DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Siddique et al. (“Biomolecular Analysis of Soil Samples and Rock Imagery for Tracing Evidence of Life Using a Mobile Robot”, 2023 IEEE 8th ICRAE, IEEE, 2 December 2023 (2023-12-02), pages 1-6, XP034561182, DOI: 10.1109/ICRAIE59459.2023.10468310) in view of Mallery et al. (“Design and Experiments with a Robot-Driven Underwater Holographic Microscope for Low-Cost In Situ Particle Measurements”, Journal of Intelligent, Springer Netherlands, Dordrecht, vol. 102, no. 2, 14 May 2021 (2021-05-14), XP037476740, ISSN: 0921-0296, DOI: 10.1007/S10846-021-01404-3).
Regarding claim 1, Siddique discloses a system (see figures 1, 3 and 4, for instance), comprising: a platform to move about an environment (see figure 1); a sampler tool coupled to the platform (“sample collection and biomolecule detection subsystem”, fig. 1); a microscope coupled to the platform (Table 1, “USB microscope”); a camera coupled to the microscope (Table 1, “1080p camera”); and a control circuit (see figure 3) to: instruct the platform to move about an environment (Fig. 3, “Deploy Rover”); instruct the sampler tool to obtain a sample (Fig. 3, “Collect Soil Samples”) and place the sample in a view field of the microscope (Fig. 3, “Analyze soil samples”); instruct the camera to capture an image of the sample (D. Analyzing Soil Test Result – “analyzing the color alteration observed by an onboard camera”); receive the image from the camera (V. Methodology, page 4, “camera imaging techniques. The acquired data is subsequently employed to categorize…”); analyze, using a neural network, the image to classify the sample (Methodology V, page 4, “A Deep Neural Network (DNN) model is utilized to categorize rock samples”); and output a classification of the sample (Methodology V, page 4, “surveying and classification”). However, Siddique does not expressly disclose the image enlarged by the microscope.
Mallery discloses a system (see figures 2-4, for instance) comprising a control circuit (3.3. Aquapod, “BCU”) to: instruct the platform to move about an environment (“manipulate to ascend and descend”); instruct the sampler tool to obtain a sample (page 4, “sample volume”) and place the sample in a view field of the microscope (page 3, “image at high speeds”); instruct the camera to capture an image of the sample (page 5, “recorded holograms”), the image enlarged by the microscope (see page 4; the images can be adjusted to be anywhere from 2048 x 2048 to 3296 x 2512 resolution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to manipulate the image size as Mallery in the system of Siddique. The motivation for doing so would have been to allow for optimization of processing execution time by gathering smaller raw images in real-time, and processing enlarged images subsequently, as taught by Mallery (page 6, 3.2 On-board Processing).
Regarding claim 2, Siddique in view of Mallery discloses the system of claim 1, comprising: a sensor coupled to the platform to collect a measurement of the environment (Algorithm 1: Bio-Sensor Data Collection); and the control circuit to: create a data record for the sample including the classification of the sample, the image, and the measurement of the environment (Fig. 3).
Regarding claim 3, Siddique in view of Mallery discloses the system of claim 2, wherein analysis of the image includes using the measurement of the environment (Fig. 3).
Regarding claim 4, Siddique in view of Mallery discloses the system of claim 1, comprising: a display coupled to the platform (Fig. 1); and the control circuit to: output the classification of the sample and the image to the display (Fig. 3).
Regarding claim 5, Siddique in view of Mallery discloses the system of claim 1, wherein the platform includes an external communications interface (Fig. 1); and the control circuit to: output the classification of the sample and the image to a server via the external communications interface (page 4, “communicated to ground control station”).
Regarding claim 6, Siddique in view of Mallery discloses the system of claim 1, comprising: a user input device coupled to the platform (“Algorithm I”); and the control circuit to: receive an instruction from the user input device (“Algorithm I”).
Regarding claim 7, Siddique in view of Mallery discloses the system of claim 1, comprising: a real-time clock/calendar (RTCC) coupled to the control circuit; and the control circuit to: create a data record for the sample including the classification of the sample, the image, and a time stamp from the RTCC (page 4, “Algorithm I”).
Regarding claim 8, Siddique in view of Mallery discloses the system of claim 1, comprising the control circuit to: retrain the neural network based on the image and the classification of the sample (page 4, “DNN”).
Regarding claim 9, Siddique discloses a method (see figures 1, 3 and 4, for instance), comprising: instructing a platform to move about an environment (see figure 1; see also Figure 3 “Deploy Rover”); instructing a sampler tool coupled to the platform to obtain a sample (“sample collection and biomolecule detection subsystem”, fig. 1) and place the sample in a view field of a microscope coupled to the platform (Fig. 3, “Analyze soil samples”); instructing a camera coupled to the microscope to capture an image of the sample (D. Analyzing Soil Test Result – “analyzing the color alteration observed by an onboard camera”); receiving the image from the camera (V. Methodology, page 4, “camera imaging techniques. The acquired data is subsequently employed to categorize…”); analyzing, using a neural network, the image to classify the sample (Methodology V, page 4, “A Deep Neural Network (DNN) model is utilized to categorize rock samples”); and outputting a classification of the sample (Methodology V, page 4, “surveying and classification”). However, Siddique does not expressly disclose the image enlarged by the microscope.
Mallery discloses a system (see figures 2-4, for instance) comprising a control circuit (3.3. Aquapod, “BCU”) to: instruct the platform to move about an environment (“manipulate to ascend and descend”); instruct the sampler tool to obtain a sample (page 4, “sample volume”) and place the sample in a view field of the microscope (page 3, “image at high speeds”); instruct the camera to capture an image of the sample (page 5, “recorded holograms”), the image enlarged by the microscope (see page 4; the images can be adjusted to be anywhere from 2048 x 2048 to 3296 x 2512 resolution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to manipulate the image size as Mallery in the method of Siddique. The motivation for doing so would have been to allow for optimization of processing execution time by gathering smaller raw images in real-time, and processing enlarged images subsequently, as taught by Mallery (page 6, 3.2 On-board Processing).
Regarding claim 10, Siddique in view of Mallery discloses the method of claim 9, comprising: receiving a measurement of the environment from a sensor coupled to the platform (Algorithm 1: Bio-Sensor Data Collection); and creating a data record for the sample including the classification of the sample, the image, and the measurement of the environment (Fig. 3).
Regarding claim 11, Siddique in view of Mallery discloses the method of claim 10, wherein analysis of the image includes using the measurement of the environment (Fig. 3).
Regarding claim 12, Siddique in view of Mallery discloses the method of claim 9, comprising: outputting the classification of the sample and the image to a display coupled to the platform (Fig. 1, Fig. 3).
Regarding claim 13. The method of claim 9, comprising: outputting the classification of the sample and the image to a server via an external communications interface on the platform (Fig. 1; page 4, “communicated to ground control station”).
Regarding claim 14, Siddique in view of Mallery discloses the method of claim 9, comprising: receiving an instruction from a user input device coupled to the platform (“Algorithm I”).
Regarding claim 15, Siddique in view of Mallery discloses the method of claim 9, comprising: retraining the neural network based on the image and the classification of the sample (page 4, “DNN”).
Regarding claim 16, Siddique discloses an apparatus (see figures 1, 3 and 4, for instance), comprising: a control circuit (see figure 3) to: instruct the platform to move about an environment (Fig. 3, “Deploy Rover”); instruct a sampler tool coupled to the platform to obtain a sample (Fig. 3, “Collect Soil Samples”) and place the sample in a view field of the a microscope coupled to the platform (Fig. 3, “Analyze soil samples”); instruct a camera coupled to the microscope to capture an image of the sample (D. Analyzing Soil Test Result – “analyzing the color alteration observed by an onboard camera”); receive the image from the camera (V. Methodology, page 4, “camera imaging techniques. The acquired data is subsequently employed to categorize…”); analyze, using a neural network, the image to classify the sample (Methodology V, page 4, “A Deep Neural Network (DNN) model is utilized to categorize rock samples”); and output a classification of the sample (Methodology V, page 4, “surveying and classification”). However, Siddique does not expressly disclose the image enlarged by the microscope.
Mallery discloses a system (see figures 2-4, for instance) comprising a control circuit (3.3. Aquapod, “BCU”) to: instruct the platform to move about an environment (“manipulate to ascend and descend”); instruct the sampler tool to obtain a sample (page 4, “sample volume”) and place the sample in a view field of the microscope (page 3, “image at high speeds”); instruct the camera to capture an image of the sample (page 5, “recorded holograms”), the image enlarged by the microscope (see page 4; the images can be adjusted to be anywhere from 2048 x 2048 to 3296 x 2512 resolution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to manipulate the image size as Mallery in the system of Siddique. The motivation for doing so would have been to allow for optimization of processing execution time by gathering smaller raw images in real-time, and processing enlarged images subsequently, as taught by Mallery (page 6, 3.2 On-board Processing).
Regarding claim 17, Siddique in view of Mallery discloses the apparatus of claim 16, comprising: the control circuit to: receive a measurement of the environment from a sensor coupled to the platform (Algorithm 1: Bio-Sensor Data Collection); analyze the image using the measurement of the environment; and create a data record for the sample including the classification of the sample, the image, and the measurement of the environment (Fig. 3).
Regarding claim 18, Siddique in view of Mallery discloses the apparatus of claim 16, comprising: the control circuit to: output the classification of the sample and the image to a display coupled to the platform (Fig. 1, Fig. 3).
Regarding claim 19, Siddique in view of Mallery discloses the apparatus of claim 16, comprising: the control circuit to: output the classification of the sample and the image to a server via an external communications interface on the platform (Fig. 1; page 4, “communicated to ground control station”).
Regarding claim 20, Siddique in view of Mallery discloses the apparatus of claim 16, comprising: the control circuit to: retrain the neural network based on the image and the classification of the sample (page 4, “DNN”).
Conclusion
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/NATHANAEL R BRIGGS/Primary Examiner, Art Unit 2871 2/9/2026