DETAILED ACTION
This Action is in response to Applicant’s response filed on 11/10/2025. Claims 1-20 are still pending in the present application. This Action is made FINAL.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/16/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Claim Rejections - 35 USC § 112: The amended claims filed on 11/10/2025 overcomes the Claim Rejections - 35 USC § 112(b) in the previous office action.
Response to Arguments
Claim Rejections - 35 USC § 101: Applicant's arguments that “a processor-readable storage device cannot be properly interpreted as a transitory signal and is patent eligible under 35 U.S.C. 101.” (Remark Page 2). After reviewing the amendments and arguments filed on 11/10/2025. The examiner has withdraw the previous 101 rejection for the following reason: a processor-readable storage device is not consider as a transitory signal.
Claim Rejections - 35 USC § 102: Applicant's arguments filed on 11/10/2025 have been fully considered but are moot in view of the new ground(s) rejection in view of Hawthorne et al (U.S. 20210344414 A1; Hawthorne).
Claim Status
Claim(s) 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kargieman et al (U.S. 201702050751 A1; Kargieman). No arguments are presented why claims 16-20 is not anticipated by the prior art; Therefore, the rejection is maintained.
Claim(s) 1-4, 6 and 8-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kargieman et al (U.S. 201702050751 A1; Kargieman), in view of Hawthorne et al (U.S. 20210344414 A1; Hawthorne).
Claim(s) 5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kargieman et al (U.S. 201702050751 A1; Kargieman), in view of Hawthorne et al (U.S. 20210344414 A1; Hawthorne), and in further view of Beckett et al (U.S. 20160300375 A1; Kargieman).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kargieman et al (U.S. 201702050751 A1; Kargieman).
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:
during a downlink session with a constrained-environment device: 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 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; (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; and 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)
Regarding claim 17, Kargieman 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 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 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 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 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 8-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kargieman et al (U.S. 201702050751 A1; Kargieman), in view of Hawthorne et al (U.S. 20210344414 A1; Hawthorne).
Regarding claim 1, Kargieman discloses an apparatus (Fig.1: a system 100) located in a ground station, 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; 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; (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; and 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)
However, Kargieman does not explicit disclose an apparatus located in a ground station
Hawthorne discloses an apparatus located in a ground station, (Paragraph 19: “The systems and methods described herein may implement a satellite antenna ground station service of a provider network.”) cause a downlink session with an orbiting satellite to be established; (Fig.1 and Paragraphs 37-38: “Provider network 102 includes data center 110 and associated ground station 142, data center 112 and associated ground station 144, and data center 114 and associated ground station 146. …. downlinked from a client satellite by a satellite antenna ground station service may be provided to, and/or stored in, a data center associated with a ground station of the satellite antenna ground station service.”; Paragraph 28: “a client may perform the client's satellite operations via the satellite antenna ground station service (e.g. satellite control, data uplink, and/or data downlink) and additionally process downlinked data via any one or more of a plurality of services offered by the provider network such as a virtual computing service, data storage service, machine learning service, data analytics service, visual recognition service, database service, or other supported network-based services.”; Paragraph 62: “a scheduler of a satellite antenna ground station service may determine a number and duration of contact sessions required to transmit the requested amount of data … a client may indicate one or more files, objects, etc. to be uplinked to a satellite or downlinked from a satellite and the scheduler of the satellite antenna ground station service may determine the amount of data needed to uplink or downlink the indicated files or objects. The scheduler of the satellite antenna ground station service may then reserve satellite antenna access time-slots sufficient to conduct a determined number and duration of contact sessions to transmit the determined amount of data.”)
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 satellite antenna ground station service that is taught by Hawthorne, to make the invention that a satellite antenna ground station service of a provider network; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the user convenient as a client may utilize one or more of these services to analyze and process downlinked data received from a satellite of the client via a satellite antenna ground station of the satellite antenna ground station service of the provider network and reducing cost such as a satellite owner/operator may minimize a number of satellite antennas the satellite/owner operator maintains access to. (Hawthorne: Paragraphs 22 and 87)
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 Hawthorne, 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; Hawthorne: Paragraph 28: “a client may perform the client's satellite operations via the satellite antenna ground station service (e.g. satellite control, data uplink, and/or data downlink) and additionally process downlinked data via any one or more of a plurality of services offered by the provider network such as a virtual computing service, data storage service, machine learning service, data analytics service, visual recognition service, database service, or other supported network-based services.”)
Regarding claim 3, Kargieman, as modified by Hawthorne, 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 Hawthorne, 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 Hawthorne, 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 Hawthorne, 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 Hawthorne, 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 Hawthorne, 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.”)) performed by a ground station, comprising:
providing a plurality of reference embeddings; (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.”)
via at least one processor included in the ground station, 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,” )
via the at least one processor included in the ground station, 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; (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).
However, Kargieman does not explicit disclose a method performed by a ground station, and the at least one processor included in the ground station,
Hawthorne discloses a method performed by a ground station, (Paragraph 19: “The systems and methods described herein may implement a satellite antenna ground station service of a provider network.”) causing a downlink session between the ground station and a constrained- environment device to be established; (Fig.1 and Paragraphs 37-38: “Provider network 102 includes data center 110 and associated ground station 142, data center 112 and associated ground station 144, and data center 114 and associated ground station 146. …. downlinked from a client satellite by a satellite antenna ground station service may be provided to, and/or stored in, a data center associated with a ground station of the satellite antenna ground station service.”; Paragraph 28: “a client may perform the client's satellite operations via the satellite antenna ground station service (e.g. satellite control, data uplink, and/or data downlink) and additionally process downlinked data via any one or more of a plurality of services offered by the provider network such as a virtual computing service, data storage service, machine learning service, data analytics service, visual recognition service, database service, or other supported network-based services.”; Paragraph 62: “a scheduler of a satellite antenna ground station service may determine a number and duration of contact sessions required to transmit the requested amount of data … a client may indicate one or more files, objects, etc. to be uplinked to a satellite or downlinked from a satellite and the scheduler of the satellite antenna ground station service may determine the amount of data needed to uplink or downlink the indicated files or objects. The scheduler of the satellite antenna ground station service may then reserve satellite antenna access time-slots sufficient to conduct a determined number and duration of contact sessions to transmit the determined amount of data.”) at least one processor included in the ground station, (Fig.12 and Paragraph 115-116: “computer system 1200 may be configured to implement various components of a satellite antenna ground station service, storage and/or compute nodes of a provider network, a data stores, and/or a client, … Computer system 1200 includes one or more processors 1210 (any of which may include multiple cores, which may be single or multi-threaded) coupled to a system memory 1220 via an input/output (I/O) interface 1230.”)
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 satellite antenna ground station service that is taught by Hawthorne, to make the invention that a satellite antenna ground station service of a provider network; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the user convenient as a client may utilize one or more of these services to analyze and process downlinked data received from a satellite of the client via a satellite antenna ground station of the satellite antenna ground station service of the provider network and reducing cost such as a satellite owner/operator may minimize a number of satellite antennas the satellite/owner operator maintains access to. (Hawthorne: Paragraphs 22 and 87)
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 Hawthorne, 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 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 Hawthorne, 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 Hawthorne, 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. 201702050751 A1; Kargieman), in view of Hawthorne et al (U.S. 20210344414 A1; Hawthorne), and in further view of Beckett et al (U.S. 20160300375 A1; Kargieman).
Regarding claim 5, Kargieman, as modified by Hawthorne, 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 Hawthorne 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 Hawthorne, 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 Hawthorne 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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/DUY TRAN/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674