Google Search Patent Update – December 18, 2020

Welcome to another week for Geeks.

The last few weeks were a little uneven, but I came up with something that is really worth reading.

As always, patents take some time to arrive, but the more you read of them, the easier it is.

So, please take some time to click and read them full.

This can only make you a better SEO.

Also, my first post of 2021 will be a round-up All Google search-related patents that I had collected by the year 2020. Be sure to stick to it.

  • Filed: April 26, 2020
  • Awarded: 10 November 2020

abstract

“Example aspects of current disclosure are directed to systems and methods that employ a machine-learned opinion classification model to classify parts of documents (e.g., sentences, phrases, paragraphs, etc.) (e.g., news articles , Web pages, etc.).) Not being opinion or opinion. Also, in some implementations, parts classified as opinions may be considered to be included in an informational display. For example, Document parts can be ranked by importance and selected to be included in informational displays based on their ranking. “

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Dave’s notes

This one, as can be done with most patents and can be used in a variety of ways, seems to indicate that a lot of the original motivation is towards news (or potential blogs).

The tenor / opinion of a document can be understood and ranked accordingly.

As stated in the patent, “a portion of the document that is classified as opinion and / or of high importance.” One can also infer that it limits a user’s exposure to new information by showing high rank information confirming one’s own perceived views on a subject (query).

By patent

“The understanding of the content contained in a document (eg, textual content) by a computing system is a challenging problem. Even in the professional news journalism space, where articles are usually written in high quality language and syntax, computing systems currently have little understanding of the actual content of news articles. Furthermore, determining how a given article compares to other news articles related to other journalists is an even more challenging task. “

“Example aspects of current disclosure are directed to systems and methods that employ a machine-learned opinion classification model to classify parts of documents (e.g., sentences, phrases, paragraphs, etc.) (e.g., news articles , Web pages, etc.).) Not being opinion or opinion. Also, in some implementations, parts classified as opinions may be considered to be included in an informational display. For example, Document parts can be ranked by importance and selected to be included in informational displays based on their ranking. “

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  • Filed: 27 March 2019
  • Awarded: 1 December 2020

Dave’s Tech

As I was digging this patent (on personalization), it discusses the PageRank / Random Surfer approach. And there are limitations.

For all those who are not intimate with it, this is the main element of the link, which is clearly an important area.

They want to optimize it by a more personalized approach and “profile rank” to improve existing (PageRank Create) search results … Anyway, interesting stuff.

By patent

“In fact, a random surfer like user never exists. When a user submits a query to a search engine, each user has their own preferences. The quality of the search results returned by the engine has to be evaluated by the satisfaction of its users. When a user’s preferences can be well defined by the query itself, or when the user’s preference is similar to a random surfer’s preference with respect to a specific query, the user is more likely to be satisfied with the search results . However, if the user’s preference is largely biased by some individual factor, which is not clearly reflected in a search query itself, or if the user’s preference is significantly different from the random user’s preference, then search from the same search engine Results may be less useful to the user, if not useless. “

  • Filed: 23 May 2019
  • Awarded: 15 December 2020

abstract

“From the content of the document, a factual unit that relates to the content of the document is determined. Material is requested for a knowledge panel. A knowledge panel is a user interface element that provides a collection of content related to the factual entity. The contents of the knowledge panel have been received for contemporary display on the user device along with the content of the document. “

Dave’s notes

Interestingly, it’s not really a ton of new elements that we won’t be familiar with when it comes to knowledge panels, but I haven’t really seen a ton of descriptive patents on them. Therefore, it is worth incorporating today.

For example, if you are not familiar with what a unit is as a whole, they describe them as, “Institutions may be involved, but a person, place, country, country, animal, historical The event may not be limited to organization, business, sports team, sporting event, film, song, album, sport, art work, or any other appropriate entity. “

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By patent

“However, when developing search queries to submit to search engines, the user is often required to provide the relevant information of the document in the query. For example, a user might write a document to describe a bear in the Smoky Mountains. The query that the user will need to create must express the need for this information. “

“In some implementations, a knowledge panel provides a summary of information for the unit. For example, a knowledge panel for a singer may include the name of the singer, image of the singer, description of the singer, one or more facts and material about the singer, identifying the songs and albums recorded by the singer Does. “

“In some implementations, a knowledge panel may provide more granular information. For example, if a documented section is about the singer’s childhood, the Knowledge Panel can provide information about the school the singer attended, a snippet about the city in which the singer grew up, And the singer remembered growing up. “

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This week’s about it for the people. If there is a specific area of ​​search that you are interested in reading, do not hesitate to contact with me.

I am always happy to help.

see you next week!

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