Nec Laboratories America, Inc Patent Portfolio Statistics

Nec Laboratories America, Inc.

Profile Summary

This article summarizes the perfomance of the assignee in the recent years. The overall statistics for this portfolio help to analyze the areas where the assignee is performing well. The filing trend, perfomance across the tech centers and the perfomance of the recent applications has been mentioned below. All the stats are calculated based on the perfomance in USPTO.

How does the overall patent portfolio of Nec Laboratories America, Inc. look like?

Total Applications: 1,809
Granted Patents: 1,301
Grant Index 82.45 %
Abandoned/Rejected Applications: 277 (17.55%)
In-Process Applications: 224
Average Grant Time: 2.58 Years
Average Office Actions: 1.1

Which Technology Area Nec Laboratories America, Inc. is filing most patents in? (Last 10 years)

Art Unit Definition Total Applications
Opap Parked GAU 133
2636 Digital and Optical Communications 106
2637 Digital and Optical Communications 51
2613 Computer Graphic Processing, 3D Animation, Display Color Attribute, Object Processing, Hardware and Memory 43
2129 AI & Simulation/Modeling 40

How many patents are Nec Laboratories America, Inc. filing every year?

Year Total Applications
2022 0*
2021 110*
2020 115
2019 91
2018 143

*The drop in the number of applications filed in last two years compared to previous years is because applications can take up to 18 months to get published

Recently filed patent applications of Nec Laboratories America, Inc. in USPTO?

Publication number: US20220028068A1
Application number: 17/380,207

Methods and systems for training a machine learning model include generating pairs of training pixel patches from a dataset of training images, each pair including a first patch representing a part of a respective training image, and a second patch, centered at the same location as the first, representing a larger part of the training image, being resized to a same size of as the first patch. A detection model is trained using the first pixel patches, to detect and locate cells in the images. A classification model is trained using the first pixel patches, to classify cells according to whether the detected cells are cancerous, based on cell location information generated by the detection model. A segmentation model is trained using the second pixel patches, to locate and classify cancerous arrangements of cells in the images.

Publication date: 2022-01-27
Applicant: Nec Laboratories America, Inc.
Inventors: Cosatto Eric

Publication number: US20220019892A1
Application number: 17/379,078

A method for training a predictive model includes training a dual-channel neural network model, which includes a static channel to process static information and a dynamic channel to process temporal information, to generate a probability score that characterizes a likelihood of a health event occurring during a dialysis procedure, based on static profile information and temporal measurement information. An augmented model is trained to generate an importance score associated with the probability score, based on the static profile information and the temporal measurement information.

Publication date: 2022-01-20
Applicant: Nec Laboratories America, Inc.
Inventors: Cheng Wei

Publication number: US20220012274A1
Application number: 17/370,498

Methods and systems of training and using a neural network model include training a time series embedding model and a text embedding model with unsupervised clustering to translate time series and text, respectively, to a shared latent space. The time series embedding model and the text embedding model are further trained using semi-supervised clustering that samples training data pairs of time series information and associated text for annotation.

Publication date: 2022-01-13
Applicant: Nec Laboratories America, Inc.
Inventors: Mizoguchi Takehiko

How are Nec Laboratories America, Inc.’s applications performing in USPTO?

Application Number Title Status Art Unit Examiner
17/380,207 Multi-Scale Tumor Cell Detection And Classification OPAP Central, Docket
17/379,078 Dialysis Event Prediction OPAP Central, Docket
17/370,498 Embedding Multi-Modal Time Series And Text Data OPAP Central, Docket
17/364,125 Compact Representation And Time Series Segment Retrieval Through Deep Learning OPAP Central, Docket
17/358,260 Approach To Determining A Remaining Useful Life Of A System Docketed New Case – Ready for Examination OPAP Central, Docket