Hitachi, Ltd.

Alcatel Lucent

Joydeep Acharya

Sudhanshu Gaur

Long Gao

Supriya Sathyanarayana

Harish Viswanathan

Yusaku Otsuka

Toshiki Shimizu

Satoshi Katsunuma

Hidenori Omiya

Sivarama Venkatesan

Yusuke Shomura

Takahiro Ohira

Ravneet Kaur

Jeremy Ostergaard

Rahul Bharadwaj

Salam Akoum

Andrew Walker

Systems and methods described herein can involve in response to receiving, at a system, a set of images associated with a first area of interest, processing the set of images to generate a first image description; communicating the first image description and the set of images to a user device to enable the user device to locate a second area of interest; in response to the user navigating to the second area of interest, using a positioning system and capturing a new image by pointing a camera in any direction, obtaining the new image at the system and processing the new image; obtaining a second image description associated with the new image; comparing the first image description with the second image description to obtain a comparison result; and based on the comparison result, communicating to the user device instructions for adjusting the camera.

Interactive method to guide a human to a precise location based on a wider image of the area around the location

A method for computing and detecting image data drift. The method may include retrieving first segment information of a plurality of segments from a drift database; receiving a number of images from a sensor; partitioning each of the received images into segments of a predetermined number; generating second segment information; computing drift in values between the first segment information and the second segment information; and detecting drift based on the computed drift in values by combining the computed drift in segments to generate overall drift, and comparing the overall drift against a drift threshold.

Method to compute drift in image data before ml model inference

A method for object defect detection. The method may include receiving an object on a production line; computing, by a processor, a motion optimized path for a robot arm, wherein the motion optimized path comprises a path for performing a sequence of rotations by the robot arm on the object for image capturing; using the robot arm to grasp the object and moving the robot arm according to the motion optimized path to rotate the object based on the sequence of rotations; capturing, by a camera, a plurality of images of the object while the object is being rotated; performing, by the processor, defect detection on the plurality of images of the object to determine object defect; and for object defect being detected, issuing, by the processor, a defect notification to an operator of the production line.

Method for motion optimized defect inspection by a robotic arm using prior knowledge from PLM and maintenance systems

Example implementations described herein involve systems and methods that involve recognizing, from sensor data, an area from the plurality of areas and a candidate task from the one or more candidate tasks associated with the area; estimating a probability of each of the plurality of candidate tasks for the each of the plurality of areas for a specific future period of time, based on referencing historical data of task sequences previously executed; accepting the ones of the plurality of candidate tasks for the each of the plurality of areas having the probability being higher than a threshold; and scheduling one or more sensors to activate and transmit in the specific future period of time in associated areas for the plurality of areas associated with other ones of the plurality of candidate tasks for the each of the plurality of areas not having the probability being higher than the threshold.

Method to operate multiple sensors for multiple instances of interconnected human activity recognition

A method for generating query response comprising receiving, by a processor, an input query from a first user; performing, by the processor, query parsing on the input query to generate a parsed query; determining, by the processor, a query type associated with the parsed query; for the query type being determined as initial query, performing: generating, by the processor, a first follow-up query to the input query based on the parsed query, and generating, by the processor, responses to the parsed query and the first follow-up query; and for the query type being determined as follow-up query to a previous query entered by a second user, performing: performing, by the processor, learning of a query sequence from the previous query to the input query, and generating, by the processor, responses to the parsed query and a second follow-up query.

Query based method to derive insight about manufacturing operations

Example implementations described herein involve systems and methods to substantially simultaneously orchestrate machine learning models over multiple resource constrained control edge devices, so that the overall system is more agile to changes in events and environmental conditions where the models have been deployed. The example implementations described herein involve multiple processes that when executed, determine a list of edge devices to be updated along with the corresponding models based on correlation.

System to invoke update of machine learning models on edge computers

A method for generating and displaying context-based information. The method may include automatically identifying, by a processor, a person; identifying, by the processor, a display device closest in proximity to the person; generating, by the processor, context information specific to the person; and displaying the context information on the display device to be viewed by the person.

Context-based information displays

Example implementations described herein involve systems and methods to select machine learning models that will be executed in a cellular Mobile Edge Computing for cellular enabled applications. In contrast to related art implementations, the example implementations described herein considers different data fidelities of received data due to the cellular wireless channel and also performs service resource allocation accordingly.

Optimizing machine learning as-a-service performance for cellular communication systems

Example implementations described herein can dynamically adapt to changing nature of sensor data traffic and through artificial intelligence (AI, strike a good tradeoff between reducing volume of sensed data, and retain enough data fidelity so that subsequent analytics applications perform well. The example implementations eliminate heuristic methods of setting sensing parameters (such as DAQ sampling rate, resolution etc.) and replaces them with an automated, AI driven edge solution core that can be readily ported on any Internet of Things (IoT) edge gateway that is connected to the DAQ.

AI enabled sensor data acquisition

Example implementations described herein are directed to systems and methods for non-invasive data extraction from digital displays. In an example implementation, a method includes receiving one or more video frames from a video capture device capturing an external display, where the external display is independent the video capture device; determining one or more locations within the external display comprising time varying data of the external display; and for each identified location of the time varying data: determining a data type; applying one or more rules based on the data type; and determining an accuracy of the time varying data within the one or more frames based on the rules.

Non-invasive data extraction from digital displays

This invention aim to improves the flexibility of data flows management from sensor to cloud, datalake or other system, which can manage the overall data flows within the system and control them dynamically. As a result, it can reduce transmission cost and storage cost properly.

Method to manage data flow for edge-core distributed analytics systems

In some examples, a computing device may determine a prediction of a network outage of a network. The computing device may determine a priority of one or more data types expected to be received during the network outage. Further, the computing device may determine a latency category of the one or more data types expected to be received during the network outage. The computing device may store a data transmission rule for the one or more data types at least partially based on the priority and the latency category. The computing device may receive, from one or more data generators, during the network outage, data for transmission to the network. The computing device may transmit at least some of the received data to the network at least partially based on the data transmission rule.

Edge processing for data transmission

Example implementations described herein are directed to a system involving a cloud architecture and an edge architecture associated with one or more vehicles. The edge architecture can involve devices associated with the one or more vehicles and can conduct edge processing to determine, from Global Positioning Satellite (GPS) information, a proximity of the first apparatus to a first Geographic Information System (GIS) waypoint relative to a second GIS waypoint, generate index information representative of the proximity of the apparatus to the first GIS waypoint relative to the second GIS waypoint; and transmit the index information to the cloud architecture.

GIS based compression and reconstruction of GPS data for transmission from a vehicular edge platform to the cloud

Example implementations described herein are directed to reducing the MME signaling overhead associated with paging and tracking area update procedures and also conserves UE battery life. This can be realized by using non-network data and applying predictive analytics algorithms to track the most probable locations of an IDLE UE, which may increase the overall efficiency of the network. In example implementations, MME signaling overhead associated with paging and TA update procedures may also be reduced, which can also conserve UE battery life.

Predictive analytics for location estimation of idle UEs in a cellular network

A communications system includes a macro base station, a plurality of UEs (user equipment), a plurality of small cells, and a network through which the macro base station, the UEs, and the small cells communicate with each other, the small cells within a macro coverage area of the macro base station. The macro base station comprises a processor, a memory, and a small cell on/off module which is operable, for each small cell of the plurality of small cells, to: determine an interference metric for the small cell; if the determined interference metric meets a preset condition for the small cell, then determine a loss in signal strength to the UEs associated with the small cell caused by switching off the small cell; and judge whether to switch off the small cell based on at least one of the determined interference metric or the determined loss in signal strength.

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Sunnyvale, CA, United States of America

Joydeep Acharya