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 .
Claims 1-24 are pending in the application.
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) 1-5, 8-13, 16-21 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lau et al. (US 20240054377 A1, hereafter Lau), in view of Williamson et al. (US 20250165754 A1, hereafter Williamson).
As per claim 1, Lau teaches the invention substantially as claimed including an image pixel classification device (FIG. 1; FIG. 33) comprising:
a quantum computing circuit (FIG. 1 Quantum Processor 106) configured to perform quantum subset summing (FIG. 9 #910 “Perform subset summing operations by the quantum processor”; FIG. 33 quantum computing circuit 337); and
a processor (FIG. 33 processor 338) configured to
generate a pairwise game theory reward matrix for a plurality of different classes of a signal FIG. 10 showing a signal classification system; FIG. 33 the processor 338 performing “generate game theory reward matrix for different deep learning models”; FIG. 20 showing a generated pairwise game theory reward matrix for a plurality of different classes (each modulation class paired with a machine learning algorithm); para. [0113] describing modulation classes and corresponding modulation feature types),
cooperate with the quantum computing circuit to perform quantum subset summing on the pairwise game theory reward matrix (FIG. 33 processor 308 performing “cooperate with QC circuit to perform quantum subset summing of game theory reward matrix”; FIG. 2-3 providing a general framework of subset summing; para. [0090]), and
select a class for the FIG. 33 processor 308 performing “select deep learning model based upon quantum subset summing of game theory reward matrix”, and “process RF signals using the selected deep learning model for RF signal classification”; para. [0212]-[0213]; FIG. 16 showing a classification example, in which the signal is assigned a QPSK modulation class since it is associated with the highest predictions score 1606; para. [0118]).
Therefore Lau teaches every limitation as recited in claim 1 except for classification of an image pixel, each class corresponding to a respective type of land feature from among a plurality of different types of land features. Note Lau mentions image classification (para. [0140]). Lau also discloses applying a fully convolutional-deconvolutional network trained end-to-end with semantic segmentation to classify land use/land cover features (para. [0139]). Image pixel classification and corresponding type of land features, however, is not apparently available.
Williamson in an analogous field discloses a geographic prediction system for generating one or more landcover predictions corresponding to one or more geographic portions within a geographic region by using one or more machine learning models (para. [0004]; FIG. 3-4). During training of the machine learning model 410 to classify the geographic region (e.g., a portion of the Earth's surface), labeled data is used. The labeled data may include geographic polygons labeled with an object class, such as a type of coverage including a type of vegetation species or other type of classification, such as city, road, grassland, and/or the like (para. [0109], [0130]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have modified Lau’s teaching by incorporating Williamson’s teaching to classify image pixel whose class corresponds to a respective type of land feature from among a plurality of different types of land features. Doing so would provide an improved image processing pipeline that is capable of automatically generating accurate, time-based geographical predictions for the geographic area as suggested by Williamson (para. [0095]).
As per claim 2, dependent upon claim 1, Lau in view of Williamson teaches wherein the processor is configured to select a deep learning model from among a plurality thereof based upon the quantum subset summing on the pairwise game theory reward matrix, and classify the image pixel based upon the selected deep learning model (Lau FIG. 20 showing pairwise game theory reward matrix representing modulation class-machine learning algorithm correspondences; FIG. 33 processor 308 performing “select deep learning model based upon quantum subset summing of game theory reward matrix”, and “process RF signals using the selected deep learning model for RF signal classification”; Williamson para. [0004]; FIG. 3-4).
As per claim 3, dependent upon claim 2, Lau in view of Williamson teaches wherein the plurality of deep learning models comprise an Adaptive Moment Estimation (ADAM) solver, a Stochastic Gradient Descent with Momentum (SGDM) solver, and a Root Mean Squared Propagation (RMSProp) solver (Lau FIG. 30; para .[0118] “The optimization algorithm can include, but is not limited to, a game theory based optimization algorithm, an Adam optimization algorithm, an a stochastic gradient decent optimization algorithm, and/or a root mean square optimization algorithm”).
As per claim 4, dependent upon claim 1, Lau in view of Williamson teaches wherein the plurality of different types of land features comprise at least some of bare earth, building, road, tower, vegetation and water (Williamson para. [0109] “An object class may be indicative of any type of object, including one or more types of a vegetation species, one or more types of geographic environments (e.g., lakes, rocks, snow, urban, etc.), one or more types of agriculture environments, and/or the like”; para. [0130] “The labeled data, for example, may include geographic polygons labeled with an object class, such as a type of coverage including a type of vegetation species or other type of classification, such as city, road, grassland, and/or the like”).
As per claim 5, dependent upon claim 1, Lau in view of Williamson teaches wherein the processor is configured to generate a land map including the image pixel rendered according to its land feature classification (Williamson FIG. 7 showing a rendered land map representing one or more geographic regions; para. [0147]; para. [0148] “the interactive geographic GUI 428 renders one or more georeferenced overlays over a geographic region reflected by and/or selected through the map interface 438 … As examples, the one or more georeferenced overlays may include a landcover overlay icon 702 reflective of one or more object classes physically located within a geographic region”).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have modified Lau’s teaching by incorporating Williamson’s teaching to generate a land map including the image pixel rendered according to its land feature classification in order to provide an interactive geographic GUI for viewing various land features (Williamson FIG. 7).
As per claim 8, dependent upon claim 1, Lau in view of Williamson teaches wherein the image pixel comprises a color image pixel (Williamson para. [0111] “In some embodiments, an image frame is a plurality of pixels (e.g., spatial, spectral, etc.) with one or more feature channels. For example, an image frame may include a single channel image frame, such as grayscale image frame, a three-channel image frame, such as a red, green, blue (RGB) image frame, and/or a hyperspectral image frame including hundreds of feature channels at different frequency spectra”).
As per claim 9, dependent upon claim 1, Lau in view of Williamson teaches wherein the image pixel comprises a grayscale image pixel (Williamson para. [0111] “In some embodiments, an image frame is a plurality of pixels (e.g., spatial, spectral, etc.) with one or more feature channels. For example, an image frame may include a single channel image frame, such as grayscale image frame, a three-channel image frame, such as a red, green, blue (RGB) image frame, and/or a hyperspectral image frame including hundreds of feature channels at different frequency spectra”).
As per claim 10, Lau teaches an image pixel classification device (FIG. 1; FIG. 33) comprising:
a quantum computing circuit (FIG. 1 Quantum Processor 106) configured to perform quantum subset summing (FIG. 9 #910 “Perform subset summing operations by the quantum processor”; FIG. 33 quantum computing circuit 337); and
a processor (FIG. 33 processor 338) configured to
generate a pairwise game theory reward matrix for a plurality of different classes of a signal FIG. 10 showing a signal classification system; FIG. 33 the processor 338 performing “generate game theory reward matrix for different deep learning models”; FIG. 20 showing a generated pairwise game theory reward matrix for a plurality of different classes (each modulation class paired with a machine learning algorithm); para. [0113] describing modulation classes and corresponding modulation feature types),
cooperate with the quantum computing circuit to perform quantum subset summing on the pairwise game theory reward matrix (FIG. 33 processor 308 performing “cooperate with QC circuit to perform quantum subset summing of game theory reward matrix”; FIG. 2-3 providing a general framework of subset summing; para. [0090]), and
select a class for the FIG. 33 processor 308 performing “select deep learning model based upon quantum subset summing of game theory reward matrix”, and “process RF signals using the selected deep learning model for RF signal classification”; para. [0212]-[0213]; FIG. 16 showing a classification example, in which the signal is assigned a QPSK modulation class since it is associated with the highest predictions score 1606; para. [0118]).
Lau teaches every limitation as recited in claim 1 except for classification of an image pixel, each class corresponding to a respective type of land feature from among a plurality of different types of land features, and generating a map including the image pixel rendered according to its land feature classification. Note Lau mentions image classification (para. [0140]). Lau also discloses applying a fully convolutional-deconvolutional network trained end-to-end with semantic segmentation to classify land use/land cover features (para. [0139]). Image pixel classification with corresponding type of land features, however, is not apparently available.
Williamson in an analogous field discloses a geographic prediction system for generating one or more landcover predictions corresponding to one or more geographic portions within a geographic region by using one or more machine learning models (para. [0004]; FIG. 3-4). During training of the machine learning model 410 to classify the geographic region (e.g., a portion of the Earth's surface), labeled data is used. The labeled data may include geographic polygons labeled with an object class, such as a type of coverage including a type of vegetation species or other type of classification, such as city, road, grassland, and/or the like (para. [0109], [0130]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have modified Lau’s teaching by incorporating Williamson’s teaching to classify image pixel whose class corresponds to a respective type of land feature from among a plurality of different types of land features. Doing so would provide an improved image processing pipeline that is capable of automatically generating accurate, time-based geographical predictions for the geographic area as suggested by Williamson (para. [0095]).
Williamson further teaches generating a land map including the image pixel rendered according to its land feature classification (Williamson FIG. 7 showing a rendered land map representing one or more geographic regions; para. [0147]; para. [0148] “the interactive geographic GUI 428 renders one or more georeferenced overlays over a geographic region reflected by and/or selected through the map interface 438 … As examples, the one or more georeferenced overlays may include a landcover overlay icon 702 reflective of one or more object classes physically located within a geographic region”).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have modified Lau’s teaching by incorporating Williamson’s teaching to generate a map including the image pixel rendered according to its land feature classification in order to provide an interactive geographic GUI for viewing various land features (Williamson FIG. 7).
Claim 11, dependent upon claim 10, is rejected as applied to claim 3 above.
Claim 12, dependent upon claim 10, is rejected as applied to claim 4 above.
As per claim 13, dependent upon claim10, Lau in view of Williamson teaches wherein the map comprises a land map (Williamson FIG. 7 showing a rendered land map representing one or more geographic regions; para. [0147]; para. [0148] “the interactive geographic GUI 428 renders one or more georeferenced overlays over a geographic region reflected by and/or selected through the map interface 438 … As examples, the one or more georeferenced overlays may include a landcover overlay icon 702 reflective of one or more object classes physically located within a geographic region”).
As per claim 16, dependent upon claim10, Lau in view of Williamson teaches wherein the image pixel comprises at least one of a color image pixel and a grayscale image pixel (Williamson para. [0111] “In some embodiments, an image frame is a plurality of pixels (e.g., spatial, spectral, etc.) with one or more feature channels. For example, an image frame may include a single channel image frame, such as grayscale image frame, a three-channel image frame, such as a red, green, blue (RGB) image frame, and/or a hyperspectral image frame including hundreds of feature channels at different frequency spectra”).
As per claim 17, an independent claim, Lau in view of Williamson teaches an image pixel classification method (Lau FIG. 10; FIG. 35) comprising:
at a processor (Lau FIG. 33),
generating a pairwise game theory reward matrix for a plurality of different classes of an image pixel, each class corresponding to a respective type of land feature from among a plurality of different types of land features,
cooperating with a quantum computing circuit to perform quantum subset summing on the pairwise game theory reward matrix, and
selecting a class for the image pixel based upon the quantum subset summing, and classify the image pixel as the corresponding type of land feature for the selected class (Claim 17 recites a method with elements corresponding to the elements recited in claim 1. Therefore, the recited elements of this claim are mapped to Lau in view of Williamson in the same manner as the corresponding elements in its corresponding apparatus claim, claim 1. Additionally, the rationale and motivation to combine Lau and Williamson presented in rejections of claim 1 apply to this claim). .
Claim 18, dependent upon claim 17, is rejected as applied to claim 2 above.
Claim 19, dependent upon claim 18, is rejected as applied to claim 3 above.
Claim 20, dependent upon claim 17, is rejected as applied to claim 4 above.
Claim 21, dependent upon claim 17, is rejected as applied to claim 5 above.
Claim 24, dependent upon claim 17, is rejected as applied to claim 16 above.
Claim(s) 6-7, 14-15 and 22-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lau et al. (US 20240054377 A1, hereafter Lau), in view of Williamson et al. (US 20250165754 A1, hereafter Williamson), as applied above to claims 1, 10 and 17 respectively, and further in view of Kneuper et al. (US 20180232097 A1, hereafter Kneuper).
As per claim 6, dependent upon claim 1, Lau in view of Williamson teaches wherein the processor is configured to generate a land map including the image pixel rendered according to its land feature classification (See rejections applied to claim 5), but does not teach a flight simulator map.
Kneuper discloses a flight planning system for navigation of an aircraft (para. [0017]). Specifically, generated flight simulator map is displayed on a touch screen display device (FIG. 1). Initially, the real-time view displayed by the touch-screen interface panel (TSIP) of an aircraft/vehicle may be captured by a high-definition (HD) camera on the exterior of the aircraft/vehicle. As shown in FIG. 1, land features with different classification is displayed. The TSIP is a digital information panel and may include a plurality of digital layers. The digital layers may overlay one another to create multiple views. For instance, one layer may be a real-time view while another layer may be a three-dimensional representation of, for example, weather while another layer may include flight instruments and may not be obstructed with any other layers or representations (para. [0099]-[0100]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Lau and Williamson by incorporating the teaching of Kneuper to generate a flight simulator map including the image pixel rendered according to its land feature classification. Generating a flight simulator map with land features would provide a user friendly, intuitive interface for receiving information and controlling the aircraft as recognized by Kneuper (para. [0095]).
As per claim 7, dependent upon claim 6, Lau in view of Williamson and Kneuper teaches wherein the processor is further configured to change the rendering of the image pixel based upon a plurality of different simulated weather conditions (Kneuper para. [0235] “The gradient-type feature of the synthetic vision application provides users the ability to dynamically adjust images. This improves situational awareness by allowing users more power in controlling the image. For example, on a foggy/cloudy day, a user may need more synthetic vision to “see” through the weather but as the fog/clouds lift, the user could reduce the amount of synthetic vision enhancements to bring in real images to better identify landmarks (e.g., roads, rivers, houses, etc.) that the synthetic vision would not show”).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Lau and Williamson by incorporating the teaching of Kneuper to generate a flight simulator map with changed rendering of the image pixel based on a plurality of different simulated weather conditions. Doing so would enable a user to visualize aircraft icon dynamically as it encounters forecasted weather representation as recognized by Kneuper (para. [0167]).
As per claim 14, dependent upon claim10, Lau in view of Williamson teaches generating a land map, but does not teach a flight simulator map.
Kneuper discloses a flight planning system for navigation of an aircraft (para. [0017]). Specifically, generated flight simulator map is displayed on a touch screen display device (FIG. 1). Initially, the real-time view displayed by the touch-screen interface panel (TSIP) of an aircraft/vehicle may be captured by a high-definition (HD) camera on the exterior of the aircraft/vehicle. As shown in FIG. 1, land features with different classification is displayed. The TSIP is a digital information panel and may include a plurality of digital layers. The digital layers may overlay one another to create multiple views. For instance, one layer may be a real-time view while another layer may be a three-dimensional representation of, for example, weather while another layer may include flight instruments and may not be obstructed with any other layers or representations (para. [0099]-[0100]).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Lau and Williamson by incorporating the teaching of Kneuper to generate a flight simulator map. Generating a flight simulator map would provide a user friendly, intuitive interface for receiving information and controlling the aircraft as recognized by Kneuper (para. [0095]).
Claim 15, dependent upon claim 14, is rejected as applied to claim 7 above.
Claim 22, dependent upon claim 17, is rejected as applied to claim 6 above.
Claim 23, dependent upon claim 22, is rejected as applied to claim 7 above.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUEMEI G CHEN whose telephone number is (571)270-3480. The examiner can normally be reached Monday-Friday 9am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John M Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/XUEMEI G CHEN/Primary Examiner, Art Unit 2661