# US 10,769,426 B2 · Inferring attributes of organizations using member graph

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- Number: US 10,769,426 B2
- Document ID: US10769426B2
- Status: Granted
- Kind: B2
- Application number: 14/929,128
- Filed: 2015-10-30
- Published: 2017-03-30
- Granted: 2020-09-08
- Earliest claimed: 2015-09-30
- Assignees: Microsoft Technology Licensing, LLC
- Applicants: Microsoft Technology Licensing, LLC
- Inventors: Songtao Guo; Ke Wang; Alex Ching Lai; Aarti Kumar; Keith Wai Kit Tsang; Rekha Thakur; Song Lin; Christopher Matthew Degiere
- Web page: https://patentgrep.com/patent/US10769426B2

## CPC

- G06F 16/215: Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
- G06F 16/24534: {Query rewriting; Transformation}
- G06F 16/24544: {Join order optimisation}
- G06F 16/285: {Clustering or classification}
- G06F 16/58: Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F 16/5838: {using colour}
- G06F 16/5854: {using shape and object relationship}
- G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)}
- G06F 16/951: Indexing; Web crawling techniques
- G06F 16/9538: Presentation of query results
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/2178: {based on feedback of a supervisor}
- G06F 18/22: Matching criteria, e.g. proximity measures
- G06F 18/24147: {Distances to closest patterns, e.g. nearest neighbour classification}
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 7/02: using fuzzy logic (computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00)
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G06Q 10/06393: {Score-carding, benchmarking or key performance indicator [KPI] analysis}
- G06Q 10/10: Office automation; Time management
- G06Q 10/40: Business processes related to social networking or social networking services
- G06V 10/40: Extraction of image or video features
- G06V 10/761: {Proximity, similarity or dissimilarity measures}
- G06V 30/19147: {Obtaining sets of training patterns; Bootstrap methods, e.g. bagging or boosting}
- G06V 30/19167: {Active pattern learning}
- G06V 30/19173: {Classification techniques}
- G06V 30/413: Classification of content, e.g. text, photographs or tables
- G06V 30/416: Extracting the logical structure, e.g. chapters, sections or page numbers; Identifying elements of the document, e.g. authors
- H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46)
- H04L 67/306: User profiles
- G06V 2201/09: Recognition of logos
- Y04S 10/50: Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- 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
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS

## Abstract

In an example embodiment, a member profile corresponding to a member of a social networking service is obtained. Usage information for the member is then obtained, and one or more member metrics are calculated based on the member profile and usage information for the corresponding member. A plurality of features are extracted from the member profile and the one or more member metrics. The plurality of features is inserted into an organization name confidence score model to obtain a confidence score for an organization name in the member profile.

## Claims

### Claim 1

A computer-implemented method comprising;
obtaining, by a processor, a plurality of sample member profiles, each sample member profile corresponding to a sample member of a social networking service;
for each of the plurality of sample member profiles:
obtaining, by the processor, usage information for a corresponding sample member;
calculating, by the processor, one or more member metrics based on the sample member profile and usage information for the corresponding sample member;
extracting, by the processor, a first plurality of features from the sample member profile and the one or more member metrics;
feeding, by the processor, the first plurality of features into a supervised machine learning organization confidence score algorithm to train an organization name confidence score model to calculate a confidence score for a particular member profile indicating a probability that an organization name in the particular member profile is accurate, the supervised machine learning organization confidence score algorithm executed by the computer to implement a supervised machine learning classifier;
obtaining, by the processor, a member profile corresponding to a member of a social networking service;
obtaining, by the processor, usage information for the member;
calculating, by the processor, one or more member metrics based on the member profile and usage information for the corresponding member;
extracting, by the processor, a second plurality of features from the member profile and the one or more member metrics;
inputting, by the processor, the second plurality of features into the organization name confidence score model to obtain a confidence score for an organization name in the member profile.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
based on the confidence score for the organization name in the member profile, combining a first organization record corresponding to the organization name with a second organization record.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
based on the confidence score for the organization name in the member profile, adding the organization name to an organization record that is missing an organization name, the organization record having at least one field matching a field in the member profile.

### Claim 4 (depends on 1)

The method of claim 1, wherein the one or more member metrics include how frequently a member accesses the social networking service.

### Claim 5 (depends on 1)

The method of claim 1, wherein the one or more member metrics include how frequently a member updates a member profile on the social networking service.

### Claim 6 (depends on 1)

The method of claim 1, wherein the one or more member metrics include how frequently a member communicates with other members via the social networking service.

### Claim 7 (depends on 1)

The method of claim 1, wherein the first plurality of features and the second plurality of features include a power user score calculated based on the one or more member metrics.

### Claim 8 (depends on 1)

The method of claim 1, wherein the first plurality of features and the second plurality of features include a connection density score calculated on based on connections in the member profile, the connection density score indicating a level at which the member is connected to other members having member profiles in a group with the member profile.

### Claim 9 (depends on 1)

The method of claim 1, wherein the first plurality of features and the second plurality of features include an email connection density score calculated based on an email address in the member profile, the connection density score indicating a level at which the email address contains a domain that is shared with email addresses of other members having member profiles in a group with the member profile.

### Claim 10

A system comprising:
a processor;
a computer-readable medium having instructions stored there on, which, when executed by the processor, cause the system to perform operations comprising:
obtaining a plurality of sample member profiles, each sample member profile corresponding to a sample member of a social networking service;
for each of the plurality of sample member profiles:
obtaining usage information for a corresponding sample member;
calculating one or more member metrics based on the sample member profile and usage information for the corresponding sample member;
extracting a first plurality of features from the sample member profile and the one or more member metrics;
feeding, using a computer, the first plurality of features into a supervised machine learning organization confidence score algorithm to train an organization name confidence score model to calculate a confidence score for a particular member profile indicating a probability that an organization name in the particular member profile is accurate, the supervised machine learning organization confidence score algorithm executed by the computer to implement a supervised machine learning classifier;
obtaining a member profile corresponding to a member of a social networking service;
obtaining usage information for the member;
calculating one or more member metrics based on the member profile and usage information for the corresponding member;
extracting a second plurality of features from the member profile and the one or more member metrics;
inputting the second plurality of features into the organization name confidence score model to obtain a confidence score for an organization name in the member profile.

### Claim 11 (depends on 10)

The system of claim 10, wherein the operations further comprise:
based on the confidence score for the organization name in the member profile, combining a first organization record corresponding to the organization name with a second organization record.

### Claim 12 (depends on 10)

The system of claim 10, wherein the operations further comprise:
based on the confidence score for the organization name in the member profile, adding the organization name to an organization record that is missing an organization name, the organization record having at least one field matching a field in the member profile.

### Claim 13 (depends on 10)

The system of claim 10, wherein the one or more member metrics include how frequently a member accesses the social networking service.

### Claim 14 (depends on 10)

The system of claim 10, wherein the one or more member metrics include how frequently a member updates a member profile on the social networking service.

### Claim 15 (depends on 10)

The system of claim 10, wherein the one or more member metrics include how frequently a member communicates with other members via the social networking service.

### Claim 16 (depends on 10)

The system of claim 10, wherein the first plurality of features and the second plurality of features include a power user score calculated based on the one or more member metrics.

### Claim 17 (depends on 10)

The system of claim 10, wherein the first plurality of features and the second plurality of features include a connection density score calculated based on connections in the member profile, the connection density score indicating a level at which the member is connected to other members having member profiles in a group with the member profile.

### Claim 18 (depends on 10)

The system of claim 10, wherein the first plurality of features and the second plurality of features include an email connection density score calculated on based on an email address in the member profile, the connection density score indicating a level at which the email address contains a domain that is shared with email addresses of other members having member profiles in a group with the member profile.

### Claim 19

A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations comprising:
obtaining a plurality of sample member profiles, each sample member profile corresponding to a sample member of a social networking service;
for each of the plurality of sample member profiles:
obtaining usage information for a corresponding sample member;
calculating one or more member metrics based on the sample member profile and usage information for the corresponding sample member;
extracting a first plurality of features from the sample member profile and the one or more member metrics;
feeding, using a computer, the first plurality of features into a supervised machine learning organization confidence score algorithm to train an organization name confidence score model to calculate a confidence score for a particular member profile indicating a probability that an organization name in the particular member profile is accurate, the supervised machine learning organization confidence score algorithm executed by the computer to implement a supervised machine learning classifier;
obtaining a member profile corresponding to a member of a social networking service;
obtaining usage information for the member;
calculating one or more member metrics based on the member profile and usage information for the corresponding member;
extracting a second plurality of features from the member profile and the one or more member metrics;
inputting the second plurality of features into the organization name confidence score model to obtain a confidence score for an organization name in the member profile.

### Claim 20 (depends on 19)

The non-tangible machine-readable medium of claim 19, wherein the operations further comprise:
based on the confidence score for the organization name in the member profile, combining a first organization record corresponding to the organization name with a second organization record.