Prior art

Most Relevant Prior Art for US11129591

To identify the most relevant prior art for US11129591, I will examine the patent's cited references. Since direct access to the USPTO database for detailed prior art analysis is not available in this environment, I will rely on the information provided within the patent text and external resources like Google Patents (which typically lists cited prior art).

Here are the prior art keywords identified in the patent's information: "echocardiographic," "image," "quality assessment," "neural network," and "parameters".

The patent itself references a provisional application: U.S. Provisional Application No. 62/325,779, entitled “PROCESS FOR IMAGING QUALITY ASSURANCE,” filed on April 21, 2016. This is the earliest priority date for the patent (2016-04-21).

I will now list the prior art explicitly cited in the US11129591 patent based on the provided text, and analyze their relevance. The patent text refers to an external academic paper related to neural network architecture:

• Full Citation: Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. International Conference on Learning Representations (ICRL) pp. 1-14 (2015).
• Publication/Filing Date: 2015 (ICRL conference publication).
• Brief Description: This paper describes the VGG architecture for very deep convolutional networks used in large-scale image recognition. The US11129591 patent references this architecture in the context of its convolutional layers, specifically noting that "all cony layers have kernels with the size of 3×3, which may, for example, follow the VGG architecture... with the number of kernels doubling for deeper cony layers". This indicates that the VGG architecture is a foundational component for the convolutional layers within the neural network used for image quality assessment in US11129591.
• Potential Anticipation (35 U.S.C. § 102): This reference, particularly concerning the VGG architecture, could potentially anticipate aspects of claims related to the neural network architecture, specifically those describing convolutional layers. For example, claims 1, 10, 20, 29, 39, and 46, which broadly refer to a "neural network" and "plurality of layers" or "sets of neural network parameters," could be seen as building upon the known techniques described in the Simonyan and Zisserman paper. It would specifically anticipate aspects related to the convolutional layer structure and kernel sizes if those features are broadly claimed without sufficient distinguishing elements.

Another academic paper referenced for optimizer details:

• Full Citation: Kingma, D. P., Ba, J. L.: Adam: a Method for Stochastic Optimization, International Conference on Learning Representations 2015 pp. 1-15 (2015).
• Publication/Filing Date: 2015 (ICRL conference publication).
• Brief Description: This paper introduces the Adam optimizer, a method for stochastic optimization. The US11129591 patent states that "the neural networks may be trained using the adam optimizer with hyper-parameters as suggested by Kingma, D. P., Ba, J. L.". This indicates that the training methodology for the neural networks in US11129591 incorporates the Adam optimizer.
• Potential Anticipation (35 U.S.C. § 102): Similar to the VGG architecture, the use of the Adam optimizer is a known technique in neural network training. Therefore, claims that broadly refer to "training the neural networks" (e.g., claims 20, 29, 38, 46) might be partially anticipated regarding the optimization method itself if not sufficiently differentiated by other novel training aspects. However, the application of this optimizer within the specific context of view-category-specific echocardiographic image quality assessment, particularly with the shared and specific layers, would be the claimed novelty.

Beyond these academic papers, a comprehensive prior art search would involve examining the "Cited by" patents listed on Google Patents or within the USPTO records for US11129591, which are typically identified during the examination process. Without direct access to the USPTO system to list all examiner-cited prior art for US11129591, I cannot provide a complete list of patent citations from the USPTO database at this time. The provided patent text only explicitly cites the two academic papers.