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 .
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.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1, 18 and 20 recite obtaining an aerial image representing an object in an environment; providing the aerial image as input to a machine learning model; generating, using the machine learning model and based on the aerial image, a textual description of a location of the object in the environment and outputting the textual description of the location of the object.
Generating, using the machine learning model and based on the obtained aerial image, a textual description of a location of the object in the environment is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “using the machine learning model,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “using the machine learning model” language, “generating” in the context of this claim encompasses the user manually visualizing a textual description. The limitation of outputting the textual description of the location, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind and manually outputted. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea
This judicial exception is not integrated into a practical application. In particular, the claim
only recites one additional element – using a machine learning model to generate textual description
step. The machine learning model at a high-level of generality (i.e., as a generic processor
performing a generic computer function of textual information based on a location of an object) such that it amounts no more than mere instructions to apply the exception using a generic
computer component. Accordingly, this additional element does not integrate the abstract idea into
a practical application because it does not impose any meaningful limits on practicing the abstract
idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract idea
into a practical application, the additional element of using a machine learning model to generate textual description information amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer
component cannot provide an inventive concept. The claims are not patent eligible. Claims 2-17 and 19 depend on claims 1 and 18, respectively. Therefore are also rejected
Claim Rejections - 35 USC § 102
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)(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.
Claims 1-7 and 12-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Callari et al (10,893,107)
Regarding claims 1, 18 and 20 Callari discloses,
Obtaining an aerial image representing an object in an environment (note col. 15 lines 4-24, and fig. 3 block 310, training data set image data received from UAV which include images);
Providing the aerial image as input to a machine learning model (note col. 15 lines 42-43 and fig 3 block 312 , supervised machine learning, training data set submitted at 312);
Generating, using the machine learning model and based on the aerial image, a textual description of a location of the object in the environment (note col. 16 lines 65- col. 17 lines 5 and col. 7 lines 7 lines 49-52, examiner interprets annotate the data can include a textual description of a location of the object in the environment); and
Outputting the textual description of the location of the object (note col. 15 lines 44-50 and fig. 3 block 306, output fig. 4 block 408 train to identify output and col. 7 lines 50-57 as sensor data added to training data set).
Regarding claim 2 Callari discloses,
Wherein the object comprises a package (note col. 11 lines 7-14, pickup items, object detection)
Regarding claim 3 Callari discloses
Wherein the package has been delivered to the environment by an unmanned aerial vehicle, and wherein the aerial image has been captured by the unmanned aerial vehicle (note col. 6 lines 22-40, UAV delivers items and camera system affixed UAV).
Regarding claim 4 Callari discloses,
Wherein the machine learning model has been trained using a plurality of training samples, wherein each respective training sample of the plurality of training samples (note col. 15 lines 27-30, machine learning technique utilizes training data for training model) comprises (i) a corresponding aerial image of a corresponding training environment (note col . 15 lines 11-19, sensor data may include images) and (ii) a corresponding textual description of a location of a training object located in the corresponding training environment (note col. 15 lines 11-19, training data suitable tasks information as item attributes).
Regarding claim 5 Callari discloses,
Wherein the machine learning model is configured to generate textual descriptions that anonymize visual information contained in the aerial images from the plurality of training samples (note col. 15 lines 11-19, training data suitable tasks information as item attributes).
Regarding claim 6 Callari discloses,
Wherein the corresponding textual description for each respective training sample does not make reference to objects outside of a designated boundary within the training environment (note col. 7 lines 46-55, user annotation based on theme references, etc).
Regarding claim 7 Callari discloses,
wherein the aerial image comprises a composite aerial image, and wherein obtaining the aerial image comprises: obtaining a plurality of aerial images of the environment, wherein at least some of the plurality of aerial images represent the object, and wherein the plurality of aerial images represent the environment from different points of view; and determining the composite aerial image by combining image data from the plurality of aerial images.
Regarding claim 12 Callari discloses,
Providing, as input to the machine learning model, a time at which the aerial image was captured (note col. 29 lines 8-4, input time) , wherein the machine learning model is configured to generate the textual description further based on the time at which the aerial image was captured (note col. 29 lines 1-8).
Regarding claim 13 Callari discloses,
Providing, as input to the machine learning model, a representation of an altitude at which the aerial image was captured, wherein the machine learning model is configured to generate the textual description further based on the representation of the altitude at which the aerial image was captured (note col. 2 lines 21-29, computing tasks can include supporting the training, retraining, and/or incremental updating of one or more machine-learning models. A data collection task may refer to any suitable task that includes collecting data (e.g., altitude)
Regarding claim 14 Callari discloses,
transmitting, to a client device, the textual description; receiving, from the client device, a response to the transmitted textual description (note col. 7 lines 14-25, transmitting to client device); and, updating, based on the response, a status associated with the object (note col. 4 lines 8-20, calculating updates).
Regarding claim 15 Callari discloses,
Modifying, based on the response, the textual description, and transmitting, to the client device, the modified textual description (note col. 18 lines 8-15, modify the training data set to include the input data collected).
Regarding claim 16 Callari discloses,
Wherein the textual description of the location of the object describes the location of the object relative to another object in the environment (note col. 3 lines 13-35, markers placed at locations).
Regarding claim 17 Callari discloses,
Wherein the textual description of the location of the object includes a cardinal direction (note col. 14 lines 12-25, coordinates of location of item delivery).
Regarding claim 19 Callari discloses,
An unmanned aerial vehicle, wherein the aerial image is captured by the unmanned aerial vehicle and wherein the operations further comprise: delivering the object to the environment (note col. 6 lines 22-40, UAV delivers items and camera system affixed UAV).
Allowable Subject Matter
Claims 8-11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter for dependent claims 8-11.
Regarding claim 8, prior art could not be found for the feature using a semantic model and based on the aerial image, a semantic map that represents, for each respective visual feature of a plurality of visual features in the aerial image, a corresponding classification of the respective visual feature; and providing the semantic map as input to the machine learning model, wherein the machine learning model is configured to generate the textual description further based on the semantic map. These features in combination with other features could not be found in the prior art. Claim 9 depend on claim 8. Therefore, are also objected.
Regarding claim 10, prior art could not be found for the features determining, based on satellite-based navigation data associated with the aerial image, an estimated location of the object in the environment; and providing the estimated location of the object as input to the machine learning model, wherein the machine learning model is configured to generate the textual description further based on the estimated location of the object. These features in combination with other features could not be found in the prior art. Claims 11 depend on claim 10. Therefore are also objected.
Related Prior Art
Okazaki (11,853,889) access aerial image (note fig. 4, block 404).
Desai et al (12,340,576) Providing the aerial image as input to a machine learning model (note fig. 3 block 210, machine learning model).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY M DESIRE whose telephone number is (571)272-7449. The examiner can normally be reached Monday-Friday 6:30am-3:00pm.
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G.D.
January 9, 2026
/GREGORY M DESIRE/Primary Examiner, Art Unit 2676