# US 12,182,133 B2 · Apparatus and methods for tracking progression of measured phenomena

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- Number: US 12,182,133 B2
- Document ID: US12182133B2
- Status: Granted
- Kind: B2
- Application number: 18/378,844
- Filed: 2023-10-11
- Published: 2024-11-07
- Granted: 2024-12-31
- Earliest claimed: 2023-05-01
- Assignees: The Strategic Coach Inc.
- Applicants: The Strategic Coach Inc.
- Inventors: Barbara Sue Smith; Daniel J. Sullivan
- Web page: https://patentgrep.com/patent/US12182133B2

## CPC

- G06F 16/24575: {using context}
- G06F 3/0482: Interaction with lists of selectable items, e.g. menus
- G06F 16/24578: {using ranking}
- G06Q 10/06393: {Score-carding, benchmarking or key performance indicator [KPI] analysis}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- 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

An apparatus and method for providing a performance data output for a user is provided. Apparatus may include a computing device including a processor, which may receive a user datum and generate an interface query data structure including at least a query including an input field based on the user datum. The interface query data structure may configure a remote display device to display the input field to the user, receive at least a user-input datum into the input field, retrieve data describing attributes of the user from a database communicatively connected with the processor, and refine the interface query data structure based on data describing attributes of the user from the database. The processor may use a machine learning model including a classifier to correlate the user datum to the interface query data structure and data multipliers into a list and accordingly generate a strategy data.

## Claims

### Claim 1

An apparatus for tracking progress of measured phenomena, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive a user datum;
generate an interface query data structure comprising at least a query comprising an input field based on the user datum, wherein the interface query data structure configures a remote display device to:
display the input field to a user;
receive at least a first user-input datum into an input field of at least a query of an interface query data;
generate multiple data multipliers based on the at least a first user-input datum, wherein:
each data multiplier comprises multiple data values comprising relatively higher data values describing data indicative of progress of the user toward matching a target;
score the multiple data multipliers as a function of the user datum and the at least a first user input datum;
identify a maximum value of at least an element of at least some data multipliers; and
generate strategy data for the user based on the at least a first user-input datum, the relatively higher data values, and an ordered list, wherein:
one or more strategies are generated, each corresponding to the progress of the user matching the target;
the strategy data is generated in response to a data multiplier instruction defined as multiplying the at least a first user-input datum by the relatively higher data values and displaying feedback to the user according to the ordered list;
the one or more strategies of the strategy data is ranked to determine a highest relevancy of matching the target;
the interface query data structure configures the remote display device to:
display at least the strategy data; and
receive a second user-input datum through the remote display device corresponding to at least some aspects of the strategy data.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein the interface query data structure is at least partially based on data describing attributes of the user that are retrieved from a database comprising categorical information correlated to a historical range of data.

### Claim 3 (depends on 1)

The apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to apply an information momentum multiplier to the user datum and the interface query data structure using a machine learning model, wherein the information momentum multiplier is defined by a second user-input datum exceeding a pre-defined numerical threshold.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to determine an interface query data structure recommendation as a function of the interface query data structure and the user datum, wherein the interface query data structure recommendation comprises a record generated by the at least a processor.

### Claim 5 (depends on 4)

The apparatus of claim 4, wherein the memory contains instructions configuring the at least a processor to:
generate the interface query data structure as a function of a user-provided response received from the interface query data structure recommendation that is transmitted to the user; and
update the interface query data structure recommendation as a function of the user-provided response iteratively through a feedback loop.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the memory contains instructions configuring the at least a processor to generate a machine-learning model for strategy data generation, wherein the machine-learning model is trained with training data comprising correlations between query data structures, user datums, and performance data outputs of the strategy data generation.

### Claim 7 (depends on 1)

The apparatus of claim 1, wherein if the second user-input datum demonstrates dissimilarity to the first user-input datum, a machine learning model iteratively recalculates the strategy data reflective of the dissimilarity such that the strategy data comprises data describing relatively more of the second user-input datum than the first user-input datum.

### Claim 8 (depends on 1)

The apparatus of claim 1, wherein the query interface data structure further configures the remote display device to display a performance data output as a function of a user data change descriptor generated based on the second user-input datum.

### Claim 9 (depends on 1)

The apparatus of claim 1, wherein the at least a first user-input datum and the second user-input datum comprise at least an element of data describing a user-responsiveness factor defined as a frequency of the user in completing activities associated with a performance data output.

### Claim 10 (depends on 1)

The apparatus of claim 1, wherein a performance data output is iteratively updated by a classifier of a machine learning model, which is further configured to classify data describing a frequency of the user completing activities describing a progress of the user toward matching the target to the strategy data.

### Claim 11

A method for tracking progress of measured phenomena, the method comprising:
receiving, by at least a processor, a user datum;
receiving, by the at least a processor, at least a first user-input datum into an input field of at least a query of an interface query data;
generating, by the at least a processor, an interface query data structure comprising at least a query comprising an input field based on the user datum, wherein the interface query data structure configures a remote display device to:
display the input field to a user;
generating, by the at least a processor, multiple data multipliers based on the at least a first user-input datum, wherein:
each data multiplier comprises multiple data values comprising relatively higher data values describing data indicative of progress of the user toward matching a target;
scoring, by the at least a processor, the multiple data multipliers as a function of the user datum and the at least a first user input datum;
identifying, by the at least a processor, a maximum value of at least an element of at least some data multipliers;
generating, by the at least a processor, one or more strategies, each corresponding the progress of the user to matching the target;
generating, by the at least a processor, strategy data for the user based on the at least a first user-input datum, the relatively higher data values, and an ordered list, wherein:
the strategy data is generated in response to a data multiplier instruction defined as multiplying the at least the at least a first user-input datum by the relatively higher data values and displaying feedback to the user according to the ordered list; and
ranking, by the at least a processor, the one or more strategies of the strategy data to determine a highest relevancy of matching the target, the interface query data structure configures the remote display device to:
display at least the strategy data; and
receive a second user-input datum through the remote display device corresponding to at least some aspects of the strategy data.

### Claim 12 (depends on 11)

The method of claim 11, further comprising:
retrieving, by the at least a processor, attributes of the user from a database comprising categorical information correlated to a historical range of data, wherein the interface query data structure is at least partially based on the data describing attributes of the user.

### Claim 13 (depends on 11)

The method of claim 11, further comprising:
applying, by the at least a processor, an information momentum multiplier to the user datum and the interface query data structure using a machine-learning model, wherein the information momentum multiplier is defined by a second user-input datum exceeding a pre-defined numerical threshold.

### Claim 14 (depends on 11)

The method of claim 11, further comprising:
determining, by the at least a processor, an interface query data structure recommendation as a function of the interface query data structure and the user datum, wherein the interface query data structure recommendation comprises a record generated by the at least a processor.

### Claim 15 (depends on 14)

The method of claim 14, further comprising:
generating, by the at least a processor, the interface query data structure as a function of a user-provided response received from the interface query data structure recommendation that is transmitted to the user; and
updating, by the at least a processor, the interface query data structure recommendation as a function of the user-provided response iteratively through a feedback loop.

### Claim 16 (depends on 11)

The method of claim 11, further comprising:
generating, by the at least a processor, a machine-learning model for strategy data generation, wherein the machine-learning model is trained with training data comprising correlations between query data structures, user datums, and performance data outputs of the strategy data generation.

### Claim 17 (depends on 11)

The method of claim 11, further comprising:
iteratively recalculating, by the at least a processor, the strategy data reflective of the dissimilarity using a machine-learning model such that the strategy data comprises data describing relatively more of the second user-input datum than the first user-input datum if the second user-input datum demonstrates dissimilarity to the first user-input datum.

### Claim 18 (depends on 11)

The method of claim 11, further comprising:
displaying, by the at least a processor and the remote display device using the query interface data structure, a performance data output as a function of a user data change descriptor generated based on the second user-input datum.

### Claim 19 (depends on 11)

The method of claim 11, wherein the at least a first user-input datum and the second user-input datum comprise at least an element of data describing a user-responsiveness factor defined as a frequency of the user in completing activities associated with a performance data output.

### Claim 20 (depends on 11)

The method of claim 11, further comprising:
iteratively updating, by the at least a processor, a performance data output using a classifier of a machine learning model, which is further configured to classify data describing a frequency of the user completing activities describing a progress of the user toward matching the target to the strategy data.