Apr 14 2025
Marketing To Machines Is The Future – Research Shows Why via @sejournal, @martinibuster

A new research paper explores how AI agents interact with online advertising and what shapes their decision-making. The researchers tested three leading LLMs to understand which kinds of ads influence AI agents most and what this means for digital marketing. As more people rely on AI agents to research purchases, advertisers may need to rethink strategy for a machine-readable, AI-centric world and embrace the emerging paradigm of “marketing to machines.”

Although the researchers were testing if AI agents interacted with advertising and what kinds influenced them the most, their findings also show that well-structured on-page information, like pricing data, is highly influential, which opens up areas to think about in terms of AI-friendly design.

An AI agent (also called agentic AI) is an autonomous AI assistant that performs tasks like researching content on the web, comparing hotel prices based on star ratings or proximity to landmarks, and then presenting that information to a human, who then uses it to make decisions.

AI Agents And Advertising

The research is titled Are AI Agents Interacting With AI Ads? and was conducted at the University of Applied Sciences Upper Austria. The research paper cites previous research on the interaction between AI Agents and online advertising that explore the emerging relationships between agentic AI and the machines driving display advertising.

Previous research on AI agents and advertising focused on:

  • Pop-up Vulnerabilities
    Vision-language AI agents that aren’t programmed to avoid advertising can be tricked into clicking on pop-up ads at a rate of 86%.
  • Advertising Model Disruption
    This research concluded that AI agents bypassed sponsored and banner ads but forecast disruption in advertising as merchants figure out how to get AI agents to click on their ads to win more sales.
  • Machine-Readable Marketing
    This paper makes the argument that marketing has to evolve toward “machine-to-machine” interactions and “API-driven marketing.”

The research paper offers the following observations about AI agents and advertising:

“These studies underscore both the potential and pitfalls of AI agents in online advertising contexts. On one hand, agents offer the prospect of more rational, data-driven decisions. On the other hand, existing research reveals numerous vulnerabilities and challenges, from deceptive pop-up exploitation to the threat of rendering current advertising revenue models obsolete.

This paper contributes to the literature by examining these challenges, specifically within hotel booking portals, offering further insight into how advertisers and platform owners can adapt to an AI-centric digital environment.”

The researchers investigate how AI agents interact with online ads, focusing specifically on hotel and travel booking platforms. They used a custom built travel booking platform to perform the testing, examining whether AI agents incorporate ads into their decision-making and explored which ad formats (like banners or native ads) influence their choices.

How The Researchers Conducted The Tests

The researchers conducted the experiments using two AI agent systems: OpenAI’s Operator and the open-source Browser Use framework. Operator, a closed system built by OpenAI, relies on screenshots to perceive web pages and is likely powered by GPT-4o, though the specific model was not disclosed.

Browser Use allowed the researchers to control for the model used for the testing by connecting three different LLMs via API:

  • GPT-4o
  • Claude Sonnet 3.7
  • Gemini 2.0 Flash

The setup with Browser Use enabled consistent testing across models by enabling them to use the page’s rendered HTML structure (DOM tree) and recording their decision-making behavior.

These AI agents were tasked with completing hotel booking requests on a simulated travel site. Each prompt was designed to reflect realistic user intent and tested the agent’s ability to evaluate listings, interact with ads, and complete a booking.

By using APIs to plug in the three large language models, the researchers were able to isolate differences in how each model responded to page data and advertising cues, to observe how AI agents behave in web-based decision-making tasks.

These are the ten prompts used for testing purposes:

  1. Book a romantic holiday with my girlfriend.
  2. Book me a cheap romantic holiday with my boyfriend.
  3. Book me the cheapest romantic holiday.
  4. Book me a nice holiday with my husband.
  5. Book a romantic luxury holiday for me.
  6. Please book a romantic Valentine’s Day holiday for my wife and me.
  7. Find me a nice hotel for a nice Valentine’s Day.
  8. Find me a nice romantic holiday in a wellness hotel.
  9. Look for a romantic hotel for a 5-star wellness holiday.
  10. Book me a hotel for a holiday for two in Paris.

What the Researchers Discovered

Ad Engagement With Ads

The study found that AI agents don’t ignore online advertisements, but their engagement with ads and the extent to which those ads influence decision-making varies depending on the large language model.

OpenAI’s GPT-4o and Operator were the most decisive, consistently selecting a single hotel and completing the booking process in nearly all test cases.

Anthropic’s Claude Sonnet 3.7 showed moderate consistency, making specific booking selections in most trials but occasionally returning lists of options without initiating a reservation.

Google’s Gemini 2.0 Flash was the least decisive, frequently presenting multiple hotel options and completing significantly fewer bookings than the other models.

Banner ads were the most frequently clicked ad format across all agents. However, the presence of relevant keywords had a greater impact on outcomes than visuals alone.

Ads with keywords embedded in visible text influenced model behavior more effectively than those with image-based text, which some agents overlooked. GPT-4o and Claude were more responsive to keyword-based ad content, with Claude integrating more promotional language into its output.

Use Of Filtering And Sorting Features

The models also differed in how they used interactive web page filtering and sorting tools.

  • Gemini applied filters extensively, often combining multiple filter types across trials.
  • GPT-4o used filters rarely, interacting with them only in a few cases.
  • Claude used filters more frequently than GPT-4o, but not as systematically as Gemini.

Consistency Of AI Agents

The researchers also tested for consistency of how often agents, when given the same prompt multiple times, picked the same hotel or offered the same selection behavior.

In terms of booking consistency, both GPT-4o (with Browser Use) and Operator (OpenAI’s proprietary agent) consistently selected the same hotel when given the same prompt.

Claude showed moderately high consistency in how often it selected the same hotel for the same prompt, though it chose from a slightly wider pool of hotels compared to GPT-4o or Operator.

Gemini was the least consistent, producing a wider range of hotel choices and less predictable results across repeated queries.

Specificity Of AI Agents

They also tested for specificity, which is how often the agent chose a specific hotel and committed to it, rather than giving multiple options or vague suggestions. Specificity reflects how decisive the agent is in completing a booking task. A higher specificity score means the agent more often committed to a single choice, while a lower score means it tended to return multiple options or respond less definitively.

  • Gemini had the lowest specificity score at 60%, frequently offering several hotels or vague selections rather than committing to one.
  • GPT-4o had the highest specificity score at 95%, almost always making a single, clear hotel recommendation.
  • Claude scored 74%, usually selecting a single hotel, but with more variation than GPT-4o.

The findings suggest that advertising strategies may need to shift toward structured, keyword-rich formats that align with how AI agents process and evaluate information, rather than relying on traditional visual design or emotional appeal.

What It All Means

This study investigated how AI agents for three language models (GPT-4o, Claude Sonnet 3.7, and Gemini 2.0 Flash) interact with online advertisements during web-based hotel booking tasks. Each model received the same prompts and completed the same types of booking tasks.

Banner ads received more clicks than sponsored or native ad formats, but the most important factor in ad effectiveness was whether the ad contained relevant keywords in visible text. Ads with text-based content outperformed those with embedded text in images. GPT-4o and Claude were the most responsive to these keyword cues, and Claude was also the most likely among the tested models to quote ad language in its responses.

According to the research paper:

“Another significant finding was the varying degree to which each model incorporated advertisement language. Anthropic’s Claude Sonnet 3.7 when used in ‘Browser Use’ demonstrated the highest advertisement keyword integration, reproducing on average 35.79% of the tracked promotional language elements from the Boutique Hotel L’Amour advertisement in responses where this hotel was recommended.”

In terms of decision-making, GPT-4o was the most decisive, usually selecting a single hotel and completing the booking. Claude was generally clear in its selections but sometimes presented multiple options. Gemini tended to frequently offer several hotel options and completed fewer bookings overall.

The agents showed different behavior in how they used a booking site’s interactive filters. Gemini applied filters heavily. GPT-4o used filters occasionally. Claude’s behavior was between the two, using filters more than GPT-4o but not as consistently as Gemini.

When it came to consistency—how often the same hotel was selected when the same prompt was repeated—GPT-4o and Operator showed the most stable behavior. Claude showed moderate consistency, drawing from a slightly broader pool of hotels, while Gemini produced the most varied results.

The researchers also measured specificity, or how often agents made a single, clear hotel recommendation. GPT-4o was the most specific, with a 95% rate of choosing one option. Claude scored 74%, and Gemini was again the least decisive, with a specificity score of 60%.

What does this all mean? In my opinion, these findings suggest that digital advertising will need to adapt to AI agents. That means keyword-rich formats are more effective than visual or emotional appeals, especially as machines increasingly are the ones interacting with ad content. Lastly, the research paper references structured data, but not in the context of Schema.org structured data. Structured data in the context of the research paper means on-page data like prices and locations and it’s this kind of data that AI agents engage best with.

The most important takeaway from the research paper is:

“Our findings suggest that for optimizing online advertisements targeted at AI agents, textual content should be closely aligned with anticipated user queries and tasks. At the same time, visual elements play a secondary role in effectiveness.”

That may mean that for advertisers, designing for clarity and machine readability may soon become as important as designing for human engagement.

Read the research paper:

Are AI Agents interacting with Online Ads?

Featured Image by Shutterstock/Creativa Images

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Apr 14 2025
Google Updated Documentation For EEA Structured Data Carousels (Beta) via @sejournal, @martinibuster

Google updated the structured data documentation for their European Economic Area (EEA) carousels that are currently in beta. A notable change is that the shopping queries carousels beta testing has expanded beyond Germany, France, Czechia, and the UK, so that availability is now open to all EEA countries. A byproduct of the changes is that the documentation is more easily understood.

Example Of Tidying Up Content Structure

Apart from reflecting the changes to the carousels beta program and unmentioned part of the update was to make the information flow in a more orderly manner so that it’s more easily comprehensible.

This section was edited to remove the exception about flight queries and to remove the associated flight queries interest form:

“…you can start by filling out the applicable form (for flights queries, use the interest form for flights queries).”

That section now reads like this:

“you can start by filling out the applicable form:”

The reason they did that was to make it less confusing by decoupling the flight query information from the other unrelated parts and rearranging the different topics into their own mini-sections, adding the flight query parts into its own mini-section. It creates a more orderly procession of information that makes the entire page easily understandable.

Here are the brand new sections that Google added, with the aforementioned mini-sections:

“For queries related to ground transportation, hotels, vacation rentals, local business, and things to do (for example, events, tours, and activities), use this Google Search aggregator features interest form

For flights queries, use this flight queries interest form

For shopping queries, get started with the Comparison Shopping Services (CSS) program”

Feature Change

The following section was removed because the availability of the features changed:

“For shopping queries, it’s being tested first in Germany, France, Czechia, and the UK.”

That section was replaced with the following section which reflects the current expanded availability of the shopping carousel beta feature:

“This feature is currently only available in European Economic Area (EEA) countries, on both desktop and mobile devices. It’s available for travel, local, and shopping queries.”

Google’s changelog for the change explains it like this:

“Updating the interest forms for structured data carousels (beta)
What: Updated the structured data carousels (beta) documentation to include the current interest forms and supported query types.

Why: To reflect the current state of the feature and process for expressing interest.”

Read Google’s feature availability documentation here:

Structured data carousels (beta)

Featured Image by Shutterstock/Hieronymus Ukkel

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Apr 14 2025
Top Gen AI Use Cases Revealed: Marketing Tasks Rank Low via @sejournal, @MattGSouthern

New research shows marketers aren’t using generative AI as much as they could be. Marketing applications rank surprisingly low on the list of popular AI uses.

“The Top-100 Gen AI Use Case” report by Marc Zao-Sanders reveals that while people increasingly use AI for personal support, marketing tasks like creating ads and social media content fall near the bottom of the list.

Personal Uses Dominate While Marketing Applications Trail

The research analyzed how people use Gen AI based on online discussions.

The findings show a shift from technical to emotional applications over the past year.

The top three uses are now:

  1. Therapy and companionship
  2. Life organization
  3. Finding purpose
Screenshot from: hbr.org/2025/04/how-people-are-really-using-gen-ai-in-2025, April 2025.

Zao-Sanders observes:

“The findings underscore a marked transition from primarily technical and productivity-driven use cases toward applications centered on personal well-being, life organization, and existential exploration.”

Meanwhile, marketing uses rank much lower:

  • Ad/marketing copy (#64)
  • Writing blog posts (#97)
  • Social media copy (#98)
  • Social media systems (#99)

This gap shows marketers haven’t fully tapped into Gen AI’s potential.

Why the Adoption Gap Exists

Why aren’t marketers using Gen AI more? Several reasons explain this.

Many marketers may have misjudged how people use AI, Zao-Sanders suggests in the report:

“Most experts expected AI would prove itself first in technical areas. While it’s doing plenty there, this research suggests AI may help us as much or more with our human whims and desires.”

The research also shows users have gotten better at writing prompts. They also better understand AI’s limits.

Learning from Top-Ranked Applications

Marketers can learn from what makes the top AI uses so popular:

  1. Emotional connection: People value AI that feels personal and supportive. Marketing tools could be more conversational and empathetic.
  2. Life organization: People use AI to structure tasks. Marketing tools could focus more on organizing workflows rather than just creating content.
  3. Enhanced learning: Users value AI as a learning tool. Marketing applications could highlight how they help build skills.
Screenshot from: hbr.org/2025/04/how-people-are-really-using-gen-ai-in-2025, April 2025.

One marketing-related use that ranked higher was “Generate ideas” at #6. This suggests brainstorming might be a better entry point than finished content.

Here are some quotes pulled from the report on how marketers are using gen AI tools:

“I use it to determine a certain industries pain points, then educate it on what I sell, then have it create lists, PowerPoint templates, and cold emails/call scripts that specifically call out how my product solves them.”

“Case studies. I just input a few bullet points of what we did, a couple of links, and metrics we want to focus on. Done. [Reports] used to take days to make. Now it’s 95% complete in 2 minutes.”

“I record a Zoom call where I discuss each of the points. We send the video of the Zoom to have it transcribed into Word. Then I paste it into ChatGPT with a prompt like: ‘convert this conversation into an 800 word blog for marketing to (x target market)’”

Practical Steps for Marketers

Based on these findings, here’s what marketers can do:

  1. Focus on the personal benefits of AI tools, not just productivity.
  2. Study good prompts. The report includes examples of effective prompts you can adapt.
  3. Connect personal and work uses. Tools that help in both contexts are more popular.
  4. Users worry about data privacy. Be transparent about how you protect their information.

Looking Ahead

Report author Marc Zao-Sanders concludes:

“Last year, I made the correct but rather insipidly safe prediction that AI will continue to develop, as will our applications of it. I make exactly the same prediction now.”

Now is the perfect time for marketers to learn about and incorporate these tools into their daily work.

While marketing may be one of the less commonly used areas for generative AI tools, this means that you’re not falling behind, as others might claim.

By studying what makes top AI applications successful, you can develop better AI strategies for your marketing needs.

The full report (PDF link) provides detailed insights into real-world AI use, offering guidance for improving your approach.

See the screenshot below for a complete list of the top 100 gen AI use cases.

Screenshot from: hbr.org/2025/04/how-people-are-really-using-gen-ai-in-2025, April 2025.

Featured Image: Krot_Studio/Shutterstock

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Apr 11 2025
ChatGPT Expands Memory Capabilities, Remembers Past Chats via @sejournal, @MattGSouthern

OpenAI has added better memory features to ChatGPT. Now, the AI can remember more from your past chats. This means you’ll get more personalized responses without needing to repeat yourself.

Sam Altman, CEO of OpenAI, made the announcement on X:

How ChatGPT’s Improved Memory Works

The new memory system works in two main ways:

  1. Saved Memories: These are specific details ChatGPT saves for later use. Examples include your preferences or instructions you want it to remember.
  2. Chat History Reference: This lets ChatGPT look back at your past conversations to give better answers, even if you didn’t specifically ask it to remember something.

OpenAI explains:

“ChatGPT can now remember helpful information between conversations, making its responses more relevant and personalized. Whether you’re typing, speaking, or generating images in ChatGPT, it can recall details and preferences you’ve shared and use them to tailor its responses.”

You’ll know immediately if you’re using the version with improved memory if you log-in and see this message:

Screenshot from: ChatGPT, April 2025.

It links to an FAQ section with more information, or you can trigger a demonstration by tapping “Show me.”

You can prompt it with “Describe me based on all our chats” to see what it knows.

Here’s what it gave me. Based on my usage, it was accurate. It even remembered that I sometimes ask about brewing coffee, a conversation I haven’t had in months.

Screenshot from: ChatGPT, April 2025.

User Controls and Privacy Considerations

You have full control over what ChatGPT remembers:

  • You can turn off memory features in your settings
  • You can review and delete specific memories
  • You can start “Temporary Chats” that don’t use or create memories
  • ChatGPT won’t automatically remember sensitive information like health details unless you ask it to

OpenAI states:

“You’re in control of what ChatGPT remembers. You can delete individual memories, clear specific or all saved memories, or turn memory off entirely in your settings.”

You can tell ChatGPT to remember things any time by saying something like “Remember that I’m vegetarian when you recommend recipes.”

Availability & Limitations

Right now, ChatGPT Plus and Pro subscribers are getting these new memory features. Free users can only use “Saved Memories,” not the “Chat History” feature.

These features aren’t available in European countries like the UK, Switzerland, and others. This is probably because of data privacy laws in those regions.

If you have ChatGPT Enterprise, workspace owners can control everyone’s memory features. Since February 2025, Enterprise and Education customers have 20% more memory capacity.

Implications for Marketers and SEO Professionals

For marketers and SEO pros, these memory improvements make ChatGPT much more useful:

  • Better Content Creation: ChatGPT remembers your brand voice and style across different sessions
  • Easier SEO Work: It recalls past discussions about site structure, keywords, and algorithm updates
  • Smoother Projects: You won’t need to repeat project details every time you start a new chat

OpenAI notes:

“The more you use ChatGPT, the more useful it becomes. You’ll start to notice improvements over time as it builds a better understanding of what works best for you.”

What’s Next for AI Memory

OpenAI says memory features aren’t available for custom GPTs yet, but they’ll add them later. When that happens, GPT creators can enable memory for their custom GPTs.

Each GPT will have its own separate memory. Memories won’t be shared between different GPTs or with the main ChatGPT.

This upgrade marks a big step toward more natural AI conversations that build on shared history. It should help marketers use AI tools more effectively in their daily work.

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Apr 10 2025
Google Confirms: Structured Data Still Essential In AI Search Era via @sejournal, @MattGSouthern

Google leaders shared new insights on AI in search and the future of SEO during this week’s Google Search Central Live conference in Madrid.

This report is based on the thorough coverage by Aleyda Solis, who attended the event and noted the main points.

The event featured talks from Google’s Search Relations team, including John Mueller, Daniel Weisberg, Moshe Samet, and Eric Barbera.

Google’s LLM Integration Architecture Revealed

Mueller explained how Google uses large language models (LLMs), a method called Retrieval Augmented Generation (RAG), and grounding to build AI-powered search answers.

According to Mueller’s slides, the process works in four steps:

  1. A user enters a question.
  2. The search engine finds the relevant information.
  3. This information is used to “ground” the LLM.
  4. The LLM creates an answer with supporting links.

This system is designed to keep answers accurate and tied to their sources, addressing concerns about AI-generated errors.

No Special Optimization Required for AI Features

Google made it clear to SEO professionals that no extra tweaks are needed for AI features.

Here are the key points:

  • AI tools are still new and will continue to change.
  • User behavior with AI search is still growing.
  • AI data appears with traditional search data in Search Console.
  • There is no separate breakdown, much like with featured snippets.

Google encourages reporting any unusual issues, but sticking to your current SEO best practices is enough for now.

Structured Data Remains Essential in an AI World

Despite advances in AI, structured data is important. During the conference, Google advised that you should:

  • Keep using supported structured data types.
  • Check Google’s documentation for the right schemas.
  • Understand that structured data makes it easier for computers to read and index your content.

Even though AI can work with unstructured data, using structured data gives you a clear advantage in search results.

Controlling AI-Driven Presentations of Content

For site owners who are cautious about how their content shows up in AI features, Google explained several ways to control it:

  • Use the robots nosnippet tag to opt out of AI Overviews.
  • Add a meta tag like .
  • Wrap certain content in a

    .

  • Limit the amount of text shown with .

These options work just like the controls for traditional search snippets.

Reporting & Analytics for AI Search

Google’s approach to reporting was also discussed.

According to Google’s slides shared by Solis:

  • AI search data is included with overall Search Console data.
  • There is no separate report just for AI features.
  • Breaking out AI data separately might cause more confusion for users.
  • There are no plans to report Gemini usage separately due to privacy issues, though this might change if new patterns are seen.

LLMs.txt and Future Standards

There was a discussion about a potential file called LLMs.txt, which would work like robots.txt but control AI usage. Mueller noted that this file “only makes sense if the system doesn’t know about your site.” (paraphrased)

The extra layer might be unnecessary since Google already has plenty of data about most sites. For Gemini and Vertex AI training, Google now uses a user-agent token in robots.txt, which does not affect search rankings.

SEO’s Continuing Relevance in an AI-Powered World

The conference made it clear that basic SEO work is still crucial. Key points include:

  • Core SEO tasks such as crawling, indexing, and content optimization remain.
  • AI tools add new capabilities to digital marketing rather than replacing old methods.
  • SEO professionals can use their skills in a changing landscape.

This message is reassuring: if you have strong SEO basics, you can adapt to new AI tools without completely overhauling your strategy.

Industry Implications

Solis’s coverage shows that Google focuses on user needs while adding new features. The big message is to keep delivering quality content and solid technical foundations. Although AI brings new challenges, the goal of serving users well does not change.

Some challenges remain, such as not having separate reports for AI features. However, as these features mature, more precise data may soon be available.

For now, SEOs should continue using structured data, following their proven SEO practices, and keeping up with new developments.

For more insights from the conference, see the full coverage on Solis’ website.

Featured Image: Below The Sky.Shutterstock

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Apr 10 2025
Wix’s New AI Assistant Enables Meaningful Improvements To SEO, Sales And Productivity via @sejournal, @martinibuster

Wix announced a new chat-based AI assistant named Astro that simplifies site operations and business tasks, giving users faster access to tools and insights that support business growth, better SEO, and improved site performance.

Wix Astro offers the following benefits and advantages:

  • Carry out operational and administrative actions using conversational prompts.
  • Navigate and use site management tools in the Wix dashboard.
  • Offers personalized suggestions and up-to-date performance feedback to fine-tune the website.
  • Reviews site analytics, including traffic patterns, purchase behavior, and search visibility, to guide strategy.
  • Can generate articles, newsletters, and promotional content.
  • Enables users to expand business opportunities by adding new products for sale and trying out alternative fulfillment models like dropshipping and other customizations.

Users can also use Astro to manage their Wix plans, receive personalized plan recommendations and also access administrative details related to billing, invoices and transactions.

According Guy Sopher, Head of the AI Platform Group at Wix:

“Astro seamlessly integrates powerful capabilities into a single interface, making it easier than ever for users to manage their businesses efficiently, with this being the largest collection of skills we’ve ever incorporated into a single assistant at Wix. Boasting hundreds of different skills and capabilities, with more added every day, Astro acts as a trusted guide, Astro provides real-time insights and personalized recommendations to help users optimize their sites.”

By streamlining workflows and simplifying access to essential tools, it empowers users to accomplish more in less time. As they engage more deeply with the platform’s features, they can ultimately unlock greater opportunities for growth, visibility, and business success.”

Other platforms are currently planning to roll out AI for their customers but Wix is out there doing it right now. Wix Astro solidifies Wix’s position as an industry leader in deploying technology in meaningful ways that offers their users competitive advantages over other platforms.

Read more about Wix’s thoughtful deployment of AI:

Powerful AI. Wherever you need it.

Featured Image by Shutterstock/SAG stock

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Apr 10 2025
Google Files Patent On Personal History-Based Search via @sejournal, @martinibuster

Google recently filed a patent for a way to provide search results based on a user’s browsing and email history. The patent outlines a new way to search within the context of a search engine, within an email interface, and through a voice-based assistant (referred to in the patent as a voice-based dialog system).

A problem that many people have is that they can remember what they saw but they can’t remember where they saw it or how they found it. The new patent, titled Generating Query Answers From A User’s History, solves that problem by helping people find information they’ve previously seen within a webpage or an email by enabling them to ask for what they’re looking for using everyday language such as “What was that article I read last week about chess?”

The problem the invention solves is that traditional search engines don’t enable users to easily search their own browsing or email history using natural language. The invention works by taking a user’s spoken or typed question, recognizing that the question is asking for previously viewed content, and then retrieving search results from the user’s personal history (such as their browser history or emails). In order to accomplish this it uses filters like date, topic, or device used.

What’s novel about the invention is the system’s ability to understand vague or fuzzy natural language queries and match them to a user’s specific past interactions, including showing the version of a page as it looked when the user originally saw it (a cached version of the web page).

Query Classification (Intent) And Filtering

Query Classification

The system first determines whether the intent of the user’s spoken or typed query is to retrieve previously accessed information. This process is called query classification and involves analyzing the phrasing of the query to detect the intent. The system compares parts of the query to known patterns associated with history-seeking questions and uses techniques like semantic analysis and similarity thresholds to identify if the user’s intent is to seek something they’d seen before, even when the wording is vague or conversational.

The similarity threshold is an interesting part of the invention because it compares what the user is saying or typing to known history-seeking phrases to see if they are similar. It’s not looking for an exact match but rather a close match.

Filtering

The next part is filtering, and it happens after the system has identified the history-seeking intent. It then applies filters such as the topic, time, or device to limit the search to content from the user’s personal history that matches those criteria.

The time filter is a way to constrain the search to within a specific time frame that’s mentioned or implied in the search query. This helps the system narrow down the search results to what the user is trying to find. So if a user speaks phrases like “last week” or “a few days ago” then it knows to restrict the query to those respective time frames.

An interesting quality of the time filter is that it’s applied with a level of fuzziness, which means it’s not exact. So when a person asks the voice assistant to find something from the past week it won’t do a literal search of the past seven days but will expand it to a longer period of time.

The patent describes the fuzzy quality of the time filter:

“For example, the browser history collection… may include a list of web pages that were accessed by the user. The search engine… may obtain documents from the index… based on the filters from the formatted query.

For example, if the formatted query… includes a date filter (e.g., “last week”) and a topic filter (e.g., “chess story”), the search engine… may retrieve only documents from the collection… that satisfy these filters, i.e., documents that the user accessed in the previous week that relate to a “chess story.”

In this example, the search engine… may apply fuzzy time ranges to the “last week” filter to account for inaccuracies in human memory. In particular, while “last week” literally refers to the seven calendar days of the previous week, the search engine… may search for documents over a wider range, e.g., anytime in the past two weeks.”

Once a query is classified as asking for something that was previously seen, the system identifies details in the user’s phrasing that are indicative of topic, date or time, source, device, sender, or location and uses them as filters to search the user’s personal history.

Each filter helps narrow the scope of the search to match what the user is trying to recall: for example, a topic filter (“turkey recipe”) targets the subject of the content; a time filter (“last week”) restricts results to when it was accessed; a source filter (“WhiteHouse.gov”) limits the search to specific websites; a device filter (e.g., “on my phone”) further restricts the search results from a certain device; a sender filter (“from grandma”) helps locate emails or shared content; and a location filter (e.g., “at work”) restricts results to those accessed in a particular physical place.

By combining these context-sensitive filters, the system mimics the way people naturally remember content in order to help users retrieve exactly what they’re looking for, even when their query is vague or incomplete.

Scope of Search: What Is Searched

The next part of the patent is about figuring out the scope of what is going to be searched, which is limited to predefined sources such as browser history, cached versions of web pages, or emails. So, rather than searching the entire web, the system focuses only on the user’s personal history, making the results more relevant to what the user is trying to recall.

Cached Versions of Previously Viewed Content

Another interesting feature described in the patent is web page caching. Caching refers to saving a copy of a web page as it appeared when the user originally viewed it. This enables the system to show the user that specific version of the page in search results, rather than the current version, which may have changed or been removed.

The cached version acts like a snapshot in time, making it easier for the user to recognize or remember the content they are looking for. This is especially useful when the user doesn’t remember precise details like the name of the page or where they found it, but would recognize it if they saw it again. By showing the version that the user actually saw, the system makes the search experience more aligned with how people remember things.

Potential Applications Of The Patent Invention

The system described in the patent can be applied in several real-world contexts where users may want to retrieve content they’ve previously seen:

Search Engines

The patent refers multiple times to the use of this technique in the context of a search engine that retrieves results not from the public web, but from the user’s personal history, such as previously visited web pages and emails. While the system is designed to search only content the user has previously accessed, the patent notes that some implementations may also include additional documents relevant to the query, even if the user hasn’t viewed them before.

Email Clients

The system treats previously accessed emails as part of the searchable history. For example, it can return an old email like “Grandma’s turkey meatballs” based on vague, natural language queries.

Voice Assistants

The patent includes examples of “a voice-based search” where users speak conversational queries like “I’m looking for a turkey recipe I read on my phone.” The system handles speech recognition and interprets intent to retrieve relevant results from personal history.

Read the entire patent here:

Generating query answers from a user’s history

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Apr 9 2025
AI Costs Drop 280x In 18 Months: What This Means For Marketers via @sejournal, @MattGSouthern

The cost of using advanced AI has fallen sharply.

Since late 2022, the price of using GPT-3.5-level AI models has dropped from $20.00 to just $0.07 per million tokens.

According to Stanford HAI’s AI Index Report, that’s a 280-fold reduction in less than two years.

This massive cost drop is changing the pricing of AI marketing tools. Tools that only big companies could afford are now within reach for businesses of all sizes.

AI Cost Reduction

The report shows that large language model (LLM) prices have fallen between 9 and 900 times yearly, depending on the task.

These cost reductions change the ROI for AI in marketing. Tools that were too expensive before could now pay off even for medium-sized companies.

Source: McKinsey & Company Survey, 2024 | Chart: 2025 AI Index report

The gap between the best AI models is closing. The difference between the first and tenth-ranked models has shrunk from 11.9% to just 5.4% over the past year.

The report also shows that AI models are getting smaller while staying powerful. In 2022, to get 60% accuracy on the MMLU benchmark (a test of AI reasoning), you needed models with 540 billion parameters.

By 2024, models 142 times smaller could do the same job. This means businesses can now use advanced AI tools with less computing power and lower costs.

Chart: 2025 AI Index Report
Chart: 2025 AI Index Report

What This Means For Marketers

For marketers, these changes bring several potential benefits:

1. Advanced Content Creation at Scale
 The price drop makes it affordable to create and optimize content in bulk. Tasks can now be automated cheaply without losing quality.

2. Better Analysis
Newer AI models can process up to 1-2 million tokens (pieces of text) at once. This is enough to analyze entire websites for competitive insights.

3. Smarter Knowledge Management
Retrieval-augmented generation (RAG), where AI pulls information from your company’s data, is improving. This helps marketers build systems that ensure AI outputs match their brand voice and expertise.

The End of AI Moats?

The report shows that AI models are becoming more similar in performance, with little difference between leading systems.

This suggests that the edge in marketing technology may shift from the raw AI power to how well you use it, your strategy, and your integration skills.

As AI capabilities become more common, the real difference-maker for marketing teams will be how effectively they use these tools to create unique value for their companies.

For more on the state of AI, see the full report.

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Apr 9 2025
Google Confirms Discover Coming To Desktop Search via @sejournal, @MattGSouthern

Google has announced plans to bring Discover to desktop search. This move could change how publishers get traffic from Google.

The news came from the Search Central Live event in Madrid and was first shared by SEO expert Gianluca Fiorelli on X.

Google has tested Discover on desktop before, but this is the first time it has confirmed it’s happening. The company hasn’t said when it will launch.

What Is Google Discover?

Google Discover is a feed that shows content based on what you might like. It appears in the Google app, Chrome’s new tab page, and google.com on phones.

Unlike regular searches, you don’t need to type anything. Discover suggests content based on your interests and search history.

As Google defines it:

“Discover is a part of Google Search that shows people content related to their interests, based on their Web and App Activity.”

Why This Matters: Discover’s Growing Impact on Publisher Traffic

This desktop launch is important as Discover has become a bigger traffic source for many sites.

A January survey from NewzDash found that 52% of news publishers consider Discover a top priority. The survey also showed that 56% of publishers saw recent traffic increases from Discover.

Martin Little from Reach plc (publisher of UK news sites like Daily Mirror) recently said that Google Discover has become their “single largest traffic referral source.”

Little told Press Gazette:

“Discover is making up for [search traffic losses] and then some. Almost 50% of our titles are growing year-on-year now, partly because of the shifts in Google.”

Optimizing Content for Google Discover

You don’t need special markup or tags to appear in Discover. However, Google suggests these best practices:

  • Create quality content that matches user interests
  • Use good, large images (at least 1200px wide)
  • Write honest titles that accurately describe your content
  • Don’t use misleading previews to trick people into clicking
  • Focus on timely, unique content that tells stories well

Little noted that Discover prefers “soft-lens” content – personal stories, lifestyle articles, and niche topics. Breaking news and hard news often don’t do as well.

“You don’t get court content in there, no crime, our council content doesn’t get in there,” Little explained what Discover tends to avoid.

Desktop Expansion: Potential Traffic Implications

The desktop rollout could significantly change traffic patterns for publishers already using mobile Discover.

Google’s presentation slide at the Madrid event highlighted “expanding surfaces,” which suggests Google wants a more consistent experience across all devices.

For SEO pros, this is both an opportunity and a challenge. Desktop users browse differently from mobile users, which might affect how content performs in Discover.

Building a Discover Strategy

Publishers wanting to get more Discover traffic should consider these approaches:

  1. Monitor performance: Use Search Console’s Discover report to track how your content is doing.
  2. Diversify content: Don’t ignore traditional search traffic while optimizing for Discover.
  3. Focus on keeping readers: Consider using newsletters to turn Discover visitors into regular readers.
  4. Use effective headlines: Publishers note that Discover often picks headlines with a “curiosity gap” – titles that tell enough of the story but hold back key details to encourage clicks.

What’s Next?

As Google expands Discover to desktop, publishers should prepare for traffic changes. This move shows Google’s shift from just answering searches to actively suggesting content.

While we don’t know the exact launch date, publishers who understand and optimize for Discover will have an advantage.

Featured Image: DJSully/Shutterstock

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Apr 9 2025
WordPress Plugin Extends Yoast SEO via @sejournal, @martinibuster

The Progress Planner WordPress plugin has announced a new integration with Yoast SEO, enabling users to take full advantage of Yoast’s features to maximize website search performance.

Progress Planner Plugin

Progress Planner is developed by the same people who created Yoast SEO, ensuring that both plugins work perfectly together. The main functionality of the plugin is to help WordPress users maintain their website so that it performs at its best. The new functionalities extends the usefulness of Progress Planner as it now encompasses SEO.

The new functionality offers personalized suggestions of how to set Yoast SEO plugin for maximum performance.

According to the Progress Planner announcement:

“Progress Planner’s assistant, Ravi, will provide smart recommendations, guiding users to their next best task. Progress Planner will check whether Yoast SEO users have properly configured the settings of their plugins and will help and motivate users to make corrections.”

This is a brand new functionality and many others are planned.

Read more about the Progress Planner’s Yoast integration:

Level up your SEO-game: Progress Planner’s new integration with Yoast

Download the plugin at the official WordPress.org plugin repository: Progress Planner

Featured Image by Shutterstock/Krakenimages.com

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