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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/13/2026 has been entered.
Claim Interpretation
The claim term “render” is interpreted to mean generate image. Thus, the relevant claim limitation is interpreted to mean that an image is generated from the sensor data.
In interpreting the claim limitations, “the threshold value includes a range of at least two values”, Examiner looks to page 12, lines 9-13 of the filed specification, which includes:
PNG
media_image1.png
247
1256
media_image1.png
Greyscale
Thus, the limitations, “range of at least two values” are interpreted to mean a range between to values.
Response to Amendments
Amendments have not overcome the 35 USC 103 rejections. New claims 20 and 21 are also rejected under 35 USC 103. See the prior art rejection section below for specificity.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive. Beginning on page 8, Applicant argues that:
PNG
media_image2.png
1267
1080
media_image2.png
Greyscale
Examiner respectfully disagrees. Ramer teaches comparing hyperspectral images to a reference spectral intensity pattern in order to determine an environmental condition. Note that the environmental condition does not have to be a compound. Ramer teaches that the environmental condition can be smoke, fungus, temperature, or light. (See the citations provided in addressing the claim limitations in the 35 USC 103 rejection below.) In these instances, the comparison does not identify a compound or concentration of a compound. Note that smoke, fungus, temperature, or light are not compounds. Particularly, note that smoke is not a compound, but rather a heterogeneous mixture. It consists of tiny solid particles (such as carbon, ash, and soot) and liquid droplets suspended in a gas (air and combustion gases like carbon dioxide and water vapor.
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, 3, 6-12, 14-16, 18, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180160510 (Ramer) in view of WO 2019141559 A1 (Sivaraman).
As per claim 1, Ramer teaches a method of detecting a condition in an environment, the method comprising:
obtaining, via a sensing mechanism, a plurality of samples taken from the environment while at least one entity from within the environment is experiencing the condition; rendering, via a processor, at least one reference image of the plurality of reference images from each of the plurality of samples taken from the environment; associating, via a processor, each of the at least one reference image with the condition; and generating, via a processor, a historical database of reference images correlating each reference image to the condition (Ramer:
Para 41: “Database 31 may be a collection of spectral reference image data including reference spectral intensity patterns stored in data files for use in conjunction with the reconfigurable sensor that includes a hyperspectral imager 12…
The collection of spectral reference image data may be updated based on changes to the reference spectral intensity pattern(s). For example, the spectral reference image data within the database 31 may include a reference spectral intensity pattern that uniquely identifies an environmental condition based on a chemical composition, a biological material, or an environmental material of a substance, for comparison to the hyperspectral image data generated by the hyperspectral imager. Alternatively or in addition, the reference spectral intensity pattern may be related to one or a combination of various different substances, such as different types of chemicals, biological materials, particulates and contaminants, such as smoke, carbon monoxide, carbon dioxide, Methicillin-resistant Staphylococcus aureus (MRSA), natural gas, or the like.”;
Para 43: “lighting devices 11A, 11B, 54 and 55 may, at various times, operate in accordance with spectral reference image data in any one of multiple files, e.g., operate in accordance with a first spectral reference image data file during daylight hours and in accordance with a second spectral reference image data file during nighttime hours or in accordance with different spectral reference image data selections from a user operator at different times, and the like. The spectral reference image data may include a reference spectral image pattern, an identifier, a name and/or harmful level, if appropriate, for each of the environmental conditions that the individual hyperspectral imager-equipped lighting device is specifically configured to detect. Depending upon the environmental condition to be detected, the first set of spectral reference image data may be different from the second spectral reference image data, or may be substantially the same. Alternatively, lighting devices 11A, 11B, 54 and 55 may only store a single spectral reference image data file.”;
Para 52: “The device 102 may receive spectral reference image data via the communication interface. The spectral reference image data may include data that enables identification of one or more substances. For example, the spectral reference image data may include a reference spectral intensity pattern that uniquely identifies an environmental condition. An environmental condition may be, for example, a substance, a chemical composition, a biological material, or an environmental material.”;
Para 57: “he updated spectral reference image data may be different from previously stored spectral reference image data. For example, the updated spectral reference image data may include one or more reference spectral intensity patterns that uniquely identifying a different chemical composition, a different biological material, or a different environmental material than the previously-stored reference spectral intensity patterns. Alternatively or in addition, the updated spectral reference image data may change one or more of the chemical composition identifier, the biological material identifier, or the environmental material identifier stored in the memory 216 and/or 218”;
Also see other arguments and citations offered below);
obtaining, via (Ramer: abstract idea: “the hyperspectral imager generates image data representative of the spectral intensity distribution”;
para 17: “The hyperspectral imager may be, for example, configured to detect the intensity of light over a continuous portion of an electromagnetic spectrum of interest. For example, the portion of the electromagnetic spectrum of interest may include light in one of the ultraviolet, visible, or infrared, both the near-infrared (NIR) and thermal infrared ranges.”;
para 18: “The image sensor is a two-dimensional (e.g., X-Y axes) sensor that detects light of a given wavelength depending upon the filter through which the light is detected by the image sensor. The image sensor may produce one or more frames of data (in X-Y axes) for each filter…
The outputted image is representative of the “spectrum” of a scene or object being imaged by the hyperspectral imager.”;
Para 21: “improving sensor capabilities associated with a lighting device to provide more useful data, e.g., spectral data, in order to provide an analysis of the environment in which the lighting device is installed.”;
Para 23: “The hyperspectral imager is configured to obtain three-dimensional spectral intensity distribution measurements of an environment in which the lighting device is located. The three-dimensional spectral intensity distribution measurements of the environment are represented as hyperspectral three-dimensional image data. The processor is coupled to the hyperspectral imager, and is configured to receive the three-dimensional image data from the hyperspectral imager, selectively detect an environmental condition present in the environment based on an image analysis of the hyperspectral image data, and generate a report of the detected for output to a data communication network.”;
Para 54: “The hyperspectral imager may be configured to output continuous-spectrum image data based on the measured intensity of a spectral range of light detected from a particular object (i.e., liquid, solid, or gas) in the field of view of the hyperspectral imager.”);
comparing, via the processor, the at least one image of the at least one sample to the plurality of reference images related to the condition within the environment (Ramer: para 19: “Each image sensing element of the hyperspectral imager measures an intensity of the different wavelengths of the spectral range represented by the light incident on respective image sensing element. The hyperspectral image data may be compared to a reference spectral intensity pattern stored in memory. A “reference spectral intensity pattern” may be a pattern of known hyperspectral image data values, or image data values over a sub-range of the detected spectra, of different compounds, objects or the like, that represent uniquely identifying characteristics of an object's spectral elements.”;
para 22: “The memory stores spectral reference image data and program instructions for processing and analyzing the hyperspectral image data.”;
Para 34: “elements of a particular lighting device may be "reconfigurable" e.g. to compare the hyperspectral image data obtained by the hyperspectral imager to the reference spectral intensity patterns stored in the memory to identify different environmental conditions, such as substances or compositions.”;
Para 41: “Database 31 may be a collection of spectral reference image data including reference spectral intensity patterns stored in data files for use in conjunction with the reconfigurable sensor that includes a hyperspectral imager 12.”;
Para 34: “compare the hyperspectral image data obtained by the hyperspectral imager to the reference spectral intensity patterns stored in the memory to identify different environmental conditions, such as substances or compositions.”;
Para 52: “There may be a number of reference spectral intensity patterns. Each reference spectral intensity pattern may be directed to a particular environmental condition having a specific chemical composition, a specific biological material, or a specific environmental material in a number of different ranges of continuous wavelengths. For example, the MRSA bacteria when imaged in different ranges of continuous wavelengths of light (visible and non-visible) may produce different spectral intensity distributions. Hence, there may be several different reference spectral intensity patterns over different ranges of continuous wavelengths.”;
Para 59: “comparing the hyperspectral image data generated by the hyperspectral imager to the spectral intensity distribution data in the spectral reference image data stored in the memory. Based on the results of the comparison, the processor 214 may identify one or more of identifiers in the spectral reference image data that correspond to the hyperspectral image data for inclusion in the outputted report.”;
Para 69: “the temperature of objects may be measured by comparing the image data to a Planckian radiation reference image data. Using a hyperspectral imager and known image analysis techniques, the temperature of the objects in an area or environment can be individually measured as well as a temperature gradient of the object's surface.”);
determining, via the processor, a feature in the at least one image of the at least one sample matches within a threshold value to a feature in at least one reference image of plurality of reference images based on the comparison, wherein the threshold value includes a range of at least two values (Ramer: para 22: “The processor controls operation of the light source, analyzes the hyperspectral image data generated by the hyperspectral imager using image analysis techniques in relation to spectral reference image data. The spectral reference image data may be a spectral signature of a respective environmental conditions, such as a material, substance, chemical or the like. For example, different environmental conditions have different and unique spectral signals that enable identification of the specific environmental condition by substantially matching subsets of the hyperspectral imager data to the known spectral signatures stored as spectral reference image data.”
: The threshold value range comprises perfect match to acceptable deviation from perfect match); and
detecting, responsive to the determination, an onset of the condition within the environment when the at least one image of the at least one sample and at least one reference image of the plurality of reference images match to within the threshold value (Ramer: para 22: “Based on the results of the image analysis of the hyperspectral image data, the processor is configured to detect an environmental condition in the environment in which the lighting device is located. The processor outputs, via the communication interface, a report of the detected environmental condition”;
para 24: “The analysis of the hyperspectral imager data for chemicals, particulates, contaminants or the like, either airborne or on a surface, is referred to herein as environmental analysis. Environmental analysis involves the collection or detection by the hyperspectral imager of data representative of the substances, the subsequent analysis of the collected data performed by a processor, and the output of an analysis result report.”;
para 25: “Other systems that may benefit from environmental analysis by adding a hyperspectral imager and related hardware to a lighting system include community water systems to constantly monitor, for example, for lead (Pb) and/or other chemicals, particulates, contaminants, hospitals to monitor hallways and entrances for bacteria and viruses, parks and nature preserves to monitor the health of vegetation and wildlife”;
para 53: “For example, the spectral reference image data may be associated with the identifier that may be related to one or more of bacteria, viruses, explosives or chemical components thereof, smoke, carbon monoxide, carbon dioxide, natural gas, or the like, and that corresponds to one or more of the image data signals generated by the hyperspectral imager. The spectral reference image data may also include other information such as values that indicate harmful levels (e.g., X parts per million) of the substance, substance names, environmental condition category (e.g., chemical, bacteria, viral, gas, and the like.) or the like. This other information may be included in the outputted report.”;
para 55: “The provided data may be simultaneously analyzed for the presence of multiple chemicals, particulates, contaminants or the like, either airborne or on a surface…
a building high volume air conditioning (HVAC) control system can take advantage of detection of humidity (H.sub.2O), carbon monoxide (CO), carbon dioxide (CO.sub.2), smoke, natural gas, biological material (e.g., bacteria (Methicillin-resistant Staphylococcus aureus (MRSA)), viruses, blood, or the like), other noxious gases, solids, liquids or the like to report on sensed conditions and/or to adjust operation of one or more controllable components of the HVAC system.”;
para 70: “the hyperspectral imager may be configured to not only measure wavelengths useful for detecting the presence and/or number of occupants in an occupiable space, but may also identify hot spots (i.e., extremely high temperature areas) that could be on fire or ready to combust. As such, the hyperspectral imager may be configured to measure wavelengths that are useful for detecting pre combustion signatures, such as outgassing, smoke signatures, flames in the occupiable space, or the like.”;
para 82: “the spectral reference image data including the reference spectral intensity pattern and the identifier may be related to different conditions associated with stress and/or well-being of an animal, human or plant.”;
PNG
media_image3.png
665
1055
media_image3.png
Greyscale
PNG
media_image4.png
616
1047
media_image4.png
Greyscale
),
wherein an onset of the condition within the environment is detected without identifying an individual compound and/or (only one of these alternatives is required) its concentration in the at least one sample taken from the environment (Ramer:
"[0019] Each image sensing element of the hyperspectral imager measures an intensity of the different wavelengths of the spectral range represented by the light incident on respective image sensing element. The hyperspectral image data may be compared to a reference spectral intensity pattern stored in memory. A “reference spectral intensity pattern” may be a pattern of known hyperspectral image data values, or image data values over a sub-range of the detected spectra, of different compounds, objects or the like, that represent uniquely identifying characteristics of an object's spectral elements."
para 37: "The lighting system elements may also include one or more hyperspectral imagers 12 used to control lighting functions, such as occupancy sensors, ambient light sensors and light, temperature sensors or environmental analysis within the that detect conditions of or produced by one or more of the lighting devices.";
para 41: "the reference spectral intensity pattern may be related to one or a combination of various different substances, such as different types of chemicals, biological materials, particulates and contaminants, such as smoke";
para 42: "It should be appreciated, however, that different lighting devices may store different spectral reference image data to selectively configure the respective hyperspectral imagers 12 to detect different substances. For example, lighting devices 54 and 55 may be configured to detect the presence of smoke, ";
para 53: "each of the... environmental materials of the particular substance may have a unique identifier associated with it. An “identifier” may be a code or a series of values that corresponds to a specific substance... environmental condition, such as the presence of smoke": Smoke is not a compound, but rather a heterogeneous mixture. It consists of tiny solid particles (such as carbon, ash, and soot) and liquid droplets suspended in a gas (air and combustion gases like carbon dioxide and water vapor).
para 55, 62, 63, 80: smoke;
para 80: "detection report indicating an unsafe environmental condition, e.g., smoke";
para 72: "identifying differences within the thermal IR range of the hyperspectral image data, the differences indicated different temperatures of portions of the imaged object";
para 89: "For example, the controller 510 may have stored in its memory (not shown) spectral reference image data of various chemical compositions, biological materials, or environmental materials of a substance, such as a fungus or gas, that are indicative of harmful agricultural conditions or atmospheric states. The controller 510 may be configured to compare the spectral reference image data stored in the memory to the hyperspectral image data as discussed in other examples.";
para 100: "The hyperspectral imager 720 provides the hyperspectral image data that the controller 710 analyzes...
spectral reference image data of various chemical compositions, biological materials, or environmental materials of a substance, such as a fungus, temperature level... that are indicative of harmful veterinary conditions or environmental states...
The controller 710 may be configured to compare the spectral reference image data stored in the memory to the hyperspectral image data as discussed in other examples."
: A fungus is not a chemical compound, but rather a eukaryotic organism belonging to its own kingdom, separate from plants and animals.
: a temperature is not a compound).
Ramer does not teach via the sensing mechanism; via the processor (emphasis added).
Sivaraman teaches obtaining, via a sensing mechanism, a plurality of samples taken from the environment; generating, via a processor, reference images; obtaining, via the sensing mechanism, at least one sample taken from the environment; rendering, via the processor, at least one image associated with the at least one sample (Sivaraman: abstract: “The method includes capturing a plurality of reference images with a camera associated with an edge node on a communication network. The reference images are received by a centralized server on the communication network….
The pruned neural network is deployed to the edge node. Real-time images are captured with the camera of the edge node and objects in the real-time images are identified with the pruned neural network.”
Page 2, lines 14-24:
PNG
media_image5.png
500
1033
media_image5.png
Greyscale
Page 12, lines 5-18:
PNG
media_image6.png
695
1032
media_image6.png
Greyscale
PNG
media_image7.png
780
1064
media_image7.png
Greyscale
).
Thus, it would have been obvious for one of ordinary skill in the art, prior to filing, to implement the teachings of Sivaraman into Ramer since both Ramer and Sivaraman suggest a practical solution and field of endeavor of training a computer vision neural network with reference images, that are communicated to be stored on a server, so that the neural network is later deployed on run-time images for detection in general and Sivaraman additionally provides teachings that can be incorporated into Ramer in that the camera at the node is used for capturing reference images and run-time images so that “the subset of objects is determined by analyzing reference images actually captured at each edge node, such that the pruned neural network is customized specifically for each particular edge node” (Sivaraman: page 2, lines 1-3). The teachings of Sivaraman can be incorporated into Ramer in that the camera at the node is used for capturing reference images and run-time images. Furthermore, one of ordinary skill in the art could have combined the elements as claimed by known methods and, in combination, each component functions the same as it does separately. One of ordinary skill in the art would have recognized that the results of the combination would be predictable.
As per claim 3, Ramer in view of Sivaraman teaches the method of claim 1, wherein comparing the at least one image to the plurality of reference images includes comparing a plurality of images of at least one sample taken over a first time period with each of the plurality of reference images (Ramer: See arguments and citations offered in rejecting claim 1 above;
Para 25 (referenced above): “constantly monitor… monitor hallways… monitor the health of vegetation and wildlife”;
Para 72: “monitoring of plant health”;
Para 74: “hyperspectral imager for environmental monitoring”;
Para 51: “frame rate… the hyperspectral imager 220 may continuously collect and analyze image data”
: the environment is continuously monitored in continuously collecting and analyzing image data at a frame rate. These captured images are compared to the plural reference images as discussed above.).
As per claim 6, Ramer in view of Sivaraman teaches the method of claim 1, wherein the environment is selected from at least one of: a greenhouse, a pond, a sea cage, an office space, a prison, an assisted living facility, a hospice facility, a barn, a chicken coop, or a livestock stable (Ramer: See arguments and citations offered in rejecting claim 1 above;
Para 35: “occupiable rooms, equipment rooms, hallways, corridors or storage areas of a building (e.g., home, hospital, office building, schools, food service areas)”).
As per claim 7, Ramer in view of Sivaraman teaches the method of claim 1, wherein the sensing mechanism is selected from at least one of: a biosensor, a biomarker sensor, a chemical sensor, an Infrared sensor, a camera, a microphone, an air composition sensor, a gas chromatograph, liquid chromatograph, a mass spectrometer, or a micro gas chromatography system (Ramer: See arguments and citations offered in rejecting claim 1 above).
As per claim 8, Ramer in view of Sivaraman teaches the method of claim 1, wherein the condition is selected from a stress condition or the outbreak of a disease (Ramer: See arguments and citations offered in rejecting claim 1 above).
As per claim 9, Ramer in view of Sivaraman teaches the method of claim 1, further comprising: sending an alert based on a positive detection of the onset of the condition (Ramer: abstract: “A controller may analyze the image data generated by the hyperspectral imager and may initiate action based on, or outputs a report indicating, an environmental condition detected by the analysis of the generated image data.”;
Para 55: “a building high volume air conditioning (HVAC) control system can take advantage of detection of humidity (H.sub.2O), carbon monoxide (CO), carbon dioxide (CO.sub.2), smoke, natural gas, biological material (e.g., bacteria (Methicillin-resistant Staphylococcus aureus (MRSA)), viruses, blood, or the like), other noxious gases, solids, liquids or the like to report on sensed conditions and/or to adjust operation of one or more controllable components of the HVAC system.”).
As per claim(s) 10, arguments made in rejecting claim(s) 1 are analogous. Ramer also teaches a system for detecting a condition in an environment comprising (Ramer: Figs. 1-3): a sensing mechanism configured to obtain at least one sample taken from the environment (Ramer: See arguments and citations offered in rejecting claim 1 above: sensors); a processor configured to (Ramer: Fig. 2 and associated text).
As per claim 11, Ramer in view of Sivaraman teaches the system of claim 10, wherein the processor is configured to compare a plurality of images taken over a first time period with the plurality of reference images associated with the condition (Ramer: See arguments and citations offered in rejecting claim 3 and 10 above).
As per claim 12, Ramer in view of Sivaraman teaches the system of claim 10, wherein the at least one image includes at least one characteristic or feature and wherein each reference image of the plurality of reference images includes at least one characteristic or feature (Ramer: See arguments and citations offered in rejecting claim 10 above).
As per claim 14, Ramer in view of Sivaraman teaches the system of claim 11, wherein the processor is further configured to send an alert based on a positive detection of the onset of the condition (Ramer: see arguments and citations offered in rejecting claim 10 above;
abstract: “A controller may analyze the image data generated by the hyperspectral imager and may initiate action based on, or outputs a report indicating, an environmental condition detected by the analysis of the generated image data.”;
Para 55: “a building high volume air conditioning (HVAC) control system can take advantage of detection of humidity (H.sub.2O), carbon monoxide (CO), carbon dioxide (CO.sub.2), smoke, natural gas, biological material (e.g., bacteria (Methicillin-resistant Staphylococcus aureus (MRSA)), viruses, blood, or the like), other noxious gases, solids, liquids or the like to report on sensed conditions and/or to adjust operation of one or more controllable components of the HVAC system.”).
As per claim 15, Ramer in view of Sivaraman teaches the system of claim 14, wherein the processor is further configured to deploy at least one counter-measure to alleviate the condition (Ramer: see arguments and citations offered in rejecting claim 10 above;
abstract: “A controller may analyze the image data generated by the hyperspectral imager and may initiate action based on, or outputs a report indicating, an environmental condition detected by the analysis of the generated image data.”;
Para 55: “a building high volume air conditioning (HVAC) control system can take advantage of detection of humidity (H.sub.2O), carbon monoxide (CO), carbon dioxide (CO.sub.2), smoke, natural gas, biological material (e.g., bacteria (Methicillin-resistant Staphylococcus aureus (MRSA)), viruses, blood, or the like), other noxious gases, solids, liquids or the like to report on sensed conditions and/or to adjust operation of one or more controllable components of the HVAC system.”).
As per claim 16, Ramer in view of Sivaraman teaches the method of claim 1, wherein the at least one sample taken from the environment is a gas sample (Ramer: see arguments and citations offered in rejecting claim 1 above; Fig. 6: mainly 612, 613: gas;
paras 3, 41, 54, 55, 62-65, 68, 71, 72, 81, 85, 90, 93-97, 100: gas, carbon dioxide, carbon monoxide, noxious gas, or natural gas).
As per claim 18, Ramer in view of Sivaraman teaches the system of claim 10, wherein the at least one sample taken from the environment is a gas sample (Ramer: see arguments and citations offered in rejecting claim 10 and 16 above).
As per claim 20, Ramer in view of Sivaraman teaches the method of claim 1, wherein the method further comprises: artificially inducing the condition to enable sampling (Ramer: para 37: "The lighting system elements may also include one or more hyperspectral imagers 12 used to control lighting functions, such as occupancy sensors, ambient light sensors and light, temperature sensors or environmental analysis within the that detect conditions of or produced by one or more of the lighting devices."
[0077] The controller 310 controls operation of the light source 330 and the hyperspectral imager 320. The controller 310 also analyzes the signals generated by the hyperspectral imager 320 in relation to spectral reference image data stored in the memory to detect an environmental condition in the environment in which the lighting device 301 is located.).
As per claim 21, Ramer in view of Sivaraman teaches the system of claim 10, wherein the condition is artificially induced to enable sampling (Ramer: para 37: "The lighting system elements may also include one or more hyperspectral imagers 12 used to control lighting functions, such as occupancy sensors, ambient light sensors and light, temperature sensors or environmental analysis within the that detect conditions of or produced by one or more of the lighting devices."
[0077] The controller 310 controls operation of the light source 330 and the hyperspectral imager 320. The controller 310 also analyzes the signals generated by the hyperspectral imager 320 in relation to spectral reference image data stored in the memory to detect an environmental condition in the environment in which the lighting device 301 is located.).
Claim(s) 4, 5, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ramer in view of Sivaraman as applied to claims 1 and 10 above, and further in view of Official Notice.
As per claim 4, Ramer in view of Sivaraman teaches the method of claim 1, wherein the at least one image includes at least one characteristic or feature and wherein each reference image of the plurality of reference images includes at least one characteristic or feature (Ramer: See arguments and citations offered in rejecting claim 1 above).
Ramer in view of Sivaraman does not teach the at least one characteristic or feature is selected from at least one of: an area above a curve provided in the at least one image or reference image, an area below the curve, at least one local maximum or peak, at least one local minimum or valley, an overall shape of the curve, a slope of at least a portion of the curve, total number of local maximums or peaks, total number of local minimums of valleys, a maximum intensity value, or (only one alternative is required) the relative position of one or more peaks or valleys.
Examiner provides Official Notice that these limitations were well known prior to filing.
One of ordinary skill in the art, prior to filing, would have recognized the advantage of effective pattern recognition. The teachings of the prior art could have been incorporated into Ramer in view of Sivaraman in that the characteristic feature is at least one image or reference image, an area below the curve, at least one local maximum or peak, at least one local minimum or valley, an overall shape of the curve, a slope of at least a portion of the curve, total number of local maximums or peaks, total number of local minimums of valleys, a maximum intensity value, or (only one alternative is required) the relative position of one or more peaks or valleys.
As per claim 5, Ramer in view of Sivaraman and Official Notice teaches the method of claim 4, wherein comparing the at least one image to the plurality of reference images includes comparing the at least one characteristic or feature of each of the plurality of reference images to the at least one characteristic or feature of the at least one image (Ramer in view of Sivaraman and Official Notice: See arguments and citations offered in rejecting claims 1 and 4 above).
As per claim(s) 13, arguments made in rejecting claim(s) 4 (and 10) are analogous.
Claim(s) 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ramer in view of Sivaraman as applied to claims 1 and 10 above, and further in view of CN 102590406 A (Kong).
As per claim 17, Ramer in view of Sivaraman teaches the method of claim 1, wherein the at least one image and the plurality of reference images are (Ramer: See arguments and citations offered in rejecting claim 16 above;
Para 41: “the hyperspectral image data generated by the hyperspectral imager. Alternatively or in addition, the reference spectral intensity pattern may be related to one or a combination of various different substances, such as different types of chemicals, biological materials, particulates and contaminants, such as smoke, carbon monoxide, carbon dioxide, Methicillin-resistant Staphylococcus aureus (MRSA), natural gas, or the like”;
Para 42: “the respective hyperspectral imagers 12 to detect different substances. For example, lighting devices 54 and 55 may be configured to detect the presence of smoke, whereas lighting devices 11A and 11B may be configured to detect carbon monoxide.”;
Para 53: “For example, the spectral reference image data may be associated with the identifier that may be related to one or more of bacteria, viruses, explosives or chemical components thereof, smoke, carbon monoxide, carbon dioxide, natural gas, or the like, and that corresponds to one or more of the image data signals generated by the hyperspectral imager.”;
Para 54: “The hyperspectral imager may be configured to output continuous-spectrum image data based on the measured intensity of a spectral range of light detected from a particular object (i.e., liquid, solid, or gas) in the field of view of the hyperspectral imager.”;
Also see paras 55, 56, 62, 63, 65-68, 71, 72, 81, 85, 89, 93, 94, 95, 96, 100).
Ramer in view of Sivaraman does not teach two-dimensional spectrograms.
Kong teaches two-dimensional spectrograms indicative of the spectral composition of respective gas samples (Kong:
abstract: “gas chromatogram-mass spectrum combined instrument for sample total ion flow and mass spectrogram performing the superposition to the obtained sample, three-dimensional omni-directional spectrum to detention time”;
para 1: “a gas phase chromatography-mass spectrometric analysis data of three-dimensional information comparing and analyzing method”;
para 2: “gas chromatography-mass spectrometry analysis…
The invention is based on gas chromatography-mass spectrometry analysis technology separating ability and comprehensive two-dimensional gas chromatography analysis technology of the spectrum analyzing method”;
Para 6: “Step I: through the gas phase chromatography-mass spectrometry instrument obtained sample total ion flow graph (TIC) and mass spectrum for three-dimensional omnidirectional spectrogram to obtain three-dimensional omnidirectional spectrum sample”;
Para 10: “two sample three-dimensional spectrogram”;
Para 13: “two sample three-dimensional spectrogram Ρ 1, Ρ 2 (FIG. 2, 3)”;
Para 39: “gas phase chromatography-mass spectrometry instrument analysis to obtain total ion flow graph and mass spectrum, the more than two two-dimensional spectrum processing to obtain three-dimensional spectrogram”;
Paras 17, 19, 29: gas, spectrogram;
PNG
media_image8.png
620
1076
media_image8.png
Greyscale
PNG
media_image9.png
625
1067
media_image9.png
Greyscale
PNG
media_image10.png
618
1065
media_image10.png
Greyscale
).
Thus, it would have been obvious for one of ordinary skill in the art, prior to filing, to implement the teachings of Kong into Kong since both Kong and Kong suggest a practical solution and field of endeavor of gas spectrum sampling and comparison analysis in general and Kong additionally provides teachings that can be incorporated into Kong in that the spectrum is represented in a spectrogram having two dimensions as to “so as to carry out characteristic analysis to spectrum graph between sample so as to realize the different sample components as compared to find profile” (Kong: abstract). The teachings of Kong can be incorporated into Kong in that the spectrum is represented in a spectrogram having two dimensions. Furthermore, one of ordinary skill in the art could have combined the elements as claimed by known methods and, in combination, each component functions the same as it does separately. One of ordinary skill in the art would have recognized that the results of the combination would be predictable.
As per claim(s) 19, arguments made in rejecting claim(s) 17 (and base claim 10) are analogous.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Atiba Fitzpatrick whose telephone number is (571) 270-5255. The examiner can normally be reached on M-F 10:00am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for Atiba Fitzpatrick is (571) 270-6255.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Atiba Fitzpatrick
/ATIBA O FITZPATRICK/
Primary Examiner, Art Unit 2677