3m Innovative Properties Company

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Solventum Intellectual Properties Company

3m Innovation Properties Company

3m Innovative Properties

Nicholas A Johnson

Gilles J Benoit

Brian E Brooks

Frederick J Arsenault

Himanshu Nayar

Tyler W Olson

Peter O Olson

Kenneth A Epstein

Michael E Lauters

Gary Timothy Boyd

John Allen Wheatley

Daniel J Schmidt

Kevin W Gotrik

Caleb T Nelson

Raymond J Kenney

Tri Dinh Pham

Robert M Emmons

Haiyan Zhang

Joseph T Aronson

Encai Hao

Michael J Sykora

Steven H Kong

Owen M Anderson

Qingbing Wang

Stephen J Etzkorn

Don Vincent West

Michael R Leaf

Shannon L Siefken

Matthew M Philippi

Catherine A Leatherdale

Michael L Steiner

Aaron K Nienaber

David Scott Thompson

Jon A Kirschhoffer

Tao Liu

Glenn E Casner

Maureen Ann Kavanagh

Benjamin D Fisher

Steven P Floeder

Thomas G Skulley

Graham M Clarke

Kristy A Gillette

James Hillis

Jonathan B Arthur

Paul J Cobian

Brett R Hemes

Brandon Alan Bartling

Kenneth A P Meyer

Thomas J Strey

David A Rosen

Oanh V Pham

Corey D Balts

Nathan J Herbst

Christopher M Brown

Patrick G Zimmerman

Timothy J Clemens

Jeffrey P Adolf

Steven G Lucht

Mark W Orlando

Douglas M Lawrence

Lori A Sjolund

Christopher Jon Farrell

Sara Suzanne Merritt

Andreas Backes

Vincent J Laraia

Jerry D Cooper

Gregory P Moriarty

Peter J Schneider

Luke A Schroeder

Miaoding Dai

Carter C Hughes

Cory D Sauer

Sarah M Mullins

Edward C Roberts

Sherie A Kristie

Andrew T Pace

Mark A Tomai

Zachary M Schaeffer

Richard V Pallarino

Susan L Woulfe

Scott L Barnett

Vincent Jl Chevrier

Trevor W Stolber

Thomas A Pritchett

Shant Daghlian

A light control film comprises a light input surface and a light output surface opposite the light input surface; alternating transmissive regions and absorptive regions disposed between the light input surface and the light output surface, wherein the absorptive regions comprise a core having a first concentration, C, of a light absorbing material sandwiched between cladding layers having a second concentration, C, of the light absorbing material, wherein C<C, and wherein the cores have an aspect ratio of at least 20.

High transmission light control film

A method of performing a process using a plurality of control signals and resulting in a plurality of measurable outcomes is described. The method includes optimizing the plurality of control signals by at least: receiving a plurality of process constraints; receiving, for each measurable outcome, an optimum range; receiving, for each control signal, a plurality of potential optimum values; iteratively performing the process, where for each process iteration, the value of each control signal is selected from among the plurality of potential optimum values received for the control signal; for each process iteration, measuring each outcome in the plurality of measurable outcomes; and generating confidence intervals for the control signals to determine a causal relationship between the control signals and the measurable outcomes. The method includes performing the process using at least the control signals determined by the causal relationship to causally affect at least one of the measurable outcomes.

Method of performing a process and optimizing control signals used in the process

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a process of polishing semiconductor wafers. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of input settings for polishing a semiconductor wafer, based on a causal model that measures current causal relationships between input settings and a quality of semiconductor wafers; ii) receiving a measure of the quality of the semiconductor wafer polished with the configuration of input settings; and iii) adjusting, based on the measure of the quality of the semiconductor wafer polished with the configuration of input settings, the causal model.

Polishing semiconductor wafers using causal models

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting control settings for the environment based on (i) a causal model that identifies causal relationships between possible settings for controllable elements in the environment and environment responses that reflect a performance of the control system in controlling the environment and (ii) current values of a set of internal parameters; and during the repeatedly selecting: monitoring environment responses to the selected control settings; determining, based on the environment responses, an indication that one or more properties of the environment have changed; and in response, modifying the current values of one or more of the internal parameters.

Determining causal models for controlling environments

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes identifying a procedural instance; selecting control settings for the procedural instance, comprising, for a particular one of the controllable elements: assigning the procedural instance to a cluster for the particular controllable element in accordance with current values of a set of clustering parameters for the particular controllable element; and selecting a setting for the particular controllable element for the procedural instances based on a causal model that is specific to the cluster; obtaining environment responses to the selected control settings that define a value of the performance metric for the procedural instance; and updating, for the particular controllable element, the causal model for the cluster for the controllable element to which the procedural instance was assigned based on the value of the performance metric.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a manufacturing process. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of control settings for a manufacturing process, based on a causal model that measures causal relationships between control settings and a measure of a success of the manufacturing process; ii) determining the measure of the success of the manufacturing process using the configuration of control settings; and iii) adjusting, based on the measure of the success of the manufacturing process using the configuration of control settings, the causal model.

Controlling a manufacturing process using causal models

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes identifying a procedural instance; determining a temporal extent for the procedural instance based on temporal extent parameters for the one or more entities in the procedural instance; selecting control settings for the procedural instance; monitoring environment responses to the control settings that are received for the one or more entities; determining which of the environment responses to attribute to the procedural instance in a causal model; and adjusting, based at least in part on the environment responses that are attributed to the procedural instance, the temporal extent parameters for the one or more entities.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting, by a control system for the environment, control settings for the environment based on internal parameters of the control system, wherein: at least some of the control settings for the environment are selected based on a causal model, and the internal parameters include a first set of internal parameters that define a number of previously received performance metric values that are used to generate the causal model for a particular controllable element; obtaining, for each selected control setting, a performance metric value; determining that generating the causal model for the at particular controllable element would result in higher system performance; and adjusting, based on the determining, the first set of internal parameters.

A liquid crystal display comprises a backlight module comprising a reflective polarizing film, a light control film and a liquid crystal panel disposed between the backlight module and the light control film. The light control film comprises a light input surface and a light output surface opposite the light input surface and alternating transmissive and absorptive regions disposed between the light input surface and the light output surface. The absorptive regions have an aspect ratio of at least 30.

Liquid crystal display having a frontside light control film

Light control films comprise a light input surface and alight output surface opposite the light input surface and alternating transmissive regions and absorptive regions disposed between the light input surface and the light output surface. The absorptive regions have an aspect ratio of at least 30 and are canted in the same direction. The alternating transmissive regions and absorbing regions have a maximum relative transmission at a viewing angle other than 0 degrees.

High transmission light control films with asymmetric light output

A method of optimizing a plurality of control signals used in operating a vehicle is described. The operation has a plurality of associated measurable parameters. The method includes: for each control signal, selecting a plurality of potential optimum values from a predetermined set; operating the vehicle in at least a first sequence of operation iterations, where for each pair of sequential first and second operation iterations in the first sequence of operation iterations, the potential optimum value of one control signal in the first operation iteration is replaced in the second operation iteration with a next potential optimum value of the control signal, while the potential optimum values of the remaining control signals are maintained; for each operation iteration, measuring each parameter in the plurality of measurable parameters; and generating confidence intervals for the control signals to determine causal relationships between the control signals and the measurable parameters.

Method of optimizing control signals used in operating vehicle

A light control film comprises a light input surface and a light output surface; alternating transmissive regions and absorptive regions disposed between the light input surface and the light output surface; and TIR cladding layers. The TIR cladding layer having a refractive index, n. The transmissive regions alternate between high refractive index transmissive regions having a refractive index, n, and low refractive index transmissive regions having a refractive index, n. The absorptive regions comprise a core having a refractive index, n, adjacent an AR cladding layer; wherein n<nand n<n. The TIR cladding layers are adjacent the high refractive index transmissive regions. The cores have an aspect ratio of at least 20. The high refractive index transmissive regions have a wall angle of 6 degrees or less.

High transmission light control film comprising alternating transmissive and absorptive regions

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes obtaining data specifying baseline probability distributions for each of a plurality of controllable elements; maintaining a causal model; repeatedly performing the following: selecting control settings for the environment based on the causal model and values for a particular internal parameter of the control system that are sampled from a range of possible values; selecting control settings for the environment based on the baseline probability distributions; monitoring environment responses to the control settings selected based on the causal model and the control settings selected based on the baseline probability distributions; determining, for each of the possible values, a measure of a difference between a current system performance and a baseline system performance; and updating how frequently each of the possible values is sampled.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a process of manufacturing a product. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of input settings for manufacturing a product, based on a causal model that measures causal relationships between input settings and a measure of a quality of the product; ii) determining the measure of the quality of the product manufactured using the configuration of input settings; and iii) adjusting, based on the measure of the quality of the product manufactured using the configuration of input settings, the causal model.

Manufacturing a product using causal models

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a process of manufacturing a biologic pharmaceutical. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of input settings for manufacturing a batch of a biologic pharmaceutical based on a causal model that measures current causal relationships between input settings and a measure of a quality of batches of the biological pharmaceutical; ii) determining a measure of the quality of a batch of the biological pharmaceutical manufactured using the configuration of input settings; and iii) adjusting, based on the measure of the quality of the batch of the biological pharmaceutical, the causal model.

Manufacturing a biologic pharmaceutical using causal models

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Burnsville, MN, United States of America

Nicholas A Johnson