# US 11,907,232 B2 · Facilitating efficient identification of relevant data

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- Number: US 11,907,232 B2
- Document ID: US11907232B2
- Status: Granted
- Kind: B2
- Application number: 17/146,651
- Filed: 2021-01-12
- Published: 2022-07-14
- Granted: 2024-02-20
- Earliest claimed: 2021-01-12
- Assignees: Adobe Inc.
- Applicants: Adobe Inc.
- Inventors: Wei Zhang; Christopher Challis
- Web page: https://patentgrep.com/patent/US11907232B2

## CPC

- G06F 16/24578: {using ranking}
- G06F 16/285: {Clustering or classification}
- G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G06Q 10/063: Operations research, analysis or management
- G06Q 10/10: Office automation; Time management
- G06Q 30/02: Marketing; Price estimation or determination; Fundraising
- H04L 67/306: User profiles
- H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)}
- G06N 20/00: Machine learning
- 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
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.

## Claims

### Claim 1

One or more computer storage media having computer-executable instructions embodied thereon that, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising:
determining a set of distribution parameters for a metric corresponding with a quantitative measure used to perform analytics, wherein a first distribution parameter is determined based on implicit positive feedback obtained in relation to the metric and usage data associated with the metric and a second distribution parameter is determined based on the usage data associated with the metric, wherein the second distribution parameter is further determined using direct implicit negative feedback obtained in accordance with the user not selecting or clicking on data associated with the metric, wherein the metric is selected from a set of metrics measuring various performance data associated with an organization and is selected based on an occurrence of an anomaly associated with the metric;
generating a distribution for the metric using the set of distribution parameters for the metric;
sampling the distribution to identify a relevance score of the metric indicating an extent of relevance of the metric to a user, wherein the relevance score indicates the metric is relevant to the user based on the relevance score being in a set of highest relevance scores among a set of relevance scores or based on the relevance score exceeding a threshold relevance value; and
based on the relevance score indicating the metric is relevant to the user, providing an indication of the metric, or data associated therewith, for presentation to the user.

### Claim 2 (depends on 1)

The computing system of claim 1, wherein the distribution is generated without any explicit feedback from the user.

### Claim 3 (depends on 1)

The computing system of claim 1, wherein the implicit positive feedback is obtained in accordance with the user selecting or clicking on data associated with the metric.

### Claim 4 (depends on 1)

The computing system of claim 1, wherein the usage data is obtained in accordance with the user accessing or using data associated with the metric.

### Claim 5 (depends on 1)

The computing system of claim 1, wherein determining the set of distribution parameters for the metric is performed via a feedback updating process to update the first distribution parameter based on the implicit positive feedback and a usage updating process to update the first distribution parameter and the second distribution parameter based on the usage data.

### Claim 6 (depends on 1)

The computing system of claim 1, wherein the set of relevance scores includes a plurality of relevance scores that correspond with other metrics.

### Claim 7 (depends on 1)

The computing system of claim 1, wherein the usage data is provided via a remote analytics system that captures usage data from user interactions with a website or an application.

### Claim 8 (depends on 1)

The method of claim 1, wherein the direct implicit negative feedback is obtained based on low usage count.

### Claim 9 (depends on 1)

The method of claim 1, wherein the direct implicit negative feedback is obtained based on usage data and a number of negative feedbacks.

### Claim 10

A computer-implemented method comprising:
identifying a set of candidate metrics for which to determine relevance to a user;
determining a set of distribution parameters for each candidate metric, each set of distribution parameters including a first distribution parameter based on implicit positive feedback associated with the metric, wherein determining the set of distribution parameters for each candidate metric includes determining the first distribution parameter based further on usage data associated with the metric and determining a second distribution parameter using the usage data associated with the metric and direct implicit negative feedback obtained in accordance with the user not selecting or clicking on data associated with a candidate metric from the set of candidate metrics and, wherein the candidate metric is selected from the set of candidate metrics measuring various performance data associated with an organization and is selected based on an occurrence of an anomaly associated with the metric;
generating a distribution for each candidate metric using the corresponding set of distribution parameters;
sampling each distribution to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric to the user, wherein the relevance score indicates the metric is relevant to the user based on the relevance score being in a set of highest relevance scores among a set of relevance scores or based on the relevance score exceeding a threshold relevance value; and
based on the relevance scores for each candidate metric, designating at least one candidate metric of the set of candidate metrics as relevant to the user.

### Claim 11 (depends on 10)

The method of claim 10, wherein the set of distribution parameters is determined without any explicit feedback provide by the user.

### Claim 12 (depends on 10)

The method of claim 10 further comprising providing an indication of the at least one candidate metric, or data associated therewith, to a user device for display.

### Claim 13

A computing system comprising:
a processor; and
computer storage memory having computer-executable instructions stored thereon which, when executed by the processor, configure the computing system to:
means for determining a set of distribution parameters for a metric, wherein a first distribution parameter is determined based on implicit positive feedback obtained in relation to the metric and usage data associated with the metric and a second distribution parameter is determined based on the usage data associated with the metric, wherein the second distribution parameter is determined using the usage data associated with the metric and direct implicit negative feedback obtained in accordance with the user not selecting or clicking on data associated with a metric from a set of metrics and wherein the metric is selected from the set of metrics measuring various performance data associated with an organization and is selected based on an occurrence of an anomaly associated with the metric;
means for generating a distribution for the metric using the set of distribution parameters for the metric; and
means for identifying a relevance score of the metric using the distribution, the relevance score indicating an extent of relevance of the metric to a user, wherein the relevance score indicates the metric is relevant to the user based on the relevance score being in a set of highest relevance scores among a set of reference scores or based on the relevance score exceeding a threshold relevance value.

### Claim 14 (depends on 13)

The media of claim 13, wherein the relevance score is identified based on a random sampling of the distribution.

### Claim 15 (depends on 13)

The media of claim 13 further comprising means for providing an indication of the metric as relevant to the user.

### Claim 16 (depends on 13)

The media of claim 13, wherein the implicit positive feedback is obtained in accordance with the user selecting or clicking on data associated with the metric, and the usage data is obtained in accordance with the user accessing or using data associated with the metric.