DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/19/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/04/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed on 05/19/2026 have been fully considered but are moot in view of the new ground(s) rejection in view of Bowers John et al (WO – 2022056638 A1; Bowers).
Claim Status
Claim(s) 1-4, 6 and 8-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kargieman et al (U.S. 20170250751 A1; Kargieman), in view of Bowers John et al (WO – 2022056638 A1; Bowers).
Claim(s) 5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kargieman et al (U.S. 20170250751 A1; Kargieman), in view of Bowers John et al (WO – 2022056638 A1; Bowers), and in further view of Beckett et al (U.S. 20160300375 A1; Beckett).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 6 and 8-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kargieman et al (U.S. 20170250751 A1; Kargieman), in view of Bowers John et al (WO – 2022056638 A1; Bowers).
Regarding claim 1, Kargieman discloses an apparatus (Fig.1: a system 100) comprising: at least one processor; (one or more processors 120 ) and a device, (Fig.1: a satellite 102; payload 104 and control system 118) including at least one memory having processor-executable code stored therein that when executed by the at least one processor cause the at least one processor (Paragraphs 22-23: “A control system 118 may be comprised of one or more on board computers for handling the functions of the payload 104 and other systems on board the satellite. The control system 18 may include one or more processors 120 and computer-readable media 122. … Much of this ability is stored as instructions, processes, and logic within the computer-readable media 122 of the satellite 102.”) to:
provide a plurality of reference embeddings corresponding to an object; (Fig.5 and Paragraph 98: “objects stored in an object database”; Paragraph 42-44: “Convolutional Neural Networks (CNN) are instanced in a CPU or GPU or an FPGA or AI accelerator onboard a satellite in orbit, and used to process images captured with a high-resolution camera, in real-time, and produce vector maps containing object shapes and object labels present in the original image data. … Once the algorithms are trained, the trained instances can be uploaded and run in orbit on the satellites; Fig.7 and Paragraph 111: “At block 704, the satellite receives artificial intelligence configuration. The artificial intelligence (“AI”) configuration may define a neural network for the artificial intelligence module (e.g., 212 of FIG. 2). The AI configuration may include a pre-trained neural network, which may be trained on the ground before being uploaded to the satellite. … The neural network may further be updated, or receive additional training, while on-board the satellite to accomplish additional tasks.”)
cause a downlink session with an orbiting satellite to be established; and during the downlink session: (Fig 2: in-orbit satellite imaging system 202; Fig.4 : in-orbit satellite image processing and analysis system 402; and Abstract: A smart satellite system is capable of decision making and prioritization on the fly to optimize the use of downlink bandwidth to deliver prioritized data based upon opportunity and the resources of available payloads.”)
receive, from the orbiting satellite, a plurality of spacecraft embeddings, wherein the plurality of spacecraft embeddings are generated onboard the orbiting satellite by applying an embedding-generation model to corresponding portions of images that are stored on the orbiting satellite and obtained from sensors on the orbiting satellite; (Fig 2: in-orbit satellite imaging system 202; Fig.4 : in-orbit satellite image processing and analysis system 402; Paragraph 42: “ Convolutional Neural Networks (CNN) are instanced in a CPU or GPU or an FPGA or AI accelerator onboard a satellite in orbit, and used to process images captured with a high-resolution camera, in real-time, and produce vector maps containing object shapes and object labels present in the original image data”; Paragraphs 90-91: “The satellite, using edge detection or object detection algorithms, for example, may segment the image data into two pixel sets, one that includes the parking lot and one that does not include the parking lot.” Paragraphs 76-78: “Vector processing: At block 328, images, mosaics or areas of interest in images or mosaics can be processed to be transformed into vector maps, representing and labeling characteristics present in the original input … Predictive Models: At 334, based on a collection of images, mosaics, areas of interest in images or mosaics, vector maps and rasters, predictive models may be used that forecast the results of future observations, and predict dependent variables, or that fit well-defined parameterized models that can be applied to the collected data. These predictive models may be maintained in orbit or downloaded to the ground. … At block 338, the processed image data may be stored on board the satellite until a downlink communications channel is established with a base station and the data can be transmitted.”)
compare, in a vector space, (Paragraph 76-77: “Vector processing: At block 328, images, mosaics or areas of interest in images or mosaics can be processed to be transformed into vector maps, representing and labeling characteristics present in the original input. For example, an object detection algorithm may be run on a high-resolution image of a road to identify cars, and may label them according to color, in a vector map representation. … an edge detection algorithm may determine the edges of the cornfield, and the boundary of the corn field may be defined by vectors. The image data may then be segmented, such as by storing the area within the vectors for further analysis …the image can be discarded when all the information required is properly represented by vectors.”) the plurality of spacecraft embeddings with the plurality of reference embeddings; (Paragraph 92-94: “Object detection 420 may utilize any suitable algorithm for identifying features or object depicted within image data. For example, an object database may be stored and features identified in the image data may be compared against entries in the object database in order to find matching objects. … Object detection may be able to analyze image data generated by the imaging sensors and through various algorithms, such as edge detection 418, determine instances of semantic objects of certain classes … By detecting the edges, object detection 420 is able to isolate likely objects and then compare these objects with objects stored in an object database to determine a match, and therefore identify objects.”; Fig.5; Paragraph 98, the person one of ordinary skill in the art would understand that the edge detection using a vector map for object detection including step comparing is interpreted as “compare, in vector space,” )
determine, based at least in part on the comparison, as to which portions of the images are high-value portions of the images that include instances of the object; (Fig.5 and Paragraphs 98-99: “By detecting the edges, object detection 420 is able to isolate likely objects and then compare these objects with objects stored in an object database to determine a match, and therefore identify objects … At block 510, image analytics may be run on the image data to collect information about the object or features depicted. … image data that is separated temporally may be compared to determine changes in,”, it shows that the “object identified” interpreted as “high-value portions of the images”; Paragraph 118; Paragraphs 124-126; Paragraph 133-135)
communicate, to the orbiting satellite, which portions of the images from among the portions of the images are the high-value portions of the images; receive, from the orbiting satellite, the high-value portions of the images; (Figs. 1-9; Paragraphs 32-33: “the image capture may be performed on-board the satellite, and some (or all) of the image processing may be performed off-board the satellite. The image processing capabilities may be shared between multiple satellites within a constellation, or between a satellite and a ground-based station. … the image analysis workflow may be shared between multiple satellites within a constellation, or between a satellite and a ground-based station.”; Paragraph 100: “At block 512, the relevant data may be transmitted, such as to a base station. In some instances, the satellite may transmit images to the base station. The images may be a subset of the total number of images captured of an area of interest. Furthermore, the images may be portions of images captured of an area of interest, having been segmented … the relevant data may be numerical, such as the number of cars, a percentage of ground cover, or an increase in water surface area.”; Paragraph 118; Paragraphs 124-126; Paragraph 133-135; Paragraphs 147-148)
process, using a trained machine-learning model, the high-value portions of the images to detect a plurality of instances of the object; (Paragraph 111; Paragraph 145: “The images may be analyzed by executing artificial intelligence or machine learning algorithms on board. Alternatively or additionally, the satellite system may analyze the images by executing instructions received from a ground-station or from another satellite.”; Paragraphs 91-93: “Image analysis 416 may be performed on image data…. . Using one or more algorithms, such as edge detection 418 or object detection 420, the cars depicted in the segmented image data may be counted, catalogued, and/or identified. … Object detection 420 may utilize any suitable algorithm for identifying features or object depicted within image data. … there a high resolution imaging sensor captures high resolution imaging data such that object detection 420 is able to isolate objects with a high degree of accuracy, the detected objects may be compared to known objects within an object database for object identification. That is, rather than simply counting the cars in the parking lot, the image processing and analysis system 402 may be able to identify the cars in the parking lot.”)
However, Kargieman does not explicit disclose an apparatus located in a ground station,
process, using a trained machine-learning model, the high-value portions of the images to detect a plurality of instances of the object and to determine, for each detected instance of the object, a corresponding geographic location and size; and
output a dataset comprising the plurality of instances of the object and, for each instance of the object, the corresponding geographic location and size.
Bowers discloses an apparatus located in a ground station, (Paragraph 165: “ The ground segment 104 includes a ground terminal 114. The term ground terminal may be used to refer to a single ground terminal or multiple ground terminals. The ground terminal 114 includes components (e.g. antennas, transmitters, receivers) for transmitting signals to and receiving signals from the imaging satellites 106, 108 and components for processing data (e.g. one or more computing devices, software modules). Processing data includes processing image data received from the satellites 106, 108.”) comprising: at least one processor; and a device, including at least one memory having processor-executable code stored therein that when executed by the at least one processor cause the at least one processor (Paragraph 224: “The ground terminal 114 includes a processor 502 in communication with a memory 504 and a communication interface 506. The processor 502 is configured to execute computer-executable instructions embodied in one or more modules or units described in Figure 5.”) to:
cause a downlink session with an orbiting satellite to be established; (Paragraph 154: “The broad area imaging satellite 106 may include a high-speed data downlink subsystem … The high-speed data downlink subsystem may provide downlink speeds which allow more images to be downlinked to a ground station (e.g. ground station 114) within a contact pass between the satellite and the ground station communications cone.”)
communicate, to the orbiting satellite, which portions of the images from among the portions of the images are the high-value portions of the images; (Paragraph 173: “ the broad area satellite 106 may collect a broad area SAR image in which a plurality of vessels (e.g. ships) are seen. The ships and ship locations may be correlated by the ground terminal 114 against AIS data from an AIS data feeding system (e.g. AIS data provider who operates its own constellation of AIS satellites). Most ships are likely transmitting an AIS signal broadcasting their respective ID, position, and heading. From this, a determination may be made using the ground terminal 114 identifying the ships in the broad area SAR image that are transmitting AIS.”)
receive, from the orbiting satellite, the high-value portions of the images; (Paragraph 189-190: “At 216, the higher resolution imaging satellite 108 transmits the second SAR data to the ground terminal 114 via downlink 120. …At 218, the ground terminal 114 receives and processes the second SAR data.”; Paragraph 238: “The ground terminal 114 further includes an image data analysis unit 542. The image data analysis unit 542 is configured to analyze the higher resolution SAR data 540 to determine image context data 544. The image context data 544 describes a context for the higher resolution SAR data 540, such as an identity of an image subject (e.g. an object in the image)”)
process, using a trained machine-learning model, the high-value portions of the images to detect a plurality of instances of the object and to determine, for each detected instance of the object, a corresponding geographic location and size; (Paragraphs 191-193; Paragraph 239; Paragraphs 303-307: “Object of interest location data 1404 may comprise an output of the location of all detected objects of interest. Object of interest location data 1404 may, for example, specify coordinates defining a location for one or more detected objects … Object characterization data 1406 may include a qualitative or quantitative characterization of each object of interest. The object characterization data may describe one or more physical characteristics of the object (e.g. vessel), such as, for example object length, object width, object surface area, etc. … the object characterization data 1406 may include an object classification (e.g. a class assignment or label), specifying a class of object to which the object of interest belongs, determined via an object detection or object classification task performed by the processor 1122. … object of interest image chips 1410 may be used as input to an object of interest classification module implementing one or more classification algorithms for classifying the object of interest in the image chip 1410. The object of interest classification algorithm may be any suitable classification algorithm. For example, the object of interest classification module may implement a machine learning-based classification model or algorithm. The object of interest classification module may be implemented on a processing unit of a ground-based terminal (e.g. ground terminal 1210, ground terminal 1212), in which case the image chip 1410 may be received by downlink from the first satellite 1202 and processed via the classification module to determine a class assignment for the object of interest.”; Paragraph 359-361) and
output a dataset comprising the plurality of instances of the object and, for each instance of the object, the corresponding geographic location and size. (Paragraphs 93 -94: “generating a detected vessel report including data describing an attribute of the detected marine vessel; and generating an image chip of the detected marine vessel from the SAR data. … The attribute may include any one or more of a size of the detected vessel, a geographic location of the detected vessel, an estimated velocity of the detected vessel, a heading of the detected vessel, and a characterization of the detected vessel.”; Paragraph 300-307; Paragraphs 359-361: “the detected object report generator module 1710 is configured to receive the object detection output and, if present, the estimated object velocity values, and generate a detected object report. Generally, the detected object report includes data describing one or more detected objects. The data may include, for example, location, characterization (object type or class), velocity, heading, etc. … The image chip generator module 1712 is configured to receive the object detection output including the SAR data and data describing the detected objects (such as location in the image) and generate an image chip of each detected object. … one image chip is generated for each detected object. The size of the image chip is based on the size of the object (e.g. vessel) and represents an extended bounding box around the object in the processed SAR image”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Kargieman by including a systems for satellite imaging including the ground segment that is taught by Bowers, to make the invention that satellite-based observation and surveillance using multiple satellites and/or with onboard image data processing; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving performance of an earth observation system, reduce the latency of an earth observation operation, as well as enhancing the user convenient as a client may utilize one or more of these services.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 2, Kargieman, as modified by Bowers, discloses the downlink session is a downlink session in a plurality of scheduled downlink sessions between the orbiting satellite and the apparatus.(Kargieman: Figs.6 and 8; Paragraphs 102 : “the satellite, using the various modules and systems described herein, determines a short-term plan. … establishing downlink communication channels, allocating time for on-board computations such as image analysis, and the like. The short-term plan may therefore influence the prioritized list of tasks where the satellite determines that it can increase efficiency by changing the priority of one or more tasks in the prioritized list of tasks”; Paragraph 120; Bowers: Paragraph 167: “The ground terminal 114 is adapted to receive data from the broad area imaging satellite 106 via downlink 116 and transmit data to the broad area imaging satellite 106 via uplink 118. … The ground terminal 114 is adapted to receive data from the higher resolution imaging satellite 108 via downlink 120 and transmit data to the higher resolution imaging satellite 108 via uplink 122.”)
Regarding claim 3, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the determination is further based on at least one of a client request, a client search, a historical client access, or a client sales pattern. (Paragraph 52: “ a customer requiring specific satellite imaging data may input a request regarding an area of interest (AOI) or a point of interest (POI), a particular type of AOI as a polygon on the surface of the Earth in a particular coordinate system. … a customer may also specify a bidding price, such as a maximum price the customer is willing to pay for accomplishing the SLA.”)
Regarding claim 4, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the spacecraft embeddings of the plurality of spacecraft embeddings are feature vectors of floating-point numbers. (Paragraph 42; Paragraph 76: “ Vector processing: At block 328, images, mosaics or areas of interest in images or mosaics can be processed to be transformed into vector maps, representing and labeling characteristics present in the original input. … The vector processing may incorporate points, lines, polygons, and other geometric forms for vector map transformation.”)
Regarding claim 6, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the images include a plurality of satellite images. (Paragraph 41: “AI accelerators or on ad-hoc hardware architectures implemented in field programmable gate arrays (“FPGAs”) or application-specific integrated circuits (“ASICs”) or other hardware embeddings, to be applied to images, or image sequences taken by the imaging sensors of the satellite 102, and used, in-orbit, as general building blocks as part of an image processing pipeline,”)
Regarding claim 8, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the sensors on the orbiting satellite include at least one of a camera, a synthetic aperture radar, a thermal imaging sensor, a hyperspectral sensor, or a video sensor. (Paragraph 38: “the sensor data module 208 may receive sensor data from satellite sensors that include, but are not limited to, wide field of view sensors, high resolution cameras, hyper-spectral cameras, thermal imaging sensors, and infrared sensors.”)
Regarding claim 9, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the embedding-generation model includes at least one of an unsupervised representation learning model, a self-supervised representation learning technique, or a supervised representation learning technique. (Paragraphs 40-42: “In some instances, the artificial intelligence (“AI”) processing module 212 is a form of neural network (NN). The AI processing module 212 is capable of training NNs through supervised learning (SL), unsupervised learning (UL) or reinforced learning (RL), to perceive, encode, predict, and classify patterns or pattern sequences in the captured image data,”)
Regarding claim 10, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses comparing the spacecraft embeddings in the plurality of spacecraft embeddings to the reference embeddings in the plurality of reference embeddings includes determining which spacecraft embeddings in the spacecraft embeddings of the plurality of spacecraft embeddings are close, in the vector space, to the reference embeddings in the set of reference embeddings. (Fig.5 and Paragraphs 76-77 and 98-99: “By detecting the edges, object detection 420 is able to isolate likely objects and then compare these objects with objects stored in an object database to determine a match, and therefore identify objects … At block 510, image analytics may be run on the image data to collect information about the object or features depicted. … image data that is separated temporally may be compared to determine changes in,”, it shows that the “match object” interpreted as “closes or similar”; Paragraph 118; Paragraphs 124-126; Paragraph 133-135)
Regarding claim 11, Kargieman discloses a method, (Fig.1: a satellite 102; payload 104 and control system 118 and Paragraphs 22-23: “A control system 118 may be comprised of one or more on board computers for handling the functions of the payload 104 and other systems on board the satellite. The control system 18 may include one or more processors 120 and computer-readable media 122. … Much of this ability is stored as instructions, processes, and logic within the computer-readable media 122 of the satellite 102.”), comprising:
providing a plurality of reference embeddings corresponding to an object; (Fig.5 and Paragraph 98: “objects stored in an object database”; Paragraph 42-44: “Convolutional Neural Networks (CNN) are instanced in a CPU or GPU or an FPGA or AI accelerator onboard a satellite in orbit, and used to process images captured with a high-resolution camera, in real-time, and produce vector maps containing object shapes and object labels present in the original image data. … Once the algorithms are trained, the trained instances can be uploaded and run in orbit on the satellites; Fig.7 and Paragraph 111: “At block 704, the satellite receives artificial intelligence configuration. The artificial intelligence (“AI”) configuration may define a neural network for the artificial intelligence module (e.g., 212 of FIG. 2). The AI configuration may include a pre-trained neural network, which may be trained on the ground before being uploaded to the satellite. … The neural network may further be updated, or receive additional training, while on-board the satellite to accomplish additional tasks.”)
causing a downlink session between the ground station and a constrained- environment device to be established; and during the downlink session: (Fig 2: in-orbit satellite imaging system 202; Fig.4 : in-orbit satellite image processing and analysis system 402; and Abstract: A smart satellite system is capable of decision making and prioritization on the fly to optimize the use of downlink bandwidth to deliver prioritized data based upon opportunity and the resources of available payloads.”; Paragraph 109: “The data may be stored on board the satellite until it is convenient for the satellite to establish a downlink communication channel with a base station and transmit the necessary data.”)
receiving, from the constrained-environment device at the ground station, (Paragraph 27: “The communications module 124 may control the communications system to provide for a communication channel with one or more base stations on the surface of the Earth or with other satellites within a satellite constellation. The communications module 124 may be responsible for receiving instructions and data from ground or airborne stations, for transmitting data to ground or airborne stations, and for transmitting and receiving data and instructions to and from other satellites within the satellite constellation”; Paragraphs 32-33: “the image capture may be performed on-board the satellite, and some (or all) of the image processing may be performed off-board the satellite. The image processing capabilities may be shared between multiple satellites within a constellation, or between a satellite and a ground-based station. … the image analysis workflow may be shared between multiple satellites within a constellation, or between a satellite and a ground-based station.”) a plurality of constrained-environment-device embeddings, wherein the constrained- environment-device embeddings in the plurality of constrained-environment-device embeddings are generated by applying an embedding-generation model to corresponding portions of images that are stored on the constrained-environment device and obtained from sensors on the constrained-environment device; (Fig 2: in-orbit satellite imaging system 202; Fig.4 : in-orbit satellite image processing and analysis system 402; Paragraph 42: “ Convolutional Neural Networks (CNN) are instanced in a CPU or GPU or an FPGA or AI accelerator onboard a satellite in orbit, and used to process images captured with a high-resolution camera, in real-time, and produce vector maps containing object shapes and object labels present in the original image data”; Paragraphs 90-91: “The satellite, using edge detection or object detection algorithms, for example, may segment the image data into two pixel sets, one that includes the parking lot and one that does not include the parking lot.” Paragraphs 76-78: “Vector processing: At block 328, images, mosaics or areas of interest in images or mosaics can be processed to be transformed into vector maps, representing and labeling characteristics present in the original input … Predictive Models: At 334, based on a collection of images, mosaics, areas of interest in images or mosaics, vector maps and rasters, predictive models may be used that forecast the results of future observations, and predict dependent variables, or that fit well-defined parameterized models that can be applied to the collected data. These predictive models may be maintained in orbit or downloaded to the ground. … At block 338, the processed image data may be stored on board the satellite until a downlink communications channel is established with a base station and the data can be transmitted.”)
comparing, in a vector space, (Paragraph 76-77: “Vector processing: At block 328, images, mosaics or areas of interest in images or mosaics can be processed to be transformed into vector maps, representing and labeling characteristics present in the original input. For example, an object detection algorithm may be run on a high-resolution image of a road to identify cars, and may label them according to color, in a vector map representation. … an edge detection algorithm may determine the edges of the cornfield, and the boundary of the corn field may be defined by vectors. The image data may then be segmented, such as by storing the area within the vectors for further analysis …the image can be discarded when all the information required is properly represented by vectors.”) the constrained-environment-device embeddings in the plurality of constrained-environment-device embeddings with the reference embeddings in the plurality of reference embeddings; (Paragraph 92-94: “Object detection 420 may utilize any suitable algorithm for identifying features or object depicted within image data. For example, an object database may be stored and features identified in the image data may be compared against entries in the object database in order to find matching objects. … Object detection may be able to analyze image data generated by the imaging sensors and through various algorithms, such as edge detection 418, determine instances of semantic objects of certain classes … By detecting the edges, object detection 420 is able to isolate likely objects and then compare these objects with objects stored in an object database to determine a match, and therefore identify objects.”; Fig.5; Paragraph 98, the person one of ordinary skill in the art would understand that the edge detection using a vector map for object detection including step comparing is interpreted as “compare, in vector space,” )
making a determination, based at least in part on the comparison, as to which portions of the images are high-value portions of the images that include instances of the object; (Fig.5 and Paragraphs 98-99: “By detecting the edges, object detection 420 is able to isolate likely objects and then compare these objects with objects stored in an object database to determine a match, and therefore identify objects … At block 510, image analytics may be run on the image data to collect information about the object or features depicted. … image data that is separated temporally may be compared to determine changes in,”, it shows that the “object identified” interpreted as “high-value portions of the images”; Paragraph 118; Paragraphs 124-126; Paragraph 133-135)
communicating, to the constrained-environment device from the ground station, which portions of the images from among the portions of the images are the high- value portions of the images; and receiving, from the constrained-environment device at the ground station, the high-value portions of the images; (Figs. 1-9; Paragraphs 32-33: “the image capture may be performed on-board the satellite, and some (or all) of the image processing may be performed off-board the satellite. The image processing capabilities may be shared between multiple satellites within a constellation, or between a satellite and a ground-based station. … the image analysis workflow may be shared between multiple satellites within a constellation, or between a satellite and a ground-based station.”; Paragraph 100: “At block 512, the relevant data may be transmitted, such as to a base station. In some instances, the satellite may transmit images to the base station. The images may be a subset of the total number of images captured of an area of interest. Furthermore, the images may be portions of images captured of an area of interest, having been segmented … the relevant data may be numerical, such as the number of cars, a percentage of ground cover, or an increase in water surface area.”; Paragraph 118; Paragraphs 124-126; Paragraph 133-135; Paragraphs 147-148).
processing, using a trained machine-learning model, the high-value portions of the images to detect a plurality of instances of the object (Paragraphs 91-93: “Image analysis 416 may be performed on image data…. . Using one or more algorithms, such as edge detection 418 or object detection 420, the cars depicted in the segmented image data may be counted, catalogued, and/or identified. … Object detection 420 may utilize any suitable algorithm for identifying features or object depicted within image data. … there a high resolution imaging sensor captures high resolution imaging data such that object detection 420 is able to isolate objects with a high degree of accuracy, the detected objects may be compared to known objects within an object database for object identification. That is, rather than simply counting the cars in the parking lot, the image processing and analysis system 402 may be able to identify the cars in the parking lot.”)
However, Kargieman does not explicit disclose a method performed by a ground station, and the at least one processor included in the ground station,
process, using a trained machine-learning model, the high-value portions of the images to detect a plurality of instances of the object and to determine, for each detected instance of the object, a corresponding geographic location and size; and
output a dataset comprising the plurality of instances of the object and, for each instance of the object, the corresponding geographic location and size.
Bowers discloses a method performed by a ground station, comprising (Paragraph 165: “ The ground segment 104 includes a ground terminal 114. The term ground terminal may be used to refer to a single ground terminal or multiple ground terminals. The ground terminal 114 includes components (e.g. antennas, transmitters, receivers) for transmitting signals to and receiving signals from the imaging satellites 106, 108 and components for processing data (e.g. one or more computing devices, software modules). Processing data includes processing image data received from the satellites 106, 108.”)
causing a downlink session between the ground station and a constrained- environment device to be established; (Paragraph 154: “The broad area imaging satellite 106 may include a high-speed data downlink subsystem … The high-speed data downlink subsystem may provide downlink speeds which allow more images to be downlinked to a ground station (e.g. ground station 114) within a contact pass between the satellite and the ground station communications cone.”)
via at least one processor included in the ground station, (Paragraph 224: “The ground terminal 114 includes a processor 502 in communication with a memory 504 and a communication interface 506. The processor 502 is configured to execute computer-executable instructions embodied in one or more modules or units described in Figure 5.”)
communicating, to the constrained-environment device from the ground station, which portions of the images from among the portions of the images are the high- value portions of the images; (Paragraph 173: “ the broad area satellite 106 may collect a broad area SAR image in which a plurality of vessels (e.g. ships) are seen. The ships and ship locations may be correlated by the ground terminal 114 against AIS data from an AIS data feeding system (e.g. AIS data provider who operates its own constellation of AIS satellites). Most ships are likely transmitting an AIS signal broadcasting their respective ID, position, and heading. From this, a determination may be made using the ground terminal 114 identifying the ships in the broad area SAR image that are transmitting AIS.”)
receiving, from the constrained-environment device at the ground station, the high-value portions of the images; (Paragraph 189-190: “At 216, the higher resolution imaging satellite 108 transmits the second SAR data to the ground terminal 114 via downlink 120. …At 218, the ground terminal 114 receives and processes the second SAR data.”; Paragraph 238: “The ground terminal 114 further includes an image data analysis unit 542. The image data analysis unit 542 is configured to analyze the higher resolution SAR data 540 to determine image context data 544. The image context data 544 describes a context for the higher resolution SAR data 540, such as an identity of an image subject (e.g. an object in the image)”)
process, using a trained machine-learning model, the high-value portions of the images to detect a plurality of instances of the object and to determine, for each detected instance of the object, a corresponding geographic location and size; (Paragraphs 191-193; Paragraph 239; Paragraphs 303-307: “Object of interest location data 1404 may comprise an output of the location of all detected objects of interest. Object of interest location data 1404 may, for example, specify coordinates defining a location for one or more detected objects … Object characterization data 1406 may include a qualitative or quantitative characterization of each object of interest. The object characterization data may describe one or more physical characteristics of the object (e.g. vessel), such as, for example object length, object width, object surface area, etc. … the object characterization data 1406 may include an object classification (e.g. a class assignment or label), specifying a class of object to which the object of interest belongs, determined via an object detection or object classification task performed by the processor 1122. … object of interest image chips 1410 may be used as input to an object of interest classification module implementing one or more classification algorithms for classifying the object of interest in the image chip 1410. The object of interest classification algorithm may be any suitable classification algorithm. For example, the object of interest classification module may implement a machine learning-based classification model or algorithm. The object of interest classification module may be implemented on a processing unit of a ground-based terminal (e.g. ground terminal 1210, ground terminal 1212), in which case the image chip 1410 may be received by downlink from the first satellite 1202 and processed via the classification module to determine a class assignment for the object of interest.”; Paragraph 359-361) and
output a dataset comprising the plurality of instances of the object and, for each instance of the object, the corresponding geographic location and size. (Paragraphs 93 -94: “generating a detected vessel report including data describing an attribute of the detected marine vessel; and generating an image chip of the detected marine vessel from the SAR data. … The attribute may include any one or more of a size of the detected vessel, a geographic location of the detected vessel, an estimated velocity of the detected vessel, a heading of the detected vessel, and a characterization of the detected vessel.”; Paragraph 300-307; Paragraphs 359-361: “the detected object report generator module 1710 is configured to receive the object detection output and, if present, the estimated object velocity values, and generate a detected object report. Generally, the detected object report includes data describing one or more detected objects. The data may include, for example, location, characterization (object type or class), velocity, heading, etc. … The image chip generator module 1712 is configured to receive the object detection output including the SAR data and data describing the detected objects (such as location in the image) and generate an image chip of each detected object. … one image chip is generated for each detected object. The size of the image chip is based on the size of the object (e.g. vessel) and represents an extended bounding box around the object in the processed SAR image”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Kargieman by including a systems for satellite imaging including the ground segment that is taught by Bowers, to make the invention that satellite-based observation and surveillance using multiple satellites and/or with onboard image data processing; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving performance of an earth observation system, reduce the latency of an earth observation operation, as well as enhancing the user convenient as a client may utilize one or more of these services.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 12, , Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the constrained-environment device is at least one of: an Internet of Things device that is in a constrained environment, an orbiting satellite, a spacecraft, or a stationary platform. (Fig.1 and Paragraph 21: “FIG. 1, a system 100 includes a satellite 102. The satellite 102 includes a payload 104, which in many cases will include an imaging system 106;” ; Paragraph 14: “FIG. 3 is a pictorial flow diagram of some of the imaging tasks performed by in-orbit satellites.”)
Regarding claim 13, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the constrained-environment device is an orbiting satellite, (Paragraph 14: “FIG. 3 is a pictorial flow diagram of some of the imaging tasks performed by in-orbit satellites.”) and wherein the downlink session is a downlink session in a plurality of scheduled downlink sessions between the orbiting satellite and a ground station that includes the at least one processor. (Figs.6 and 8; Paragraphs 102 : “the satellite, using the various modules and systems described herein, determines a short-term plan. … establishing downlink communication channels, allocating time for on-board computations such as image analysis, and the like. The short-term plan may therefore influence the prioritized list of tasks where the satellite determines that it can increase efficiency by changing the priority of one or more tasks in the prioritized list of tasks”; Paragraph 120)
Regarding claim 14, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the determination is further based on at least one of a client request, a client search, a historical client access, or a client sales pattern.(Paragraph 52: “ a customer requiring specific satellite imaging data may input a request regarding an area of interest (AOI) or a point of interest (POI), a particular type of AOI as a polygon on the surface of the Earth in a particular coordinate system. … a customer may also specify a bidding price, such as a maximum price the customer is willing to pay for accomplishing the SLA.”)
Regarding claim 15, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the constrained-environment-device embeddings of the plurality of constrained-environment-device embeddings are feature vectors of floating-point numbers. (Paragraph 76: “ Vector processing: At block 328, images, mosaics or areas of interest in images or mosaics can be processed to be transformed into vector maps, representing and labeling characteristics present in the original input. … The vector processing may incorporate points, lines, polygons, and other geometric forms for vector map transformation.”)
Regarding claim 16, Kargieman discloses a processor-readable storage device, having stored thereon processor- executable code that, upon execution by at least one processor, enables actions, (Paragraphs 22-23: “A control system 118 may be comprised of one or more on board computers for handling the functions of the payload 104 and other systems on board the satellite. The control system 18 may include one or more processors 120 and computer-readable media 122. … Much of this ability is stored as instructions, processes, and logic within the computer-readable media 122 of the satellite 102.”) comprising:
provide a plurality of reference embeddings corresponding to an object; (Fig.5 and Paragraph 98: “objects stored in an object database”; Paragraph 42-44: “Convolutional Neural Networks (CNN) are instanced in a CPU or GPU or an FPGA or AI accelerator onboard a satellite in orbit, and used to process images captured with a high-resolution camera, in real-time, and produce vector maps containing object shapes and object labels present in the original image data. … Once the algorithms are trained, the trained instances can be uploaded and run in orbit on the satellites; Fig.7 and Paragraph 111: “At block 704, the satellite receives artificial intelligence configuration. The artificial intelligence (“AI”) configuration may define a neural network for the artificial intelligence module (e.g., 212 of FIG. 2). The AI configuration may include a pre-trained neural network, which may be trained on the ground before being uploaded to the satellite. … The neural network may further be updated, or receive additional training, while on-board the satellite to accomplish additional tasks.”)
during a downlink session with a constrained-environment device: (Fig 2: in-orbit satellite imaging system 202; Fig.4 : in-orbit satellite image processing and analysis system 402; and Abstract: A smart satellite system is capable of decision making and prioritization on the fly to optimize the use of downlink bandwidth to deliver prioritized data based upon opportunity and the resources of available payloads.”)
receive, from the constrained-environment device, a plurality of constrained-environment-device embeddings, wherein the constrained-environment- device embeddings in the plurality of constrained-environment-device embeddings are generated by applying an embedding-generation model to corresponding portions of images that are stored on the constrained-environment device and obtained from sensors on the constrained-environment device; (Fig 2: in-orbit satellite imaging system 202; Fig.4 : in-orbit satellite image processing and analysis system 402; Paragraph 42: “ Convolutional Neural Networks (CNN) are instanced in a CPU or GPU or an FPGA or AI accelerator onboard a satellite in orbit, and used to process images captured with a high-resolution camera, in real-time, and produce vector maps containing object shapes and object labels present in the original image data”; Paragraphs 90-91: “The satellite, using edge detection or object detection algorithms, for example, may segment the image data into two pixel sets, one that includes the parking lot and one that does not include the parking lot.” Paragraphs 76-78: “Vector processing: At block 328, images, mosaics or areas of interest in images or mosaics can be processed to be transformed into vector maps, representing and labeling characteristics present in the original input … Predictive Models: At 334, based on a collection of images, mosaics, areas of interest in images or mosaics, vector maps and rasters, predictive models may be used that forecast the results of future observations, and predict dependent variables, or that fit well-defined parameterized models that can be applied to the collected data. These predictive models may be maintained in orbit or downloaded to the ground. … At block 338, the processed image data may be stored on board the satellite until a downlink communications channel is established with a base station and the data can be transmitted.”)
compare, in a vector space,(Paragraph 76-77: “Vector processing: At block 328, images, mosaics or areas of interest in images or mosaics can be processed to be transformed into vector maps, representing and labeling characteristics present in the original input. For example, an object detection algorithm may be run on a high-resolution image of a road to identify cars, and may label them according to color, in a vector map representation. … an edge detection algorithm may determine the edges of the cornfield, and the boundary of the corn field may be defined by vectors. The image data may then be segmented, such as by storing the area within the vectors for further analysis …the image can be discarded when all the information required is properly represented by vectors.”) the constrained-environment-device embeddings in the plurality of constrained-environment-device embeddings with reference embeddings in the plurality of reference embeddings; (Paragraph 92-94: “Object detection 420 may utilize any suitable algorithm for identifying features or object depicted within image data. For example, an object database may be stored and features identified in the image data may be compared against entries in the object database in order to find matching objects. … Object detection may be able to analyze image data generated by the imaging sensors and through various algorithms, such as edge detection 418, determine instances of semantic objects of certain classes … By detecting the edges, object detection 420 is able to isolate likely objects and then compare these objects with objects stored in an object database to determine a match, and therefore identify objects.”; Fig.5; Paragraph 98 ; Fig.7 and Paragraph 111; the person one of ordinary skill in the art would understand that the edge detection using a vector map for object detection including step comparing is interpreted as “compare, in vector space,” )
make a determination, based at least in part on the comparison, as to which portions of the images are high-value portions of the images that include instances of the object; (Fig.5 and Paragraphs 98-99: “By detecting the edges, object detection 420 is able to isolate likely objects and then compare these objects with objects stored in an object database to determine a match, and therefore identify objects … At block 510, image analytics may be run on the image data to collect information about the object or features depicted. … image data that is separated temporally may be compared to determine changes in,”, it shows that the “object identified” interpreted as “high-value portions of the images”; Paragraph 118; Paragraphs 124-126; Paragraph 133-135)
communicate, to the constrained-environment device, which portions of the images from among the portions of the images are the high-value portions of the images; receive, from the constrained-environment device, the high-value portions of the images; (Figs. 1-9; Paragraph 100: “At block 512, the relevant data may be transmitted, such as to a base station. In some instances, the satellite may transmit images to the base station. The images may be a subset of the total number of images captured of an area of interest. Furthermore, the images may be portions of images captured of an area of interest, having been segmented … the relevant data may be numerical, such as the number of cars, a percentage of ground cover, or an increase in water surface area.”; Paragraph 118; Paragraphs 124-126; Paragraph 133-135; Paragraphs 147-148)
process, using a trained machine-learning model, the high-value portions of the images to detect a plurality of instances of the object; , (Paragraph 111: “At block 704, the satellite receives artificial intelligence configuration. The artificial intelligence (“AI”) configuration may define a neural network for the artificial intelligence module (e.g., 212 of FIG. 2). The AI configuration may include a pre-trained neural network, which may be trained on the ground before being uploaded to the satellite. The pre-trained neural network may be trained for any suitable task, such as analyzing images to detect a specific object, or may be trained as a land use classifier, or for determining environmental indicators, for example.”; Paragraph 145: “The images may be analyzed by executing artificial intelligence or machine learning algorithms on board. Alternatively or additionally, the satellite system may analyze the images by executing instructions received from a ground-station or from another satellite.”; Paragraphs 91-93: “Image analysis 416 may be performed on image data. Continuing with the previous example of the parking lot, once the image has been segmented such that the remaining image data contains object or features of interest, the image processing and analysis system 402 may analyze the remaining pixel set. Using one or more algorithms, such as edge detection 418 or object detection 420, the cars depicted in the segmented image data may be counted, catalogued, and/or identified. … Object detection 420 may utilize any suitable algorithm for identifying features or object depicted within image data. … there a high resolution imaging sensor captures high resolution imaging data such that object detection 420 is able to isolate objects with a high degree of accuracy, the detected objects may be compared to known objects within an object database for object identification. That is, rather than simply counting the cars in the parking lot, the image processing and analysis system 402 may be able to identify the cars in the parking lot.”) and
However, Kargieman does not disclose process, using a trained machine-learning model, the high-value portions of the images to detect a plurality of instances of the object and to determine, for each detected instance of the object, a corresponding geographic location and size;
output a dataset comprising the plurality of instances of the object and, for each instance of the object, the corresponding geographic location and size.
Bowers discloses a processor-readable storage device, having stored thereon processor-executable code that, upon execution by at least one processor, enables actions, comprising: (Paragraph 224: “The ground terminal 114 includes a processor 502 in communication with a memory 504 and a communication interface 506. The processor 502 is configured to execute computer-executable instructions embodied in one or more modules or units described in Figure 5.”)
during a downlink session with a constrained-environment device: (Paragraph 154: “The broad area imaging satellite 106 may include a high-speed data downlink subsystem … The high-speed data downlink subsystem may provide downlink speeds which allow more images to be downlinked to a ground station (e.g. ground station 114) within a contact pass between the satellite and the ground station communications cone.”)
communicate, to the constrained-environment device, which portions of the images from among the portions of the images are the high-value portions of the images; (Paragraph 173: “ the broad area satellite 106 may collect a broad area SAR image in which a plurality of vessels (e.g. ships) are seen. The ships and ship locations may be correlated by the ground terminal 114 against AIS data from an AIS data feeding system (e.g. AIS data provider who operates its own constellation of AIS satellites). Most ships are likely transmitting an AIS signal broadcasting their respective ID, position, and heading. From this, a determination may be made using the ground terminal 114 identifying the ships in the broad area SAR image that are transmitting AIS.”)
receive, from the constrained-environment device, the high-value portions of the images; (Paragraph 189-190: “At 216, the higher resolution imaging satellite 108 transmits the second SAR data to the ground terminal 114 via downlink 120. …At 218, the ground terminal 114 receives and processes the second SAR data.”; Paragraph 238: “The ground terminal 114 further includes an image data analysis unit 542. The image data analysis unit 542 is configured to analyze the higher resolution SAR data 540 to determine image context data 544. The image context data 544 describes a context for the higher resolution SAR data 540, such as an identity of an image subject (e.g. an object in the image)”)
process, using a trained machine-learning model, the high-value portions of the images to detect a plurality of instances of the object and to determine, for each detected instance of the object, a corresponding geographic location and size; (Paragraphs 191-193; Paragraph 239; Paragraphs 303-307: “Object of interest location data 1404 may comprise an output of the location of all detected objects of interest. Object of interest location data 1404 may, for example, specify coordinates defining a location for one or more detected objects … Object characterization data 1406 may include a qualitative or quantitative characterization of each object of interest. The object characterization data may describe one or more physical characteristics of the object (e.g. vessel), such as, for example object length, object width, object surface area, etc. … the object characterization data 1406 may include an object classification (e.g. a class assignment or label), specifying a class of object to which the object of interest belongs, determined via an object detection or object classification task performed by the processor 1122. … object of interest image chips 1410 may be used as input to an object of interest classification module implementing one or more classification algorithms for classifying the object of interest in the image chip 1410. The object of interest classification algorithm may be any suitable classification algorithm. For example, the object of interest classification module may implement a machine learning-based classification model or algorithm. The object of interest classification module may be implemented on a processing unit of a ground-based terminal (e.g. ground terminal 1210, ground terminal 1212), in which case the image chip 1410 may be received by downlink from the first satellite 1202 and processed via the classification module to determine a class assignment for the object of interest.”; Paragraph 359-361) and
output a dataset comprising the plurality of instances of the object and, for each instance of the object, the corresponding geographic location and size. (Paragraphs 93 -94: “generating a detected vessel report including data describing an attribute of the detected marine vessel; and generating an image chip of the detected marine vessel from the SAR data. … The attribute may include any one or more of a size of the detected vessel, a geographic location of the detected vessel, an estimated velocity of the detected vessel, a heading of the detected vessel, and a characterization of the detected vessel.”; Paragraph 300-307; Paragraphs 359-361: “the detected object report generator module 1710 is configured to receive the object detection output and, if present, the estimated object velocity values, and generate a detected object report. Generally, the detected object report includes data describing one or more detected objects. The data may include, for example, location, characterization (object type or class), velocity, heading, etc. … The image chip generator module 1712 is configured to receive the object detection output including the SAR data and data describing the detected objects (such as location in the image) and generate an image chip of each detected object. … one image chip is generated for each detected object. The size of the image chip is based on the size of the object (e.g. vessel) and represents an extended bounding box around the object in the processed SAR image”)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Kargieman by including a systems for satellite imaging including the ground segment that is taught by Bowers, to make the invention that satellite-based observation and surveillance using multiple satellites and/or with onboard image data processing; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving performance of an earth observation system, reduce the latency of an earth observation operation, as well as enhancing the user convenient as a client may utilize one or more of these services.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 17, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the constrained- environment device is at least one of: an Internet of Things device that is in a constrained environment, an orbiting satellite, a spacecraft, or a stationary platform. (Fig.1 and Paragraph 21: “FIG. 1, a system 100 includes a satellite 102. The satellite 102 includes a payload 104, which in many cases will include an imaging system 106;” ; Paragraph 14: “FIG. 3 is a pictorial flow diagram of some of the imaging tasks performed by in-orbit satellites.”)
Regarding claim 18, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the constrained- environment device is an orbiting satellite, (Paragraph 14: “FIG. 3 is a pictorial flow diagram of some of the imaging tasks performed by in-orbit satellites) and wherein the downlink session is a downlink session in a plurality of scheduled downlink sessions between the orbiting satellite and a ground station that includes the at least one processor. (Figs.6 and 8; Paragraphs 102 : “the satellite, using the various modules and systems described herein, determines a short-term plan. … establishing downlink communication channels, allocating time for on-board computations such as image analysis, and the like. The short-term plan may therefore influence the prioritized list of tasks where the satellite determines that it can increase efficiency by changing the priority of one or more tasks in the prioritized list of tasks”; Paragraph 120)
Regarding claim 19, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the determination is further based on at least one of a client request, a client search, a historical client access, or a client sales pattern. (Paragraph 52: “ a customer requiring specific satellite imaging data may input a request regarding an area of interest (AOI) or a point of interest (POI), a particular type of AOI as a polygon on the surface of the Earth in a particular coordinate system. … a customer may also specify a bidding price, such as a maximum price the customer is willing to pay for accomplishing the SLA.”)
Regarding claim 20, Kargieman, as modified by Bowers, discloses all the claims invention. Kargieman further discloses the constrained- environment-device embeddings of the plurality of constrained-environment-device embeddings are feature vectors of floating-point numbers. (Paragraph 76: “ Vector processing: At block 328, images, mosaics or areas of interest in images or mosaics can be processed to be transformed into vector maps, representing and labeling characteristics present in the original input. … The vector processing may incorporate points, lines, polygons, and other geometric forms for vector map transformation.”)
Claim(s) 5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kargieman et al (U.S. 20170250751 A1; Kargieman), in view of Bowers John et al (WO – 2022056638 A1; Bowers), and in further view of Beckett et al (U.S. 20160300375 A1; Beckett).
Regarding claim 5, Kargieman, as modified by Bowers, discloses all the claims invention except wherein the spacecraft embeddings of the plurality of spacecraft embeddings are feature vectors each having at least 256 dimensions.
Beckeet discloses wherein the spacecraft embeddings of the plurality of spacecraft embeddings are feature vectors each having at least 256 dimensions. (Paragraph 41-43: “the compressed band image and masks are 1024 pixels by 1024 pixels, although other sizes may be used. Encoded tiles may be stored in a memory device or across multiple memory devices. … Layer Mask: Using a bounding polygon to clip image tiles to a specific area of interest to provide context for vector layers within a map. … Map Tile Service (MTS): The Map Tile Service is responsible for serving imagery and data products to external and internal clients, for example, as rasterized 256 by 256 pixels map tiles.”; Paragraph 277)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Kargieman and Bowers by including generate encoded tiles and map tiles that is taught by Beckett, to make the invention that processing and distributing Earth observation imagery; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving scalability and performance and may also reduce costs (Beckett: Paragraph 38)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Regarding claim 7, Kargieman, as modified by Bowers, all the claims invention except wherein the portions of the images are image tiles that are evenly-sized portions of the images.
Beckett discloses the portions of the images are image tiles that are evenly-sized portions of the images. (Fig. 16: “an image scene is divided into encoded tiles and rendered into map tiles for a specified polygon.”, show that the encode tiles are evenly-size as square tiles.)
Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Kargieman and Bowers by including generate encoded tiles and map tiles that is taught by Beckett, to make the invention that processing and distributing Earth observation imagery; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving scalability and performance and may also reduce costs (Beckett: Paragraph 38)
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chong et al (U.S. 20170070939 A1), “System and Method for Providing Continuous Communications Access to Satellites in Geocentric, Non-Geosynchronous Orbits”, teaches about a small satellite constellation architecture in LEO to enable continuous communications services for other geocentric satellites. The continuous communication is performed by relaying data to the aforementioned small-satellite constellation via an inter-satellite link, and a set of strategically placed ground stations.
Turner (U.S. 20180288374 A1), “Low Earth Orbiting Spacecraft with A Dual – Use Directional Antenna”, teaches about low-latency transmission of imaging data from a first satellite in a low earth orbit to a ground station via a second satellite in a higher earth orbit, using a downlink antenna on the first spacecraft to establish an inter-satellite link (“crosslink”) with the second satellite.. It also teaches about a low earth orbiting spacecraft (LEO spacecraft) operable in a first earth orbit ; During a first period of time, the main body is oriented such that the data collection payload views a region of interest on the earth; During a second period of time, the main body and the first directional antenna are oriented such that the first directional antenna is directed toward a first ground station. During a third period of time, the main body and the first directional antenna are oriented such that the first directional antenna is directed toward a second spacecraft operating in a second orbit.
Godwin, IV et al (U.S. 20210342669 A1), “ Method, System, and Medium for Processing Satellite Orbital Information Using a Generative Adversarial Network”, teaches about method, electronic device, system, and computer-readable medium embodiments include a signal processing workflow incorporating a graphical user interface for displaying orbital information for satellites and other spacecraft. In some embodiments, a generative adversarial network (GAN) is employed for evaluating satellite orbital positions, for predicting future orbital movements, for detecting orbital maneuvers of a satellite, and for analyzing such maneuvers for potential nefarious intent.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Duy A Tran whose telephone number is (571)272-4887. The examiner can normally be reached Monday-Friday 8:00 am - 5:00 pm.
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/DUY TRAN/ Examiner, Art Unit 2674
/ONEAL R MISTRY/ Supervisory Patent Examiner, Art Unit 2674
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