Patents by Inventor Aveek N. Chatterjee
Aveek N. Chatterjee has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 10418505Abstract: A method including installing solar pods at varying heights on a tower, where a size of each of the solar pods is inversely related its installation height on the tower, each of the solar pods including a transparent ovoid enclosure symmetrical about an axis, and a reflector and a solar cell both contained within the transparent ovoid enclosure, the solar cell positioned at a common focal point of the reflector such that substantially all light reflected by the reflector is directed at the solar cell.Type: GrantFiled: May 18, 2017Date of Patent: September 17, 2019Assignee: International Business Machines CorporationInventors: Aveek N. Chatterjee, Kota V. R. M. Murali, Ninad D. Sathaye, Rajesh Sathiyanarayanan
-
Patent number: 10102477Abstract: A method, computer program product, and system for generating flavor profile models are provided. An alpha flavor model is determined. An electrode signal is transmitted to a taste simulator based on the alpha flavor model. User feedback is received. A variance between the alpha flavor model and the user feedback is determined. The variance is compared to a predetermined threshold. In response to determining that the variance is greater than the predetermined threshold, a beta flavor model is generated.Type: GrantFiled: May 26, 2016Date of Patent: October 16, 2018Assignee: International Business Machines CorporationInventors: Siddique M. Adoni, Aveek N. Chatterjee, Dhandapani Shanmugam
-
Patent number: 10095979Abstract: A method, computer program product, and system for generating flavor profile models are provided. An alpha flavor model is determined. An electrode signal is transmitted to a taste simulator based on the alpha flavor model. User feedback is received. A variance between the alpha flavor model and the user feedback is determined. The variance is compared to a predetermined threshold. In response to determining that the variance is greater than the predetermined threshold, a beta flavor model is generated.Type: GrantFiled: July 6, 2015Date of Patent: October 9, 2018Assignee: International Business Machines CorporationInventors: Siddique M. Adoni, Aveek N. Chatterjee, Dhandapani Shanmugam
-
Publication number: 20170256664Abstract: A method including installing solar pods at varying heights on a tower, where a size of each of the solar pods is inversely related its installation height on the tower, each of the solar pods including a transparent ovoid enclosure symmetrical about an axis, and a reflector and a solar cell both contained within the transparent ovoid enclosure, the solar cell positioned at a common focal point of the reflector such that substantially all light reflected by the reflector is directed at the solar cell.Type: ApplicationFiled: May 18, 2017Publication date: September 7, 2017Inventors: Aveek N. Chatterjee, Kota V.R.M. Murali, Ninad D. Sathaye, Rajesh Sathiyanarayanan
-
Patent number: 9705021Abstract: A solar pod system, comprising of an oval transparent enclosure. The oval transparent enclosure encapsulates a circular paraboloidal reflector mounted on solar cell. The solar cell extends over the circular parabolic reflector to place the focus of the paraboloidal reflector on the solar cell, whereby the solar cell receives light reflected by the circular parabolic reflector.Type: GrantFiled: October 30, 2014Date of Patent: July 11, 2017Assignee: International Business Machines CorporationInventors: Aveek N. Chatterjee, Kota V. R. M. Murali, Ninad D. Sathaye, Rajesh Sathiyanarayanan
-
Patent number: 9654414Abstract: A method for scheduling cost efficient data center load distribution is described. The method includes receiving a task to be performed by computing resources within a set of data centers. The method further includes determining, all available data centers to perform the task. The method further includes determining lowest computing cost task schedule from available data centers. The method further includes scheduling the task to be completed at an available data center with the lowest cost computing.Type: GrantFiled: September 18, 2014Date of Patent: May 16, 2017Assignee: International Business Machines CorporationInventors: Aveek N. Chatterjee, Hendrik F. Hamann, Shankar Km, Siyuan Lu, Kota V. R. M. Murali
-
Publication number: 20170011145Abstract: A method, computer program product, and system for generating flavor profile models are provided. An alpha flavor model is determined. An electrode signal is transmitted to a taste simulator based on the alpha flavor model. User feedback is received. A variance between the alpha flavor model and the user feedback is determined. The variance is compared to a predetermined threshold. In response to determining that the variance is greater than the predetermined threshold, a beta flavor model is generated.Type: ApplicationFiled: May 26, 2016Publication date: January 12, 2017Inventors: Siddique M. Adoni, Aveek N. Chatterjee, Dhandapani Shanmugam
-
Publication number: 20170011164Abstract: A method, computer program product, and system for generating flavor profile models are provided. An alpha flavor model is determined. An electrode signal is transmitted to a taste simulator based on the alpha flavor model. User feedback is received. A variance between the alpha flavor model and the user feedback is determined. The variance is compared to a predetermined threshold. In response to determining that the variance is greater than the predetermined threshold, a beta flavor model is generated.Type: ApplicationFiled: July 6, 2015Publication date: January 12, 2017Inventors: Siddique M. Adoni, Aveek N. Chatterjee, Dhandapani Shanmugam
-
Patent number: 9530092Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.Type: GrantFiled: November 10, 2015Date of Patent: December 27, 2016Assignee: International Business Machines CorporationInventors: Aveek N. Chatterjee, Siddique M. Adoni, Dhandapani Shanmugam
-
Patent number: 9385087Abstract: Various embodiments include resistor structures. Particular embodiments include a resistor structure having multiple oxide layers, at least one of which includes a modified oxide. The modified oxide can aid in controlling the thermal capacitance and the thermal time constant of the resistor structure, or the thermal dissipation within the resistor structure.Type: GrantFiled: October 18, 2013Date of Patent: July 5, 2016Assignee: GLOBALFOUNDRIES INC.Inventors: Debarsi Chakraborty, Aveek N. Chatterjee
-
Publication number: 20160126393Abstract: A solar pod system, comprising of an oval transparent enclosure. The oval transparent enclosure encapsulates a circular paraboloidal reflector mounted on solar cell. The solar cell extends over the circular parabolic reflector to place the focus of the paraboloidal reflector on the solar cell, whereby the solar cell receives light reflected by the circular parabolic reflector.Type: ApplicationFiled: October 30, 2014Publication date: May 5, 2016Inventors: Aveek N. Chatterjee, Kota V. R. M. Murali, Ninad D. Sathaye, Rajesh Sathiyanarayanan
-
Publication number: 20160087909Abstract: A method for scheduling cost efficient data center load distribution is described. The method includes receiving a task to be performed by computing resources within a set of data centers. The method further includes determining, all available data centers to perform the task. The method further includes determining lowest computing cost task schedule from available data centers. The method further includes scheduling the task to be completed at an available data center with the lowest cost computing.Type: ApplicationFiled: September 18, 2014Publication date: March 24, 2016Inventors: Aveek N. Chatterjee, Hendrik F. Hamann, Shankar Km, Siyuan Lu, Kota V. R. M. Murali
-
Publication number: 20160063373Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.Type: ApplicationFiled: November 10, 2015Publication date: March 3, 2016Inventors: Aveek N. Chatterjee, Siddique M. Adoni, Dhandapani Shanmugam
-
Patent number: 9230208Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.Type: GrantFiled: December 18, 2013Date of Patent: January 5, 2016Assignee: International Business Machines CorporationInventors: Aveek N. Chatterjee, Siddique M. Adoni, Dhandapani Shanmugam
-
Patent number: 9218565Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.Type: GrantFiled: June 19, 2014Date of Patent: December 22, 2015Assignee: International Business Machines CorporationInventors: Aveek N. Chatterjee, Siddique M. Adoni, Dhandapani Shanmugam
-
Publication number: 20150170024Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.Type: ApplicationFiled: June 19, 2014Publication date: June 18, 2015Inventors: Aveek N. Chatterjee, Siddique M. Adoni, Dhandapani Shanmugam
-
Publication number: 20150170023Abstract: In a method for training an artificial neural network based algorithm designed to monitor a first device, a processor receives a first data. A processor determines a first service action recommendation for a first device using the received first data and an artificial neural network (ANN) algorithm. A processor causes a second device to provide haptic feedback using the received first data. A processor receives a second service action recommendation for the first device based on the haptic feedback. A processor adjusts at least one parameter of the ANN algorithm such that the ANN algorithm determines a third service action recommendation for the first device using the received first data, wherein the third service action recommendation is equivalent to the second service action recommendation.Type: ApplicationFiled: December 18, 2013Publication date: June 18, 2015Applicant: International Business Machines CorporationInventors: Aveek N. Chatterjee, Siddique M. Adoni, Dhandapani Shanmugam
-
Publication number: 20150108608Abstract: Various embodiments include resistor structures. Particular embodiments include a resistor structure having multiple oxide layers, at least one of which includes a modified oxide. The modified oxide can aid in controlling the thermal capacitance and the thermal time constant of the resistor structure, or the thermal dissipation within the resistor structure.Type: ApplicationFiled: October 18, 2013Publication date: April 23, 2015Applicant: International Business Machines CorporationInventors: Debarsi Chakraborty, Aveek N. Chatterjee