Prior Art Research Articles

Analisis patentability alat sunat sekali pakai pada pengembangan desain industri A00202202524

Industrial Design A00202202524, is about a circumcision device, which is called a modern circumcision device or a disposable circumcision device. The tool was designed taking into account the creator's experience, prior art, functional review and the anatomy in which the tool is used. Design development has been obtained and the product has been tested with good success. As a follow-up, we want to know the patent ability of the new design. The method used is a search of the closest related patents as a comparison document, both Indonesian and international. Analysis of similarities and differences between the invention product and the comparison document or prior art. From the patents found, 5 Indonesian patents and 8 international patents were selected. Indonesia. The conclusions obtained are: the product/invention is different from the comparison documents. Certain features of findings or creations or inventions are the answer to a technical problem, which has an invention step, and is new. Such creations or inventions have the potential to obtain a patent if the draft patent is properly formulated

Contact Edit: Artist Tools for Intuitive Modeling of Hand-Object Interactions

Posing high-contact interactions is challenging and time-consuming, with hand-object interactions being especially difficult due to the large number of degrees of freedom (DOF) of the hand and the fact that humans are experts at judging hand poses. This paper addresses this challenge by elevating contact areas to first-class primitives. We provide end-to-end art-directable (EAD) tools to model interactions based on contact areas, directly manipulate contact areas, and compute corresponding poses automatically. To make these operations intuitive and fast, we present a novel axis-based contact model that supports real-time approximately isometry-preserving operations on triangulated surfaces, permits movement between surfaces, and is both robust and scalable to large areas. We show that use of our contact model facilitates high quality posing even for unconstrained, high-DOF custom rigs intended for traditional keyframe-based animation pipelines. We additionally evaluate our approach with comparisons to prior art, ablation studies, user studies, qualitative assessments, and extensions to full-body interaction.

Circulatory Support: Artificial Muscles for the Future of Cardiovascular Assist Devices.

Artificial muscles enable the design of soft implantable devices which are poised to transform the way wemechanically support the heart today. Heart failure is a prevalent and deadly disease, which is treated with the implantation of rotary blood pumps as the only alternative to heart transplantation. The clinically used mechanical devices are associated with severe adverse events, which are reflected here in a comprehensive list of critical requirements for soft active devices of the future: low power, no blood contact, pulsatile support, physiological responsiveness, high cycle life, and less-invasive implantation. In this review, prior art in artificial muscles for their applicability in the short and long term is investigated and critically evaluated. The main challenges regarding the effectiveness, controllability, and implantability of recently proposed actuators are highlighted and the future perspectives for attachment, physiological responsiveness, durability, and biodegradability as well as equitable design considerations are explored.

RCM adjustment for a double-parallelogram based RCM mechanism used for MIS robots

The inevitable errors in the manufacturing, assembly, and testing of a remote center-of-motion (RCM) mechanism lead to the erroneous link parameters which deviate its geometry. Consequently, the motion, as well as the force, become non-zero at the entry port to the patient’s body which is undesirable. To address this issue, the current work first presents the accurate kinematic model of the RCM of an exemplary double-parallelogram-based RCM mechanism. Thereafter, it introduces the RCM adjustment concept and a flexure-based compliant mechanism to nullify/minimize the said deviation. For the analytical design of the flexure element, this paper makes the required design calculations and performs the finite element analysis. It is followed by the disclosure of the RCM Adjustment System and a systematized method of RCM adjustment for a mechanically constrained RCM device. The devised system is based on the prescribed compliant mechanism design and is advantageous, over the traditional RCM adjustments. The potential of the proposed concept lies in the fact that it is not limited to the adopted mechanism only rather, it is extendable to other mechanisms as well which was missing in the prior art. In addition, it offers a more simplified and economical design. Further, an initial prototype of the exemplary mechanism is manufactured using laser micromachining and rapid prototyping techniques. Both analytical as well as physical validations have been performed which proved the efficacy of the proposed kinematic modeling concept. Furthermore, the prototype of the introduced RCM Adjustment System is manufactured as a single-piece design which demonstrated its effectiveness. The preferred embodiment of the same is the said double-parallelogram-based RCM mechanism which is most widely used for robot-assisted minimally invasive surgery (RAMIS). It is designed in a SolidWorks environment and manufactured using Al6101-T6 and SS304 as link material, and a variety of standard elements.

Text Classification Based on Neural Network Fusion

The goal of text classification is to identify the category to which the text belongs. Text categorization is widely used in email detection, sentiment analysis, topic marking and other fields. However, good text representation is the point to improve the capability of NLP tasks. Traditional text representation adopts bag-of-words model or vector space model, which loses the context information of the text and faces the problems of high latitude and high sparsity,. In recent years, with the increase of data and the improvement of computing performance, the use of deep learning technology to represent and classify texts has attracted great attention. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and RNN with attention mechanism are used to represent the text, and then to classify the text and other NLP tasks, all of which have better performance than the traditional methods. In this paper, we design two sentence-level models based on the deep network and the details are as follows: (1) Text representation and classification model based on bidirectional RNN and CNN (BRCNN). BRCNN’s input is the word vector corresponding to each word in the sentence; after using RNN to extract word order information in sentences, CNN is used to extract higher-level features of sentences. After convolution, the maximum pool operation is used to obtain sentence vectors. At last, softmax classifier is used for classification. RNN can capture the word order information in sentences, while CNN can extract useful features. Experiments on eight text classification tasks show that BRCNN model can get better text feature representation, and the classification accuracy rate is equal to or higher than that of the prior art. (2) Attention mechanism and CNN (ACNN) model uses the RNN with attention mechanism to obtain the context vector; Then CNN is used to extract more advanced feature information. The maximum pool operation is adopted to obtain a sentence vector; At last, the softmax classifier is used to classify the text. Experiments on eight text classification benchmark data sets show that ACNN improves the stability of model convergence, and can converge to an optimal or local optimal solution better than BRCNN.

Optimizing Secure Decision Tree Inference Outsourcing

Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on the proprietary decision tree of the model provider and the private input data of the client. In this paper, we design, implement, and evaluate a new system that allows highly efficient outsourcing of decision tree inference. Our system significantly improves upon prior art in the overall online end-to-end secure inference service latency at the cloud as well as the local-side performance of the model provider. We first present a new scheme which securely shifts most of the processing of the model provider to the cloud, resulting in a substantial reduction on the model provider&#x0027;s performance complexities. We further devise a scheme which substantially optimizes the performance for secure decision tree inference at the cloud, particularly the communication round complexities. The synergy of these techniques allows our new system to achieve up to <inline-formula><tex-math notation="LaTeX">$8 \times$</tex-math></inline-formula> better overall online end-to-end secure inference latency at the cloud side over realistic WAN environment, as well as bring the model provider up to <inline-formula><tex-math notation="LaTeX">$19 \times$</tex-math></inline-formula> savings in communication and <inline-formula><tex-math notation="LaTeX">$18 \times$</tex-math></inline-formula> savings in computation.

ReAFM: A Reconfigurable Nonlinear Activation Function Module for Neural Networks

Deep neural networks (DNNs) with various nonlinear activation functions (NAFs) have achieved unprecedented successes, sparking interest in efficient DNN hardware implementation. However, most existing NAF implementations focus on one type of functions with dedicated architectures, which is not suited for supporting versatile DNN accelerators. In this brief, based on a proposed reciprocal approximation optimization (RAO) method, an efficient reconfigurable nonlinear activation function module (ReAFM) is devised to implement various NAFs. The computational logic and dataflow of certain NAFs are merged and reused to minimize hardware consumption by leveraging the correlations among different NAFs. In addition, a precision adjustable exponential unit (PAEU) is developed to obtain a good tradeoff between the approximation accuracy and hardware cost. Compared to the prior art, the experimental results demonstrate that the proposed ReAFM can support many more NAF types with comparable or even better performance. Furthermore, evaluation results on some prevalent neural networks show that the proposed approximation method causes negligible accuracy loss (< 0.1%).

Virtual Service Embedding With Time-Varying Load and Provable Guarantees

Deploying services efficiently while satisfying their quality requirements is a major challenge in network slicing. Effective solutions place instances of the services' virtual network functions (VNFs) at different locations of the cellular infrastructure and manage such instances by scaling them as needed. In this work, we address the above problem and the very relevant aspect of sub-slice reuse among different services. Further, unlike prior art, we account for the services' finite lifetime and time-varying traffic load. We identify two major sources of inefficiency in service management: (i) the overspending of computing resources due to traffic of multiple services with different latency requirements being processed by the same virtual machine (VM), and (ii) the poor packing of traffic processing requests in the same VM, leading to opening more VMs than necessary. To cope with the above issues, we devise an algorithm, called REShare, that can dynamically adapt to the system's operational conditions and find an optimal trade-off between the aforementioned opposite requirements. We prove that REShare has low algorithmic complexity and is asymptotic 2-competitive under a non-decreasing load. Numerical results, leveraging real-world scenarios, show that our solution outperforms alternatives, swiftly adapting to time-varying conditions and reducing service cost by over 25%.

Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems

State-of-the-art order dispatching algorithms for ridesharing batch passenger requests and allocate them to a fleet of vehicles in a centralized manner, optimizing over the estimated values of each vehicle-passenger matching using integer linear programming. Using good estimates of future value, such ILP-based approaches are able to significantly increase the service rates (percentage of requests served) for a fixed fleet of vehicles. However, such approaches that focus solely on maximizing efficiency can lead to disparities for both drivers (e.g., income inequality) and passengers (e.g., inequality of service for different groups). Existing approaches that consider fairness only do it for naive assignment policies, require extensive training or look at only single-sided fairness. We propose a simple incentive-based fairness scheme (SI) that can be implemented online as a part of this ILP formulation that allows us to improve fairness over a variety of fairness metrics. Deriving from a lens of variance minimization, we describe how these fairness incentives can be formulated for two distinct use cases for passenger groups (SIP) and driver (SID) fairness. We show that under mild conditions, our approach can guarantee an improvement in the chosen metric for the worst-off individual. We also show empirically that our Simple Incentives approach significantly outperforms prior art, despite requiring no retraining; indeed, it often leads to a large improvement over the state-of-the-art fairness-aware approach in both overall service rate and fairness.

Grace Period Harmonization in the Open Innovation Era

Abstract This article urges European companies to discard their prejudices and work with their patent offices to adopt a grace period in the European Patent Convention (‘EPC’) in exchange for the United States’ reconciliation of the definition of the prior art, harmonizing the differences remaining after the America Invents Act (AIA). The negative view that European companies have taken of a grace period is contrary to the experience in Japan, where an unrestricted grace period helped large companies engage in open innovation and increase their use. Empirical studies have substantiated the negative impacts of the lack of a grace period on European SMEs and Universities. After highlighting the differences in the prior art under AIA and EPC, this article will conclude with a proposal for a 12-month, U.S.-style grace period to harmonize the patent systems in Paris Union member states. The adoption of this proposed grace period would result in a win-win outcome for both the United States and European countries.

Distributionally Robust Optimization with Probabilistic Group

Modern machine learning models may be susceptible to learning spurious correlations that hold on average but not for the atypical group of samples. To address the problem, previous approaches minimize the empirical worst-group risk. Despite the promise, they often assume that each sample belongs to one and only one group, which does not allow expressing the uncertainty in group labeling. In this paper, we propose a novel framework PG-DRO, which explores the idea of probabilistic group membership for distributionally robust optimization. Key to our framework, we consider soft group membership instead of hard group annotations. The group probabilities can be flexibly generated using either supervised learning or zero-shot approaches. Our framework accommodates samples with group membership ambiguity, offering stronger flexibility and generality than the prior art. We comprehensively evaluate PG-DRO on both image classification and natural language processing benchmarks, establishing superior performance.

Learning Continuous Depth Representation via Geometric Spatial Aggregator

Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision. While arbitrary scale DSR is a more realistic setting in this scenario, previous approaches predominantly suffer from the issue of inefficient real-numbered scale upsampling. To explicitly address this issue, we propose a novel continuous depth representation for DSR. The heart of this representation is our proposed Geometric Spatial Aggregator (GSA), which exploits a distance field modulated by arbitrarily upsampled target gridding, through which the geometric information is explicitly introduced into feature aggregation and target generation. Furthermore, bricking with GSA, we present a transformer-style backbone named GeoDSR, which possesses a principled way to construct the functional mapping between local coordinates and the high-resolution output results, empowering our model with the advantage of arbitrary shape transformation ready to help diverse zooming demand. Extensive experimental results on standard depth map benchmarks, e.g., NYU v2, have demonstrated that the proposed framework achieves significant restoration gain in arbitrary scale depth map super-resolution compared with the prior art. Our codes are available at https://github.com/nana01219/GeoDSR.

Infusing Definiteness into Randomness: Rethinking Composition Styles for Deep Image Matting

We study the composition style in deep image matting, a notion that characterizes a data generation flow on how to exploit limited foregrounds and random backgrounds to form a training dataset. Prior art executes this flow in a completely random manner by simply going through the foreground pool or by optionally combining two foregrounds before foreground-background composition. In this work, we first show that naive foreground combination can be problematic and therefore derive an alternative formulation to reasonably combine foregrounds. Our second contribution is an observation that matting performance can benefit from a certain occurrence frequency of combined foregrounds and their associated source foregrounds during training. Inspired by this, we introduce a novel composition style that binds the source and combined foregrounds in a definite triplet. In addition, we also find that different orders of foreground combination lead to different foreground patterns, which further inspires a quadruplet-based composition style. Results under controlled experiments on four matting baselines show that our composition styles outperform existing ones and invite consistent performance improvement on both composited and real-world datasets. Code is available at: https://github.com/coconuthust/composition_styles

Scalable Theory-Driven Regularization of Scene Graph Generation Models

Several techniques have recently aimed to improve the performance of deep learning models for Scene Graph Generation (SGG) by incorporating background knowledge. State-of-the-art techniques can be divided into two families: one where the background knowledge is incorporated into the model in a subsymbolic fashion, and another in which the background knowledge is maintained in symbolic form. Despite promising results, both families of techniques face several shortcomings: the first one requires ad-hoc, more complex neural architectures increasing the training or inference cost; the second one suffers from limited scalability w.r.t. the size of the background knowledge. Our work introduces a regularization technique for injecting symbolic background knowledge into neural SGG models that overcomes the limitations of prior art. Our technique is model-agnostic, does not incur any cost at inference time, and scales to previously unmanageable background knowledge sizes. We demonstrate that our technique can improve the accuracy of state-of-the-art SGG models, by up to 33%.

Are patent offices substitutes?

This paper evaluates whether and to what extent search and examination processes at patent offices are substitutes. Based on an original dataset comprising 4.884 patents filed simultaneously in Japan, the USA and Europe, the empirical analysis confirms that the degree of substitution is significant within the PCT framework. When patent offices have access to the search for prior art of other offices, they search into less technology classes, send fewer communications, or rely more on international citations. They also cite 12 % to 32 % less prior art. Further substitution could still be achieved, as 8 % to 27 % of remaining backward citations are duplicates -voluntarily or not - of prior work. Taken together, work reductions and overlaps witness a degree of substitution of about 37 % between the three patent offices.

Exploration of 3D motorcycle complexes from hexahedral meshes

Shape decompositions that are guided by a motorcycle graph endow topological properties that are relevant for many engineering applications, such as T-spline fitting, shape compression and structured mesh generation. While for the surface case this is a widely studied and well-established construction, the concept of motorcycle graph was lifted to volumes only recently (Brückler et al., 2021). Due to this recent introduction, the generation of volumetric motorcycle graphs that fulfill application dependent criteria, such as minimal number of blocks or high approximation capabilities, is still an open problem. In this article we study and compare two alternative approaches to the computation of volume shape decompositions guided by a motorcycle graph. The proposed methodologies are designed to optimize alternative application-dependent quality criteria and, overall, perform better than prior art in most of the cases.

Rethinking Label Flipping Attack: From Sample Masking to Sample Thresholding.

Nowadays, machine learning (ML) and deep learning (DL) methods have become fundamental building blocks for a wide range of AI applications. The popularity of these methods also makes them widely exposed to malicious attacks, which may cause severe security concerns. To understand the security properties of the ML/DL methods, researchers have recently started to turn their focus to adversarial attack algorithms that could successfully corrupt the model or clean data owned by the victim with imperceptible perturbations. In this paper, we study the Label Flipping Attack (LFA) problem, where the attacker expects to corrupt an ML/DL model's performance by flipping a small fraction of the labels in the training data. Prior art along this direction adopts combinatorial optimization problems, leading to limited scalability toward deep learning models. To this end, we propose a novel minimax problem which provides an efficient reformulation of the sample selection process in LFA. In the new optimization problem, the sample selection operation could be implemented with a single thresholding parameter. This leads to a novel training algorithm called Sample Thresholding. Since the objective function is differentiable and the model complexity does not depend on the sample size, we can apply Sample Thresholding to attack deep learning models. Moreover, since the victim's behavior is not predictable in a poisonous attack setting, we have to employ surrogate models to simulate the true model employed by the victim model. Seeing the problem, we provide a theoretical analysis of such a surrogate paradigm. Specifically, we show that the performance gap between the true model employed by the victim and the surrogate model is small under mild conditions. On top of this paradigm, we extend Sample Thresholding to the crowdsourced ranking task, where labels collected from the annotators are vulnerable to adversarial attacks. Finally, experimental analyses on three real-world datasets speak to the efficacy of our method.

TreeSensing: Linearly Compressing Sketches with Flexibility

A Sketch is an excellent probabilistic data structure, which records the approximate statistics of data streams. Linear additivity is an important property of sketches. This paper studies how to keep the linear property after sketch compression. Most existing compression methods do not keep the linear property. We propose TreeSensing, an accurate, efficient, and flexible framework to linearly compress sketches. In TreeSensing, we first separate a sketch into two parts according to counter values. For the sketch with small counters, we propose a technique called TreeEncoding to compress it into a hierarchical structure. For the sketch with large counters, we propose a technique called SketchSensing to compress it using compressive sensing. We theoretically analyze the accuracy of TreeSensing. We use TreeSensing to compress 7 sketches and conduct two end-to-end experiments: distributed measurement and distributed machine learning. Experimental results show that TreeSensing outperforms prior art on both accuracy and efficiency, which achieves up to 100× smaller error and 5.1× higher speed than state-of-the-art Cluster-Reduce. All related codes are open-sourced.

A method to reduce false positives in a patent query

The aim of this paper is to present a method that allows researchers and analysts to reduce the number of false positives in a patent query. Patents are not only used for prior art searches but increasingly for competitive analyses and the analysis of the evolution of technology. When these cases focus on specific technological domains, non-experts will aim to identify patents related to their focus technology. In certain cases, this can require complex queries to contain thousands of patents. It then becomes difficult to identify false positives. We present a method that allows researchers and analysts to refine their queries on large datasets.

RIS-Aided mmWave MIMO Channel Estimation Using Deep Learning and Compressive Sensing

Reconfigurable intelligent surface (RIS) assisted wireless systems require accurate channel state information (CSI) to control wireless channels and improve both the bandwidth and energy efficiency. However, CSI acquisition is non-trivial for two reasons: 1) the passive nature of RIS does not allow transceiving and processing pilot signals, and 2) the dimensions of the cascaded channel between transceivers increases with the large number of RIS elements, which yields high training overhead and computational complexity. While prior art has mainly focused on frequency-flat channel estimation, this paper proposes novel data-driven and compressive sensing based approaches for estimating both frequency-flat and frequency-selective cascaded channels of RIS-assisted multi-user millimeter-wave large multiple input multiple output (MIMO) systems with limited training overhead. The proposed methods exploit the common sparsity property among the different subcarriers and the double-structured sparsity property of the angular cascaded channel matrices as different angular cascaded channels observed by different users share completely common non-zero rows and user-specific column supports. The proposed data-driven cascaded channel estimation approaches use denoising neural networks to accurately detect channel supports. Alternatively, when data-training capabilities are not available, the compressive sensing based orthogonal matching pursuit (OMP) approach relies on sparsity properties and applies simultaneous OMP to detect the channel supports. Simulation results show that the pilot overhead required by the proposed scheme is lower than existing schemes. When compared to other OMP approaches that achieve an NMSE gap of 5 to 6 dB with respect to the Oracle least square lower bound, the proposed algorithms reduce the lower bound gap to only 1 dB, while reducing complexity by more than two orders of magnitude.