Patents by Inventor Babak Hodjat

Babak Hodjat 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).

  • Publication number: 20180322395
    Abstract: Roughly described, in an evolutionary technique for finding optimal solutions to a provided problem, a computer system uses a grouping algorithm that is better able to find diverse and optimum solutions in data mining environment with multiple solution landscapes and a plurality of candidate individuals. Each candidate individual identifies with a potential solution, and is associated with a testing experience level and one or more partition tags. Each candidate individual is assigned into one of a plurality of competition groups in dependence upon the individual's testing experience level and partition tag. During competition among candidate individuals, a candidate individual can only replace another candidate individual if both the candidate individuals have a common partition tag and are in the same competition group. A candidate individual cannot replace another candidate individual if they have different partition tags or are in different competition groups.
    Type: Application
    Filed: July 17, 2018
    Publication date: November 8, 2018
    Applicant: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Hormoz Shahrzad, Babak Hodjat
  • Publication number: 20180260713
    Abstract: The technology disclosed proposes a novel asynchronous evaluation strategy (AES) that increases throughput of evolutionary algorithms by continuously maintaining a queue of K individuals ready to be sent to the worker nodes for evaluation and evolving the next generation once a fraction Mi of the K individuals have been evaluated by the worker nodes, where Mi<<K. A suitable value for Mi is determined experimentally, balancing diversity and efficiency. The technology disclosed is extended to coevolution of deep neural network supermodules and blueprints in the form of AES for cooperative evolution of deep neural networks (CoDeepNEAT-AES). Applied to image captioning domain, a threefold speedup is observed on 200 graphics processing unit (GPU) worker nodes, demonstrating that the disclosed AES and CoDeepNEAT-AES are promising techniques for evolving complex systems with long and variable evaluation times.
    Type: Application
    Filed: March 7, 2018
    Publication date: September 13, 2018
    Applicant: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Jason Zhi LIANG, Hormoz SHAHRZAD, Babak HODJAT, Risto MIIKKULAINEN
  • Patent number: 10025700
    Abstract: Roughly described, a training database contains N segments of data samples. Candidate individuals identify a testing experience level, a fitness estimate, a rule set, and a testing set TSi of the data samples on which it is tested. The testing sets have fewer than all of the data segments and they are not all the same. Testing involves testing on only the individual's assigned set of data segments, updating the fitness estimates and testing experience levels, and discarding candidates through competition. If an individual reaches a predetermined maturity level of testing experience, then validating involves further testing it on samples of the testing data from a testing data segment other than those in the individual's testing set TSi. Those individuals that satisfy validation criteria are considered for deployment.
    Type: Grant
    Filed: March 15, 2016
    Date of Patent: July 17, 2018
    Assignee: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Hormoz Shahrzad, Kaivan Kamali, Babak Hodjat, Daniel Edward Fink
  • Publication number: 20180113977
    Abstract: Roughly described, a computer-implemented evolutionary data mining system implements a genetic algorithm. The Genetic algorithm includes a requirements checkpoint, which selects individuals for discarding from the pool of candidate genomes which do not meet a predetermined minimum behavioral requirement for operating in production. The requirements checkpoint enforces an absolute minimum threshold for a behavioral characteristic of the individual, and is different from a competition step in which individuals are selected for removal on the basis of comparisons with each other. A requirements checkpoint may be inserted at various points within the genetic algorithm flow or at reasonable intervals during the training cycle. If at any of these checkpoints the minimum requirement is not met, the candidate individual may be removed from the candidate pool.
    Type: Application
    Filed: October 23, 2017
    Publication date: April 26, 2018
    Applicant: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Babak Hodjat, Hormoz Shahrzad
  • Publication number: 20180114118
    Abstract: Roughly described, a problem solving platform distributes the solving of the problem over a evolvable individuals, each of which also evolves its own pool of actors. The actors have the ability to contribute collaboratively to a solution at the level of the individual, instead of each actor being a candidate for the full solution. Populations evolve both at the level of the individual and at the level of actors within an individual. In an embodiment, an individual defines parameters according to which its population of actors can evolve. The individual is fixed prior to deployment to a production environment, but its actors can continue to evolve and adapt while operating in the production environment. Thus a goal of the evolutionary process at the level of individuals is to find populations of actors that can sustain themselves and survive, solving a dynamic problem for a given domain as a consequence.
    Type: Application
    Filed: December 21, 2017
    Publication date: April 26, 2018
    Applicant: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Babak Hodjat, Hormoz Shahrzad
  • Publication number: 20180083996
    Abstract: Roughly described, anomalous behavior of a machine-learned computer-implemented individual can be detected while operating in a production environment. A population of individuals is represented in a computer storage medium, each individual identifying actions to assert in dependence upon input data. As part of machine learning, the individuals are tested against samples of training data and the actions they assert are recorded in a behavior repository. The behavior of an individual is characterized from the observations recorded during training. In a production environment, the individuals are operated by applying production input data, and the production behavior of the individual is observed and compared to the behavior of the individual represented in the behavior repository. A determination is made from the comparison of whether the individual's production behavior during operation is anomalous.
    Type: Application
    Filed: September 20, 2017
    Publication date: March 22, 2018
    Applicant: Sentient Technologies (Barbados) Limited
    Inventor: Babak HODJAT
  • Publication number: 20170323219
    Abstract: Roughly described, an evolutionary data mining system includes at least two processing units, each having a pool of candidate individuals in which each candidate individual has a fitness estimate and experience level. A first processing unit tests candidate individuals against training data, updates an individual's experience level, and assigns each candidate to one of multiple layers of the candidate pool based on the individual's experience level. Individuals within the same layer of the same pool compete with each other to remain candidates. The first processing unit selects a set of candidates to retain based on the relative novelty of their responses to the training data. The first processing unit reports successful individuals to the second processing unit, and receives individuals for further testing from the second processing unit. The second processing unit selects individuals to retain based on their fitness estimate.
    Type: Application
    Filed: May 4, 2016
    Publication date: November 9, 2017
    Applicant: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Hormoz Shahrzad, Babak Hodjat, Risto Miikkulainen
  • Publication number: 20170293849
    Abstract: In many environments, rules are trained on historical data to predict an outcome likely to be associated with new data. Described is a ruleset which predicts the probability of a particular outcome. Roughly described, an individual identifies a ruleset, where each of the rules has a plurality of conditions and also indicates a rule-level probability of a predetermined classification. The conditions indicate a relationship (e.g. ‘<’ or ‘!<’) between an input feature and a corresponding value. The rules are evaluated against input data to derive a certainty for each condition, and aggregated to a rule-level certainty. The rule probabilities are combined using the rule-level certainty values to derive a probability output for the ruleset, which can be used to provide a basis for decisions. In an embodiment, the per-condition certainty values are fuzzy values aggregated by fuzzy logic. A novel genetic algorithm can be used to derive the ruleset.
    Type: Application
    Filed: April 7, 2017
    Publication date: October 12, 2017
    Inventors: Babak Hodjat, Hormoz Shahrzad
  • Patent number: 9734215
    Abstract: Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database in which each candidate individual has a respective fitness estimate; a gene pool processor which tests individuals from the candidate gene pool on training data and updates the fitness estimate associated with the individuals in dependence upon the tests; and a gene harvesting module providing for deployment selected ones of the individuals from the gene pool, wherein the gene pool processor includes a competition module which selects individuals for discarding from the gene pool in dependence upon both their updated fitness estimate and their testing experience level. Preferably the gene database has an elitist pool containing multiple experience layers, and the competition module causes individuals to compete only with other individuals in their same experience layer.
    Type: Grant
    Filed: June 10, 2016
    Date of Patent: August 15, 2017
    Assignee: Sentient Technologies (Barbados) Limited
    Inventors: Babak Hodjat, Hormoz Shahrzad, Greg S. Hornby
  • Patent number: 9710764
    Abstract: Roughly described, individuals in both a training system and in a production system include a label field in their rule outputs. Positions entered by an individual are maintained in a status record for the individual, including the label output by the rule which triggered entry of that position. Rules that assert exiting or partial exiting of a position also output the label from the rule which triggered the assertion, and are effective only so far as matching positions exist or remain in the individual's status record, including a matching label. Labels present in the status record also can be referenced in conditions of a rule. During evolution, a rule's output label is subject to crossover and/or mutation just like the conditions and output assertions.
    Type: Grant
    Filed: March 13, 2014
    Date of Patent: July 18, 2017
    Assignee: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Babak Hodjat, Hormoz Shahrzad, Kaivan Kamali, Daniel E. Fink
  • Patent number: 9684875
    Abstract: Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database in which each candidate individual has a respective fitness estimate; a gene pool processor which tests individuals from the candidate gene pool on training data and updates the fitness estimate associated with the individuals in dependence upon the tests; and a gene harvesting module providing for deployment selected ones of the individuals from the gene pool, wherein the gene pool processor includes a competition module which selects individuals for discarding from the gene pool in dependence upon both their updated fitness estimate and their testing experience level. Preferably the gene database has an elitist pool containing multiple experience layers, and the competition module causes individuals to compete only with other individuals in their same experience layer.
    Type: Grant
    Filed: November 12, 2014
    Date of Patent: June 20, 2017
    Assignee: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Babak Hodjat, Hormoz Shahrzad, Greg S. Hornby
  • Patent number: 9466023
    Abstract: Roughly described, a data mining arrangement for developing high quality classifiers using an evolutionary algorithm, includes a plurality of “mid-chain” evolutionary coordinators, down-chain of a main (top-chain) evolutionary coordinator and up-chain of evolutionary engines. Multiple levels of mid-chain evolutionary coordinators can be used in a hierarchy, and the various branches of the hierarchy need not have equal length. Each evolutionary coordinator (other than the top-chain evolutionary coordinator) appears to its up-chain neighbor as if it were an evolutionary engine, though it does not actually perform any evolution itself. Similarly, each evolutionary coordinator (including the top-chain evolutionary coordinator) also appears to its down-chain neighbors as a top-chain evolutionary coordinator. Each mid-chain evolutionary coordinator maintains its own local candidate pool, reducing the load on the top-chain evolutionary coordinator pool, as well as reducing bandwidth requirements.
    Type: Grant
    Filed: August 27, 2013
    Date of Patent: October 11, 2016
    Assignee: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Hormoz Shahrzad, Babak Hodjat
  • Publication number: 20160283563
    Abstract: Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database in which each candidate individual has a respective fitness estimate; a gene pool processor which tests individuals from the candidate gene pool on training data and updates the fitness estimate associated with the individuals in dependence upon the tests; and a gene harvesting module providing for deployment selected ones of the individuals from the gene pool, wherein the gene pool processor includes a competition module which selects individuals for discarding from the gene pool in dependence upon both their updated fitness estimate and their testing experience level. Preferably the gene database has an elitist pool containing multiple experience layers, and the competition module causes individuals to compete only with other individuals in their same experience layer.
    Type: Application
    Filed: June 10, 2016
    Publication date: September 29, 2016
    Applicant: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Babak Hodjat, Hormoz Shahrzad, Greg S. Hornby
  • Patent number: 9367816
    Abstract: Roughly described, a data mining system for use in evolving individuals based on data samples in a training database. The individuals have a plurality of conditions and, for each of a plurality of the data items in a sample, output an action which depends upon application of the individual's conditions to the data item. The conditions include a state of the individual, and the actions include at least one which affects the state of the individual if asserted. Each candidate individual further has a fitness estimate which the system updates in dependence upon the testing results. Testing involves applying the conditions of the individual to data items of the samples to assert actions, and for a subset of at least one but less than all of the data items to which the conditions are applied, modifying the asserted action for the data items in the subset.
    Type: Grant
    Filed: July 16, 2013
    Date of Patent: June 14, 2016
    Assignee: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Gilles Stéphane Demaneuf, Babak Hodjat, Hormoz Shahrzad
  • Patent number: 9304895
    Abstract: Roughly described, a training database contains N segments of data samples. Candidate individuals identify a testing experience level, a fitness estimate, a rule set, and a testing set TSi of the data samples on which it is tested. The testing sets have fewer than all of the data segments and they are not all the same. Testing involves testing on only the individual's assigned set of data segments, updating the fitness estimates and testing experience levels, and discarding candidates through competition. If an individual reaches a predetermined maturity level of testing experience, then validating involves further testing it on samples of the testing data from a testing data segment other than those in the individual's testing set TSi. Those individuals that satisfy validation criteria are considered for deployment.
    Type: Grant
    Filed: July 18, 2013
    Date of Patent: April 5, 2016
    Assignee: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Hormoz Shahrzad, Kaivan Kamali, Babak Hodjat, Daniel Edward Fink
  • Patent number: 9256837
    Abstract: Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database containing active and shadow individuals; a gene pool processor which tests only active individuals on training data and updates their fitness estimates; a competition module which selects individuals (both active and shadow) for discarding from the gene pool in dependence upon both their updated fitness estimate and their testing experience level; and a gene harvesting module providing for deployment selected ones of the individuals from the gene pool. The gene database has an experience layered elitist pool, and individuals to compete only with other individuals in their same layer. Shadow individuals are created in each layer for active individuals that survive all competition with the layer before their testing experience exceeds the testing experience range for the layer.
    Type: Grant
    Filed: March 14, 2013
    Date of Patent: February 9, 2016
    Assignee: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
    Inventors: Babak Hodjat, Hormoz Shahrzad
  • Patent number: 9002759
    Abstract: Roughly described, a data mining system includes a candidate gene database in which each candidate individual has a respective fitness estimate. A gene pool processor tests individuals from the candidate gene pool on training data and updates the fitness estimate of the individuals. A gene harvesting module deploys selected individuals from the gene pool. The gene pool processor includes a competition module which selects individuals for discarding in dependence upon their updated fitness estimate. The system maintains a fitness training history for each of the candidate individuals, identifying the data samples on which the individual has been tested. The historical information can be used to assist in any one or more of the following: competition among the individuals, avoiding re-testing of an individual on the same data sample, removing duplicate test data before merging fitness evaluations, improving gene pool diversity, and selecting individuals for deployment.
    Type: Grant
    Filed: January 25, 2012
    Date of Patent: April 7, 2015
    Assignee: Sentient Technologies (Barbados) Limited
    Inventors: Babak Hodjat, Hormoz Shahrzad
  • Patent number: 8977581
    Abstract: Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database in which each candidate individual has a respective fitness estimate; a gene pool processor which tests individuals from the candidate gene pool on training data and updates the fitness estimate associated with the individuals in dependence upon the tests; and a gene harvesting module for deploying selected individuals from the gene pool, wherein the gene pool processor includes a competition module which selects individuals for discarding in dependence upon both their testing experience level and a diversity measure of individuals in the gene pool.
    Type: Grant
    Filed: July 2, 2012
    Date of Patent: March 10, 2015
    Assignee: Sentient Technologies (Barbados) Limited
    Inventors: Babak Hodjat, Hormoz Shahrzad
  • Patent number: 8918349
    Abstract: A server computer and a multitude of client computers form a network computing system that is scalable and adapted to continue to evaluate the performance characteristics of a number of genes generated using a software application running on the client computers. Each client computer continues to periodically receive data associated with the genes stored in its memory. Using this data, the client computers evaluate the performance characteristic of their genes by comparing a solution provided by the gene with the periodically received data associated with that gene. Accordingly, the performance characteristic of each gene may be updated and varied with each periodically received data. The performance characteristic of a gene defines its fitness. The genes may be virtual asset traders that recommend trading options, and the data associated with the genes may be historical trading data.
    Type: Grant
    Filed: August 29, 2013
    Date of Patent: December 23, 2014
    Assignee: Genetic Finance (Barbados) Limited
    Inventors: Babak Hodjat, Hormoz Shahrzad, Antoine Blondeau, Adam Cheyer, Peter Harrigan
  • Patent number: 8909570
    Abstract: Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database in which each candidate individual has a respective fitness estimate; a gene pool processor which tests individuals from the candidate gene pool on training data and updates the fitness estimate associated with the individuals in dependence upon the tests; and a gene harvesting module providing for deployment selected ones of the individuals from the gene pool, wherein the gene pool processor includes a competition module which selects individuals for discarding from the gene pool in dependence upon both their updated fitness estimate and their testing experience level. Preferably the gene database has an elitist pool containing multiple experience layers, and the competition module causes individuals to compete only with other individuals in their same experience layer.
    Type: Grant
    Filed: July 15, 2011
    Date of Patent: December 9, 2014
    Assignee: Genetic Finance (Barbados) Limited
    Inventors: Babak Hodjat, Hormoz Shahrzad, Greg S. Hornby