DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without a practical application or significantly more.
Regarding claims 1 and 12, these claims recite the following limitations which are found to be abstract ideas not reciting a practical application or significantly more, with claim 1 being exemplary:
receiving first image data associated with a scene; determining, using a machine learning model, a first image setting based on the first image data; providing an indication of the first image setting; (abstract idea as a mental process since a human mind and knowledge to determine a setting in an image by looking at it)
receiving first user input associated with the first image setting; and (abstract idea as a mental process since a human mind can provide input in regards to what they determine was the setting)
determining, for use in training the machine learning model, a first training dataset based on the first user input and the first image setting. (abstract idea as a mental process since a human mind can determine which images that they want to be used for training a model. Note: the limitation does not require any actual training of a machine learning model)
This judicial exception is not integrated into a practical application for the following reasons. Claim 12 further recite additional elements: claim 12 contains an infrared imaging system. While the infrared imaging system of claim 12 contain an infrared imager, a logic device are additional elements, they are not sufficient to recite a practical application of the abstract ideas recited in claims 1 and 12 as they amount to mere generic computer elements and thus amount to no more than a recitation of the words "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. see MPEP §2106.05(f).
Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claims 1 and 12 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the claimed “infrared imager” is simple data gathering and “logic device” perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
The dependent claims are also directed to an abstract idea such that the elements can be done mentally.
The claims do not recite additional elements that integrate the judicial exception into a practical application because these additional elements in the claim do no more than automate the mental process that a person may perform.
Claim Rejections - 35 USC § 103
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kamangar US2022/0076851 in view of Ligocki et al. Fully Automated DCNN-Based Thermal Images Annotation Using Neural Network Pre-Trained on RGB Data hereinafter referred as Ligocki.
As per Claim 1, Kamangar teaches a method comprising:
receiving first image data associated with a scene; determining, using a machine learning model, a first image setting based on the first image data; providing an indication of the first image setting; (Kamangar, Paragraph [0070], “The service provider SPCI-app of a device 320A may forward the image(s) to the SPCI server 50 to employ neural logic, AI, or machine learning to analyze the infrared image of the client to determine their temperature and report same to the service provider SPCI-app of a device 320A. In an embodiment, the SPCI server 50 may forward the image(s) to an expert system 70B to analyze the infrared image of the client to determine their temperature and report same to the service provider SPCI-app of a device 320A”)
Kamangar does not explicitly teach receiving first user input associated with the first image setting; and
determining, for use in training the machine learning model, a first training dataset based on the first user input and the first image setting.
Ligocki teaches receiving first user input associated with the first image setting; and (Ligocki, page 3, Section 1.2, “By adding small human intervention in dataset creation, we improved the training process, so it outperforms training neural networks on these days’ largest human-annotated datasets” and page 10, Section 4.3 Thermal Images Annotation and Figure 10, describes a process to created annotated IR images to be used for training)
determining, for use in training the machine learning model, a first training dataset based on the first user input and the first image setting. (Ligocki, page 10, Section 4.3 Thermal Images Annotation and Figure 10, describes a process to created annotated IR images to be used for training)
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Ligocki into Kamangar because by utilizing a training method such as Ligocki to train the model of temperature determination of Kamangar will enhance the accuracy of the determination process.
Therefore it would have been obvious to one of ordinary skill to combine the two references to obtain the invention in Claim 1.
As per Claim 2, Kamangar in view of Ligocki teaches the method of claim 1, further comprising: adjusting the machine learning model based on the first training dataset to obtain an adjusted machine learning model; receiving second image data associated with the scene; and determining, using the adjusted machine learning model, a second image setting based on the second image data. (Ligocki, page 10, Section 4.3 Thermal Images Annotation and Figure 10, describes a process to created annotated IR images to be used for training. Multiple images may be inputted as seen in Figure 10)
The rationale applied to the rejection of claim 1 has been incorporated herein.
As per Claim 3, Kamangar in view of Ligocki teaches the method of claim 2, further comprising: receiving second user input associated with the second image setting; determining, for use in training the adjusted machine learning model, a second training dataset based on the second user input and the second image setting; and adjusting the adjusted machine learning model based on the second training dataset to obtain a further adjusted machine learning model. (Ligocki, page 10, Section 4.3 Thermal Images Annotation and Figure 10, describes a process to created annotated IR images to be used for training. Multiple images may be inputted as seen in Figure 10. Other data will affect the annotated IR data that will be used to further train a neural network)
The rationale applied to the rejection of claim 2 has been incorporated herein.
As per Claim 4, Kamangar in view of Ligocki teaches the method of claim 1, further comprising generating an image based on the first image setting, wherein the first image setting comprises an emissivity associated with an object in the scene, a reflected temperature associated with the object, a distance between the object and an image sensor device when the first image data is captured by the image sensor device, an atmospheric temperature, a temperature associated with optics of the image sensor device, and/or an atmospheric humidity. (Kamangar, Paragraph [0070], “The service provider SPCI-app of a device 320A may forward the image(s) to the SPCI server 50 to employ neural logic, AI, or machine learning to analyze the infrared image of the client to determine their temperature and report same to the service provider SPCI-app of a device 320A”)
The rationale applied to the rejection of claim 1 has been incorporated herein.
As per Claim 5, Kamangar in view of Ligocki teaches the method of claim 1, wherein the indication comprises: an image that represents the first image data and has one or more pixel values determined based in part on the first image setting, a value associated with the first image setting, and/or a location associated with the first image setting. (Ligocki, page 10, Section 4.3 Thermal Images Annotation and Figure 10 and Kamangar, Paragraph [0070])
The rationale applied to the rejection of claim 1 has been incorporated herein.
As per Claim 6, Kamangar in view of Ligocki teaches the method of claim 1, further comprising generating second image data based on the first image setting and the first image data, wherein: the second image data comprises an image and temperature data associated with a portion of the image, the first image setting comprises a first temperature measurement location indicative of the portion of the image, and the first user input comprises an adjustment from the first temperature measurement location to a second temperature measurement location. (Ligocki, page 10, Section 4.3 Thermal Images Annotation and Figure 10 and Kamangar, Paragraph [0070])
The rationale applied to the rejection of claim 1 has been incorporated herein.
As per Claim 7, Kamangar in view of Ligocki teaches the method of claim 6, wherein the first training dataset is based on a difference between the first temperature measurement location and the second temperature measurement location. (Ligocki, page 10, Section 4.3 Thermal Images Annotation and Figure 10 and Kamangar, Paragraph [0070])
The rationale applied to the rejection of claim 6 has been incorporated herein.
As per Claim 8, Kamangar in view of Ligocki teaches the method of claim 6, further comprising displaying the image and the indication of the first image setting overlaid on the image, wherein the first image data comprises thermal infrared image data and visible-light image data, and wherein the generating the second image data comprises combining the thermal infrared image data and the visible-light image data to obtain the image. (Ligocki, page 10, Section 4.3 Thermal Images Annotation and Figure 10 and Kamangar, Paragraph [0070])
The rationale applied to the rejection of claim 6 has been incorporated herein.
As per Claim 9, Kamangar in view of Ligocki teaches the method of claim 1, further comprising: determining, using the machine learning model, a second image setting based on the first image data; and receiving second user input associated with the second image setting, wherein the first training dataset is further based on the second image setting and the second user input, and wherein the second image setting comprises an emissivity associated with an object in the scene, a reflected temperature associated with the object, a distance between the object and an image sensor device when the first image data is captured by the image sensor device, an atmospheric temperature, a temperature associated with optics of the image sensor device, an atmospheric humidity, a measurement location, a measurement function, a palette to apply to the first image data, a fusion mode setting, a temperature alarm, a gain level, a fault severity assessment, a recommended action, an equipment type classification, or an annotation. (Ligocki, page 10, Section 4.3 Thermal Images Annotation and Figure 10 and Kamangar, Paragraph [0070])
The rationale applied to the rejection of claim 1 has been incorporated herein.
As per Claim 10, Kamangar in view of Ligocki teaches the method of claim 1, further comprising determining a weight associated with the first user input, wherein the first training dataset is further based on the weight. (Kamangar, Paragraph [0092], “The input vector I and output vector O may include many entries and each node may include a weighted matrix that is applied to the upstream vector where the weight matrix is developed by the training database 56 and training systems 50”)
The rationale applied to the rejection of claim 1 has been incorporated herein.
As per Claim 11, Kamangar in view of Ligocki teaches the method of claim 1, wherein the machine learning model comprises a neural network-based machine learning model or a decision tree-based machine learning model. (Ligocki, page 10, Section 4.3 Thermal Images Annotation and Figure 10)
The rationale applied to the rejection of claim 1 has been incorporated herein.
As per Claim 12, Claim 12 claims An infrared imaging system comprising: an infrared imager and a logic device (Kamangar, Paragraph [0069] and [0070]) performing the method as claimed in Claim 1. Therefore the rejection and rationale are analogous to that made in Claim 1.
As per Claim 13, Claim 13 claims the same limitation as Claim 2 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 2.
As per Claim 14, Claim 14 claims the same limitation as Claim 3 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 3.
As per Claim 15, Claim 15 claims the same limitation as Claim 4 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 4.
As per Claim 16, Claim 16 claims the same limitation as Claim 6 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 6.
As per Claim 17, Claim 17 claims the same limitation as Claim 7 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 7.
As per Claim 18, Claim 18 claims the same limitation as Claim 8 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 8.
As per Claim 19, Claim 19 claims the same limitation as Claim 9 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 9.
As per Claim 20, Claim 20 claims the same limitation as Claim 11 and is dependent on a similarly rejected independent claim. Therefore the rejection and rationale are analogous to that made in Claim 11.
Conclusion
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/MING Y HON/Primary Examiner, Art Unit 2666
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