DETAILED ACTION
Claims 1 and 6 have been amended.
Claims 3 and 8 are Originals
Claims 2 and 7 are previously presented.
Claims 4, 5, 9 and 10 are Canceled.
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3 and 6- 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, Claim 1-3 to a method (i.e., a process), Claim 6- 8 is to an apparatus (i.e., a machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1 and 6 are (Claim 1 being representative):
A disaster management method comprising:
receiving satellite images to monitor a disaster from a plurality of satellites having different orbits, different capture time points, and different satellite specifications including a resolution, a magnification, a focus, and a view angle;
setting a location of an area for disaster monitoring and a type of disaster to be monitored and selecting satellite images associated with the location of the area and the type of disaster among the received satellite images through a satellite image selection model;
synthesizing the selected satellite images through a satellite image synthesis model and generating disaster image data which enables to monitor a disaster; and
monitoring a disaster of the area for disaster monitoring by using the disaster image data;
wherein the disaster image data is data in which boundaries between various kinds of geography including mountains, water, cities, and land are divided by weight,
the satellite image selection model is trained to select a satellite image of which at least one of a capture angle, geographic coordinates, and a capture time point included in each of the satellite images is associated with the location of the area,
the satellite image synthesis model is a DNN (deep neural network) trained through artificial intelligence technology based on selected satellite images selected by the satellite image selection model, selects any one of the selected satellite images as a reference satellite image, is trained to match at least one of the resolution, the magnification, the focus, the view angle, and a window size of remaining ones of the selected satellite images to the reference satellite image and generates disaster image data by synthesizing the matched satellite image, and
due to different characteristics of satellites providing the satellite images, the satellite image synthesis model is trained to perform preprocessing and correction to match information of the remaining ones of the satellite images with the reference satellite image when synthesizing the satellite images, and
wherein the monitoring of the disaster comprises monitoring the disaster by analyzing whether a disaster occurs, a degree of risk, and a degree of damage in the area for disaster monitoring by comparing the disaster image data generated before and after a predetermined time point.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, “a certain satellite system trained to receive, collect, select and monitor a disaster”. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Examiner notes that the additional elements, considered at Step 2A Prong Two and Step 2B, are bolded above.)
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f) and MPEP 2106.05(h).
Claim 1 and 6 recites the following additional elements: from a plurality of satellites having different orbits, different capture time points, and different satellite specifications including a resolution, a magnification, a focus, and a view angle; the satellite image selection model is trained; the satellite image synthesis model is a DNN (deep neural network) trained through artificial intelligence technology based on selected satellite images selected by the satellite image selection model; is trained to; These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [Page number 7 and figure 3, page 9-10] of applicant’s specification as filed, for example.
The Examiner interprets “from a plurality of satellites having different orbits, different capture time points, and different satellite specifications including a resolution, a magnification, a focus, and a view angle” is an additional element” described in [Page 6] of applicant’s specification as generally linking to the field of use, per MPEP 2106.05(h).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f) or Linking to the field of use. See MPEP 2106.05(h).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Claim 2 and 7 recites additional abstract steps and/or information that further narrow the abstract idea: using image data received, observation data, measurement data, and big data associated with the disaster to monitor the disaster. Simply narrowing the abstract idea does not integrate it into practical application and/or add significantly more. The claim also recites further additional elements: from a device installed on a ground; of a system measuring the disaster; received through a communication network. Similar to above, these are generic computing elements that are merely facilitating the tasks of the narrowed abstract idea. Whether viewed alone or in combination with those additional elements highlighted previously, this does not integrate the abstract idea into practical application and/or add significantly more. See MPEP 2106.05(f).
Claim 3 and 8 recites additional abstract steps and/or information that further narrow the abstract idea: the satellite image selection model is trained to select a satellite image associated with the type of disaster based on a specification of the satellite image. Simply narrowing the abstract idea does not integrate it into practical application and/or add significantly more. The claim also recites further additional elements: of training the image model to select a satellite image these are generic computing elements that are merely facilitating the tasks of the narrowed abstract idea. Whether viewed alone or in combination with those additional elements highlighted previously, this does not integrate the abstract idea into practical application and/or add significantly more. See MPEP 2106.05(f)
Accordingly, claims 1-3 and 6-8 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
In conclusion the claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non - statutory subject matter.
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) 1-3 and 6- 8 are rejected under 35 U.S.C. 103 as being unpatentable over Rutschman et al [US 2018/0239948 A1] hereafter Rutschman, in view of Boman et al [US9495614 B1] hereafter Boman, in further view of Cao et al [CN 112382043 A], hereafter Cao.
As per claim 1 and 6 (Similar scope);
Rutschman discloses;
A disaster management method comprising:
receiving satellite images to monitor a disaster from a plurality of satellites
{[0207] In one embodiment, obtaining imagery using the at least one imager of the satellite that is part of a constellation of satellites providing machine vision at 3516 includes obtaining imagery using the satellite 500 that is part of a satellite constellation 1600. The imagery can be obtained using a plurality of satellites 500 and 500N that are part of the constellation 1600. Satellites 500 and 500N can each include the imaging system 100 or components thereof.}
Rutschman discloses the Orbiting in paragraph [0053], field or angle of view in paragraph [0060], magnification in paragraph [0263] and Resolution in paragraph [0264];
having different orbits, different capture time points, and different satellite specifications including a resolution, a magnification, a focus, and a view angle;
{[0053] The satellite imaging system 100 can include approximately nine to twelve steerable spot imagers 104 that are independently configured to focus, dwell, and/or scan for select targets. Each spot imager 104 can pivot approximately +/−seventy degrees and can include proximity sensing to avoid lens crashing. The steerable spot imagers 104 can provide an approximately 20 km diagonal field of view of approximately 4:3 aspect ratio. Resolution can be approximately one to three meters (nadir) in the visible and infrared or near-infrared range obtained using image sensors 316 and 318 of approximately 8 million pixels per square degree. Resolution can be increased to super-resolution when the spot imagers 104 dwell on a particular target to collect multiple image frames, which multiple image frames are combined to increase the resolution of a still image.
[0060] Furthermore, any given satellite imaging system 100 can include more than one field of view 400, such as a front field of view 400 and a back field of view 400 (e.g., one pointed at Earth and another directed to outer space). Alternatively, an additional field of view 400 can be directed ahead, behind, or to a side of an orbital path of a satellite. The fields of view 400 in this context can be different or identical.
[0263] In one embodiment, the controlling one or more imagers based on the at least one interpretation of the imagery at 3912 includes the image processor 504 or the hub processor 502 steering, directing, aligning, panning, zooming, dwelling, fixating, or other similar action with respect to an imager of the global imaging array 102 or the spot imager 104, in response to an interpretation of imagery obtained using the satellite imaging system 100. Steering, directing, or aligning can include mechanical movement of one or more imagers. Panning and zooming can include digital panning and/or zooming, such as through selective pixel retention and decimation, or mechanical panning and/or zooming, such as moving or focusing one or imagers.
[0264] Due to the relatively large field of view and lower spatial resolution imagery collected by the fisheye imager 210, the image processor 504N can direct the spot imager 104 to align with the possible Caribou to obtain higher spatial resolution imagery of the same. The image processor 504N can perform interpretive analysis using the higher spatial resolution imagery obtained from the spot imager 104, such as neural network analysis to confirm an instance of Caribou migration, quantifying the Caribou, and determining a location and travel speed of the Caribou. This data can be communicated from the satellite 500 to an environmentalist, a government agency, a hunting application, or an educational facility for further analysis.}
Rutschman discloses;
setting a location of an area for disaster monitoring and
{[0239] Location GPS coordinates of the forest fire along with size, growth, intensity, and trend information can be communicated from the satellite 500 in near-real-time to alert one or more first responders or emergency personnel}
Rutschman discloses;
a type of disaster to be monitored and selecting satellite images associated with the location of the area and the type of disaster among the received satellite images through a satellite image selection model;
{[0005] In another embodiment, a computer process executed by at least one computer processor of at least one satellite for providing machine vision for disaster-relief support, includes, but is not limited to, obtaining imagery using at least one imager of the at least one satellite; detecting at least one event by analyzing at least one aspect of the imagery; and executing at least one operation based on the at least one event.}
Rutschman discloses;
synthesizing the selected satellite images through a satellite image synthesis model and generating disaster image data which enables to monitor a disaster; and monitoring a disaster of the area for disaster monitoring by using the disaster image data;
{[0074] perform physical or geographical area monitoring;
[0269] monitoring for one or more aspects based on the at least one interpretation of the imagery at 4002; initiating at least one specific application based on the at least one interpretation of the imagery at 4004; generating data based on the at least one interpretation of the imagery at 4006;}
Rutschman does not explicitly disclose the following, however Boman discloses the training of the model, See [Page 13, paragraph 13, Line 30 -36], a location or geographical coordinate, See [Page 18, Paragraph 24, Line 16 – 24] and time point or timestamp, See [Page 14, Paragraph 15, Line 56- 63];
the satellite image selection model is trained to select a satellite image of which at least one of a capture angle, geographic coordinates, and a capture time point included in each of the satellite images is associated with the location of the area,
{[Page 13, paragraph 13, Line 30- 36] Any of several well known image recognition techniques, including object recognition, can be used to detect image features in the image, including geographical features. For example, object classes can be determined in the image based on machine learning, where an object recognition technique is trained with many sample images to find objects of particular classes.
[Page 14, Paragraph 15, Line 56- 63] implementations can examine characteristics or personal information related to the user associated with the image to assist recognition or recognized label generation (e.g., if the user has provided permission for use of such personal information). For example, user information indicating locations the user has visited can be coordinated with a timestamp of the image to generate one or more recognized labels for the image based on a visited location.
[Page 18, Paragraph 24, Line 16 – 24] Image 400 can be obtained by a system as described above with reference to FIGS. 2 and 3. Location information 402 is associated with image 400, e.g., geographic location metadata that was sensed by a device capturing the image or a device located at or near where the image was captured, using, for example, GPS sensors. In this example, the location information includes location coordinates 402, e.g., a representation of latitude and longitude in some implementations.}
Boman discloses matching the image;
is trained to match at least one of the resolution, the magnification, the focus, the view angle, and a window size of remaining ones of the selected satellite images to the reference satellite image and generates disaster image data by synthesizing the matched satellite image, and
{[Page 13, paragraph 13, Line 30- 45] Any of several well known image recognition techniques, including object recognition, can be used to detect image features in the image, including geographical features. For example, object classes can be determined in the image based on machine learning, where an object recognition technique is trained with many sample images to find objects of particular classes. In another example, a bag-of-visual-words model can be used to treat image features (e.g., small image patches) as words, including building descriptors on the features, keeping occurrence counts of a vocabulary of local image features, and comparing/matching the features against a database of labeled images. Some techniques can use features such as weak supervision and sparse segmentation. Other image recognition techniques can also or alternatively be used, e.g., other pattern matching and feature recognition techniques.}
Rutschman does not explicitly discloses the weight, however, Boman discloses,
wherein the disaster image data is data in which boundaries between various kinds of geography including mountains, water, cities, and land are divided by weight,
{[Page 12, Col. 11, line 49- 61] Since the image may not depict all of these geographic features, one or more descriptor labels may not accurate describe the image content. In some implementations, this check is for multiple descriptor labels of the same type for the location, e.g., generic descriptor type and proper name type of labels. For example, the method can check if there are multiple generic descriptor labels. In one example, if the generic descriptor labels “monument” and “lake” are both obtained in block 304 for the geographic location, then the method can consider there to be a possible discrepancy (while a generic label and a proper name label would not indicate a possible discrepancy).
[Page 14, Col. 16, Line 4-20] For example, the image recognition techniques can determine how much (e.g., percentage) of an entire image area is taken up by a recognized feature, which can influence how much weight or importance to provide other recognized features in the image. In some implementations, some recognized labels can be assigned a greater importance score than other recognized labels based on the depicted content. In one example, a face is determined via facial recognition techniques to take up a large (e.g., over a threshold percentage) of the entire area of the image. Other, smaller features recognized in the image, such as a building or monument located in a background area behind the face in the image, can be given less weight and lower importance score to their recognized labels. The importance score can be used in operations described below, e.g., to filter out some matches or assign matches less confidence, and/or to provide prioritization of verified labels associated with the image.}
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since Rutschman system and method provide the disaster monitoring with the inclusion of a training model, a location or geographical coordinate, time point or timestamp, and determining the images on the map by weight to Determine the boundaries on the image map. See Boman [Page 12, Col. 11, line 49- 61], See [Page 13, paragraph 13, Line 30 -36], and [Page 14, Col. 16, Line 4-20] and See [Page 18, Paragraph 24, Line 16 – 24].
The combination of Rutschman and Boman, does not explicitly disclose the training of the model. However, Cao does disclose the following limitations:
the satellite image synthesis model is a DNN (deep neural network) trained through artificial intelligence technology based on selected satellite images selected by the satellite image selection model, selects any one of the selected satellite images as a reference satellite image,
{[Page 4, Second paragraph] In the invention, a real-time monitoring image of a monitoring area is obtained through satellite monitoring; acquiring a preset disaster image set through satellite monitoring; performing deep learning training on the initial target monitoring model according to the preset disaster image set to obtain a deep learning disaster monitoring model; and inputting the real-time monitoring image into the deep learning disaster monitoring model, and performing disaster early warning according to an output result. According to the method, the disaster monitoring model is obtained through deep learning, the specific disaster in the specific area is effectively monitored, when the disaster area exists, the disaster trend is predicted, and disaster prevention and control information is provided for disaster prevention personnel; and when no disaster area exists, predicting the disaster occurrence probability of the monitoring area, and alarming the disaster occurrence probability according to the degree to avoid the disaster occurrence. According to the method, the meteorological information is combined with the satellite monitoring image, interference factors in the satellite monitoring image are eliminated, and the efficiency and accuracy of disaster early warning are improved.}
Cao discloses adjusting and correction of the information;
due to different characteristics of satellites providing the satellite images, the satellite image synthesis model is trained to perform preprocessing and correction to match information of the remaining ones of the satellite images with the reference satellite image when synthesizing the satellite images, and
{[Page 6, third paragraph] It should be understood that, when the monitoring area is a mountain forest area, for example, the wind and rain are adjusted in the second preset time period, the disaster risk is low, the mountain forest vegetation grows rapidly, compensation coefficients are generated according to the information such as the illumination amount and the rainfall amount acquired from the meteorological information, and the real-time monitoring image is compensated to eliminate the influence factors caused by the natural change of the monitoring area.
[Page 6, Fourth paragraph] Further, the disaster area includes a fire area or a landslide area; the step of determining whether a disaster area exists in the compensated and corrected real-time monitoring image according to the preset disaster characteristics specifically includes: determining whether a fire area exists in the compensated and corrected real-time monitoring image according to a preset fire characteristic; or determining whether a mountain landslide area exists in the real-time monitoring image after compensation and correction according to preset landslide characteristics.}
Cao discloses;
wherein the monitoring of the disaster comprises monitoring the disaster by analyzing whether a disaster occurs, a degree of risk, and a degree of damage in the area for disaster monitoring by comparing the disaster image data generated before and after a predetermined time point.
{[Page 7, third paragraph] It is easy to understand that disaster trend prediction is performed according to the output result, the current humidity information and the current gas proportion. And predicting the fire spreading direction according to the current wind direction and the future wind direction, and combining the topographic information of the monitored area and the oxygen content in the air to ensure that the fire spreading direction can be more accurately predicted. For example: the monitoring area is a mountain forest area, villages exist on mountain feet, a tree cutting area exists in the current area according to human activity analysis, and the current combustion area is closer to the tree cutting area.
[Page 7] In specific implementation, according to the vegetation coverage and the vegetation coverage change rate in the vegetation coverage information, whether human activities exist in the monitored area, whether trees are felled, whether construction settings exist, and the like can be known. For example: the monitoring area is a landscape sightseeing area, an airport is planned and constructed at the scenic sightseeing area to facilitate visitors to visit, the vegetation coverage rate is reduced, and people's activities and trees felling are known to exist in the monitoring area. Since the natural environment is damaged, the probability of occurrence of a disaster in the monitoring area increases.}
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since the system and method provide disaster monitoring with the inclusion of a trained DNN to select the satellite images and to generate disaster image data geographical fixtures to enable the monitoring of a disaster by synthesizing the satellite images. See Cao [Page 4, Second paragraph], [Page 6, second paragraph] and [Page 7, second paragraph].
As per claim 2 and 7 (Similar scope);
Rutschman discloses;
The disaster management method of claim 1, further comprising using image data received from a device installed on a ground, observation data, measurement data of a system measuring the disaster, and big data associated with the disaster received through a communication network to monitor the disaster.
{[0121] For example, the hub processing unit 502 is linked to the at least one first imaging unit 202 and the at least one second imaging unit 204 and is configured to at least one of manage, triage, delegate, coordinate, or satisfy one or more incoming requests received via the communication interface or gateway 506. Requests received via the communication interface or gateway 506 can include program requests or user requests from a ground station or device.}
As per claim 3 and 8 (Similar scope);
Cao discloses;
The disaster management method of claim 1, wherein the satellite image selection model is trained to select a satellite image associated with the type of disaster based on a specification of the satellite image.
{[Page 4, second paragraph] According to the method, the disaster monitoring model is obtained through deep learning, the specific disaster in the specific area is effectively monitored, when the disaster area exists, the disaster trend is predicted, and disaster prevention and control information is provided for disaster prevention personnel; and when no disaster area exists, predicting the disaster occurrence probability of the monitoring area, and alarming the disaster occurrence probability according to the degree to avoid the disaster occurrence. According to the method, the meteorological information is combined with the satellite monitoring image, interference factors in the satellite monitoring image are eliminated, and the efficiency and accuracy of disaster early warning are improved.}
Motivation: The combination would have been obvious because a person of ordinary skill in the art, since the system and method provide disaster monitoring with the inclusion of a trained DNN to select the satellite images and to generate disaster image data geographical fixtures to enable enables to monitor a disaster by synthesizing the satellite images. See Cao [Page 4, Second paragraph], [Page 6, second paragraph] and [Page 7, second paragraph].
Rejection of Claims under 35 USC § 101
In response to the argument filled February, 20, 2026, on page 7-12, regarding the 101 rejections, the Examiner Respectfully disagrees.
Applicant argues that the claims are not directed to an abstract idea and reflect an improvement to the technology of disaster monitoring and technical field.
Examiner Respectfully disagrees.
The Examiner notes that the aspect of processing, resolving inconsistencies, capturing, magnifying, synthesizing, selecting of satellite images, setting and aligning selected images, the Examiner viewed as steps of the identified abstract idea in the Step 2A Prong 1 Analysis and the codes as an additional element in the Step 2A Prong 2 Analysis. Therefore, the Examiner maintains the Mental Processes – Concepts Performed in the Human Mind grouping of abstract idea.
Applicant argues that the generated disaster image data is structured such that boundaries between different kinds of geography, including mountains, water, cities, and land, are divided by weight and as a technically improvement tailored to monitor disaster.
Examiner Respectfully disagrees.
The Examiner notes that the DNN (Deep neural network) for simulating, generating and monitoring are merely generic technology with no technical improvement rather an improvement to the abstract idea using generic technology. See Applicant specification [Page number 7 and figure 3, page 9-10].
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) Mental processes [] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 19 3, 197 (1978).
The Examiner maintains these claims recite an abstract idea.
Therefore, for the foregoing reasons the Examiner has maintained the 35 USC 101 rejection.
Regarding the prior art rejections, the Examiner respectfully disagrees.
In terms of the arguments, Rutschman does not teach specific limitations as amended, however, the Examiner now references under 35 USC 103 rejection, Rutschman in view of Boman et al, in further view of Cao et al.
Cao discloses the training model, monitoring and analyzing the disaster, degree of risk, comparing disaster image data generated before and after a predetermined time point. See Cao [Page 4, Second paragraph], [Page 6, second paragraph] and [Page 7, second paragraph].
Boman et al discloses the training of the model, See [Page 13, paragraph 13, Line 30 -36], a location or geographical coordinate, See [Page 18, Paragraph 24, Line 16 – 24], a time point or timestamp, See [Page 14, Paragraph 15, Line 56- 63]; and also Land and other geographical fixtures divided by weight. See (Page 12, Col. 11, line 49-61] and [Page 14, Col.16, line 4-20]).
Based on the considered amendments cited, 35 USC 103 references have been utilized to teach the claimed invention (claim 1 and 6).
As such, claim 1 and 6 are maintaining the 35 USC 103 rejection as considered above in light of the amended claim limitation.
Lacking any further argument, claims 1-3 and 6-8 are maintaining the 35 USC 103 rejection, as considered above in light of the amended claim limitation above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Watanabe et al (JP 2005078566 A) – Is directed towards providing a disaster occurrence monitoring method and a device therefor which can early and surely detect the occurrence of a disaster and to provide a fire-fighting emergency response device which reduces the burden of a call receiver and allows an emergency call to be quickly and accurately responded to.
M. R. Khaefi, Z. Pramestri, I. Amin and J. G. Lee, "Nowcasting Air Quality by Fusing Insights from Meteorological Data, Satellite Imagery and Social Media Images Using Deep Learning," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 2018, pp. 393-396.
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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/VICTOR ESONU/
Examiner, Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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