Prior art

To identify the most relevant prior art for US Patent 11030491, I would typically access the patent document directly from the USPTO database and review the "References Cited" section. The provided text contains the full patent text, so I will extract the prior art from there.

Based on the provided full patent text of US11030491, the patent itself cites several documents, which are considered prior art.

Here are the cited prior art references and their descriptions as found in the patent text:

Non-Patent Literature:

• "Network In Network" by M. Lin et al. (published in the International Conference on Learning Representations, 2014, arXiv:1409.1556)
  • Publication Date: 2014
  • Brief Description: This paper describes the "Network in Network" (NIN) model, where multiple layers of artificial perception outcomes are generated using micro neural networks with complex structures. These outcomes are then stacked and averaged to create a single global average pooling layer for classification. The patent mentions NIN as a machine learning classifier that has demonstrated superior performance and is less storage-intensive than conventional CNN processing.
  • Potentially Anticipates (under 35 U.S.C. § 102): The general concept of using advanced neural network architectures, specifically NIN, for classification tasks in machine learning. Claims pertaining to the use of NIN as a machine learning model for analyzing features (e.g., in claims involving "applying a deep learning analysis model to the features," or where "the deep learning analysis model may be NIN") could be considered. For example, the description states: "Analyzing the features to determine a property characteristic may include applying a deep learning analysis model to the features. The deep learning analysis model may be NIN."
• "ImageNet Classification with Deep Convolutional Neural Networks" by Krizhevsky et al. (Advances in neural information processing systems. 2012)
  • Publication Date: 2012
  • Brief Description: This paper describes Alexnet, an example of a Convolutional Neural Network (CNN) processing model. The patent refers to CNN as a "well-established and popular machine-learning methodology" for preprocessing images and classifying property features.
  • Potentially Anticipates (under 35 U.S.C. § 102): The broad application of Convolutional Neural Networks (CNNs) for image classification and feature extraction. Claims involving a "convolutional neural network (CNN) to preprocess the aerial image... and to classify the property features" could be potentially anticipated.

Other References Mentioned as Sources (not explicitly cited as "Prior Art" in the formal sense, but as general background information or potential data sources):

These are listed as sources for imagery or general concepts, not necessarily as prior art for the inventive steps. They highlight the existing landscape of image acquisition and mapping.

• Google® Earth images by Google, Inc.
  • Brief Description: Private industry database for aerial imagery.
• NTT Geospace Corporation of Japan
  • Brief Description: Private industry database for aerial imagery.
• Geospatial Information Authority (GSI) of Japan
  • Brief Description: Publicly owned organization database for aerial imagery and urban planning maps.
• United States Geological Survey
  • Brief Description: Publicly owned organization database for aerial imagery.
• Federal Agency for Cartography and Geodesy of Germany
  • Brief Description: Publicly owned organization database for aerial imagery.
• QGIS by the Open Source Geospatial Foundation (OSGeo)
  • Brief Description: Open Source Geographic Information System (GIS) for collecting aerial imagery.
• Zenrin Co. Ltd. of Japan
  • Brief Description: Source for shape map images, similar to urban planning maps.
• Google® Street View by Google, Inc.
  • Brief Description: Source for terrestrial (street view) images.
• Bing® Maps Streetside by Microsoft Corp.
  • Brief Description: Source for terrestrial (street view) images.
• Mapillary by Mapillary AB of Sweden
  • Brief Description: Source for terrestrial (street view) images.