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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/19/2024 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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhong et al (US10852419) in view of Shaker et al (US20210197834).
Regarding claim 1, Zhong teaches a learning apparatus (fig. 3), comprising:
preprocessing circuitry (fig. 3), which, in operation, performs processing of converting relative phase differences (col. 9 lines 25-36, the initial phase of the IF signal is the difference between the phase of the transmitted chirp and the phase of the received chirp at the time of the start of the IF signal) and relative intensity differences (col. 9 lines 52-55, the intensity of a radar signal is proportional to the amplitude) between a plurality of transmission/reception waves (col. 9 lines 13-17) into a color image (col. 9 lines 17-19, angle; col. 12 lines 59-65, where the angle and magnitude of the motion vector determine the hue and saturation of the pixel color. The magnitude is interpreted to be a measure of the intensity), the plurality of transmission/reception waves being based on radiation of radio waves to an object to be measured (col. 9 lines 13-16),
wherein the first color image and a second color image being processed by the preprocessing circuitry (col. 13 lines 42-44, it would be obvious to generate the consecutive frames using the previous steps).
Zhong fails to teach learning circuitry, which, in operation, learns, by using a first color image and training data, an identification model for identifying a type of an internal state of the object to be measured, the training data being training data in which a second color image and the type of the internal state are associated, the first color image and the second color image being processed by the preprocessing circuitry.
However Shaker teaches learning circuitry which learns by using a first color image and training data (para. [0134], ML is initially trained with feature data generated from the radar; para. [0139], A supervised machine learning scheme is then used to build a classification model of different concentrations by mapping the measured radar readings), an identification model for identifying a type of an internal state of the object to be measured (para. [0073], a high frequency radar (operating anywhere between 30 GHz to 300 GHz) is used as the sensor and data is classified using machine learning and machine intelligence models, which classifies individuals based on their palm/fingerprint/eye radio signatures; para. [0134], The feature data (i.e. range, total energy received from the returning radar signal, spatial dispersion, along with the IQ data for each channel) quantifies the magnitudes and phase changes from backscattered waves received by the radar), the training data being training data in which a second color image and the type of the internal state are associated (para. [0143], Initially, all the measurements taken by the radar sensor were used as training data to build classification models by mapping the measurements. It would be obvious to apply the steps of Zhong to the measurements from the electromagnetic waves of Shaker, as in para. [0020], to generate a plurality of images).
Therefore taking the combined teachings of Zhong and Shaker as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Shaker into the apparatus of Zhong. The motivation to combine Shaker and Zhong would be to differentiate between a large set of objects with very high degrees of accuracy and precision (para. [0073] of Shaker).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhong et al (US10852419) and Shaker et al (US20210197834) in view of Gonciulea et al (US20210256745).
Regarding claim 2, the modified apparatus of Zhong fails to teach a learning apparatus wherein the preprocessing circuitry assigns the relative phase differences to hue and assigns the relative intensity differences to at least one of saturation and/or value.
However Gonciulea teaches assigning a phase to hue (para. [0048], For example, the phase of the amplitude may be converted into a hue (the “H” in HSV)) and assigning an intensity to at least one of saturation and/or value (para. [0048], the magnitude may be converted to a saturation (the “S”)). It would be obvious to apply the steps to the difference values determined in claim 1.
Therefore taking the combined teachings of Zhong and Shaker with Gonciulea as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Gonciulea into the apparatus of Zhong and Shaker. The motivation to combine Shaker, Gonciulea and Zhong would be to optimize a visual impression (para. [0047] of Gonciulea).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhong et al (US10852419) and Shaker et al (US20210197834) in view of Zhang et al (US20190370955).
Regarding claim 3, the modified apparatus of Zhong fails to teach a learning apparatus comprising identification circuitry, which, in operation, identifies, by using the identification model, the type of the internal state of the object to be measured according to the first color image,
wherein the learning circuitry adjusts a parameter of the identification model according to an identification result of the identification circuitry and according to the training data.
However Zhang teaches identifying, using an identification model (para. [0011], defect classifier), a type of the internal state of an object to be measured according to a first color image (para. [0011], The defect classifier is configured for classifying defects detected on the specimen using the images generated by the imaging subsystem; para. [0101], The multi-class classifier may separate the defects into different classes (hence the multi-class nomenclature), which may include different classes like bridges, particles, missing features, scratches, and the like), and
adjusting a parameter of the identification model according to an identification result of the identification circuitry and according to training data (para. [0097], Training the defect classifier using the set of labeled data may be performed in any suitable manner known in the art (e.g., by inputting the data points into the defect classifier and modifying one or more parameters of the defect classifier until the output of the defect classifier for the input data points matches the labels acquired for the data points)).
Therefore taking the combined teachings of Zhong and Shaker with Zhang as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Zhang into the apparatus of Zhong and Shaker. The motivation to combine Shaker, Zhang and Zhong would be to break the inter-dependency between defect sampling and model training for enhanced defect sampling (para. [0026] of Zhang).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhong et al (US10852419) in view of Shaker et al (US20210197834) and Gonciulea et al (US20210256745).
Regarding claim 7, Zhong teaches a learning method of a learning apparatus that identifies an internal state of an object to be measured, the learning method comprising:
performing processing of converting relative phase differences (col. 9 lines 25-36, the initial phase of the IF signal is the difference between the phase of the transmitted chirp and the phase of the received chirp at the time of the start of the IF signal) and relative intensity differences (col. 9 lines 52-55, the intensity of a radar signal is proportional to the amplitude) between a plurality of transmission/reception waves (col. 9 lines 13-17) into a color image (col. 9 lines 17-19, angle; col. 12 lines 59-65, where the angle and magnitude of the motion vector determine the hue and saturation of the pixel color. The magnitude is interpreted to be a measure of the intensity), the plurality of transmission/reception waves being based on radiation of radio waves to the object to be measured (col. 9 lines 13-16); and
wherein the first color image and a second color image being processed (col. 13 lines 42-44, it would be obvious to generate the consecutive frames using the previous steps).
Zhong fails to teach wherein the color image includes hue, saturation, and value. However Gonciulea teaches wherein a color image includes hue, saturation, and value (para. [0048]).
Therefore taking the combined teachings of Zhong and Gonciulea as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Gonciulea into the method of Zhong. The motivation to combine Gonciulea and Zhong would be to optimize a visual impression (para. [0047] of Gonciulea).
Zhong also fails to teach learning, by using a first color image and training data, an identification model for identifying a type of the internal state, the training data being training data in which a second color image and the type of the internal state are associated.
However Shaker teaches learning circuitry which learns by using a first color image and training data (para. [0134], ML is initially trained with feature data generated from the radar; para. [0139], A supervised machine learning scheme is then used to build a classification model of different concentrations by mapping the measured radar readings), an identification model for identifying a type of an internal state of the object to be measured (para. [0073], a high frequency radar (operating anywhere between 30 GHz to 300 GHz) is used as the sensor and data is classified using machine learning and machine intelligence models, which classifies individuals based on their palm/fingerprint/eye radio signatures; para. [0134], The feature data (i.e. range, total energy received from the returning radar signal, spatial dispersion, along with the IQ data for each channel) quantifies the magnitudes and phase changes from backscattered waves received by the radar), the training data being training data in which a second color image and the type of the internal state are associated (para. [0143], Initially, all the measurements taken by the radar sensor were used as training data to build classification models by mapping the measurements. It would be obvious to apply the steps of Zhong to the measurements from the electromagnetic waves of Shaker, as in para. [0020], to generate a plurality of images).
Therefore taking the combined teachings of modified Zhong and Shaker as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Shaker into the method of modified Zhong. The motivation to combine Shaker and modified Zhong would be to differentiate between a large set of objects with very high degrees of accuracy and precision (para. [0073] of Shaker).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhong et al (US10852419) in view of Shaker et al (US20210197834), Gonciulea et al (US20210256745), and Zhang et al (US20190370955).
Regarding claim 8, Zhong teaches a nondestructive inspection system, comprising:
preprocessing circuitry (fig. 3), which, in operation, performs processing of converting relative phase differences (col. 9 lines 25-36, the initial phase of the IF signal is the difference between the phase of the transmitted chirp and the phase of the received chirp at the time of the start of the IF signal) and relative intensity differences (col. 9 lines 52-55, the intensity of a radar signal is proportional to the amplitude) between a plurality of transmission/reception waves (col. 9 lines 13-17) into a color image (col. 9 lines 17-19, angle; col. 12 lines 59-65, where the angle and magnitude of the motion vector determine the hue and saturation of the pixel color. The magnitude is interpreted to be a measure of the intensity), the plurality of transmission/reception waves being based on radiation of radio waves to an object to be measured (col. 9 lines 13-16);
wherein the first color image and a second color image are being processed by the preprocessing circuitry (col. 13 lines 42-44, it would be obvious to generate the consecutive frames using the previous steps); and
a monitor, which, in operation, displays an identification result of the identification circuitry (136 in fig. 3).
Zhong fails to teach wherein the color image includes hue, saturation, and value. However Gonciulea teaches wherein a color image includes hue, saturation, and value (para. [0048]).
Therefore taking the combined teachings of Zhong and Gonciulea as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the feature of Gonciulea into the system of Zhong. The motivation to combine Gonciulea and Zhong would be to optimize a visual impression (para. [0047] of Gonciulea).
Zhong also fails to teach learning, by using a first color image and training data, an identification model for identifying a type of the internal state, the training data being training data in which a second color image and the type of the internal state are associated.
However Shaker teaches learning circuitry which learns by using a first color image and training data (para. [0134], ML is initially trained with feature data generated from the radar; para. [0139], A supervised machine learning scheme is then used to build a classification model of different concentrations by mapping the measured radar readings), an identification model for identifying a type of an internal state of the object to be measured (para. [0073], a high frequency radar (operating anywhere between 30 GHz to 300 GHz) is used as the sensor and data is classified using machine learning and machine intelligence models, which classifies individuals based on their palm/fingerprint/eye radio signatures; para. [0134], The feature data (i.e. range, total energy received from the returning radar signal, spatial dispersion, along with the IQ data for each channel) quantifies the magnitudes and phase changes from backscattered waves received by the radar), the training data being training data in which a second color image and the type of the internal state are associated (para. [0143], Initially, all the measurements taken by the radar sensor were used as training data to build classification models by mapping the measurements. It would be obvious to apply the steps of Zhong to the measurements from the electromagnetic waves of Shaker, as in para. [0020], to generate a plurality of images).
Therefore taking the combined teachings of modified Zhong and Shaker as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Shaker into the system of modified Zhong. The motivation to combine Shaker and modified Zhong would be to differentiate between a large set of objects with very high degrees of accuracy and precision (para. [0073] of Shaker).
Zhong further fails to teach identification circuitry, which, in operation, identifies, by using the identification model, the type of the internal state of the object to be measured according to the first color image.
However Zhang teaches identifying, using an identification model (para. [0011], defect classifier), a type of the internal state of an object to be measured according to a first color image (para. [0011], The defect classifier is configured for classifying defects detected on the specimen using the images generated by the imaging subsystem; para. [0101], The multi-class classifier may separate the defects into different classes (hence the multi-class nomenclature), which may include different classes like bridges, particles, missing features, scratches, and the like).
Therefore taking the combined teachings of modified Zhong and Zhang as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Zhang into the system of modified Zhong. The motivation to combine Zhang and modified Zhong would be to break the inter-dependency between defect sampling and model training for enhanced defect sampling (para. [0026] of Zhang).
Allowable Subject Matter
Claims 4-6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Related Art
Zhao et al (US20170214901) – see para. [0009], [0019], [0086], [0113]
Conclusion
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663