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Google’s Hummingbird Update: How It Changed Search

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Google's Hummingbird Update: How It Changed Search

Google Hummingbird was a rewrite of Google’s algorithm that consciously anticipated the needs of searching on mobile devices, in particular by enabling conversational search.

Hummingbird set the stage for dramatic advances in search.

Google never published an explainer of what Hummingbird was.

However, there are records of Googlers explaining what it is.

Let’s take a look at what Google’s Hummingbird update did, how it impacted natural language search, and what Googlers and SEO industry experts had to say about it.

Google Hummingbird

The Google Hummingbird update was put into place in August 2013 and announced one month later, in September 2013.

The Hummingbird update has been described by Google as the biggest change to the algorithm since 2001.

It was also described by multiple Googlers as a total rewrite of the core algorithm.

Yet, despite the scale of this update, the immediate effect was so subtle that the update was largely unnoticed.

It seems contradictory for an update to be both wide-scale and unnoticeable.

The contradiction, however, is made more understandable when Hummingbird is viewed as the starting point for subsequent waves of innovations that were made possible by it.

Hummingbird Defined

The update was called Hummingbird because it is said to make Google’s core algorithm more precise and fast.

We all know what fast means.

Arguably the most important part of Hummingbird is the word “precise” because precision is about accuracy and being exact.

As you’ll see in the following linked conversations by Googlers, Hummingbird enabled Google to be more precise about what a query meant.

And, by moving away from matching keywords in a query to keywords on a webpage, Google became more precise about showing pages that matched the topic inherent in the search query.

A Complete Rewrite Of The Core Algorithm

Former Google Software Engineer Matt Cutts described Hummingbird as a rewrite of the entire core algorithm.

That doesn’t mean it was a brand new algorithm but rather the core algorithm was rewritten in a way that makes it able to do its job better.

In a December 4, 2013 video interview, Matt Cutts said that the Hummingbird algorithm was a rewrite of Google’s core search algorithm.

Matt Cutts explained (at the 1:20:00 mark of this video):

“Hummingbird is a rewrite of the core search algorithm.

Just to do a better job of matching the users queries with documents, especially for natural language queries, you know the queries get longer, they have more words in them and sometimes those words matter and sometimes they don’t.”

Some people think of Hummingbird as a component of Google’s core algorithm, much like Panda and Penguin are parts of the core algorithm.

Matt Cutts makes it clear that Hummingbird was not a part of the core algorithm. It was a rewrite of the core algorithm.

One of the goals of the rewrite was to make the core algorithm better able to match queries to webpages and to be able to handle longer conversational search queries.

Hummingbird Affected 90% Of Searches

Matt Cutts followed up by sharing that the precision and quickness of Hummingbird were present in 90% of searches.

Matt said:

“And so Hummingbird affects 90% of all searches.

But usually just to a small degree because we’re saying this particular document isn’t really about what the user searched for because maybe they said, ‘Okay Google, now how do I put a rutabaga up into space, what really matters is rutabaga and space and not how do I’.”

Hummingbird And Natural Language Search

When Hummingbird came out, some in the search community advised that it might be a good idea to change how content is written in order to match how searchers were searching.

Common advice was to convert articles to use more phrases like, how to.

While the advice was well-intentioned, it was also misguided.

What Hummingbird did was to make long conversational search queries understandable to the search engine.

In Matt’s example, Google was ignoring certain words in order to better understand what the search query really meant.

In the old algorithm, Google would try to rank a webpage that contained all the words in a search query, to do a word-for-word match between the search query and the webpage.

What Matt was explaining is that Google was now ignoring certain words in order to understand the queries and then use that understanding to rank a webpage.

Hummingbird enabled Google to stop relying on matching keywords to webpages, and instead, focus more on what the search query means.

That’s what he meant when he started his explanation of Hummingbird by saying:

“Just to do a better job of matching the users queries with documents, especially for natural language queries…”

Is There A Hummingbird Patent?

Some of the things that Hummingbird was doing with search queries was rewriting them by using techniques like query expansion.

For example, there are multiple ways to search for the same thing, using different words.

Five different search queries can be equal to one search query, with the only difference being that they use different words that are synonyms of each other.

With something like query expansion, Google could use synonyms to broaden the group of potential webpages to rank.

After Hummingbird, Google was no longer exact matching keywords in search queries to keywords in webpages.

This was something different that began happening after the Hummingbird update.

Bill Slawski wrote about a patent that describes things that the Hummingbird algorithm is said to be able to do, especially with regard to natural language queries.

Bill writes in his article:

“When the Hummingbird patent came out on Google’s 15th Birthday, it was like an overhaul of Google’s infrastructure, such as the Caffeine update, in the way that Googles index worked.

One thing that we were told was that the process behind Hummingbird was to rewrite queries more intelligently.”

The patent that Bill discovered and wrote about describes a breakthrough in how search queries are handled.

This patent described a way to make a search engine perform better for natural language search queries.

Thanks to Matt Cutts, we know that Hummingbird was a total rewrite of Google’s search algorithm.

Thanks to Bill Slawski, we can read a patent that describes some of the new things that the Hummingbird update made possible.

Does The Hummingbird Update Do New Things?

Similar to what Bill Slawski touched on about the patent he discovered, Matt Cutts said that the Hummingbird update allows Google to remove words from a mobile search query.

Matt Cutts said at a Pubcon 2013 keynote session that Hummingbird allows the algorithm to remove words that aren’t relevant to the context of what a user wants to find from a mobile voice search query.

You can watch Matt discuss Google Hummingbird in this video at the 6:35 minute mark:

“…the idea behind Hummingbird is, if you’re doing a query, it might be a natural language query, and you might include some word that you don’t necessarily need, like uh… [what’s the capital of Texas my dear]?

Well, ‘my dear’ doesn’t really add anything to that query.

It would be totally fine if you said just, [what is the capital of Texas?]

Or, [what is the capital of ever lovin’ Texas?]

Or, [what is the capital of crazy rebel beautiful Texas?]

Some of those words don’t matter as much.

And previously, Google used to match just the words in the query.

Now, we’re starting to say which ones are actually more helpful and which ones are more important.

And so Hummingbird is a step in that direction, where if you are saying or typing a longer query then we’re going to figure out which words matter more…”

There are three key takeaways from Matt’s explanation of what Hummingbird does:

  • Google no longer relies on just matching keywords in the search query.
  • Google identifies which words in a query are important and which are not.
  • Hummingbird is a step in the direction of understanding queries more precisely.

Hummingbird Did Not Initially Affect SEO

As previously mentioned, some SEOs advised updating webpages to make them match longer conversational search queries.

But just because Google was learning to understand conversational search queries did not mean that webpages needed to become more conversational.

In the above video recording of the 2013 Pubcon keynote address, Matt goes on to remark that Hummingbird doesn’t affect SEO.

Matt observed:

“Now, there’s a lot of articles written about Hummingbird, when even when just the code name was known, people were like, okay, how will Hummingbird affect SEO?

And even though people don’t know exactly what Hummingbird is they’re still going to write 500 words about how Hummingbird affects SEO.

And the fact is it doesn’t affect it that much.”

The Effect Of Hummingbird On Search Was Subtle

Matt next describes how the changes that Hummingbird introduced were subtle and not disruptive.

He said that the effect of the Hummingbird update was wide but the effect itself was small.

Matt explained:

“It affected 90% of queries but only to a small degree and we rolled it out over a month without people even noticing.

So it’s a subtle change, it’s not something that you need to worry about. It’s not going to rock your world like Panda and Penguin.

It’s just going to make the results a little bit better and especially on those long-tail queries or really specific queries, make them much better.”

Hummingbird & Long-Tail Keywords

Cutts continued his discussion about Hummingbird by describing its effect on sites that targeted extremely specific long-tail keywords.

We have to stop here and talk about long-tail phrases in order to better understand Matt Cutts is talking about because this part of the Hummingbird update had an effect on some SEO practices.

Long-tail keywords are search phrases that aren’t searched very often.

Many people associate long-tail with keyword phrases that have a lot of words in them – but that’s not what long-tail is.

Long tail, within the context of SEO, simply describes keyword phrases that are rarely searched for.

While some long-tail phrases may have a lot of words in them, the amount of words in a search query is not the defining characteristic of a long-tail search phrase.

The rarity of how often a phrase is used as a search query is what defines what a long-tail search query is.

The opposite of a Long-tail Search Query is a Head Phrase Search Query.

Head phrases are keyword phrases that have a high search query volume.

Screenshot by author, March 2022

Because there are so many people using the internet, spammers figured out that it was easy to rank for rare search queries so they began targeting millions of long-tail search phrases in order to attract thousands of site visitors every day and make money from ads.

Prior to Hummingbird, many legitimate sites also routinely targeted rare keyword phrase combinations for the same reason as the spammers, because they were easy to rank for.

After Hummingbird, Google began using some of the techniques that Bill Slawski reviewed in his article about the Google patent.

This change to how Google handled long-tail keyword phrases that Hummingbird introduced had a profound effect on how content was written, as many publishers learned it was not profitable to focus on thousands of granular long-tail search queries.

Cutts explained this long-tail aspect of the Hummingbird update:

“So unless you are a spammer and you’re targeting, ‘how many SEOs does it take to change a light bulb,’ and you’ve got all the keywords, you’ve got 15 variants of it, you’ve got a page for each one, you know.

If you’re doing those really long-tail things, then it might affect you.

But in general people don’t need to worry that much about Hummingbird.”

Despite his confidence that this change wouldn’t affect normal sites, Hummingbird did affect some legitimate non-spam sites that optimized webpages for highly specific search queries.

Hummingbird Was A Step Toward Conversational Search

Because Hummingbird was a rewrite of the old algorithm, which made it more precise and fast, it can be seen as a step toward today’s more modern search engine.

All of that one-to-one matching of keywords in the search query to keywords on a webpage was gone.

Combined with other improvements, such as the introduction of the Knowledge Graph, Google was now on its way to developing a deeper understanding of what users meant with their search queries and what webpages were really about.

That’s a vast improvement over the old search engine that matched keywords in the search queries to webpage content.

The improvements introduced by Google Hummingbird may have made this direction possible.

And though Cutts described the initial effect as subtle, these changes eventually lead to a more robust spoken language search experience that had a profound effect on what webpages were ranked and which pages were not ranked.

Search Innovations Sped Up After Hummingbird

What we know about Hummingbird is that it helped Google to better understand conversational search queries; it was a rewrite of the old Google core algorithm; that it helped Google understand the context of search queries; and that Google improved its ability to answer long-tail search queries.

Many significant changes to Google’s algorithm happened within months of the release of the Hummingbird update.

User Intent

Of course, when the conversation is about understanding user search queries, we’re now getting into the realm of understanding user intent.

Being able to remove superfluous words and get to the meaning of what a search query means is a step closer to understanding the user intent.

Fast Conversational Search – June 11, 2014

Conversational search began taking off in a big way in the spring of 2014, about six months after Hummingbird was introduced.

That was when Google was able to integrate the moment current events into the search results.

Read: Let Google Be Your Guide to the Beautiful Game with Real-time Highlights and Trends

Google Hummingbird was so-named because it was fast and accurate.

This new feature gave Google Search the ability to display sports scores in real-time.

There’s nothing faster than real-time, and sports scores are an example of precise information.

Ok Google Comes Online – June 26, 2014

A few weeks later Google unveiled the “Ok Google” conversational search product.

The introduction of the “Ok Google” voice command could be said to be the moment Google finally achieved its goal of providing a true conversational search experience.

Read:Ok Google” From Any Screen 

Conversational search depends heavily on understanding what people mean when they ask a question. That’s a huge leap forward.

Many other breakthroughs in conversational search followed

Conversational Search And Planning – October 14, 2014

Pravir Gupta, Senior Director of Engineering, Google Assistant posted an article on Google’s blog instructing how to utilize conversational search for doing things like verbally asking Google to find a restaurant or to give the user a reminder.

Read: Fall into Easier Planning with Google

Maybe it’s a coincidence or maybe it’s not that many of these conversational search innovations were released within months of Google’s Hummingbird update.

Regardless, these kinds of conversational search improvements are the sorts of things that Google Hummingbird was meant to support.

Though our understanding of Google Hummingbird could be better, what we do know makes it very clear that the Hummingbird update set Google on course to meet the challenges of mobile search and caused the SEO community to re-evaluate what it meant to build search optimized content.

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Featured Image: Henk Bogaard/Shutterstock

In-post Image #2: D-Krab/Shutterstock, modified by author, March 2022 

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Google Clarifies Organization Merchant Returns Structured Data

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Google updates organization structured data for merchant returns

Google quietly updated their organization structured data documentation in order to clarify two points about merchant returns in response to feedback about an ambiguity in the previous version.

Organization Structured Data and Merchant Returns

Google recently expanded their Organization structured data so that it could now accommodate a merchant return policy. The change added support for adding a sitewide merchant return policy.

The original reason for adding this support:

“Adding support for Organization-level return policies

What: Added documentation on how to specify a general return policy for an Organization as a whole.

Why: This makes it easier to define and maintain general return policies for an entire site.”

However that change left unanswered about what will happen if a site has a sitewide return policy but also has a different policy for individual products.

The clarification applies for the specific scenario of when a site uses both a sitewide return policy in their structured data and another one for specific products.

What Takes Precedence?

What happens if a merchant uses both a sitewide and product return structured data? Google’s new documentation states that Google will ignore the sitewide product return policy in favor of a more granular product-level policy in the structured data.

The clarification states:

“If you choose to provide both organization-level and product-level return policy markup, Google defaults to the product-level return policy markup.”

Change Reflected Elsewhere

Google also updated the documentation to reflect the scenario of the use of two levels of merchant return policies in another section that discusses whether structured data or merchant feed data takes precedence. There is no change to the policy, merchant center data still takes precedence.

This is the old documentation:

“If you choose to use both markup and settings in Merchant Center, Google will only use the information provided in Merchant Center for any products submitted in your Merchant Center product feeds, including automated feeds.”

This is the same section but updated with additional wording:

“If you choose to use both markup (whether at the organization-level or product-level, or both) and settings in Merchant Center, Google will only use the information provided in Merchant Center for any products submitted in your Merchant Center product feeds, including automated feeds.”

Read the newly updated Organization structured data documentation:

Organization (Organization) structured data – MerchantReturnPolicy

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What Is It & How To Write It

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What Is It & How To Write It

In this guide, you will learn about alternative text (known as alt text): what it is, why it is important for on-page SEO, how to use it correctly, and more.

It’s often overlooked, but every image on your website should have alt text. More information is better, and translating visual information into text is important for search engine bots attempting to understand your website and users with screen readers.

Alt text is one more source of information that relates ideas and content together on your website.

This practical and to-the-point guide contains tips and advice you can immediately use to improve your website’s image SEO and accessibility.

What Is Alt Text?

Alternative text (or alt text) – also known as the alt attribute or the alt tag (which is not technically correct because it is not a tag) – is simply a piece of text that describes the image in the HTML code.

What Are The Uses Of Alt Text?

The original function of alt text was simply to describe an image that could not be loaded.

Many years ago, when the internet was much slower, alt text would help you know the content of an image that was too heavy to be loaded in your browser.

Today, images rarely fail to load – but if they do, then it is the alt text you will see in place of an image.

Screenshot from Search Engine Journal, May 2024

Alt text also helps search engine bots understand the image’s content and context.

More importantly, alt text is critical for accessibility and for people using screen readers:

  • Alt text helps people with disabilities (for example, using screen readers) learn about the image’s content.

Of course, like every element of SEO, it is often misused or, in some cases, even abused.

Let’s now take a closer look at why alt text is important.

Why Alt Text Is Important

The web and websites are a very visual experience. It is hard to find a website without images or graphic elements.

That’s why alt text is very important.

Alt text helps translate the image’s content into words, thus making the image accessible to a wider audience, including people with disabilities and search engine bots that are not clever enough yet to fully understand every image, its context, and its meaning.

Why Alt Text Is Important For SEO

Alt text is an important element of on-page SEO optimization.

Proper alt text optimization makes your website stand a better chance of ranking in Google image searches.

Yes, alt text is a ranking factor for Google image search.

Depending on your website’s niche and specificity, Google image search traffic may play a huge role in your website’s overall success.

For example, in the case of ecommerce websites, users very often start their search for products with a Google image search instead of typing the product name into the standard Google search.

Screenshot from search for [Garmin forerunner]Screenshot from search for [Garmin forerunner], May 2024

Google and other search engines may display fewer product images (or not display them at all) if you fail to take care of their alt text optimization.

Without proper image optimization, you may lose a lot of potential traffic and customers.

Why Alt Text Is Important For Accessibility

Visibility in Google image search is very important, but there is an even more important consideration: Accessibility.

Fortunately, in recent years, more focus has been placed on accessibility (i.e., making the web accessible to everyone, including people with disabilities and/or using screen readers).

Suppose the alt text of your images actually describes their content instead of, for example, stuffing keywords. In that case, you are helping people who cannot see this image better understand it and the content of the entire web page.

Let’s say one of your web pages is an SEO audit guide that contains screenshots from various crawling tools.

Would it not be better to describe the content of each screenshot instead of placing the same alt text of “SEO audit” into every image?

Let’s take a look at a few examples.

Alt Text Examples

Finding many good and bad examples of alt text is not difficult. Let me show you a few, sticking to the above example with an SEO audit guide.

Good Alt Text Examples

So, our example SEO guide contains screenshots from tools such as Google Search Console and Screaming Frog.

Some good examples of alt text may include:

”The
”Google
”List
”Screaming

Tip: It is also a good idea to take care of the name of your file. Using descriptive file names is not a ranking factor, but I recommend this as a good SEO practice.

Bad And/Or Spammy Alt Text Examples

I’ve also seen many examples of bad alt text use, including keyword stuffing or spamming.

Here is how you can turn the above good examples into bad examples:

”google search console coverage report
”google
”seo
”seo

As you can see, the above examples do not provide any information on what these images actually show.

You can also find examples and even more image SEO tips on Google Search Central.

Common Alt Text Mistakes

Stuffing keywords in the alt text is not the only mistake you can make.

Here are a few examples of common alt text mistakes:

  • Failure to use the alt text or using empty alt text.
  • Using the same alt text for different images.
  • Using very general alt text that does not actually describe the image. For example, using the alt text of “dog” on the photo of a dog instead of describing the dog in more detail, its color, what it is doing, what breed it is, etc.
  • Automatically using the name of the file as the alt text – which may lead to very unfriendly alt text, such as “googlesearchconsole,” “google-search-console,” or “photo2323,” depending on the name of the file.

Alt Text Writing Tips

And finally, here are the tips on how to write correct alt text so that it actually fulfills its purpose:

  • Do not stuff keywords into the alt text. Doing so will not help your web page rank for these keywords.
  • Describe the image in detail, but still keep it relatively short. Avoid adding multiple sentences to the alt text.
  • Use your target keywords, but in a natural way, as part of the image’s description. If your target keyword does not fit into the image’s description, don’t use it.
  • Don’t use text on images. All text should be added in the form of HTML code.
  • Don’t write, “this is an image of.” Google and users know that this is an image. Just describe its content.
  • Make sure you can visualize the image’s content by just reading its alt text. That is the best exercise to make sure your alt text is OK.

How To Troubleshoot Image Alt Text

Now you know all the best practices and common mistakes of alt text. But how do you check what’s in the alt text of the images of a website?

You can analyze the alt text in the following ways:

Inspecting an element (right-click and select Inspect when hovering over an image) is a good way to check if a given image has alt text.

However, if you want to check that in bulk, I recommend one of the below two methods.

Install Web Developer Chrome extension.

Screenshot of Web Developer Extension in Chrome by authorScreenshot from Web Developer Extension, Chrome by author, May 2024

Next, open the page whose images you want to audit.

Click on Web Developer and navigate to Images > Display Alt Attributes. This way, you can see the content of the alt text of all images on a given web page.

The alt text of images is shown on the page.Screenshot from Web Developer Extension, Chrome by author, May 2024

How To Find And Fix Missing Alt Text

To check the alt text of the images of the entire website, use a crawler like Screaming Frog or Sitebulb.

Crawl the site, navigate to the image report, and review the alt text of all website images, as shown in the video guide below.

You can also export only images that have missing alt text and start fixing those issues.

Alt Text May Not Seem Like A Priority, But It’s Important

Every source of information about your content has value. Whether it’s for vision-impaired users or bots, alt text helps contextualize the images on your website.

While it’s only a ranking factor for image search, everything you do to help search engines understand your website can potentially help deliver more accurate results. Demonstrating a commitment to accessibility is also a critical component of modern digital marketing.

FAQ

What is the purpose of alt text in HTML?

Alternative text, or alt text, serves two main purposes in HTML. Its primary function is to provide a textual description of an image if it cannot be displayed. This text can help users understand the image content when technical issues prevent it from loading or if they use a screen reader due to visual impairments. Additionally, alt text aids search engine bots in understanding the image’s subject matter, which is critical for SEO, as indexing images correctly can enhance a website’s visibility in search results.

Can alt text improve website accessibility?

Yes, alt text is vital for website accessibility. It translates visual information into descriptive text that can be read by screen readers used by users with visual impairments. By accurately describing images, alt text ensures that all users, regardless of disability, can understand the content of a web page, making the web more inclusive and accessible to everyone.

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Google Dials Back AI Overviews In Search Results, Study Finds

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Photo of a mobile device in mans hand with generative google AI Overview on the screen.

According to new research, Google’s AI-generated overviews have undergone significant adjustments since the initial rollout.

The study from SE Ranking analyzed 100,000 keywords and found Google has greatly reduced the frequency of AI overviews.

However, when they appear, they’re more detailed than they were previously.

The study digs into which topics and industries are more likely to get an AI overview. It also looks at how the AI snippets interact with other search features like featured snippets and ads.

Here’s an overview of the findings and what they mean for your SEO efforts.

Declining Frequency Of AI Overviews

In contrast to pre-rollout figures, 8% of the examined searches now trigger an AI Overview.

This represents a 52% drop compared to January levels.

Yevheniia Khromova, the study’s author, believes this means Google is taking a more measured approach, stating:

“The sharp decrease in AI Overview presence likely reflects Google’s efforts to boost the accuracy and trustworthiness of AI-generated answers.”

Longer AI Overviews

Although the frequency of AI overviews has decreased, the ones that do appear provide more detailed information.

The average length of the text has grown by nearly 25% to around 4,342 characters.

In another notable change, AI overviews now link to fewer sources on average – usually just four links after expanding the snippet.

However, 84% still include at least one domain from that query’s top 10 organic search results.

Niche Dynamics & Ranking Factors

The chances of getting an AI overview vary across different industries.

Searches related to relationships, food and beverages, and technology were most likely to trigger AI overviews.

Sensitive areas like healthcare, legal, and news had a low rate of showing AI summaries, less than 1%.

Longer search queries with ten words were more likely to generate an AI overview, with a 19% rate indicating that AI summaries are more useful for complex information needs.

Search terms with lower search volumes and lower cost-per-click were more likely to display AI summaries.

Other Characteristics Of AI Overviews

The research reveals that 45% of AI overviews appear alongside featured snippets, often sourced from the exact domains.

Around 87% of AI overviews now coexist with ads, compared to 73% previously, a statistic that could increase competition for advertising space.

What Does This Mean?

SE Ranking’s research on AI overviews has several implications:

  1. Reduced Risk Of Traffic Losses: Fewer searches trigger AI Overviews that directly answer queries, making organic listings less likely to be demoted or receive less traffic.
  2. Most Impacted Niches: AI overviews appear more in relationships, food, and technology niches. Publishers in these sectors should pay closer attention to Google’s AI overview strategy.
  3. Long-form & In-Depth Content Essential: As AI snippets become longer, companies may need to create more comprehensive content beyond what the overviews cover.

Looking Ahead

While the number of AI overviews has decreased recently, we can’t assume this trend will continue.

AI overviews will undoubtedly continue to transform over time.

It’s crucial to monitor developments closely, try different methods of dealing with them, and adjust game plans as needed.


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