2021 BRN Discussion, page-33478

Hi all,

Firstly, wishing all a safe New Year. Thanks to all the valued contributors on HC.

I have full confidence and look forward to an explosive 2022!

I’m currently indisposed and in front of a desktop instead of my iphone so I thought I might do a bit of research. There are a lot of patents concerning Neural Networks. Numerous companies have some including Google, Intel, IBM, VERITON, SONY, SONOS. We’re not alone in our quest but also indicates we are on a prosperous path if we can maintain a lead in commercialisation.

We’re aware of a link with TATA and BRAINCHIP. No guarantees we’re gonna be used in them all but here are several TATA NN patents:

There’s been numerous discussions of Fridges telling us when our food goes off:

APPARATUS AND METHOD FOR MULTIMODAL SENSING AND MONITORING OF PERISHABLE COMMODITIES

The system 100 stores the health model in the memory 101, wherein the health model is a combination of a plurality of machine learning algorithms. In an embodiment, the health model includes at least one Convolution Neural network (CNN) model. The at least one CNN model is trained with a plurality of time series images to extract a plurality of vector representations which represent degradation in a plurality of perishable commodities, further wherein the vector representation of the time series images are annotated as representing a degree of freshness level.

https://patents.justia.com/patent/20210405009 Oct 25, 2019

I read this one and still not sure what it does?

NEURAL NETWORKS FOR HANDLING VARIABLE-DIMENSIONAL TIME SERIES DATA

Several applications capture data from sensors resulting in multi-sensor time series. Existing neural networks-based approaches for such multi-sensor/multivariate time series modeling assume fixed input-dimension/number of sensors. Such approaches can struggle in practical setting where different instances of same device/equipment come with different combinations of installed sensors. In the present disclosure, neural network models are trained from such multi-sensor time series having varying input dimensionality, owing to availability/installation of different sensors subset at each source of time series.

Neural network (NN) architecture is provided for zero-shot transfer learning allowing robust inference for multivariate time series with previously unseen combination of available dimensions/sensors at test time. Such combinatorial generalization is achieved by conditioning layers of core NN-based time series model with “conditioning vector” carrying information of available sensors combination for each time series and is obtained by summarizing learned “sensor embedding vectors set” corresponding to available sensors in time series

https://patents.justia.com/patent/20210406603 Feb 22, 2021

This appears to be document scanning and interpreting:

Method and system to resolve ambiguities in regulations

To disambiguate a given regulatory sentence the method augments the regulation sentence with relevant internal information extracted using a set of predefined linguistic patterns and relevant external information extracted from external sources identified using a Neural Network (NN) model.

https://patents.justia.com/patent/11120221 Mar 26, 2019

Akida BALLISTA