# US 2021/0241859 A1 · Trial design platform

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- Number: US 2021/0241859 A1
- Document ID: US20210241859A1
- Status: Published application
- Kind: A1
- Application number: 17/163,423
- Filed: 2021-01-30
- Published: 2021-08-05
- Earliest claimed: 2020-01-31
- Assignees: Cytel Corporation
- Applicants: Cytel Inc.
- Inventors: Jaydeep Bhattacharya; James Bolognese; Alexandre Buer; Eric Edwards; Stanley Y. Huang; Yannis Jemiai; Cyrus Mehta; Nitin Patel; Anne Pelz; Ajay Prabhakar Sathe; Joshua A. Schultz; Pralay Senchaudhuri
- Web page: https://patentgrep.com/patent/US20210241859A1

## CPC

- G16H 10/20: for electronic clinical trials or questionnaires
- G06F 30/10: Geometric CAD
- G06F 30/12: characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 5/04: Inference or reasoning models
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- G06Q 10/06315: {Needs-based resource requirements planning or analysis}
- G06Q 10/067: Enterprise or organisation modelling
- G06Q 10/0674: {using process simulation}
- G06Q 10/101: Collaborative creation, e.g. joint development of products or services
- G06Q 30/0205: {based on location or geographical consideration}
- G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
- G16H 40/67: for remote operation
- G16H 50/50: for simulation or modelling of medical disorders
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G06F 3/0484: for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
- G06F 2111/02: CAD in a network environment, e.g. collaborative CAD or distributed simulation
- G06F 2111/04: Constraint-based CAD
- G06F 2111/06: Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
- G06F 2111/08: Probabilistic or stochastic CAD
- G06F 2111/16: Customisation or personalisation
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

A method for determining trial designs is provided. The method includes obtaining simulation data for a set of trial designs. The simulation data includes performance parameters and performance parameter values associated with each design in the set of designs for a set of criteria; determining an optimality criteria for evaluating the trial designs; searching, within the set of trial designs, for globally optimum designs based on the optimality criteria; and recommending globally optimum designs.

## Claims

### Claim at position 1

1-434. (canceled)

### Claim 435

A method for determining trial designs, the method comprising:
obtaining simulation data for a set of trial designs, wherein the simulation data includes performance parameters and performance parameter values associated with each design in the set of designs for a set of criteria;
determining an optimality criteria for evaluating the trial designs;
searching, within the set of trial designs, for globally optimum designs based on the optimality criteria; and
recommending globally optimum designs.

### Claim 436 (depends on 435)

The method of claim 435, wherein the set of trial designs includes all combinations of design options for a set of criteria.

### Claim 437 (depends on 435)

The method of claim 435, wherein the optimality criteria is based on historical data and includes performance parameters of a benchmark design.

### Claim 438 (depends on 435)

The method of claim 435, wherein the optimality criteria is based on a weighted sum of performance criteria values of each of the set of trial designs.

### Claim 439 (depends on 435)

The method of claim 435, further comprising:
changing the optimality criteria based on a number of globally optimum designs.

### Claim 440 (depends on 435)

The method of claim 435, further comprising:
determining a second optimality criteria; and
searching, within the set of trial designs, for a second set of globally optimum designs based on the second optimality criteria.

### Claim 441 (depends on 435)

The method of claim 435, further comprising:
determining a second optimality criteria; and
searching, within the set of globally optimum designs, for a second set of globally optimum designs based on the second optimality criteria.

### Claim 442 (depends on 435)

The method of claim 435, further comprising:
dynamically changing the optimality criteria in response to properties of globally optimum designs.

### Claim 443 (depends on 435)

The method of claim 435, further comprising:
dynamically changing the optimality criteria in response to user feedback.

### Claim 444 (depends on 435)

The method of claim 435, wherein the optimality criteria includes Pareto optimality.

### Claim 445 (depends on 435)

The method of claim 435, wherein the optimality criteria includes convex hull optimality.

### Claim 446 (depends on 435)

The method of claim 435, wherein the optimality criteria includes Pareto optimality and the optimality criteria includes convex hull optimality.

### Claim 447 (depends on 435)

The method of claim 435, further comprising:
evaluating historical trial design selections to identify one or more trial design parameters based at least in part on one or more trial design criteria determined from a user via an interactive interface, wherein obtaining the simulation data is based at least in part on a quick search data structure and the one or more trial design parameters;
generating a substitute for at least some of the simulation data based at least in part on a relationship between the simulation data and supplemental data;
generating a performance surface based at least in part on the set of trial designs;
evaluating one or more trial designs based at least in part on the performance surface; and
calculating a score based on normalized score component values corresponding to the simulation data.

### Claim 448

An apparatus comprising:
a data processing circuit configured to obtain design data for a set of trial designs;
an optimality determining circuit configured to:
determine an optimality criteria for evaluating the trial designs, and search, from the set of trial designs, for globally optimum designs based on the optimality criteria; and
a design analysis circuit configured to analyze the globally optimum designs and determine a modification to the optimality criteria;
wherein the optimality determining circuit is structured to receive the modification and determine a second set of globally optimum designs.

### Claim 449 (depends on 448)

The apparatus of claim 448, wherein the set of trial designs includes all combinations of design options for a set of criteria.

### Claim 450 (depends on 448)

The apparatus of claim 448, wherein the optimality criteria is based on historical data and includes performance parameters of a benchmark design.

### Claim 451 (depends on 448)

The apparatus of claim 448, wherein the optimality criteria is based on a weighted sum of performance criteria values of each of the set of trial designs.

### Claim 452 (depends on 448)

The apparatus of claim 448, wherein the modification is based on a number of globally optimum designs.

### Claim 453 (depends on 448)

The apparatus of claim 448, wherein the modification is in response to user feedback.

### Claim 454

A non-transitory computer-readable medium storing instructions that adapt at least one processor to:
obtain a simulation data for a set of trial designs, wherein the simulation data includes performance parameters and performance parameter values associated with each design in the set of designs for a set of criteria;
determine an optimality criteria for evaluating the trial designs;
search, within the set of trial designs, for globally optimum designs based on the optimality criteria; and
recommend globally optimum designs.

### Claim at position 22

455-696. (canceled)