DETAILED ACTION
Status of Application
This action is a Non-Final Rejection. This action is in response to the request for continued examination filed on March 10, 2026.
Claims 1, 9, 13, 14, and 19 have been amended.
Claims 1-20 are pending and rejected.
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 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.
Response to Arguments
Regarding the rejection under 35 U.S.C. 112(b) regarding the limitation from claim 1 of “determining, using a third set of one or more machine learning models, whether each damaged object is to be repaired or replaced based on the assessment of the state of the real property and the repair information,” the rejection has been withdrawn in light of Applicant’s amendments and remarks. Applicant asserts that paragraph 0049 of the Specification “indicates that when the machine learning models detect a unique object which is damaged, the model determines whether the unique object is damaged to a degree of needing only to be repaired, or to a degree severe enough to be replaced.” Therefore, the amended limitation is interpreted to mean that it is determined whether a damaged object needs to be repaired or whether it needs to be replaced.
Regarding the rejection under 35 U.S.C. 112(b) regarding the limitation from claim 1 of “at least a first image,” Applicant has amended the claim to include commas before and after “at least” (e.g., “…,at least, the first image…”). These terms are interpreted as referring back to “at least a first image.” If Applicant intends for these terms to have a different interpretation, Applicant should provide clarification.
Regarding the rejection under 35 U.S.C. § 101, Applicant argues that the method of claim 1 “denotes a technological process, rather than a fundamental economic principle in view of MPEP 2106.04(a).” However, the machine learning models are being used to implement a business process of determining whether a damaged object is to be either repaired or replaced. Applicant further argues that “the method claimed is to improve image processing” and points to paragraph 0048 of the Specification. However, the Specification does not describe an improvement to image processing technology. Instead, paragraph 0048 states that “computer vision techniques may be used to count and track each unique object shown in the image data.” This is described at a high level and without any specificity that would show a technological improvement.
Applicant further argues that the claims are eligible for reasons similar to claims 1 and 3 of Example 47. Claim 1 of Example 47 was eligible because it did not recite a judicial exception. Claim 3 of Example 47 was eligible because the claims included a technological improvement by “detect[ing] network intrusions and tak[ing] real-time remedial actions, including dropping suspicious packets and blocking traffic from suspicious source addresses.” The instant claims do not provide a similar type of technological improvement.
Applicant further argues that the claimed invention is not directed to a mental process and references the limitation of “receiving a series of images.” However, claim 1 recites “receiving a series of images of a real property from one or more viewpoints.” One could receive these images on paper or electronically and observe them. Similarly, one could identify multiple objects related to a property captured in an image. The machine learning models are being applied to a process could that could otherwise be performed in the mind or manually.
Applicant further argues that the steps of claim 1 amount “to more than merely invoking a tool to implement steps to apply the purported abstract idea because the method relies on two trained models to determine specific objects of real property within the images, assess the images for damage, and determine from those images, materials needed to repair or replace the specific damage to the real property.” However, the models are recited at a high level and are being used as a tool to implement the recited abstract idea. Paragraphs 0048-0052 and 0056 of the Specification have been reviewed and a technological improvement has not been described. For example, Applicant has not described an improved computer vision technique. As such, the rejection under 35 U.S.C. 101 has been maintained.
Regarding the rejection under 35 U.S.C. 103, the rejection has been withdrawn in light of Applicant’s amendments. Although individual claim features were found in the art (see previous 103 rejection and prior art cited below), claim 1 as a whole is not obvious in light of the prior art.
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 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Yes, with respect to claims 1-20, which recite a method and, therefore, are directed to the statutory class of process.
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a))
The following claims identify the limitations that recite the abstract idea in regular text and that recite additional elements in bold:
1. A method, comprising:
receiving a series of images of a real property from one or more viewpoints;
identifying, for at least a first image from the series of images, using a first set of one or more machine learning models, multiple objects related to the real property captured in, at least, the first image;
determining, for at least the first image, a number of unique objects that are shown in, at least, the first image, wherein each unique object is only tracked and accounted for a single time based on computer vision techniques; and
generating, for at least the first image, using a second set of one or more machine learning models, an assessment of a state of the real property, wherein the assessment includes a determination of one or more damaged objects;
identifying, for the one or more damaged objects, using the second set of one or more machine learning models, details related to the damaged objects, wherein the details include the dimension of the damaged object, sections of the object that is damaged, and material of the damaged object;
receiving, for at least the first image, repair information for the one or more damaged objects, the repair information comprising at least an estimated cost of repair; and
determining, using a third set of one or more machine learning models, whether each damaged object is to be either repaired or replaced based on the assessment of the state of the real property and the repair information.
2. The method of claim 1, wherein the first set of one or more machine learning models and the second set of one or more machine learning models are a same set of one or more machine learning models.
3. The method of claim 1, wherein generating the assessment of the state of the real property further comprises: determining a damage state for at least one unique object.
4. The method of claim 3, wherein the damage state includes at least one of a location of damage or a severity of damage.
5. The method of claim 3, wherein the damage state includes at least one of an estimated repair cost, a repair methodology and an estimated number of labor hours to perform a repair.
6. The method of claim 1, wherein generating the assessment of the state of the real property further comprises: determining physical dimensions for at least one unique object.
7. The method of claim 1, wherein generating the assessment of the state of the real property further comprises: determining one or more materials for at least one unique object.
8. The method of claim 1, wherein the image data includes at least one of satellite images, images captured by a drone or images captured during an aerial fly over of the real property.
9. The method of claim 1, further comprising:
providing feedback to a user device that captured at least a portion of the image data, wherein the feedback is displayed in an interface comprising the feedback and a view of a camera of the user device.
10. The method of claim 9, wherein the feedback includes an alert configured to indicate a request to a user to change a distance or angle between the camera and the real property.
11. The method of claim 10, wherein the request to change the distance or the angle is based on a presence of an object of interest, a region of interest relative to one or more objects or a region of damage relative to one or more objects.
12. The method of claim 9, wherein the feedback includes an alert configured to indicate a request to a user during recording of video to change a manner in which the user is moving the camera.
13. The method of claim 1, further comprising:
receiving predicted weather related data; and
determining, using a third set of one or more machine learning models, predicted weather related damage for the real property.
14. The method of claim 1, further comprising:
constructing, based on at least the image data, a two-dimensional (2D) or three-dimensional (3D) model of the real property.
15. The method of claim 14, wherein the 2D model or 3D model are constructed using augmented reality (AR) or virtual reality (VR) techniques.
16. The method of claim 14, further comprising:
requesting feedback related to the 2D model or 3D model from a user, wherein the feedback is related to identifying a region of interest in the 2D model or 3D model.
17. The method of claim 1, further comprising:
receiving feedback from a user related to the assessment of the state of the real property.
18. The method of claim 1, further comprising:
receiving non-image data related to the real property, wherein the assessment of the state of the real property is generated based on the non-image data.
19. The method of claim 1, further comprising:
identifying one or more of the multiple objects or an object occluding the one or more of the multiple objects based on segmenting the image data.
20. The method of claim 1, further comprising:
verifying an accuracy of the image data based on images received from a third party source.
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite certain methods of organizing human activity. The claims are directed to assessing a state of real property. This type of method of organizing human activity is a fundamental economic practice because it involves insurance and a commercial interaction such as agreements in the form of contracts, legal obligations, and business relations. The claims also recite mental processes. For example, “receiving a series of images” includes observation, “identifying…multiple objects related to the real property captured in the first image” includes evaluation and judgment, “determining a number of unique objects that are shown in the first image” includes evaluation and judgment, “generating…an assessment of a state of the real property” includes judgment or opinion, “receiving…the repair information” includes observation, and “determining…whether each damaged object is to be repaired or replaced” includes judgment and opinion. Thus, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d))
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components (i.e., additional elements that are in bold above) are recited at a high level of generality and are merely invoked as a tool to implement the steps. For example, only a programmed general purpose computing device is needed to implement the claimed process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05)
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claims, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claims do not provide an inventive concept.
As such, the claims are not patent eligible.
Relevant Prior Art
The following references are relevant to Applicant’s invention:
Spader et al., U.S. Patent Number 10,832,333 B1. This reference teaches generating a proposed insurance claim for an object that is shown in a 3D image.
Howe et al., U.S. Patent Application Publication Number 2017/0270650 A1. This reference teaches image analysis for property damage assessment and verification. Paragraph 0068 recites “Alternatively and/or additionally, the system may detect the damage type automatically through the use of various techniques in computer vision, machine learning, and/or other imagery analysis techniques.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH H ROSEN whose telephone number is (571) 270-1850 and email address is elizabeth.rosen@uspto.gov. The examiner can normally be reached Monday - Friday, 10 AM ET - 7 PM ET.
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/ELIZABETH H ROSEN/Primary Examiner, 3693
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