X Development LLC

Development Limited

Antonio Raymond Papania-Davis

Weishi Yan

Jeffrey Bush

Ray Anthony Nagatani, Jr

Bin Ni

Allen Richard Zhao

Neil David Treat

Charles Stephen Spirakis

Ray Jr Anthony Nagatani

Kartikye Mittal

Shelby Lin

Kathy Feng Liu

Scott Glover

Brian Howell

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterization of aggregate particles. A method includes obtaining, from a set of low fidelity sensors, first sensor data of a first portion of particles; obtaining, from a set of high fidelity sensors, second sensor data of the first portion of particles, the second sensor data comprising a higher fidelity representation of characteristics of the first portion of particles than the first sensor data; training a characterization model using the first sensor data and the second sensor data, the training comprising: providing, as training data to the characterization model, the second sensor data; and processing the second sensor data with the characterization model to correlate the first sensor data with the second sensor data. The first sensor data can indicate shape characteristics of each particle; and the second sensor data indicates a surface area of each particle.

High throughput characterization of aggregate particles

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing recycled concrete aggregate (RCA). One of the methods includes obtaining first optical measurements of RCA particles as the RCA particles are conveyed past the first optical sensors; determining, based on the first measurements, an initial characterization of the RCA particles; iteratively performing a carbonation process on the RCA particles, obtaining second optical measurements of the RCA particles, and determining, from the second measurements, a second characterization of the RCA particles, wherein conditions of the carbonation process are initially set based on the initial characterization, and the conditions of the carbonation process are adjusted based on the second characterization; ceasing the iterative performance of the carbonation process in response to the second characterization meeting target carbonation characteristics; iteratively performing a densification process on the RCA particles, obtaining third optical measurements of the RCA particles, and determining, from the third measurements, a third characterization of the RCA particles, wherein conditions of the densification process are initially set based on the initial characterization or the second characterization, and the conditions of the densification process are adjusted based on the third characterization; and ceasing the iterative performance of the densification process in response to the third characterization meeting target densification characteristics.

Recycled concrete preparation using sensors to characterize particles and control carbonation and densification process steps

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for preparing a concrete mixture. One of the methods includes controlling an ingredient metering system to measure and add a plurality of ingredients to a concrete mixture, measuring characteristics of at least one ingredient of the ingredients using a particle analyzer, determining an estimated rheometry measurement of for the concrete mixture, obtaining an actual rheometry measurement of the concrete mixture, and selectively controlling the ingredient metering system to add one or more additional ingredients to the concrete mixture based on a comparison of the estimated rheometry measurement with the actual rheometry measurement.

Concrete preparation and recipe optimization

Methods, systems, and apparatus for generating a recipe for a concrete mixture, comprising: obtaining an optical characterization of a set of particles; determining, based on the optical characterization, physical characteristics of the set of particles; generating a multispherical approximation of the set of particles; selecting, based on the physical characteristics of the set of particles and from a database of performance rules, performance rules applicable to the set of particles; predicting performance of a proposed recipe for a concrete mixture formed from the set of particles by: determining a wet flowability rating of the proposed recipe based on the selected performance rules; and determining a dry packing rating of the proposed recipe based on the multispherical approximation; iteratively altering the proposed recipe and predicting performance of the altered proposed recipe until the predicted performance satisfies performance criteria to obtain a final recipe; and outputting the final recipe.

Method to produce evolving concrete mixture heuristic

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for simulating a concrete mixture. One of the methods includes obtaining an optical characterization of physical particles, generating a multispherical approximation of the physical particles, the multispherical approximation having reduced dimensionality compared to the optical characterization, simulating an aggregate mixture by applying the multispherical approximation of the particles to a physics simulator to obtain a predicted performance of the proposed aggregate mixture, selectively altering the aggregate mixture based on a comparison with performance metrics and simulating the altered aggregate mixture until the predicted performance satisfies the performance metrics to obtain a final aggregate mixture, and outputting the final aggregate mixture.

Simulation and optimization of concrete recipe

Techniques are described for using stenography to protect sensitive information within conversational audio data by generating a pseudo-language representation of conversational audio data. In some implementations, audio data corresponding to an utterance is received. The audio data is classified as likely sensitive audio data. A particular set of sentiments associated with the audio data is determined. Data indicating the particular set of sentiments associated with the audio data is provided to a model. The model is trained to output, for each of different sets of sentiments, desensitized, pseudo-language audio data that exhibits the set of sentiments, and is not classified as likely sensitive audio data. A particular desensitized, pseudo-language audio data is received from the model. The audio data is replaced with the particular desensitized, pseudo-language audio data and stored within an audio data repository.

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Oakland, CA, United States of America

Antonio Raymond Papania-Davis