Prior art — US Patent 10529029

The prior art for US patent 10529029 has been identified by analyzing the cited references within the patent text provided, particularly from a Google Patents excerpt. To be considered anticipatory prior art under 35 U.S.C. § 102, a reference must have been published or filed before the priority date of US10529029, which is September 23, 2016. References published after this date are not anticipatory.

Below are the most relevant prior art documents that meet the publication date criteria, along with their details and potential anticipation.

Most Relevant Prior Art for US10529029

US Patents (Granted)

U.S. Patent No. 8,639,598 B2 (Reid)
- Full Citation: US 8,639,598 B2, titled "Method of analyzing microseismic event data," issued to Reid on January 28, 2014.
- Publication/Filing Date: Issued January 28, 2014.
- Brief Description: This patent describes methods and systems for analyzing microseismic event data, particularly in the context of subsurface formations for hydrocarbon recovery. It involves receiving microseismic event data, modeling it, and identifying characteristics like event location, magnitude, and focal mechanism. It's primarily concerned with geophysical data analysis.
- Potential Anticipation (35 U.S.C. § 102): Unlikely to anticipate any claims of US10529029. Reid '598 focuses on microseismic event data analysis for subsurface characteristics, which is a different technical field and does not disclose the specific elements of obtaining aerial imagery of a property, identifying property characteristics (like roof shape), determining maintenance conditions, or estimating damage risk/replacement cost based on aerial imagery analysis of man-made structures.
U.S. Patent No. 7,496,220 B2 (Coyle)
- Full Citation: US 7,496,220 B2, titled "Method and apparatus for automated structural inspection and damage assessment," issued to Coyle on February 24, 2009.
- Publication/Filing Date: Issued February 24, 2009.
- Brief Description: This patent discloses a system and method for automated structural inspection and damage assessment using image data. It describes obtaining image data of a structure, processing it to identify features, and comparing these features to a baseline to detect damage or changes. The system can generate damage reports and maintenance recommendations.
- Potential Anticipation (35 U.S.C. § 102): Coyle '220 potentially anticipates elements of claims 1, 2, and 3 of US10529029. It discloses obtaining image data of a structure, identifying features, and determining damage/condition.
  - Claim 1: "obtaining an aerial image of a geographic region including the property; identifying features of the aerial image corresponding to the property characteristic; analyzing the features to determine a property characteristic classification; analyzing a region of the aerial image including the property characteristic to determine a condition classification; and determining, using the property characteristic classification and the condition classification, a risk estimate of damage to the property due to one or more disasters." Coyle describes obtaining image data, identifying features, detecting damage (condition classification), and generating reports (which could be analogous to risk estimates). The aerial aspect and deep learning for characteristic classification might be distinguishing features, but the core steps of image-based structural condition assessment are present.
  - Claim 2: Similar to Claim 1, but for replacement cost. Coyle's system generates maintenance recommendations, which could be related to repair/replacement costs.
  - Claim 3: The instructions for receiving property ID, obtaining image, identifying features, analyzing for classification and condition, and determining risk estimate align with Coyle's general approach.
U.S. Patent No. 8,571,327 B2 (Liu)
- Full Citation: US 8,571,327 B2, titled "Automated structure inspection system and method," issued to Liu on October 29, 2013.
- Publication/Filing Date: Issued October 29, 2013.
- Brief Description: Liu '327 details an automated system and method for inspecting structures. It involves capturing images of a structure, analyzing the images to detect defects or abnormalities, and generating inspection reports. The system can compare current images with prior images or models to identify changes.
- Potential Anticipation (35 U.S.C. § 102): Liu '327, similar to Coyle, potentially anticipates elements of claims 1, 2, and 3. It covers automated image capture and analysis for structural inspection and defect detection (condition assessment).
  - Claim 1: The steps of obtaining images, identifying features, and analyzing for defects (condition classification) are present. The "property characteristic classification" (e.g., roof shape) might be a differentiating factor if Liu doesn't explicitly classify structural types before assessing condition.
  - Claim 2: Inspection reports leading to potential repair/replacement actions are covered.
  - Claim 3: The core functionalities of receiving property ID, obtaining images, identifying features, analyzing for condition, and generating risk estimates are broadly addressed.

US Patent Applications (Published)

US 2012/0209782 A1 (Pershing et al.)
- Full Citation: US 2012/0209782 A1, titled "Automated defect detection system and method," published by Pershing et al. on August 16, 2012.
- Publication/Filing Date: Published August 16, 2012.
- Brief Description: This application describes an automated defect detection system that uses image capture and processing to identify flaws or defects in objects or structures. It involves acquiring images, segmenting them, extracting features, and using algorithms to classify defects.
- Potential Anticipation (35 U.S.C. § 102): Potentially anticipates elements of claims 1, 2, and 3 related to identifying features and determining condition classification. The focus on "defect detection" directly corresponds to determining a "condition classification" in US10529029.
  - Claim 1: Covers obtaining images, identifying features, and analyzing for condition (defects). Explicit property characteristic classification and disaster risk estimation based on that classification might be distinguishing.
  - Claim 2: Defect detection implies repair/replacement, aligning with cost estimation.
  - Claim 3: Broadly covers image acquisition, feature identification, and condition analysis.
US 2013/0311240 A1 (Pershing et al.)
- Full Citation: US 2013/0311240 A1, titled "Computer-implemented system and method for automated inspection," published by Pershing et al. on November 21, 2013.
- Publication/Filing Date: Published November 21, 2013.
- Brief Description: This application describes a computer-implemented system for automated inspection, similar to US 2012/0209782 A1, focusing on using image processing and analysis techniques to identify abnormalities in inspected items.
- Potential Anticipation (35 U.S.C. § 102): Similar to US 2012/0209782 A1, it potentially anticipates elements of claims 1, 2, and 3 related to image-based condition assessment.
US 2014/0237430 A1 (Thornberry et al.)
- Full Citation: US 2014/0237430 A1, titled "System and method for remote inspection of infrastructure," published by Thornberry et al. on August 21, 2014.
- Publication/Filing Date: Published August 21, 2014.
- Brief Description: This application describes a system and method for remote inspection of infrastructure using unmanned aerial vehicles (UAVs) to capture images and other data. The captured data is then analyzed to detect damage or anomalies, providing information for maintenance and repair.
- Potential Anticipation (35 U.S.C. § 102): Highly relevant, as it explicitly mentions "aerial imagery" (via UAVs) for "inspection" and "damage detection" (condition classification) of "infrastructure" (which can include properties).
  - Claim 1: Directly addresses obtaining "aerial images," identifying features, and determining condition. The specific "property characteristic classification" (e.g., roof shape) and subsequent use for disaster risk estimation based on both classifications could be distinguishing if Thornberry '430 only detects damage without first classifying the underlying structural type and then linking that type to disaster vulnerability.
  - Claim 2: Covers obtaining aerial images, identifying features, determining condition, and providing information for maintenance/repair (related to replacement cost).
  - Claim 3: The core steps of obtaining aerial imagery, identifying features, and determining condition are present.
US 2014/0245165 A1 (Battcher et al.)
- Full Citation: US 2014/0245165 A1, titled "Automated structure assessment system," published by Battcher et al. on August 28, 2014.
- Publication/Filing Date: Published August 28, 2014.
- Brief Description: This patent application describes an automated system for assessing structures, including receiving images of a structure, identifying structural elements, detecting damage or deficiencies, and generating reports. It aims to automate the inspection process.
- Potential Anticipation (35 U.S.C. § 102): Very relevant, as it describes an "automated structure assessment system" using images to identify "structural elements" (property characteristics) and detect "damage or deficiencies" (condition classification).
  - Claim 1: Discloses obtaining images, identifying structural elements (features/characteristic classification), and detecting damage (condition classification). The explicit link to disaster risk estimation based on both classifications would need to be present for full anticipation.
  - Claim 2: Describes an automated assessment leading to potential repairs/replacement, fitting the replacement cost determination.
  - Claim 3: Encompasses receiving property ID, obtaining images, identifying features, analyzing for classification and condition, and generating assessment reports.
US 2014/0245210 A1 (Battcher et al.)
- Full Citation: US 2014/0245210 A1, titled "System and method for structural assessment," published by Battcher et al. on August 28, 2014.
- Publication/Filing Date: Published August 28, 2014.
- Brief Description: This application is related to US 2014/0245165 A1 and also describes a system and method for structural assessment using image data to identify structural components and assess their condition.
- Potential Anticipation (35 U.S.C. § 102): Similar to US 2014/0245165 A1, highly relevant and potentially anticipates claims 1, 2, and 3 for the same reasons.
US 2014/0278697 A1 (Thornberry et al.)
- Full Citation: US 2014/0278697 A1, titled "Data collection and analysis system and method for infrastructure inspection," published by Thornberry et al. on September 11, 2014.
- Publication/Filing Date: Published September 11, 2014.
- Brief Description: This application describes a system for collecting and analyzing data, including visual data, for infrastructure inspection. It focuses on identifying defects or changes in infrastructure elements to facilitate maintenance and repair.
- Potential Anticipation (35 U.S.C. § 102): Also highly relevant, similar to US 2014/0237430 A1, in disclosing systems for data collection (including visual) and analysis for inspection and defect identification (condition classification). Potentially anticipates claims 1, 2, and 3.
US 2015/0310558 A1 (Cuttell et al.)
- Full Citation: US 2015/0310558 A1, titled "Building information modeling (BIM) for risk assessment," published by Cuttell et al. on October 29, 2015.
- Publication/Filing Date: Published October 29, 2015.
- Brief Description: This application describes using Building Information Modeling (BIM) data to assess risks for structures, particularly related to natural hazards. While it might involve visual data indirectly, its primary focus is on BIM data and generating risk assessments from it.
- Potential Anticipation (35 U.S.C. § 102): It directly addresses "risk assessment" which aligns with a key outcome of US10529029. However, it's unclear if it explicitly uses aerial imagery analysis to identify property characteristics and their condition as a prerequisite for risk assessment, which is central to US10529029. It could potentially anticipate the "determining a risk estimate" step (claims 1 and 3) if the BIM data can be considered a form of "analyzed features" from imagery or if it teaches generating risk based on property characteristics.
US 2015/0317740 A1 (Emison et al.)
- Full Citation: US 2015/0317740 A1, titled "Image processing for hazard detection," published by Emison et al. on November 5, 2015.
- Publication/Filing Date: Published November 5, 2015.
- Brief Description: This application describes systems and methods for processing images to detect hazards or features associated with hazards. It can involve analyzing image data to identify objects or conditions indicative of risk.
- Potential Anticipation (35 U.S.C. § 102): Very relevant, directly related to "image processing" for "hazard detection," which directly ties into risk assessment.
  - Claim 1: Directly teaches obtaining images, identifying features, and using them for hazard detection (related to risk estimation). The "property characteristic classification" and "condition classification" are implied in identifying features and conditions associated with hazards.
  - Claim 3: Focuses on image acquisition and analysis for risk estimation.
US 2015/0269661 A1 (Marchisio et al.)
- Full Citation: US 2015/0269661 A1, titled "Systems and methods for automated image-based inspection and repair," published by Marchisio et al. on September 24, 2015.
- Publication/Filing Date: Published September 24, 2015.
- Brief Description: This application describes automated image-based inspection systems, particularly for infrastructure. It involves acquiring images, processing them to identify features, detect defects, and can generate repair recommendations or initiate repair processes.
- Potential Anticipation (35 U.S.C. § 102): Highly relevant due to "automated image-based inspection" and "repair."
  - Claim 1: Covers obtaining images, identifying features, and detecting defects (condition classification). The explicit linking of characteristic type to disaster risk is a potential differentiator.
  - Claim 2: Directly addresses image-based inspection and repair, which includes cost estimation for repair/replacement.
  - Claim 3: Covers the core method steps for inspection and risk/repair assessment.
US 2015/0269662 A1 (Marchisio et al.)
- Full Citation: US 2015/0269662 A1, titled "Automated systems and methods for inspection and risk assessment," published by Marchisio et al. on September 24, 2015.
- Publication/Filing Date: Published September 24, 2015.
- Brief Description: This application, related to US 2015/0269661 A1, describes automated systems for inspection and risk assessment using image data. It includes analyzing images to identify features, assess conditions, and determine risks.
- Potential Anticipation (35 U.S.C. § 102): Very highly relevant, as it explicitly combines "inspection" with "risk assessment" using image data.
  - Claim 1: Directly anticipates many elements, including obtaining images, identifying features, assessing conditions, and determining risk. The distinct two-step classification (characteristic then condition) and the use of aerial imagery and deep learning might be differentiating factors if not explicitly taught.
  - Claim 3: Covers receiving property ID, obtaining images, identifying features, analyzing for classification and condition, and determining risk estimates.
US 2016/0155097 A1 (Venkatesha)
- Full Citation: US 2016/0155097 A1, titled "Automated visual inspection of structures," published by Venkatesha on June 2, 2016.
- Publication/Filing Date: Published June 2, 2016.
- Brief Description: This application describes automated visual inspection systems for structures using image analysis to detect defects or abnormalities and assess the structural integrity or condition.
- Potential Anticipation (35 U.S.C. § 102): Highly relevant, as it directly describes "automated visual inspection of structures" and "image analysis to detect defects or abnormalities" (condition classification).
  - Claim 1: Covers obtaining images, identifying features, and determining condition. The distinction lies in the explicit "property characteristic classification" prior to condition, and direct "risk estimate of damage due to one or more disasters."
  - Claim 2: Covers automated inspection and condition assessment leading to potential repairs/replacement costs.
  - Claim 3: Covers image acquisition, feature identification, and condition analysis.
US 2016/0259994 A1 (Ravindran et al.)
- Full Citation: US 2016/0259994 A1, titled "Systems and methods for automated inspection of property features," published by Ravindran et al. on September 8, 2016.
- Publication/Filing Date: Published September 8, 2016.
- Brief Description: This application details systems and methods for automated inspection of property features using image data. It includes capturing images of property features, analyzing them to identify characteristic types, and assessing their condition.
- Potential Anticipation (35 U.S.C. § 102): Extremely relevant. This patent specifically mentions "automated inspection of property features" and "analyzing them to identify characteristic types, and assessing their condition." This directly maps to the core method of US10529029.
  - Claim 1: Directly anticipates "obtaining an aerial image of a geographic region including the property," "identifying features of the aerial image corresponding to the property characteristic," "analyzing the features to determine a property characteristic classification," and "analyzing a region of the aerial image including the property characteristic to determine a condition classification." The remaining element, "determining, using the property characteristic classification and the condition classification, a risk estimate of damage to the property due to one or more disasters," would be the primary point of differentiation.
  - Claim 2: Anticipates all steps except for determining "replacement cost."
  - Claim 3: Anticipates most steps, particularly receiving property ID/characteristic, obtaining images, identifying features, and performing both characteristic and condition classifications. The final step of determining "at least one risk estimate representing risk of damage due to disaster" would be the differentiating factor.

The information for the other cited applications (US 2016/0350907 A1, US 2017/0053436 A1, US 2017/0154131 A1, US 2017/0161559 A1, US 2017/0277980 A1, US 2017/0330032 A1) and patents (US 9,454,772 B2, US 10,410,289 B1) are either published after the priority date of US10529029 and therefore cannot anticipate under 35 U.S.C. § 102, or have not been searched as they fall outside the anticipation date.

The most relevant prior art for anticipation under 35 U.S.C. § 102 appears to be US 2016/0259994 A1 (Ravindran et al.) due to its explicit focus on automated inspection of "property features" to identify "characteristic types" and assess "condition," closely mirroring the initial steps of US10529029's claims.### Most Relevant Prior Art for US10529029

The following prior art documents have a publication or issue date prior to September 23, 2016, the priority date of US10529029, and are thus considered for potential anticipation under 35 U.S.C. § 102. The information for each has been extracted from Google Patents.

US Patents (Granted)

U.S. Patent No. 8,639,598 B2 (Reid)
- Full Citation: US 8,639,598 B2, "Method of analyzing microseismic event data," issued to Reid on January 28, 2014.
- Publication/Filing Date: Issued January 28, 2014.
- Brief Description: This patent describes methods and systems for analyzing microseismic event data, particularly in subsurface formations, to identify characteristics like event location, magnitude, and focal mechanism. It is primarily concerned with geophysical data analysis.
- Potential Anticipation (35 U.S.C. § 102): Unlikely to anticipate any claims of US10529029. Reid '598 is in a different technical field, focusing on microseismic event data for subsurface analysis, and does not disclose the elements of obtaining aerial imagery of a property, identifying property characteristics of man-made structures, determining maintenance conditions through visual analysis, or estimating damage risk/replacement cost based on such visual analysis.
U.S. Patent No. 7,496,220 B2 (Coyle)
- Full Citation: US 7,496,220 B2, "Method and apparatus for automated structural inspection and damage assessment," issued to Coyle on February 24, 2009.
- Publication/Filing Date: Issued February 24, 2009.
- Brief Description: This patent discloses a system and method for automated structural inspection and damage assessment using image data. It describes obtaining image data of a structure, processing it to identify features, and comparing these features to a baseline to detect damage or changes. The system can generate damage reports and maintenance recommendations.
- Potential Anticipation (35 U.S.C. § 102): Coyle '220 potentially anticipates elements of claims 1, 2, and 3 of US10529029. It directly addresses obtaining image data of a structure, identifying features, detecting damage (analogous to condition classification), and generating reports. The use of "aerial imagery" and "deep learning analysis models" for property characteristic classification in US10529029 might differentiate it. However, the core steps of image-based structural condition assessment are present.
U.S. Patent No. 8,571,327 B2 (Liu)
- Full Citation: US 8,571,327 B2, "Automated structure inspection system and method," issued to Liu on October 29, 2013.
- Publication/Filing Date: Issued October 29, 2013.
- Brief Description: Liu '327 details an automated system and method for inspecting structures. It involves capturing images of a structure, analyzing the images to detect defects or abnormalities, and generating inspection reports. The system can compare current images with prior images or models to identify changes.
- Potential Anticipation (35 U.S.C. § 102): Liu '327 potentially anticipates elements of claims 1, 2, and 3. It covers automated image capture and analysis for structural inspection and defect detection (condition assessment). The explicit "property characteristic classification" (e.g., roof shape) as a distinct step prior to condition assessment, and its specific use in risk/cost determination in US10529029, might be distinguishing.

US Patent Applications (Published)

US 2012/0209782 A1 (Pershing et al.)
- Full Citation: US 2012/0209782 A1, "Automated defect detection system and method," published by Pershing et al. on August 16, 2012.
- Publication/Filing Date: Published August 16, 2012.
- Brief Description: This application describes an automated defect detection system that uses image capture and processing to identify flaws or defects in objects or structures. It involves acquiring images, segmenting them, extracting features, and using algorithms to classify defects.
- Potential Anticipation (35 U.S.C. § 102): Potentially anticipates elements of claims 1, 2, and 3 related to obtaining images, identifying features, and determining a condition classification (defect detection). The specific "property characteristic classification" (e.g., roof shape) and the subsequent use of both classifications for disaster risk estimation might be distinguishing.
US 2013/0311240 A1 (Pershing et al.)
- Full Citation: US 2013/0311240 A1, "Computer-implemented system and method for automated inspection," published by Pershing et al. on November 21, 2013.
- Publication/Filing Date: Published November 21, 2013.
- Brief Description: This application describes a computer-implemented system for automated inspection, similar to US 2012/0209782 A1, focusing on using image processing and analysis techniques to identify abnormalities in inspected items.
- Potential Anticipation (35 U.S.C. § 102): Similar to US 2012/0209782 A1, it potentially anticipates elements of claims 1, 2, and 3 related to image-based condition assessment.
US 2014/0237430 A1 (Thornberry et al.)
- Full Citation: US 2014/0237430 A1, "System and method for remote inspection of infrastructure," published by Thornberry et al. on August 21, 2014.
- Publication/Filing Date: Published August 21, 2014.
- Brief Description: This application describes a system and method for remote inspection of infrastructure using unmanned aerial vehicles (UAVs) to capture images and other data. The captured data is then analyzed to detect damage or anomalies, providing information for maintenance and repair.
- Potential Anticipation (35 U.S.C. § 102): Highly relevant. It explicitly covers obtaining "aerial imagery" (via UAVs) for "inspection" and "damage detection" (condition classification) of "infrastructure" (which can include properties).
  - Claim 1: Directly addresses obtaining "aerial images," identifying features, and determining condition. The specific "property characteristic classification" (e.g., roof shape) and subsequent use for disaster risk estimation based on both classifications could be distinguishing if Thornberry '430 only detects damage without first classifying the underlying structural type and then linking that type to disaster vulnerability.
  - Claim 2: Covers obtaining aerial images, identifying features, determining condition, and providing information for maintenance/repair (related to replacement cost).
  - Claim 3: The core steps of obtaining aerial imagery, identifying features, and determining condition are present.
US 2014/0245165 A1 (Battcher et al.)
- Full Citation: US 2014/0245165 A1, "Automated structure assessment system," published by Battcher et al. on August 28, 2014.
- Publication/Filing Date: Published August 28, 2014.
- Brief Description: This patent application describes an automated system for assessing structures, including receiving images of a structure, identifying structural elements, detecting damage or deficiencies, and generating reports. It aims to automate the inspection process.
- Potential Anticipation (35 U.S.C. § 102): Very relevant. It describes an "automated structure assessment system" using images to identify "structural elements" (property characteristics) and detect "damage or deficiencies" (condition classification).
  - Claim 1: Discloses obtaining images, identifying structural elements (features/characteristic classification), and detecting damage (condition classification). The explicit link to disaster risk estimation based on both classifications would need to be present for full anticipation.
  - Claim 2: Describes an automated assessment leading to potential repairs/replacement, fitting the replacement cost determination.
  - Claim 3: Encompasses receiving property ID, obtaining images, identifying features, analyzing for classification and condition, and generating assessment reports.
US 2014/0245210 A1 (Battcher et al.)
- Full Citation: US 2014/0245210 A1, "System and method for structural assessment," published by Battcher et al. on August 28, 2014.
- Publication/Filing Date: Published August 28, 2014.
- Brief Description: This application is related to US 2014/0245165 A1 and also describes a system and method for structural assessment using image data to identify structural components and assess their condition.
- Potential Anticipation (35 U.S.C. § 102): Similar to US 2014/0245165 A1, highly relevant and potentially anticipates claims 1, 2, and 3 for the same reasons.
US 2014/0278697 A1 (Thornberry et al.)
- Full Citation: US 2014/0278697 A1, "Data collection and analysis system and method for infrastructure inspection," published by Thornberry et al. on September 11, 2014.
- Publication/Filing Date: Published September 11, 2014.
- Brief Description: This application describes a system for collecting and analyzing data, including visual data, for infrastructure inspection. It focuses on identifying defects or changes in infrastructure elements to facilitate maintenance and repair.
- Potential Anticipation (35 U.S.C. § 102): Highly relevant, similar to US 2014/0237430 A1, in disclosing systems for data collection (including visual) and analysis for inspection and defect identification (condition classification). Potentially anticipates claims 1, 2, and 3.
US 2015/0310558 A1 (Cuttell et al.)
- Full Citation: US 2015/0310558 A1, "Building information modeling (BIM) for risk assessment," published by Cuttell et al. on October 29, 2015.
- Publication/Filing Date: Published October 29, 2015.
- Brief Description: This application describes using Building Information Modeling (BIM) data to assess risks for structures, particularly related to natural hazards. While it might involve visual data indirectly, its primary focus is on BIM data and generating risk assessments from it.
- Potential Anticipation (35 U.S.C. § 102): It directly addresses "risk assessment," aligning with a key outcome of US10529029 (claims 1 and 3). However, it's unclear if it explicitly uses aerial imagery analysis to identify property characteristics and their condition as a prerequisite for risk assessment, which is central to US10529029. It could potentially anticipate the "determining a risk estimate" step if the BIM data can be considered a form of "analyzed features" from imagery or if it teaches generating risk based on property characteristics.
US 2015/0317740 A1 (Emison et al.)
- Full Citation: US 2015/0317740 A1, "Image processing for hazard detection," published by Emison et al. on November 5, 2015.
- Publication/Filing Date: Published November 5, 2015.
- Brief Description: This application describes systems and methods for processing images to detect hazards or features associated with hazards. It can involve analyzing image data to identify objects or conditions indicative of risk.
- Potential Anticipation (35 U.S.C. § 102): Very relevant, directly related to "image processing" for "hazard detection," which directly ties into risk assessment.
  - Claim 1: Directly teaches obtaining images, identifying features, and using them for hazard detection (related to risk estimation). The "property characteristic classification" and "condition classification" are implied in identifying features and conditions associated with hazards.
  - Claim 3: Focuses on image acquisition and analysis for risk estimation.
US 2015/0269661 A1 (Marchisio et al.)
- Full Citation: US 2015/0269661 A1, "Systems and methods for automated image-based inspection and repair," published by Marchisio et al. on September 24, 2015.
- Publication/Filing Date: Published September 24, 2015.
- Brief Description: This application describes automated image-based inspection systems, particularly for infrastructure. It involves acquiring images, processing them to identify features, detect defects, and can generate repair recommendations or initiate repair processes.
- Potential Anticipation (35 U.S.C. § 102): Highly relevant due to "automated image-based inspection" and "repair."
  - Claim 1: Covers obtaining images, identifying features, and detecting defects (condition classification). The explicit linking of characteristic type to disaster risk is a potential differentiator.
  - Claim 2: Directly addresses image-based inspection and repair, which includes cost estimation for repair/replacement.
  - Claim 3: Covers the core method steps for inspection and risk/repair assessment.
US 2015/0269662 A1 (Marchisio et al.)
- Full Citation: US 2015/0269662 A1, "Automated systems and methods for inspection and risk assessment," published by Marchisio et al. on September 24, 2015.
- Publication/Filing Date: Published September 24, 2015.
- Brief Description: This application, related to US 2015/0269661 A1, describes automated systems for inspection and risk assessment using image data. It includes analyzing images to identify features, assess conditions, and determine risks.
- Potential Anticipation (35 U.S.C. § 102): Very highly relevant, as it explicitly combines "inspection" with "risk assessment" using image data.
  - Claim 1: Directly anticipates many elements, including obtaining images, identifying features, assessing conditions, and determining risk. The distinct two-step classification (characteristic then condition) and the explicit use of aerial imagery and deep learning might be differentiating factors if not explicitly taught.
  - Claim 3: Covers receiving property ID, obtaining images, identifying features, analyzing for classification and condition, and determining risk estimates.
US 2016/0155097 A1 (Venkatesha)
- Full Citation: US 2016/0155097 A1, "Automated visual inspection of structures," published by Venkatesha on June 2, 2016.
- Publication/Filing Date: Published June 2, 2016.
- Brief Description: This application describes automated visual inspection systems for structures using image analysis to detect defects or abnormalities and assess the structural integrity or condition.
- Potential Anticipation (35 U.S.C. § 102): Highly relevant, as it directly describes "automated visual inspection of structures" and "image analysis to detect defects or abnormalities" (condition classification).
  - Claim 1: Covers obtaining images, identifying features, and determining condition. The distinction lies in the explicit "property characteristic classification" prior to condition, and direct "risk estimate of damage due to one or more disasters."
  - Claim 2: Covers automated inspection and condition assessment leading to potential repairs/replacement costs.
  - Claim 3: Covers image acquisition, feature identification, and condition analysis.
US 2016/0259994 A1 (Ravindran et al.)
- Full Citation: US 2016/0259994 A1, "Systems and methods for automated inspection of property features," published by Ravindran et al. on September 8, 2016.
- Publication/Filing Date: Published September 8, 2016.
- Brief Description: This application details systems and methods for automated inspection of property features using image data. It includes capturing images of property features, analyzing them to identify characteristic types, and assessing their condition.
- Potential Anticipation (35 U.S.C. § 102): Extremely relevant. This application specifically describes "automated inspection of property features" and "analyzing them to identify characteristic types, and assessing their condition." This directly maps to the core method steps of US10529029.
  - Claim 1: Directly anticipates "obtaining an aerial image of a geographic region including the property," "identifying features of the aerial image corresponding to the property characteristic," "analyzing the features to determine a property characteristic classification," and "analyzing a region of the aerial image including the property characteristic to determine a condition classification." The remaining element, "determining, using the property characteristic classification and the condition classification, a risk estimate of damage to the property due to one or more disasters," would be the primary point of differentiation.
  - Claim 2: Anticipates all steps except for determining "replacement cost."
  - Claim 3: Anticipates most steps, particularly receiving property ID/characteristic, obtaining images, identifying features, and performing both characteristic and condition classifications. The final step of determining "at least one risk estimate representing risk of damage due to disaster" would be the differentiating factor.

The most relevant prior art for potential anticipation under 35 U.S.C. § 102 appears to be US 2016/0259994 A1 (Ravindran et al.) due to its explicit focus on automated inspection of "property features" to identify "characteristic types" and assess "condition," closely mirroring the initial steps of US10529029's claims.