Applied Underwriters, Inc.

Silicon Laboratories Inc.

David Alan Clark

Justin N Smith

Mark S Nowotarski

Wenming Zhang

Breena Gloriana Fraga

Moshe Morrie Altmejd

Qin Wang

Yahui Zhang

A computer-implemented semantic analysis system for ranking search results has instructions including the steps: receive a query about a first document authored by a person; read in data identifying the first document; read in a second document about the person authored after the first document; identify indicator terms in the second document related to whether or not there is a significant discrepancy between the second document and the first document; read in weighting factors for the identified indicator terms; determine an aggregate weighting factor that decreases with an increasing number of indicator terms found; and calculate a score of how much of a discrepancy there is between the second document and the first document using the weighting factors. When multiple first documents are returned for multiple persons, present the links in order from high score to low score.

Semantic analysis system for ranking search results

An artificial intelligence system for training a classifier has a database of training data and a modeling system for building a classification model based on the training data. The database has a binary classification for each entity and binary tokens indicating whether or not one or more indicators about the entity are true. The classification model is based on a tempered indication of the tokens. The tempered indication is a ratio of a weighted sum of the tokens for each entity divided by a tempering factor for each of the entities. The tempering factor is a function of the unweighted sum of the tokens for each entity. Thus, the tempering factor will reduce the tempered indication when large numbers of low weight tokens are present so that the model does not over predict the probability of an entity being in the classification.

Artificial intelligence system for training a classifier

An apparatus includes a first electromagnetic sensor configured to generate a first sensed signal based at least in part on detection of a first signal having a first wavelength. The apparatus includes a second electromagnetic sensor configured to generate a second sensed signal based at least in part on detection of a second signal having a second wavelength different from the first wavelength. The apparatus includes a processing circuit configured to generate a plethysmogram based at least in part on the first sensed signal and the second sensed signal. The apparatus may include a first emitter configured to emit an optical signal having the first wavelength. The apparatus may include a second emitter configured to emit a reference signal. The first wavelength may be a human-blood-sensitive and human-skin-penetrable wavelength and the second wavelength may be at least one of a human-blood-insensitive wavelength and a human-skin-impenetrable wavelength.

Plethysmography heart rate monitor noise reduction using differential sensors

An artificial intelligence expert system detects an anomaly between an application and a publication provided by an applicant in a given class. The system calculates an application score then uses a decision tree to determine a publication score based on the presence or absence of tokens in the publication. An anomaly is detected when the ratio of the application score to the publication score is greater than a threshold value. The decision tree is trained using prior applications in the same class. The publication score of each leaf node of the decision tree is set equal to an average application score of the prior applications associated with the leaf node.

Artificial intelligence expert system for anomaly detection

An artificial intelligence system for training a classifier has a database of training data and a modeling system for building a classification model based on the training data. The database has a binary classification for each entity and binary tokens indicating whether or not one or more indicators about the entity are true. The classification model is based on a tempered indication of the tokens. The tempered indication is a ratio of a weighted sum of the tokens for each entity divided by a tempering factor for each of the entities. The tempering factor is a function of the unweighted sum of the tokens for each entity. Thus the tempering factor will reduce the tempered indication when large numbers of low weight tokens are present so that the model does not over predict the probability of an entity being in the classification.

An artificial intelligence expert system for screening provides characteristic profiles to candidates to perform a particular task. The profiles have individual screening items within them that are expected to be related to whether or not a person is suitable for the task. The responses from the persons to the items are received by a computer implemented expert system. The expert system applies a combined model to the responses to generate a forecasted performance of the person to the task. The combined model is a linear combination of two or more path dependent regressions performed on data from a set of N training persons with known abilities to do the task. The number of parameters in each path dependent model is limited to a fraction of the number N so that the path dependent models are not over fit to the data. A suitable fraction is &#x2155;.

Growing community of inventors

Austin, TX, United States of America

David Alan Clark