International Business Machines Corporation patent applications on August 29th, 2024
Patent Applications by International Business Machines Corporation on August 29th, 2024
International Business Machines Corporation: 37 patent applications
International Business Machines Corporation has applied for patents in the areas of G06N3/08 (4), H01L21/768 (3), H01L23/528 (3), G06F16/35 (2), H01L23/522 (2) G06N3/08 (2), G06N3/084 (2), G06N20/00 (1), G06Q10/08355 (1), G08G1/164 (1)
With keywords such as: computer, data, based, code, processor, embodiment, output, key, between, and include in patent application abstracts.
Patent Applications by International Business Machines Corporation
Inventor(s): JAMES MCDONAGH of Frodsham (GB) for international business machines corporation, Benjamin Hardy Wunsch of Mt. Kisco NY (US) for international business machines corporation, Theodore G. van Kessel of Millbrook NY (US) for international business machines corporation, FLAVIU CIPCIGAN of Warrington (GB) for international business machines corporation
IPC Code(s): B01D53/14, B01D53/18, B01D53/62, B01D53/78, B01D53/96
CPC Code(s): B01D53/1493
Abstract: a composition for carbon dioxide (co2) capture includes 2-aminocyclohexanol, cis-1,2-diaminocyclohexane, meglumine, or combinations thereof. a process of cocapture includes mixing a co-containing gas with the composition. a system includes a component for receiving a gas and the composition, which absorbs cofrom the gas. a further composition includes a first amine solvent selected from 2-aminocyclohexanol, cis-1,2-diaminocyclohexane, and meglumine and a second amine solvent independently selected from 2-aminocyclohexanol, cis-1,2-diaminocyclohexane, meglumine, ethyl diethanolamine, dimethylethanolamine, piperidine, 2-amino-2-methyl-1-propanol, and monoethanolamine. a further process includes reacting cowith an amine selected from 2-aminocyclohexanol, cis-1,2-diaminocyclohexane, and meglumine.
Inventor(s): YANG LIANG of Beijing (CN) for international business machines corporation, Su Liu of Austin TX (US) for international business machines corporation
IPC Code(s): B60W40/08, G06F3/01
CPC Code(s): B60W40/08
Abstract: a method for proactively assisting a driver to avoid road driving risks. the method detects, in real-time, a current driving status of a driver in a vehicle. the method further learns a risk type of the driver based on driving history data and creates a corresponding action pattern for the learned risk type of the driver. the method further determines whether the vehicle is about to encounter a dangerous event and assists the driver to avoid the dangerous event using real-time physiological stimulation, wherein real-time physiological stimulation comprises electric muscle stimulation (ems) and galvanic vestibular stimulation (gvs) signals. the method maps the ems and gvs signals to muscles of the driver related to the corresponding action pattern necessary to avoid the dangerous event.
Inventor(s): Daniel WADDINGTON of Morgan Hill CA (US) for international business machines corporation, Saransh GUPTA of San Jose CA (US) for international business machines corporation
IPC Code(s): G06F3/06
CPC Code(s): G06F3/0613
Abstract: embodiments of the present disclosure provide an enhanced system and methods for optimizing data placement in a memory hierarchy. a disclosed non-limiting computer-implemented method configures a counter block comprising access frequency counters mapped into an application memory space, and configures a counter map, where each entry in the counter map associates an application-defined memory region with the access frequency counters of the counter block. a memory controller identifies a memory access in a given application-defined memory region and compares an access address with a mask in the counter map to track the memory access. the memory controller generates a heatmap representing a frequency count of accesses to quantized memory using the access frequency counters. generating the heatmap is performed by memory controller hardware.
Inventor(s): Charan Acharya Chandrashekar of Bangalore (IN) for international business machines corporation, Srikkanth R Kulkarni of Bengaluru (IN) for international business machines corporation, Shrinidhi Kulkarni of Bengaluru (IN) for international business machines corporation
IPC Code(s): G06F3/06
CPC Code(s): G06F3/064
Abstract: a computer-implemented method, a system, and a computer program product for cloud object storage format conversion are provided. a processor identifies a target object format and an object storage bucket in a cos environment. the processor determines a function associated with objects in the storage bucket. the processor generates a conversion policy that includes converting a first format of an object to a target format, based on the function, the object storage bucket, the first format, and input from a user. the processor receives a request to perform the function on a object having a first format and an object storage bucket corresponding to performing the function on the object, and the processor triggering conversion of the object having the first format to the target format within the cloud environment, based on the conversion policy for the object storage bucket.
Inventor(s): Mu Qiao of San Jose CA (US) for international business machines corporation, Martin G. Keen of Durham NC (US) for international business machines corporation, Jeremy R. Fox of Austin TX (US) for international business machines corporation
IPC Code(s): G06F8/71, G06F8/73
CPC Code(s): G06F8/71
Abstract: a context aware code snippet recommendation method, system, and computer program product that includes generating comments for source code, learning semantically meaningful sentence embeddings based on the comments, and retrieving code snippets from the source code based on the learned semantically meaningful sentence embeddings.
Inventor(s): Julian Timothy Dolby of Bronx NY (US) for international business machines corporation, Kiran A. Kate of Chappaqua NY (US) for international business machines corporation, Martin Hirzel of Chappaqua NY (US) for international business machines corporation, Jason Tsay of White Plains NY (US) for international business machines corporation, Kavitha Srinivas of Port Chester NY (US) for international business machines corporation
IPC Code(s): G06F8/72, G06F8/41, G06N3/08
CPC Code(s): G06F8/72
Abstract: a computer-implemented method, system and computer program product for refactoring code using a machine learning model. parallel corpora is generated using a single directional code transform. a single directional code transform refers to a transformation performed by a refactoring tool which refactors computer code (“code”) to restructure the code to include styles, which are often undesirable, such as “for” loops. parallel corpora refers to a collection of code of a first style of code (e.g., dictionary comprehensions) in the code prior to refactoring (non-refactored code) and a second style of code (e.g., “for” loops) in the refactored code. a machine learning model is then trained to perform code refactoring in a reverse direction of the single directional code transform using the parallel corpora. new computer code is then refactored using the trained machine learning model, where the refactored code now includes a desired style of code (e.g., dictionary comprehensions).
Inventor(s): Hiroshi Itagaki of Yokohama-shi (JP) for international business machines corporation, Tohru Hasegawa of Tokyo (JP) for international business machines corporation, Atsushi Abe of Ebina (JP) for international business machines corporation, Tsuyoshi Miyamura of Yokohama-shi (JP) for international business machines corporation, Noriko Yamamoto of Tokyo (JP) for international business machines corporation, Shinsuke Mitsuma of Machida-shi (JP) for international business machines corporation
IPC Code(s): G06F11/07
CPC Code(s): G06F11/0769
Abstract: in one general embodiment, a method includes receiving, at a data storage device that performs data operations on magnetic media, a command indicating a read range of records to read. a determination is made as to whether one or more of the records within the read range is expected to cause an error. in response to determining that one or more of the records within the read range is known to cause an error, an indication indicative of the record known to cause the error is returned.
20240289260. TEST CASE GENERATION_simplified_abstract_(international business machines corporation)
Inventor(s): Vijay Arya of Gurgaon (IN) for international business machines corporation, Diptikalyan Saha of Bangalore (IN) for international business machines corporation, Devika Sondhi of Noida (IN) for international business machines corporation, Kahini Wadhawan of Ferozepur (IN) for international business machines corporation
IPC Code(s): G06F11/36, G06N3/08
CPC Code(s): G06F11/3684
Abstract: a processor can receive input data to a computer code and output data that the computer code produces corresponding to the input data. based on the input data and the output data, the processor can build a decision tree that links input conditions to the output data. using the input conditions, the processor can run a condition generator model created by fine-tuning a pre-trained programming language model, where the condition generator model predicts next condition that is likely to occur in the computer code. based on the next condition, the processor can generate input instances and run the computer code using the generated input instances. based on output instances output by the computer code based on the generated input instances, the processor can detect at least one flow path in the computer code, which had not been previously built in the decision tree.
Inventor(s): Vaibhav Sudhakar Dantale of Pune (IN) for international business machines corporation, Ashwini Ramani of Bangalore (IN) for international business machines corporation
IPC Code(s): G06F16/21, G06F16/25
CPC Code(s): G06F16/214
Abstract: an embodiment includes generating source inventory data representative of information technology assets associated with a source site, the source site comprising information technology infrastructure of an enterprise, the information technology assets comprising a source application database and a source peripheral entity associated with the source application database. the embodiment determines a target platform for migrating the source application database and determining a degree of compatibility between the source peripheral entity and the target platform. the embodiment creates a migration plan defining characteristics of a migration of the source application database from the source site to the target platform, wherein the target platform is selected from among a plurality of candidate target platforms based at least in part on the degree of compatibility between the source peripheral entity and the target platform.
Inventor(s): Aldo Guzman Saenz of White Plains NY (US) for international business machines corporation, Aritra Bose of White Plains NY (US) for international business machines corporation, Daniel Enoch Platt of Putnam Valley NY (US) for international business machines corporation, Laxmi Parida of Mohegan Lake NY (US) for international business machines corporation, Niina Haiminen of Tampere (FI) for international business machines corporation
IPC Code(s): G06F16/248, G06F16/2455
CPC Code(s): G06F16/248
Abstract: a cumulant-based network analysis visualizer (cunaviz) includes an interactive dashboard with a user interface and a display that allows a user to query a network of multi-modal biomarkers for phenotypes associated with a complex disease and to visualize answers to the queries as subgraphs. the subgraphs include highlighted nodes and edges where the highlighted nodes represent the multi-modal biomarkers from the network that are associated with the queried phenotypes for the complex disease and the highlighted edges represent the interactions between the multi-modal biomarkers that are associated with the queried phenotypes for the complex disease. the cunaviz allows a user to identify important multi-modal biomarkers and neighborhoods of multi-modal biomarkers specific to a complex disease.
Inventor(s): Marcos Martínez Galindo of Dublin (IE) for international business machines corporation, Leopold Fuchs of Stuttgart (DE) for international business machines corporation, Gabriele Picco of Dublin (IE) for international business machines corporation, Thanh Lam Hoang of Maynooth (IE) for international business machines corporation, Vanessa Lopez Garcia of Dublin (IE) for international business machines corporation, Marco Luca Sbodio of Castaheany (IE) for international business machines corporation
IPC Code(s): G06F16/35, G06F16/31, G06F40/30
CPC Code(s): G06F16/35
Abstract: automatically enriching the descriptions of an entity mentioned in a sentence corpus includes generating multiple enriched descriptions corresponding to a label of the entity. each of the multiple enriched descriptions is ranked. the ranking is generated by a machine learning model that is configured to determine a likelihood that an enriched description correctly describes the entity. the sentence corpus are annotated by coupling each mention of the entity with one or more of the enriched descriptions. the one or more enriched descriptions are selected based on the ranking. as annotated, the sentence corpus can be output.
Inventor(s): Roy BAR-HAIM of Ramat Gan (IL) for international business machines corporation, Arie CATTAN of Jerusalem (IL) for international business machines corporation, Lilach EDEN of Haifa (IL) for international business machines corporation, Yoav KANTOR of Haifa (IL) for international business machines corporation
IPC Code(s): G06F16/35, G06F16/332
CPC Code(s): G06F16/358
Abstract: an example system includes a processor to execute a key point analysis to extract key points from a number of input sentences. the processor can compute directional relation scores between the extracted key points. the processor can construct a key point graph based on the extracted key points and the directional relation scores. the processor can generate a key point graph visualization based on the key point graph.
Inventor(s): Bharat Sukhwani of Sudbury MA (US) for international business machines corporation, Sameh Asaad of Briarcliff Manor NY (US) for international business machines corporation, Mohit Kapur of Westchester NY (US) for international business machines corporation
IPC Code(s): G06F16/901, H04L45/7453
CPC Code(s): G06F16/9014
Abstract: a computer-implemented method, system and computer program product for minimizing hash collisions of composite keys. each component (“key component”) of the composite key is converted into a linear index that uniquely represents that key component. such a one-to-one mapping reduces the length (i.e., range of values the key component represents) of each component without introducing any collisions thereby effectively resulting in the lossless compression of the individual key components. such a linear index for each converted key component is stored in a separate lookup memory. a reduced composite key is then created by joining together the linear indices that represent the components of the composite key thereby reducing the total length or domain (i.e., range of values the composite key represents) of the original composite key in a lossless manner. in this manner, by reducing the total length of the composite key, hash collisions of composite keys are minimized.
Inventor(s): Louis P. GOMES of Poughkeepsie NY (US) for international business machines corporation, David Harold SURMAN of Milton NY (US) for international business machines corporation, Peter Jeremy RELSON of Ulster Park NY (US) for international business machines corporation, Seth E. LEDERER of Staatsburg NY (US) for international business machines corporation, Thomas MATHIAS of Vestal NY (US) for international business machines corporation
IPC Code(s): G06F21/44, G06F21/62
CPC Code(s): G06F21/44
Abstract: an instruction to be executed within the computing environment is obtained. the instruction includes an operation code indicating a diagnose operation. the instruction is executed. the executing includes obtaining a token as an input to the instruction and comparing the token to a current token for a configuration of the computing environment. based on the token matching the current token, one or more verification certificates are returned from a certificate store.
Inventor(s): Shachee Mishra Gupta of Gurgaon (IN) for international business machines corporation, Ashok Pon Kumar Sree Prakash of BANGALORE (IN) for international business machines corporation, Abhishek Jain of PUNE (IN) for international business machines corporation
IPC Code(s): G06F21/57, G06F8/61, G06F21/55
CPC Code(s): G06F21/577
Abstract: an example operation may include one or more of determining that a software artifact hosted by a pod within a cluster of a host platform contains code that is a security vulnerability, identifying a function within the software artifact that requests the code, activating a filter within the pod of the host platform which prevents execution of the function within the software artifact, and installing a fix for the security vulnerability within the software artifact.
Inventor(s): TERRI VANESSA DILLON of COSTA MESA CA (US) for international business machines corporation, JEREMY R. FOX of AUSTIN TX (US) for international business machines corporation, ANDREW R. JONES of AUSTIN TX (US) for international business machines corporation, RAGHUVEER PRASAD NAGAR of BANGALORE (IN) for international business machines corporation
IPC Code(s): G06F40/35, H04L51/21
CPC Code(s): G06F40/35
Abstract: according to the computer-implemented method, an active communication between multiple users is identified. a context of the active communication is determined. historical communications are searched for a similar context to the context of the active communication. the searched historical communications are analyzed for digital content that is relevant to the context. a recommendation is generated to use the digital content in the active communication based on the similar context.
Inventor(s): Masayasu Muraoka of Tokyo (JP) for international business machines corporation, Yang Zhao of Tokyo (JP) for international business machines corporation
IPC Code(s): G06F40/40, G06F40/20
CPC Code(s): G06F40/40
Abstract: techniques for fine-tuning free evaluation of large language models with enhanced interpretability using a debiased output probability distribution of a large language model and a probability distribution of a k-nearest neighbor search result are provided. in one aspect, a method for performing a downstream task with a language model includes: constructing a datastore by applying the language model to a training set; applying the language model to a prompt-applied sentence from a testing set to obtain a language model feature vector; performing a k-nearest neighbor search of the datastore using the language model feature vector as a query vector; and interpolating a probability distribution of results from the k-nearest neighbor search and an output probability distribution of the language model to obtain a prediction for the downstream task.
Inventor(s): Irem Boybat Kara of Adliswil (CH) for international business machines corporation, Hadjer Benmeziane of AULNOY-LEZ-VALENCIENNES (FR) for international business machines corporation, Manuel Le Gallo-Bourdeau of Horgen (CH) for international business machines corporation, Kaoutar El Maghraoui of Yorktown Heights NY (US) for international business machines corporation, Malte Johannes Rasch of Chappaqua NY (US) for international business machines corporation, HsinYu Tsai of Cupertino CA (US) for international business machines corporation
IPC Code(s): G06N3/08
CPC Code(s): G06N3/08
Abstract: a method, computer program product, and system to generate a processor design via a deep neural network is provided. a processor selects an architecture search space and a hardware components space. a processor selects an initial deep neural network from the architecture search space. a processor determines an initial current chip design for executing the current deep neural network, wherein the initial chip design has a hardware performance metric for implementing the current deep neural network. a processor repeatedly executes an optimization method, the optimization method comprising modifying the chip design one or more times using components from the hardware components space and optimizing the current deep neural network by selecting a deep neural network from the architecture search space. a processor provides the optimized chip design and the specific deep neural network for performing the machine learning task.
Inventor(s): Amadou Ba of Navan (IE) for international business machines corporation, Bradley Eck of Dublin (IE) for international business machines corporation, Duygu Kabakci Zorlu of Dublin (IE) for international business machines corporation
IPC Code(s): G06N3/08, G06N3/045
CPC Code(s): G06N3/08
Abstract: disclosed embodiments provide techniques for automated drift detection in multidimensional data. disclosed embodiments utilize deep learning to estimate high-density regions of multidimensional, multivariate, and/or multimodal data and combine the results with hypothesis testing. a hypothesis of drift or no drift is tested using a mathematical test, and if drift is detected, mitigation actions, including retraining are performed, to enable the continuation of reliable, high-quality results from the neural network.
Inventor(s): Vagner Figueredo de Santana of São Paulo (BR) for international business machines corporation, Larissa Monteiro da Fonseca Galeno of Niterói (BR) for international business machines corporation, Emilio Ashton Vital Brazil of Rio de Janeiro (BR) for international business machines corporation, Renato Fontoura de Gusmao Cerqueira of Rio de Janeiro (BR) for international business machines corporation
IPC Code(s): G06N3/084
CPC Code(s): G06N3/084
Abstract: in a first aspect of the invention, there is a computer-implemented method including: generate, by a processor set, an interaction usage graph based on user interaction data on user interactions with an analytical system user interface; generate, by the processor set, an interaction embedding model in a vector space based on the interaction usage graph; determine, by the processor set and based on the interaction embedding model in the vector space, a similarity of a portion of the interaction embedding model that corresponds to a particular analytical task among the user interactions with the analytical system user interface with a particular machine learning model from a set of one or more machine learning models; and output, by the processor set, to the analytical system user interface, an indication of the particular machine learning model.
Inventor(s): Ron SHMELKIN of Givatayim (IL) for international business machines corporation, Abigail GOLDSTEEN of Haifa (IL) for international business machines corporation, Ariel FARKASH of Shimshit (IL) for international business machines corporation, Tal ZOHAR of Haifa (IL) for international business machines corporation
IPC Code(s): G06N3/084
CPC Code(s): G06N3/084
Abstract: provided are a computer program product, system, and method for editing a target model to forget data samples. forget data samples of data samples to forget are inputted into a reference model, trained on a non-private data set, to produce reference output. the forget data samples to forget are inputted to a target model, trained on a total data set comprising the non-private data set and a private data set, to produce target output. the private data set includes the forget data samples a loss function is calculated to measure a divergence of the reference output and the target output. a determination is made of gradients that minimize an error of the loss function. optimized gradients are calculated from the determined gradients. the optimized gradients are applied to update weights in the target model to produce an edited target model.
Inventor(s): Naoto Sato of Kawasaki-shi (JP) for international business machines corporation
IPC Code(s): G06N5/047
CPC Code(s): G06N5/047
Abstract: a method of determining causal relations includes receiving a set of events, a selected event and a request to determine whether the set of events has a causal relation with the selected event, receiving a causality collection including a plurality of causality pairs, and receiving an entailment collection including a plurality of entailment pairs, each entailment pair including a first event and a second event. a first group of events is selected from the causality and entailment collections, and the first group of events is formulated as a first domain-specific algebraic structure. the method further includes selecting a second group of events from the causality and entailment collections, formulating the second group of events as a second domain-specific algebraic structure, and determining a causal relation between the set of events and the selected event based on a relation between the first structure and the second structure.
Inventor(s): Francesco Fusco of Zurich (CH) for international business machines corporation, Diego Matteo Antognini of Ruvigliana (CH) for international business machines corporation
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: according to one embodiment, a method and computer program product for generating a model including a term encoder is provided. the embodiment may include training the model on a training dataset that associates training terms with first embeddings of the training terms. the training includes generating, with the term encoder, second embeddings from numerical representations of word subunits of the training terms with an objective of minimizing distances between the first embeddings and the second embeddings. the word subunits form part of a predetermined set of word subunits. the training includes predicting confidence scores based on the minimized distances. the embodiment may include deploying the model as part of an executable algorithm to allow a user to infer third embeddings and corresponding confidence scores from any input terms written based on word subunits of the predetermined set.
Inventor(s): Shoichiro Watanabe of Tokyo (JP) for international business machines corporation, Yasutaka Nishimura of Yamato-shi (JP) for international business machines corporation, Kenichi Takasaki of Tokyo (JP) for international business machines corporation, Sanehiro Furuichi of Tokyo (JP) for international business machines corporation, Mari Abe Fukuda of Tokyo (JP) for international business machines corporation
IPC Code(s): G06Q10/0835
CPC Code(s): G06Q10/08355
Abstract: a system, method, and computer program product receive a request for delivery of a package from an origin to a destination with a desired degree of information disclosure using the physical internet. a first carrier for a first leg of the delivery is determined based on the origin and an area coverage of the first carrier. the first carrier is not provided destination information as dictated by the desired degree of information disclosure. a candidate list of other carriers for transporting the package in one or more subsequent legs is identified based on the historical performances of each of the other carriers and the desired degree of information disclosure. a final carrier from the candidate list is determined for a final leg of the delivery. the destination information is disclosed to the final carrier.
Inventor(s): Michael S. Gordon of Croton on Hudson NY (US) for international business machines corporation, Kevin W. Brew of Niskayuna NY (US) for international business machines corporation, Mattias Fitzpatrick of Mount Kisco NY (US) for international business machines corporation, Matthew Beck of Danbury CT (US) for international business machines corporation, Brian Paul Gaucher of Brookfield CT (US) for international business machines corporation
IPC Code(s): G08G1/16, B60W50/14, B60W60/00, G06V20/56, G06V20/58, G08G1/056
CPC Code(s): G08G1/164
Abstract: according to one embodiment, a method, computer system, and computer program product for wrong way driving detection is provided. the embodiment may include monitoring indications of wrong way traversal along a traversal path through one or more on-board, internet of things (iot) sensors. the embodiment may also include determining whether wrong way traversal of the traversal path is occurring based on a risk assessment value satisfying a preconfigured threshold. the embodiment may further include, in response to determining the risk assessment value satisfies the preconfigured threshold, performing a corrective action to ameliorate the wrong way traversal.
Inventor(s): Paul R. Bastide of Ashland MA (US) for international business machines corporation, SAMIR NASSER of Durham NC (US) for international business machines corporation
IPC Code(s): G16H40/63
CPC Code(s): G16H40/63
Abstract: according to one embodiment, a method, computer system, and computer program product for biomarker prioritization is provided. the embodiment may include collecting data from a medical device associated with a user. the embodiment may also include identifying, for each type of data, at least one biomarker generated by the medical device associated with the user. the embodiment may further include associating each biomarker with a concerned component and/or a medical model. the embodiment may also include assigning a biomarker priority to a related component. the embodiment may further include generating a schedule for resource processing based on the biomarker priority. the embodiment may also include executing the schedule for resource processing.
Inventor(s): Tao Li of Slingerlands NY (US) for international business machines corporation, Richard C. Johnson of Selkirk NY (US) for international business machines corporation, Kisik Choi of Watervliet NY (US) for international business machines corporation, Ruilong Xie of Niskayuna NY (US) for international business machines corporation
IPC Code(s): H01L21/768, H01L23/528, H01L29/06, H01L29/66
CPC Code(s): H01L21/76897
Abstract: according to the embodiment of the present invention, a semiconductor device includes a first nanodevice and a second nanodevice. the second nanodevice is adjacent to and parallel to the first nanodevice along an x-axis. a dielectric fill is located between the first nanodevice and the second nanodevice. a dielectric liner is comprised of a first dielectric liner and a second dielectric liner. the first dielectric liner is located between the first nanodevice and the dielectric fill. the second dielectric liner is located between the second nanodevice and the dielectric fill. a plurality of self-aligned contact (sac) caps is comprised of a head section and a shaft section. the head section extends a first width parallel to the x-axis and is in direct contact with a frontside of the dielectric liner. the shaft section extends a second width parallel to the x-axis. the first width is greater than the second width.
Inventor(s): Tsung-Sheng Kang of Ballston Lake NY (US) for international business machines corporation, Tao Li of Slingerlands NY (US) for international business machines corporation, Ruilong Xie of Niskayuna NY (US) for international business machines corporation, Daniel Charles Edelstein of White Plains NY (US) for international business machines corporation
IPC Code(s): H01L23/48, H01L21/768, H01L23/522, H01L23/528
CPC Code(s): H01L23/481
Abstract: a semiconductor ic device includes a super via. the super via includes a lower skip via and an upper skip via. a connecting wire may be directly between or may separate the lower skip via and the upper skip via. the upper skip via may be adequately electrically isolated from a surrounding wire by an upper liner. the lower skip via may be adequately electrically isolated from a surrounding wire by a lower liner. the super via along with the connecting wire may connect a wire in the lowest wiring level with a wire in the highest wiring level. because of the lower liner and/or the upper liner, the super via may be utilized for wiring and routing through a backside level of the semiconductor ic device (e.g., potential or signal routing) and be utilized as a heat transfer conduit through the backside of the semiconductor ic device.
Inventor(s): Nicholas Anthony Lanzillo of Wynantskill NY (US) for international business machines corporation, Albert M. Chu of Nashua NH (US) for international business machines corporation, Ruilong Xie of Niskayuna NY (US) for international business machines corporation, REINALDO VEGA of Mahopac NY (US) for international business machines corporation, Lawrence A. Clevenger of Saratoga Springs NY (US) for international business machines corporation, Brent A. Anderson of Jericho VT (US) for international business machines corporation
IPC Code(s): H01L23/528, H01L21/768, H01L23/522
CPC Code(s): H01L23/5283
Abstract: embodiments of present invention provide a semiconductor structure. the semiconductor structure includes a metal level, the metal level includes a metal strip including a notch at a side of the metal strip or a pass-through inside the metal strip, wherein the notch or the pass-through is at least partially filled with a dielectric material. the metal level further includes a conductive wiring that vertically passes through the metal strip. the conductive wiring is at least partially inside the notch or inside the pass-through and is insulated from the metal strip by the dielectric material. methods of manufacturing the semiconductor structure are also provided.
Inventor(s): Sathyanarayanan Raghavan of Ballston Lake NY (US) for international business machines corporation, Mukta Ghate Farooq of HOPEWELL JCT NY (US) for international business machines corporation, Prabudhya Roy Chowdhury of Albany NY (US) for international business machines corporation
IPC Code(s): H01L23/00, H01L21/304, H01L23/053, H01L25/065
CPC Code(s): H01L23/562
Abstract: a semiconductor structure for tailoring a die stiffness. the semiconductor structure may include a packaging substrate, a lid, and a first semiconductor die between the packaging substrate and the lid. the first semiconductor die may have a frontside attached to the packaging substrate that has semiconductor devices. the first semiconductor die may also have a backside, opposite the frontside, that has grooves less than a thickness of the first semiconductor die.
Inventor(s): Julien Frougier of ALBANY NY (US) for international business machines corporation, Nicolas Jean Loubet of GUILDERLAND NY (US) for international business machines corporation, Andrew M. Greene of Slingerlands NY (US) for international business machines corporation, Andrew Gaul of Halfmoon NY (US) for international business machines corporation, Ruilong Xie of Niskayuna NY (US) for international business machines corporation, Shogo Mochizuki of Mechanicville NY (US) for international business machines corporation, Curtis S. Durfee of Schenectady NY (US) for international business machines corporation, Eric Miller of ALBANY NY (US) for international business machines corporation, Ronald Newhart of Lebanon PA (US) for international business machines corporation, Choudhury Mahboob Ellahi of Halfmoon NY (US) for international business machines corporation, Anthony I. Chou of Guilderland NY (US) for international business machines corporation, Susan Ng Emans of ALBANY NY (US) for international business machines corporation
IPC Code(s): H01L29/423, H01L29/06, H01L29/66, H01L29/775, H01L29/786
CPC Code(s): H01L29/42392
Abstract: a semiconductor structure includes a substrate and a gate-all-around field effect transistor disposed over the substrate. the gate-all-around field effect transistor includes a first source-drain region; a second source-drain region; at least one channel region interconnecting the first and second source drain regions; and a gate structure surrounding the at least one channel region. a self-aligned substrate isolation (sasi) layer is located between the substrate and the gate structure and extends over a width of the gate structure.
Inventor(s): Giulio Zizzo of Dublin (IE) for international business machines corporation, Stefano Braghin of Dublin (IE) for international business machines corporation, Ambrish Rawat of Dublin (IE) for international business machines corporation, Mark Purcell of Naas (IE) for international business machines corporation
IPC Code(s): H04L9/00, G06N3/098
CPC Code(s): H04L9/008
Abstract: a computer-implemented method, system and computer program product for verifying the trustworthiness of an aggregation scheme utilized by an aggregator in the federated learning technique. a bit mask is received from each client used for training a machine learning algorithm using the federated learning technique. such a bit mask contains values of ones and zeros, where a value of one indicates that the updated parameter of the global model corresponds to a parameter used by the local model trained on the client and a value of zero indicates that is not the case. these bit masks, which are encrypted, may then be combined using a homomorphic additive encryption scheme into a mask containing a matrix of values. if the mask contains a matrix of values of only the value of one, then the aggregator is deemed to be trustworthy. otherwise, the aggregator is deemed to be untrustworthy.
Inventor(s): Allon ADIR of Kiryat Tivon (IL) for international business machines corporation, Ramy MASALHA of Kafr Qari (IL) for international business machines corporation, Eyal KUSHNIR of Kfar Vradim (IL) for international business machines corporation, Ehud AHARONI of Kfar Saba (IL) for international business machines corporation, Omri SOCEANU of Haifa (IL) for international business machines corporation
IPC Code(s): H04L9/30, H04L9/00
CPC Code(s): H04L9/3026
Abstract: an example system can include a processor to receive a ciphertext including a fully homomorphic encrypted (fhe) time series from a client device. the processor can train an arima model on the ciphertext using an estimated error and approximating polynomials. the processor can generate an encrypted report and send the encrypted report to the client device.
Inventor(s): Avirup Saha of Kolkata (IN) for international business machines corporation, Neelamadhav Gantayat of Bangalore (IN) for international business machines corporation, Renuka Sindhgatta Rajan of Bengaluru (IN) for international business machines corporation, Ravi Shankar Arunachalam of Bangalore (IN) for international business machines corporation, Geomy George of Thrissur (IN) for international business machines corporation, SAMPATH DECHU of Acton MA (US) for international business machines corporation
IPC Code(s): H04L41/147, H04L41/16
CPC Code(s): H04L41/147
Abstract: mechanisms are provided for forecasting information technology (it) and environmental impacts on key performance indicators (kpis). machine learning (ml) computer model(s) are trained on historical data representing events and kpis of organizational processes (ops). the ml computer model(s) forecast kpi impact given events. correlation graph data structure(s) are generated that map at least one of events to it computing resources, or kpi impacts to ops. a unified model is trained to model ops and it resources. the trained ml computer model(s) and unified model process input data to generate a forecast output that specifies at least one of a forecasted it event or a kpi impact. the forecasted output is correlated with at least one of it computing resource(s) or op(s), at least by applying the correlation graph data structure(s) to the forecast output to generate a correlation output. a remedial action recommendation that comprises a resource allocation is generated based on the forecast output and correlation output.
Inventor(s): Rakesh Jain of San Jose CA (US) for international business machines corporation, Sandeep Gopisetty of Morgan Hill CA (US) for international business machines corporation
IPC Code(s): H04L45/76, H04L45/74
CPC Code(s): H04L45/76
Abstract: one or more computer processors route one or more packets within an application that comprises a plurality of pods distributed in a multi-cloud environment. the one or more computer processors deploy one or more created proxies as one or more sidecar containers for each pod in the plurality of pods, wherein the sidecar containers run with an application container. the one or more computer processors apply a set of routing rules to each pod in the plurality of pods, wherein all traffic is routed between the one or more created proxies based on the set of routing rules.
Inventor(s): Juan F. Vargas of Cary NC (US) for international business machines corporation, Mark E. Maresh of Oro Valley AZ (US) for international business machines corporation
IPC Code(s): H04L9/40, G06K19/06
CPC Code(s): H04L63/1483
Abstract: cybersecurity is provided for a transaction initiated by a matrix barcode using a smart device connected to a computer network by a device having a computer receiving a reading of a matrix barcode on an item. the matrix barcode electronically pointing to a url (uniform resource locator) for a web-based resource accessible using the device via a computer network. using the computer of the device or a service computer, determining information parameters about the matrix barcode, and determining when the url is identified as a registered service provider in a knowledge corpus communicating with the service computer. the url can be validated as registered, when the url is identified as corresponding to the registered service provider and based on the information parameters.
Inventor(s): Oded Sofer of Midreshet Cen Gurion (IL) for international business machines corporation, Guy Galil of Jerusalem (IL) for international business machines corporation
IPC Code(s): H04L9/40
CPC Code(s): H04L63/1491
Abstract: a computer-implemented method comprising: automatically monitoring a honeypot trap environment, to capture activity data within the honeypot trap environment, wherein the honeypot trap environment comprises a plurality of software and hardware resources that are intended to attract attempts at unauthorized use of the honeypot trap environment; automatically extracting, from the captured activity data, a plurality of attributes representing entities, events, and relations between the entities and events; automatically applying an analytics suite to identify specific combinations of the attributes as representing a likelihood of being associated with an unauthorized intrusion attempt into the honeypot environment; automatically assigning a risk score to each of the specific combinations, wherein the risk score reflect the likelihood of being associated with an unauthorized intrusion attempt into the honeypot environment; and automatically generating at least one security rule for an intrusion detection and prevention system, based on at least one of the specific combinations.
International Business Machines Corporation patent applications on August 29th, 2024
- International Business Machines Corporation
- B01D53/14
- B01D53/18
- B01D53/62
- B01D53/78
- B01D53/96
- CPC B01D53/1493
- International business machines corporation
- B60W40/08
- G06F3/01
- CPC B60W40/08
- G06F3/06
- CPC G06F3/0613
- CPC G06F3/064
- G06F8/71
- G06F8/73
- CPC G06F8/71
- G06F8/72
- G06F8/41
- G06N3/08
- CPC G06F8/72
- G06F11/07
- CPC G06F11/0769
- G06F11/36
- CPC G06F11/3684
- G06F16/21
- G06F16/25
- CPC G06F16/214
- G06F16/248
- G06F16/2455
- CPC G06F16/248
- G06F16/35
- G06F16/31
- G06F40/30
- CPC G06F16/35
- G06F16/332
- CPC G06F16/358
- G06F16/901
- H04L45/7453
- CPC G06F16/9014
- G06F21/44
- G06F21/62
- CPC G06F21/44
- G06F21/57
- G06F8/61
- G06F21/55
- CPC G06F21/577
- G06F40/35
- H04L51/21
- CPC G06F40/35
- G06F40/40
- G06F40/20
- CPC G06F40/40
- CPC G06N3/08
- G06N3/045
- G06N3/084
- CPC G06N3/084
- G06N5/047
- CPC G06N5/047
- G06N20/00
- CPC G06N20/00
- G06Q10/0835
- CPC G06Q10/08355
- G08G1/16
- B60W50/14
- B60W60/00
- G06V20/56
- G06V20/58
- G08G1/056
- CPC G08G1/164
- G16H40/63
- CPC G16H40/63
- H01L21/768
- H01L23/528
- H01L29/06
- H01L29/66
- CPC H01L21/76897
- H01L23/48
- H01L23/522
- CPC H01L23/481
- CPC H01L23/5283
- H01L23/00
- H01L21/304
- H01L23/053
- H01L25/065
- CPC H01L23/562
- H01L29/423
- H01L29/775
- H01L29/786
- CPC H01L29/42392
- H04L9/00
- G06N3/098
- CPC H04L9/008
- H04L9/30
- CPC H04L9/3026
- H04L41/147
- H04L41/16
- CPC H04L41/147
- H04L45/76
- H04L45/74
- CPC H04L45/76
- H04L9/40
- G06K19/06
- CPC H04L63/1483
- CPC H04L63/1491
