Have Teachable Moments at Google Been Replaced with Related Questions?

Was Google to Help Searchers find Search Results with Teachable Moments?

Google was granted a patent this month about what it refers to as providing “teachable moments” to searchers to “promote efficient interaction with a search system.”

Google is concerned about helping searchers find what they might be looking for. This patent tells us about some of the steps that they may take to help searchers learn more about finding things through the search engine.

This patent may be obsolete because of related questions that Google is showing in search results.

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The purpose of the patent is to assist searchers looking for information, based upon a series of queries that they perform.

The patent describes to us what it calls “innovative aspects of the subject matter described in this specification.”

Those involve:

• Receiving a series of queries from a searcher
• Obtaining a query pattern from the queries based on entities and other aspects that are associated with queries
• Deciding partially based on the query pattern that a teachable moment interface should be displayed with search results
• Transmitting content to be displayed in the teachable moment interface on searcher’s device

Some additional features described in the patent:

1. They want to make sure that there is a query pattern a searcher is using that they can include in a teachable moment
2. They want to limit the number of times that they might show teachable moments to searchers to a threshold number
3. A query pattern includes one or more entities and sets of aspects related to those, comparing the entities, and the sets of aspect across queries, and that the searcher would benefit from being shown the teachable moment information

This teachable moments patent can be found at:

Identifying teachable moments for contextual search
Inventors: Behshad Behzadi
Assignee: Google LLC
US Patent: 10,528,564
Granted: January 7, 2020
Filed: September 25, 2018

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a series of queries provided from a user device, the series of queries including two or more queries, obtaining a query pattern of the series of queries based on one or more entities and one or more aspects associated with the two or more queries, and determining, at least partially based on the query pattern, that a teachable moment interface is to be displayed with search results, and in response, transmitting content to be displayed in the teachable moment interface on a computing device.

When Teachable Moments Might Take Place

The drawings from the patent show off an example of when Google might take action in a teachable moment here:

The patent also provides details of when teachable moments might be shown, and that they might be based upon recent queries from a searcher:

Following implementations of the present disclosure, the example environment also includes a search assistance system communicably coupled to the search system, e.g., directly coupled or coupled over a network such as a network. Although the search system and the search assistance system are depicted as separate systems in FIG. 1, it is contemplated that the search system can include the search assistance system.

In some implementations, the search assistance system identifies opportunities to assist users in interacting with the search system based on queries received from the users. In some examples, and as described in further detail herein, the search assistance system can trigger a user interface, referred to herein as a teachable moment interface, which can be displayed in the search results display. In some examples, the teachable moment interface informs a user of a manner, in which to interact with the search system to improve the efficiency of the information retrieval process.

For example, the teachable moment interface can include content that is based on one or more queries recently submitted by a user, and that informs the user of a more efficient manner to submit queries to the search system.

The patent tells us that these teachable moments might be typed textual queries or even spoken queries.

Entities in Query Patterns

in 2012, “introducing the Knowledge Graph” told us about indexing Real World Things.

Before Google introduced us to its knowledge graph in 2012, it used to have us search to match the words in a query we performed with words on a web page. With the Knowledge Graph, Google told us that they were indexing real-world things or entities, which are specific people, places, and things. This patent talks about entities when telling us about the teachable moments that it might show. It does this when telling us about query patterns:

In some implementations, a query pattern is based on one or more entities associated with queries in the series of queries. For example, a query can be associated with an entity. As another example, a query can be associated with a plurality of entities. For example, a query can include one or more terms, e.g., words, phrases, and at least one term can correspond to an entity. In some examples, an entity can include a person, place, country, landmark, animal, historical event, organization, business, sports team, sporting event, movie, song, album, game, work of art, or any other appropriate entity.

Google uses this entity information from a query pattern to identify an entity or entities from an entity graph and information associated with them:

In some examples, one or more terms of a query are provided to an entity annotator that selects one or more entities from an entity graph, and the one or more entities are included in the set of entities associated with the query.

The patent tells us about examples of the kinds of entity information that could be found in a query pattern:

• A number of entities and information associated with them can be stored as structured data in the entity graph
• An entity graph includes a number of nodes and edges between nodes
• A node represents an entity and an edge represents a relationship between entities
• The entity graph can be provided based on an example schema that structures data based on domains, types and properties
• A domain includes one or more types that share a namespace
• A namespace is provided as a directory of uniquely named objects, where each object in the namespace has a unique name (identifier)
• A type denotes an “is a” relationship about a topic, and is used to hold a collection of properties
• A topic represents an entity, such as a person, place or thing
• Each topic can have one or more types associated with them
• A property is associated with a topic and defines a “has a” relationship between the topic and a value of the property
• The value of the property can include another topic
• An entity can be associated with a unique identifier within the entity graph (For example, the entity Alcatraz Island can be assigned the identifier /m/0h594.)

In other cases, a number of entities could be provided in one or more databases, such as a tale that can provide data associated with each entity like:

• A name of the entity
• A location of the entity
• One or more types assigned to the entity
• One or more ratings associated with the entity
• A set of entity query patterns associated with the entity
• Any other appropriate information that can be provided for the entity

Associated Terms in Query Patterns

A query can include a term that is associated with an entity. It can be a primary entity and a second entity. The patent gives us this information to flesh this out:

In the example query [obama white house], a primary entity can include “Barrack Obama,” and secondary entities can include “President of the United States,” and “White House.”

Another example tells us about a primary entity and secondary entities and additional non-entity terms:

An example series of queries can include [obama white house], [obama white house speech], and [obama white house speech today]. In this example series of queries, entity terms can include “Obama,” associated with the primary entity “Barrack Obama” and the secondary entity “President of the United States,” and “white house,” associated with the secondary entity “White House.” In this example series of queries, non-entity terms can include “speech” and “today.” In this example, the query pattern can include the primary entity and the secondary entities being consistent within the series of queries, e.g., associated with each query of the series of queries, and the non-entity terms being inconsistent within the series of queries.

Since this patent is about how Google might generate teachable moments from query patterns, they are providing several examples of queries, like this one, too:

Another example series of queries can include [obama white house speeches], [bush white house speeches], and [reagan white house speeches]. In this example series of queries, entity terms can include “Obama,” “bush,” and “Reagan” respectively associated with the primary entity “Barrack Obama,” the primary entity “George W. Bush,” the primary entity “George H. W. Bush,” the primary entity “Ronald Reagan,” and the secondary entity “President of the United States,” and “white house,” associated with the secondary entity “White House.” In this example series of queries, a non-entity term can include “speeches.” In this example, the query pattern can include the primary entity being inconsistent within the series of queries, and the non-entity term(s) being consistent within the series of queries.

We are told that “a query pattern can be recognized for a series of queries.”

The patent tells us that as a series of queries is received it might review entity terms and non-entity terms in those queries for consistency. If there is consistency, Google might recognize a query pattern from that series of queries. When it recognizes a query pattern from a series of queries, it might show a teachable moment interface to a searcher, to aid them in “more efficiently interacting with the search system.”

Query Patterns and Respective characteristics

Query pattern also include respective characteristics. These can be things such as:

• A length of queries
• Consistency (or lack of consistency) of aspects across queries in a series of queries

Query patterns may indicate whether it is associated with a teachable moment.

An Example:

afirst query patterns include relatively long, similar queries in a series that indicate the same entit[y/ies]. The first query pattern can be indicated as corresponding to a teachable moment. That is, for a series of queries reflecting the first query pattern, a teachable moment interface can be provided.

An example series of queries reflecting the first query pattern can include:

[who hosted the world cup in 1990]
[who hosted the world cup in 1994]
[who hosted the world cup in 1998]

Another example of a query pattern

[how tall is the empire state building]
[when was the empire state building built]
[what restaurants are near the empire state building]

Instead of presenting me with a teachable moment, when I search for the first two of the queries in the second set of examples, Google shows me “people also search for” images and “people also ask” related questions, like this:

The “people also ask” related questions seem like good examples that may be based upon a desire to show teachable moment type queries based upon a search that wouldn’t necessarily have to analyze query templates.

performing one of the world cup queries also returns a featured snippet and a set of related questions that are good examples of queries that fit into the same pattern:

As another example, the following example series of queries, introduced above, can be considered: [dog pictures] [cat pictures] [fox pictures]

In response to this example series of queries, a teachable moment interface is not displayed to the user. More particularly, because the queries in the series are relatively short and are about the same aspect, e.g., pictures, even though of different entities, e.g., dog, cat, fox, the example series of queries does not represent a teachable moment that could promote a more conversational and efficient interaction with search systems.

Takeaways

While it may be possible that Google may decide, after seeing a series of queries from a searcher, to show a teachable moment, the related questions that Google is showing after receiving some queries are good examples of searches related in some way the query that triggers them. If related questions are inspired by a desire to show searchers teachable moments related to a query they perform, they do a good job of showing those off.

Maybe we will not see teachable moments, and maybe Google will decide that they are still worth showing.

Regardless of whether Google does or doesn’t show off those teachable moments, one thing I found interesting in this patent was they way they described query patterns that may trigger teachable moments in terms of entities that might be in those queries. It shows off the entity-first approach at Google that has taken the place of matching keywords in a query with keywords in documents.

Google Related questions may be the examples of queries that Google wants to show searchers to help them perform related queries, even doing the searches for them. The related questions that Google shows may be based upon query patterns involving entities just like described in this patent when it might show teachable moments based on those query patterns (I will be spending more time looking at related questions now to see if they appear to fit into patterns.)

About Bill Slawski

With more than 26 years of SEO experience and a Juris Doctor Degree, Bill Slawski is the foremost expert on Google’s patents as related to SEO. Patent Exploration is one of the quickest and most detailed ways to find new information about SEO. Bill is the Editor of SEO by the Sea, a prominent search engine optimization blog, where he is the author of over 1,300 posts. Bill’s experience includes Fortune 500 brands and some of the largest websites in the world. Bill is a contributing author for Moz, Search Engine Land, and Search Engine Journal. In 2014-2021, he spoke at industry-leading international conferences about topics including search engine algorithms, universal and blended search, personalization in search, search and social, and duplicate content problems, structured data, and schema