comprising: analysing, by a system comprising a processor, … actual use data for an object in a non-artificial environment ([0018] an accelerated weathering test apparatus, [0025] a processor and a memory that stores programming instructions, [0053] The multi-variable micro-environment cycle may be selected from the group consisting of an outdoor micro-environment, an indoor micro-environment and a laboratory-generated micro-environment), wherein the actual use data comprises: measured ambient temperatures in the non-artificial environment at first different times over a defined period of time ([0029] operating parameters that are at least two selected from the group consisting of temperature, ultraviolet irradiance and moisture, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature, [[0025] adjusting the programming instructions so that the run-time variables reconcile to the operating parameters); and for each surface of a group of surfaces of the object, measured surface temperatures at the surface at the first different times ([0053] A micro-environment detector 10 may be installed in any imaginable location in order to record a desired or selected micro-environment cycle for a certain material, such as, for example only, on an exterior surface 22 of a building 20, such as a roof 24 or a wall 26, on an interior surface of the building 20, such as a frame of an exterior window 28 of the building 20, the interior walls or the floor, on continuously or non-continuously exposed components 30 of the building 22 structure, such as deck or porch railing, on an exterior surface 42 of a vehicle 40, on an interior surface 44 of the vehicle 40, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature); determining, by the system…, simulation data for simulating the actual use data for the object in an artificial environment in a condensed period of time that is shorter than the defined period of time ([0044] a controller to ensure very accurate simulation of the end-use multi-variable micro-environment cycles inside the artificial accelerated weathering test apparatus so that the degradation of the test specimen exposed inside the artificial accelerated weathering test apparatus is accurately simulated, [0046] edit the operating or function parameters before reproduction of such cycles in the accelerated weathering test apparatus in order to achieve an accelerated test while still maintaining the basic micro-environment cycle characteristics for accurate simulation of material degradation in end-use), wherein the simulation data comprises: ambient temperatures in the artificial environment at second different times over the condensed period of time; for each surface of the group of surfaces of the object, surface temperatures at the surface at the second different times ([0052] The operating or function parameters to remove non-critical time periods from such operating parameters, supplement the operating parameters for acceleration, adjust the operating parameters with respect to one of the group consisting of frequency, duration and sequence, average a plurality of micro-environment cycles, generate a statistically probable worst case scenario and interpolate the operating parameters to produce a generally smooth continuous rate of change, [0055] the temperature adjusting source 110 may include any combination of heating (e.g. direct or indirect heat source), cooling (e.g., direct or indirect cooling source) and air movement (e.g., fan, ducting, dampers, mixing devices or other air moving devices) components as commonly known as suitable for the intended application in the accelerated weathering art, so that the temperature exposure in the test chamber 104 may be adjusted up, down or the same based on the operating or function parameters under instruction from the controller 114, [0035] obtaining the same degradation results for identical materials or test specimens regardless if exposed to artificial or laboratory accelerated exposure cycles or natural end-use multi-variable micro-environment exposure cycles by generating exposures in the artificial accelerated weathering test apparatus that are an accurate simulation of the natural multi-variable micro-environment cycles (i.e., exposing the test specimen to the same operating or function parameters of a natural multi-variable micro-environment cycle within the accelerated weathering test apparatus)); and dynamic configurations of one or more radiation generators and one or more airflow generators over the condensed period of time to achieve the ambient temperatures in the artificial environment and the respective surface temperatures of the surfaces of the object at the second different times; and controlling, by the system… , the one or more radiation generators and the one or more airflow generators in the artificial environment according to the dynamic configurations over the condensed period of time to simulate the actual use data on the object ([0055] the controller 114. In one embodiment, the temperature adjusting source 110 may include any combination of heating (e.g. direct or indirect heat source), cooling (e.g., direct or indirect cooling source) and air movement (e.g., fan, ducting, dampers, mixing devices or other air moving devices) components as commonly known as suitable for the intended application in the accelerated weathering art, so that the temperature exposure in the test chamber 104 may be adjusted up, down or the same based on the operating or function parameters under instruction from the controller 114, [0025] a method for accurate service life prediction may include controlling an accelerated weathering test apparatus that may have a test chamber, a mount in the test chamber for a test specimen, an irradiance source in the test chamber, a temperature adjustment source in the accelerated weathering test apparatus, a moisture adjustment source in the accelerated weathering test apparatus).
Hardcastle does not teach using machine learning, using the machine learning
Ashdown teaches using machine learning, using the machine learning ([0011] accurately model the physical environment and simulate its performance. If the virtual simulation includes representations of the physical sensors and can reasonably approximate their outputs over time, then a spatially dense array of virtual sensors can be used to provide both supervised training data for the artificial intelligence engine, [0020] The system includes a trainable artificial intelligence (AI) based controller that is trained by the predicted environmental conditions and the output control signals)
Regarding claim 9, the combination of Hardcastle and Ashdown teach The method of claim 8, wherein the simulation data further comprises respective locations of temperature measurement devices on the group of surfaces of the object in the artificial environment (Hardcastle, [0053] A micro-environment detector 10 may be installed in any imaginable location in order to record a desired or selected micro-environment cycle for a certain material, such as, for example only, on an exterior surface 22 of a building 20, such as a roof 24 or a wall 26, on an interior surface of the building 20, such as a frame of an exterior window 28 of the building 20, the interior walls or the floor, on continuously or non-continuously exposed components 30 of the building 22 structure, such as deck or porch railing, on an exterior surface 42 of a vehicle 40, on an interior surface 44 of the vehicle 40… the micro-environment detector 10 may be installed in an accelerated weathering test apparatus, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature, [0035] obtaining the same degradation results for identical materials or test specimens regardless if exposed to artificial or laboratory accelerated exposure cycles or natural end-use multi-variable micro-environment exposure cycles by generating exposures in the artificial accelerated weathering test apparatus that are an accurate simulation of the natural multi-variable micro-environment cycles (i.e., exposing the test specimen to the same operating or function parameters of a natural multi-variable micro-environment cycle within the accelerated weathering test apparatus)).
Regarding claim 11, the combination of Hardcastle and Ashdown The method of claim 8, wherein the determining the simulation data comprises at least one of interpolating or condensing the actual use data to determine the respective surface temperatures of the surfaces of the object at the second different times over the condensed period of time (Hardcastle, Fig. 2, Fig. 7, Fig. 9 [0030] editing the operating parameters may include removing non-critical time periods of the operating parameters from the multi-variable micro-environment cycle, adjusting the operating parameters with respect to one of the group consisting of frequency, duration and sequence, averaging a plurality of micro-environment cycles or generating a statistically probable worst case scenario, [0058] editing of the operating parameters and the generating of the function parameters, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature).
Regarding claim 12, the combination of Hardcastle and Ashdown teach The method of claim 8, wherein the determining the simulation data comprises dividing the defined period of time into segments, and for each segment of the segments, at least one of interpolating or condensing the actual use data in the segment to determine the respective surface temperatures of the surfaces of the object at the second different times in a corresponding segment of the condensed period of time (Hardcastle, Fig. 2, Fig. 7, Fig. 9 [0030] editing the operating parameters may include removing non-critical time periods of the operating parameters from the multi-variable micro-environment cycle, adjusting the operating parameters with respect to one of the group consisting of frequency, duration and sequence, averaging a plurality of micro-environment cycles or generating a statistically probable worst case scenario, [0058] editing of the operating parameters and the generating of the function parameters, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature).
Regarding claim 15, Hardcastle teaches A non-transitory computer-readable medium having instructions stored thereon that, in response to execution, cause a system comprising a processor to perform operations ([0025] a processor and a memory that stores programming instructions) comprising: analysing, … actual use data for an object in a non-artificial environment ([0053] The multi-variable micro-environment cycle may be selected from the group consisting of an outdoor micro-environment, an indoor micro-environment and a laboratory-generated micro-environment, [0018] an accelerated weathering test apparatus), wherein the actual use data comprises: measured ambient temperatures in the non-artificial environment at first different times over a defined period of time ([0029] operating parameters that are at least two selected from the group consisting of temperature, ultraviolet irradiance and moisture, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature, [[0025] adjusting the programming instructions so that the run-time variables reconcile to the operating parameters); and for each surface of a group of surfaces of the object, measured surface temperatures at the surface at the first different times ([0053] A micro-environment detector 10 may be installed in any imaginable location in order to record a desired or selected micro-environment cycle for a certain material, such as, for example only, on an exterior surface 22 of a building 20, such as a roof 24 or a wall 26, on an interior surface of the building 20, such as a frame of an exterior window 28 of the building 20, the interior walls or the floor, on continuously or non-continuously exposed components 30 of the building 22 structure, such as deck or porch railing, on an exterior surface 42 of a vehicle 40, on an interior surface 44 of the vehicle 40, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature); determining, … simulation data for simulating the actual use data for the object in an artificial environment in a condensed period of time that is shorter than the defined period of time ([0044] a controller to ensure very accurate simulation of the end-use multi-variable micro-environment cycles inside the artificial accelerated weathering test apparatus so that the degradation of the test specimen exposed inside the artificial accelerated weathering test apparatus is accurately simulated, [0046] edit the operating or function parameters before reproduction of such cycles in the accelerated weathering test apparatus in order to achieve an accelerated test while still maintaining the basic micro-environment cycle characteristics for accurate simulation of material degradation in end-use), wherein the simulation data comprises: ambient temperatures in the artificial environment at second different times over the condensed period of time; for each surface of the group of surfaces of the object, surface temperatures at the surface at the second different times; and dynamic configurations of one or more radiation generators and one or more airflow generators over the condensed period of time to produce the ambient temperatures in the artificial environment and the respective surface temperatures of the surfaces of the object at the second different times ([0052] The operating or function parameters to remove non-critical time periods from such operating parameters, supplement the operating parameters for acceleration, adjust the operating parameters with respect to one of the group consisting of frequency, duration and sequence, average a plurality of micro-environment cycles, generate a statistically probable worst case scenario and interpolate the operating parameters to produce a generally smooth continuous rate of change, [0055] the temperature adjusting source 110 may include any combination of heating (e.g. direct or indirect heat source), cooling (e.g., direct or indirect cooling source) and air movement (e.g., fan, ducting, dampers, mixing devices or other air moving devices) components as commonly known as suitable for the intended application in the accelerated weathering art, so that the temperature exposure in the test chamber 104 may be adjusted up, down or the same based on the operating or function parameters under instruction from the controller 114, [0035] obtaining the same degradation results for identical materials or test specimens regardless if exposed to artificial or laboratory accelerated exposure cycles or natural end-use multi-variable micro-environment exposure cycles by generating exposures in the artificial accelerated weathering test apparatus that are an accurate simulation of the natural multi-variable micro-environment cycles (i.e., exposing the test specimen to the same operating or function parameters of a natural multi-variable micro-environment cycle within the accelerated weathering test apparatus)); and controlling…, the one or more radiation generators and the one or more airflow generators in the artificial environment according to the dynamic configurations over the condensed period of time to simulate the actual use data on the object ([0055] the controller 114. In one embodiment, the temperature adjusting source 110 may include any combination of heating (e.g. direct or indirect heat source), cooling (e.g., direct or indirect cooling source) and air movement (e.g., fan, ducting, dampers, mixing devices or other air moving devices) components as commonly known as suitable for the intended application in the accelerated weathering art, so that the temperature exposure in the test chamber 104 may be adjusted up, down or the same based on the operating or function parameters under instruction from the controller 114, [0025] a method for accurate service life prediction may include controlling an accelerated weathering test apparatus that may have a test chamber, a mount in the test chamber for a test specimen, an irradiance source in the test chamber, a temperature adjustment source in the accelerated weathering test apparatus, a moisture adjustment source in the accelerated weathering test apparatus).
Hardcastle does not teach using machine learning, using the machine learning
Ashdown teaches using machine learning, using the machine learning ([0011] accurately model the physical environment and simulate its performance. If the virtual simulation includes representations of the physical sensors and can reasonably approximate their outputs over time, then a spatially dense array of virtual sensors can be used to provide both supervised training data for the artificial intelligence engine, [0020] The system includes a trainable artificial intelligence (AI) based controller that is trained by the predicted environmental conditions and the output control signals).
Regarding claim 16, the combination of Hardcastle and Ashdown teach The non-transitory computer-readable medium of claim 15, wherein the simulation data further comprises respective locations of temperature measurement devices on the group of surfaces of the object in the artificial environment (Hardcastle, [0053] A micro-environment detector 10 may be installed in any imaginable location in order to record a desired or selected micro-environment cycle for a certain material, such as, for example only, on an exterior surface 22 of a building 20, such as a roof 24 or a wall 26, on an interior surface of the building 20, such as a frame of an exterior window 28 of the building 20, the interior walls or the floor, on continuously or non-continuously exposed components 30 of the building 22 structure, such as deck or porch railing, on an exterior surface 42 of a vehicle 40, on an interior surface 44 of the vehicle 40… the micro-environment detector 10 may be installed in an accelerated weathering test apparatus, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature, [0035] obtaining the same degradation results for identical materials or test specimens regardless if exposed to artificial or laboratory accelerated exposure cycles or natural end-use multi-variable micro-environment exposure cycles by generating exposures in the artificial accelerated weathering test apparatus that are an accurate simulation of the natural multi-variable micro-environment cycles (i.e., exposing the test specimen to the same operating or function parameters of a natural multi-variable micro-environment cycle within the accelerated weathering test apparatus)).
Regarding claim 19, the combination of Hardcastle and Ashdown teach The non-transitory computer-readable medium of claim 15, wherein the determining the simulation data comprises dividing the defined period of time into segments, and for each segment of the segments, at least one of interpolating or condensing the actual use data in the segment to determine the respective surface temperatures of the surfaces of the object at the second different times in a corresponding segment of the condensed period of time (Hardcastle, Fig. 2, Fig. 7, Fig. 9 [0030] editing the operating parameters may include removing non-critical time periods of the operating parameters from the multi-variable micro-environment cycle, adjusting the operating parameters with respect to one of the group consisting of frequency, duration and sequence, averaging a plurality of micro-environment cycles or generating a statistically probable worst case scenario, [0058] editing of the operating parameters and the generating of the function parameters, [0010] Material exposure temperatures are a complex function of material characteristics such as solar absorbance, emittance and thermal conductivity characteristics of the material as well as environmental variable characteristics such as ambient temperature).
Claim(s) 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hardcastle et al. (US20110224905A1, herein Hardcastle), in view of Ashdown et al. (US20190353985, herein Ashdown), in further view of Albaghajati et al. (US20170199525A1, herein Albaghajati).
Regarding claim 3, the combination of Hardcastle and Ashdown teach The artificial weathering system of claim 2, … temperature measurement device ([0053] The micro-environment detector may include any suitable multi-variable exposure measuring device or sensor (e.g., irradiance, temperature, moisture, etc.)) measurement devices at the respective locations on the group of surfaces ([0053] A micro-environment detector 10 may be installed in any imaginable location in order to record a desired or selected micro-environment cycle for a certain material, such as, for example only, on an exterior surface 22 of a building 20, such as a roof 24 or a wall 26, on an interior surface of the building 20, such as a frame of an exterior window 28 of the building 20).
Ashdown further teaches wherein the controller is further configured to control, using the machine learning ([0020] The system includes a trainable artificial intelligence (AI) based controller that is trained by the predicted environmental conditions and the output control signals),
The combination of Hardcastle and Ashdown do not teach one or more robotic devices to place the …measurement devices
Albaghajati teaches one or more robotic devices to place the …measurement devices ([0095] If the current location of the robot is at the target point, then at step S208 the computer system uses the sensor handling assembly to deploy a seismic sensor at the target point, [0038] The internal compartment 129 houses a sensor handling assembly 130 that includes a robotic arm and control circuitry for deployment and recovery of seismic sensors from the sensor load/unload position 152)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hardcastle’s teaching of generating simulation data from temperature sensors placed of surfaces with Albaghajati’s teaching of a robot placing the sensors. The combined teaching provides an expected result of generating simulation data from temperature sensors placed by robots on surfaces. Therefore, one of ordinary skill in the art would be motivated to improve user experience and safety shown by Albaghajati [0006] the deployment of sensors in these regions is arduous and sometimes dangerous. Accordingly, there is a requirement for a technique of deploying sensors in an efficient manner.
Regarding claim 10, the combination of Hardcastle and Ashdown teach The method of claim 9, further comprising controlling, by the system, …temperature measurement devices at the respective locations on the group of surfaces (Hardcastle, [0053] The micro-environment detector may include any suitable multi-variable exposure measuring device or sensor (e.g., irradiance, temperature, moisture, etc.), [0053] A micro-environment detector 10 may be installed in any imaginable location in order to record a desired or selected micro-environment cycle for a certain material, such as, for example only, on an exterior surface 22 of a building 20, such as a roof 24 or a wall 26, on an interior surface of the building 20, such as a frame of an exterior window 28 of the building 20).)
Ashdown further teaches using the machine learning ([0020] The system includes a trainable artificial intelligence (AI) based controller that is trained by the predicted environmental conditions and the output control signals),
The combination of Hardcastle and Ashdown do not teach one or more robotic devices to place the …measurement devices
Albaghajati teaches one or more robotic devices to place the …measurement devices ([0095] If the current location of the robot is at the target point, then at step S208 the computer system uses the sensor handling assembly to deploy a seismic sensor at the target point, [0038] The internal compartment 129 houses a sensor handling assembly 130 that includes a robotic arm and control circuitry for deployment and recovery of seismic sensors from the sensor load/unload position 152)
Regarding claim 17, the combination of Hardcastle and Ashdown teach The non-transitory computer-readable medium of claim 16, wherein the operations further comprise controlling, …temperature measurement devices at the respective locations on the group of surfaces (Hardcastle, [0053] The micro-environment detector may include any suitable multi-variable exposure measuring device or sensor (e.g., irradiance, temperature, moisture, etc.), [0053] A micro-environment detector 10 may be installed in any imaginable location in order to record a desired or selected micro-environment cycle for a certain material, such as, for example only, on an exterior surface 22 of a building 20, such as a roof 24 or a wall 26, on an interior surface of the building 20, such as a frame of an exterior window 28 of the building 20).)
Ashdown further teaches using the machine learning ([0020] The system includes a trainable artificial intelligence (AI) based controller that is trained by the predicted environmental conditions and the output control signals),
The combination of Hardcastle and Ashdown do not teach one or more robotic devices to place the …measurement devices
Albaghajati teaches one or more robotic devices to place the …measurement devices ([0095] If the current location of the robot is at the target point, then at step S208 the computer system uses the sensor handling assembly to deploy a seismic sensor at the target point, [0038] The internal compartment 129 houses a sensor handling assembly 130 that includes a robotic arm and control circuitry for deployment and recovery of seismic sensors from the sensor load/unload position 152)
Claim(s) 6-7, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hardcastle et al. (US20110224905A1, herein Hardcastle), in view of Ashdown et al. (US20190353985, herein Ashdown), in further view of Fainberg et al. (US20160048614, herein Fainberg).
Regarding claim 6, the combination of Hardcastle and Ashdown teach The artificial weathering system of claim 5, wherein the determining the simulation data further comprises determining for each segment and for each surface a …radiation at the surface during the segment based on the measured surface temperatures at the surface at the first different times during the segment (Hardcastle, Fig. 8, Fig. 9, [0036] The run-time variables (e.g., temperature, UV irradiance, moisture, etc.) of the artificial accelerated weathering test apparatus exposure cycle of the present disclosure are always changing in the same manner and with the same generally smooth continuous rates of change as in the operating or function parameters of the desired or selected multi-variable micro-environment cycles (e.g., frequency, duration, sequence, etc., [0044] recording operating parameters of multi-variable micro-environment cycles, including, but not limited to, solar UV irradiance, temperature and moisture exposure conditions with a micro-environment detector or data logger and then playing back the recorded operating parameters using an artificial accelerated weathering test apparatus including irradiance (other than solar), temperature adjusting and moisture adjusting sources, an exposure detector or data logger in the test chamber as a feedback device and a controller to ensure very accurate simulation of the end-use multi-variable micro-environment cycles inside the artificial accelerated weathering test apparatus so that the degradation of the test specimen exposed inside the artificial accelerated weathering test apparatus is accurately simulated) .
The combination of Hardcastle and Ashdown do not teach sum total of radiation
Fainberg teaches sum total of radiation ([0071] model for surface radiation is that of determining the values of the incident radiation at each part of the radiant surface, [0074] the integral in (4) with a total through the contributions of the individual subdivisions of the radiant surface)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hardcastle’s teaching of monitoring run-time variables (e.g., temperature, UV irradiance, moisture, etc.) of the artificial accelerated weathering test apparatus with Fainberg’s teaching of monitoring a total of radiation of subdivisions of the radiant surface. The combined teaching provides an expected result of monitoring run time variables including a total of radiation of subdivisions of radiant surfaces of the weather testing apparatus . Therefore, one of ordinary skill in the art would be motivated to improve simulation accuracy.
Regarding claim 7, the combination of Hardcastle, Ashdown, and Fainberg teach The artificial weathering system of claim 6, wherein the surface temperatures of the surface at the second different times in the corresponding segment are based on the … radiation at the surface during the segment ([0044] recording operating parameters of multi-variable micro-environment cycles, including, but not limited to, solar UV irradiance, temperature and moisture exposure conditions with a micro-environment detector or data logger and then playing back the recorded operating parameters using an artificial accelerated weathering test apparatus including irradiance (other than solar), temperature adjusting and moisture adjusting sources, an exposure detector or data logger in the test chamber as a feedback device and a controller to ensure very accurate simulation of the end-use multi-variable micro-environment cycles inside the artificial accelerated weathering test apparatus so that the degradation of the test specimen exposed inside the artificial accelerated weathering test apparatus is accurately simulated).
Fainberg further teaches sum total of radiation ([0071] model for surface radiation is that of determining the values of the incident radiation at each part of the radiant surface, [0074] the integral in (4) with a total through the contributions of the individual subdivisions of the radiant surface)
Regarding claim 13, the combination of Hardcastle and Ashdown teach The method of claim 12, wherein the determining the simulation data further comprises determining for each segment and for each surface a … radiation at the surface during the segment based on the measured surface temperatures at the surface at the first different times during the segment (Hardcastle, [0044] recording operating parameters of multi-variable micro-environment cycles, including, but not limited to, solar UV irradiance, temperature and moisture exposure conditions with a micro-environment detector or data logger and then playing back the recorded operating parameters using an artificial accelerated weathering test apparatus including irradiance (other than solar), temperature adjusting and moisture adjusting sources, an exposure detector or data logger in the test chamber as a feedback device and a controller to ensure very accurate simulation of the end-use multi-variable micro-environment cycles inside the artificial accelerated weathering test apparatus so that the degradation of the test specimen exposed inside the artificial accelerated weathering test apparatus is accurately simulated).
The combination of Hardcastle and Ashdown do not teach sum total of radiation
Fainberg teaches sum total of radiation ([0071] model for surface radiation is that of determining the values of the incident radiation at each part of the radiant surface, [0074] the integral in (4) with a total through the contributions of the individual subdivisions of the radiant surface)
Regarding claim 14, the combination of Hardcastle, Ashdown, and Fainberg teach The method of claim 13, wherein the surface temperatures of the surface at the second different times in the corresponding segment are based on the …radiation at the surface during the segment.
Fainberg further teaches sum total of radiation ([0071] model for surface radiation is that of determining the values of the incident radiation at each part of the radiant surface, [0074] the integral in (4) with a total through the contributions of the individual subdivisions of the radiant surface)
Regarding claim 20, the combination of Hardcastle and Ashdown teach The non-transitory computer-readable medium of claim 19, wherein the determining the simulation data further comprises determining for each segment and for each surface a …radiation at the surface during the segment based on the measured surface temperatures at the surface at the first different times during the segment, and wherein the surface temperatures of the surface at the second different times in the corresponding segment are based on the … radiation at the surface during the segment.
The combination of Hardcastle and Ashdown do not teach sum total of radiation
Fainberg teaches sum total of radiation ([0071] model for surface radiation is that of determining the values of the incident radiation at each part of the radiant surface, [0074] the integral in (4) with a total through the contributions of the individual subdivisions of the radiant surface)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Venkatadri (US20200134494) discloses a system for generating artificial scenarios for an autonomous vehicle.
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/YVONNE T FOLLANSBEE/
Examiner, Art Unit 2117
/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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