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. None of the claims has been amended.
Response to Arguments
Applicant's arguments filed 5/6/26 have been fully considered but they are not persuasive.
Arguments (REMARKS pages 12-14)
Nevertheless, the Examiner looked to the secondary reference to Williamson et al. in an attempt to supply these missing teachings from the primary reference. Williamson et al. discloses a geographic prediction system comprising memory and one or more processors communicatively coupled to the memory. Further, "the one or more processors are configured to receive one or more hyperspectral image frames corresponding to at least a portion of a geographic region; generate, using one or more machine learning models, one or more landcover predictions for the geographic region based at least in part on the one or more hyperspectral image frames". (Emphasis added, paragraph [0004]).
Applicant respectfully submits that Williamson et al. uses hyperspectral image frames to determine landcover predictions. It has nothing to do with 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, selecting a class for the image pixel based upon the quantum subset summing, and classifying the image pixel as the corresponding type of land feature for the selected class, as in the claimed invention. These claim recitations are not mere implementation details; they define a particular integration of quantum subset summing and per-pixel landcover classification that neither reference, alone or in combination, sets out.
The Examiner took the position that it would have been obvious to one skilled in the art "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 a plurality of different types of land features." The Examiner contended that 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])."
Applicant submits that the Examiner is using impermissible hindsight to combine bits and pieces of the references in an attempt to produce the claimed invention. The quantum subset summing of Lau et al. is deployed in a decision-making/model-selection context, while the landcover pipeline of Williamson et al. is concerned with deterministic machine learning classification over hyperspectral inputs. Accordingly, one of skill in the art looking on improving landcover predictions from Williamson et al. would naturally look to better ML architectures, feature reduction, or training data, not to a quantum reward-matrix engine designed for RF strategy optimization as in the Lau et al.
The primary reference to Lau et al. focuses on quantum registers, qubit encodings of reward matrices, quantum adders/comparators, and decision histograms, with outputs driving electronic devices (e.g., cognitive radios, object detectors, RF transmitters). Its imaging embodiment relies on VAE latent spaces, GAN-generated data, and quantum Z-tests for cluster membership-again for object detection and model selection, not for geospatial landcover labeling feeding environmental overlays.
In contrast to Lau et al., the teachings of Williamson et al. are deeply tied to raster/polygon landcover datasets, environmental rasters, and UI overlays (carbon, species, landcover), with alignment to lookup tables, burned-area rasters, soil carbon stock rasters, etc. The ML classifier produces probabilities or labels that feed into carbon and species overlays and time-dependent environmental insights.
Applicant submits that one of skill in the art would not look to make the selective combination of Lau et al. and Williamson et al. as suggested by the Examiner. Accordingly, independent Claims 1, 10 and 17 are patentable over the prior art. Their dependent claims, which recite yet further distinguishing features, are also patentable and require no further discussion herein.
Response
As stated in last OA, primary reference 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. Instead Lau teaches a different application, which is classification of signals (FIG. 10), in which each class corresponding to a respective type of modulation feature from among a plurality of different types of modulation features. Note signal recognition and classification is just one illustrative use case for the quantum processor in Lau (para. [0095]). Lau’s approach can be applied in various applications (see below para. [0059], annotation added).
[0059] The present approach can be used in various applications. For example, the present approach can be used in (i) sensor control applications (e.g., to determine which action(s) should be taken or task(s) sensor(s) should be performing at any given time), (ii) vehicle or craft navigation applications (e.g., to determine which direction a ship should travel to avoid an obstacle), (iii) dynamic network applications (e.g., to determine which is the best plan for dynamic resource allocation), (iv) search and rescue applications (e.g., to determine which grid a drone or other sensor device should proceed to for efficient search and rescue), (v) unmanned vehicle control applications (e.g., to determine what is an optimal positioning of an unmanned vehicle to achieve communication linking), (vi) natural resource exploration applications (e.g., to determine which acoustic ray trace should be used for oil and gas exploration), (vii) image analysis applications (e.g., to determine which land use land cover tag should be used to label a pixel for image feature extraction), (viii) robot control applications (e.g., to determine which path is the most efficient for a robot to travel to a destination), (ix) observation applications (e.g., which machine learning algorithm or model in an ensemble should be used for a given observation—which frequency should a transmitter hop to avoid jamming or which modulation type is received), (x) network node or personnel management applications (e.g., to determined which network node or person is the most important or influential), (xi) situational awareness applications (e.g., to determine which emotion or personality is being displayed by a person), (xii) business applications (e.g., to determine which opportunity should a business pursue, and/or what training does each employee need to achieve a next level most efficiently), and/or (xiii) aircraft control applications (e.g., to determine what is an optimal aircraft for noise mitigation).
Therefore Lau specifically mentions that the same approach used for signal classification can be applied in image analysis to determine a pixel label which is associated with a land feature. Secondary reference Williamson expressly fills the gap by discloses an application of 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. Therefore the motivation would be use of known technique to improve similar devices (methods, or products) in the same way (MPEP 2141 III).
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.
Conclusion
The following prior art is considered closely related to the current application.
Rahmes et al. (US 20220300843 A1) discloses a method for operating a quantum processor (Abstract). The methods comprise: receiving a reward matrix at the quantum processor, the reward matrix comprising a plurality of values that are in a given format and arranged in a plurality of rows and a plurality of columns; converting, by the quantum processor, the given format of the plurality of values to a qubit format; performing, by the quantum processor, subset summing operations to make a plurality of row selections based on different combinations of the values in the qubit format; using, by the quantum processor, the plurality of row selections to determine a normalized quantum probability for a selection of each row of the plurality of rows; making, by the quantum processor, a decision based on the normalized quantum probabilities; and causing, by the quantum processor, operations of an electronic device to be controlled or changed based on the decision (Abstract; FIG. 1). Rahmes further discloses a signal classification system using the quantum processor (FIG. 10; para. [0080]).
Prior art searched but not cited is recorded in PTO-892.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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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.
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/XUEMEI G CHEN/Primary Examiner, Art Unit 2661
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