# US 2021/0241144 A1 · Trial design platform with recommendation engine

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- Number: US 2021/0241144 A1
- Document ID: US20210241144A1
- Status: Published application
- Kind: A1
- Application number: 17/163,427
- 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/US20210241144A1

## 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, according to some implementations, includes obtaining trial design simulation results for a set of trial designs and determining a set of Pareto designs in the set of trial designs based at least in part on the trial design simulation results and one or more performance parameters. The method further includes determining a set of convex hull designs in the set of trial designs, determining a set of recommended designs based at least in part on the set of Pareto designs and the set of convex hull designs, and transmitting the set of recommended designs.

## Claims

### Claim at position 1

-615. (canceled)

### Claim 616

A method comprising:
obtaining trial design simulation results for a set of trial designs;
determining a set of Pareto designs in the set of trial designs based at least in part on the trial design simulation results and one or more performance parameters;
determining a set of convex hull designs in the set of trial designs;
determining a set of recommended designs based at least in part on the set of Pareto designs and the set of convex hull designs; and
transmitting the set of recommended designs.

### Claim 617 (depends on 616)

The method of claim 616 further comprising:
filtering one or more of the set of trial designs that are dominated by at least one of the set of convex hull designs.

### Claim 618 (depends on 616)

The method of claim 616 further comprising:
filtering one or more of the set of trial designs that are dominated by at least one of the set of Pareto designs.

### Claim 619 (depends on 616)

The method of claim 616, wherein determining the set of recommended designs comprises:
determining at least one of the set of recommended designs within an epsilon-distance from at least one of the set of Pareto designs.

### Claim 620 (depends on 616)

The method of claim 616, wherein determining the set of recommended designs comprises:
determining at least one of the set of recommended designs within an epsilon-distance from at least one of the set of convex hull designs.

### Claim 621 (depends on 616)

The method of claim 616, wherein determining a set of recommended designs comprises:
performing simulated annealing on one or more of the set of trial designs.

### Claim 622 (depends on 621)

The method of claim 621, wherein simulated annealing is based at least in part on facets of the set of convex hull designs.

### Claim 623 (depends on 616)

The method of claim 616 further comprising:
identifying different design types in the set of Pareto designs.

### Claim 624 (depends on 616)

The method of claim 616, wherein:
the set of Pareto designs is determined prior to the set of convex hull designs;
the set of convex hull designs is derived from the set of Pareto designs such that each of the set of convex hull designs is in the set of Pareto designs; and
at least one of the set of recommended designs is in the set of convex hull designs.

### Claim 625 (depends on 616)

The method of claim 616, wherein:
the set of convex hull designs is determined prior to the set of Pareto designs;
the set of Pareto designs is derived from the set of convex hull designs such that each of the set of Pareto designs is in the set of convex hull designs; and
at least one of the set of recommended designs is in the set of convex hull designs.

### Claim 626 (depends on 616)

The method of claim 616 further comprising:
identifying a number of trial designs in the set of Pareto designs;
wherein:
determining the set of convex hull designs occurs when the number is greater-than-or-equal-to a threshold; and
the set of convex hull designs is derived from the set of Pareto designs.

### Claim 627 (depends on 616)

The method of claim 616 further comprising:
identifying a number of trial designs in the set of Pareto designs; and
if the number is less-than-or-equal-to a threshold, identifying, for inclusion in the set of recommended designs, one or more of the set of trial designs within an epsilon distance of at least one of the set of Pareto designs.

### Claim 628 (depends on 616)

The method of claim 616 further comprising:
for each of the set of Pareto designs, determining a design type;
determining a first trial design of the set of Pareto designs is of a different design type from and within an epsilon-distance to a second trial design of the set of Pareto designs; and
including the first trial design and the second trial design in the set of recommended designs.

### Claim 629 (depends on 616)

The method of claim 616, 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 trial design simulation results 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 trial design simulation results based at least in part on a relationship between the trial design simulation results and supplemental data;
generating a performance surface based at least in part on a set of trial designs corresponding to the trial design simulation results;
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 trial design simulation results.

### Claim 630

An apparatus comprising:
a results processing circuit structured to interpret trial design simulation results for a set of trial designs;
a Pareto evaluation circuit structured to determine a set of Pareto designs in the set of trial designs based at least in part on the trial design simulation results and one or more performance parameters;
a convex hull evaluation circuit structured to determine a set of convex hull designs in the set of trial designs;
a recommendation circuit structured to determine a set of recommended designs based at least in part on the set of Pareto designs and the set of convex hull designs; and
a recommendation provisioning circuit structured to transmit the set of recommended designs.

### Claim 631 (depends on 630)

The apparatus of claim 630 further comprising:
a filtering circuit structured to filter one or more of the set of trial designs that are dominated by at least one of the set of convex hull designs.

### Claim 632 (depends on 630)

The apparatus of claim 630 further comprising:
a filtering circuit structured to filter one or more of the set of trial designs that are dominated by at least one of the set of Pareto designs.

### Claim 633 (depends on 630)

The apparatus of claim 630, wherein the recommendation circuit is further structured to:
determine at least one of the set of recommended designs within an epsilon-distance from at least one of the set of Pareto designs.

### Claim 634

A non-transitory computer-readable medium storing instructions that adapt at least one processor to:
interpret trial design simulation results for a set of trial designs;
determine a set of Pareto designs in the set of trial designs based at least in part on the trial design simulation results and one or more performance parameters;
determine a set of convex hull designs in the set of trial designs;
determine a set of recommended designs based at least in part on the set of Pareto designs and the set of convex hull designs; and
transmit the set of recommended designs.

### Claim 635 (depends on 634)

The non-transitory computer-readable medium of claim 634, wherein the stored instructions further adapt the at least one processor to:
filter one or more of the set of trial designs that are dominated by at least one of the set of convex hull designs.

### Claim at position 22

-696. (canceled)