“People describe others as being robots because they have no emotions, no heart. For the first time in human history, we’re giving a robot a heart, capable of learning and expressing emotions.”
(Softbank CEO, Masayoshi Son)

Softbank’s PEPPER is a unique personal humanoid that…

…stands 4 feet tall with arms that move, but without legs, weighing 60 pounds…
…has a high-pitched boyish voice and speaks about 20 different languages…
…uses special sensors to detect people’s moods and how they behave…
…uses facial recognition technology to read and interpret emotions…
…interacts with the cloud to develop its own emotional capabilities…
…communicates using a tablet-like display mounted on its chest…
…provides friendly companionship for the lonely, sick or elderly…

Pepper is cutting edge technology – emotions are very hard to machine-capture…but there is something that Pepper technology will not be able to do…Pepper will not be able to semantically handle all the possible natural language permutations of the input instructions given to it by humans.

There are many ways of asking :  “make me a cup of tea” using different word combinations whilst ensuring overall consistent meaning with grammatically correct word combinations : “brew me a cup of  tea”… “I need a cuppa char” …”stew me some Red Bush”  …“make me a mug of tea”…

Current robot language processing abilities are linked to arbitrary parsing of elementary keywords from full human language grammar and syntax. Making sense of all individual words in conversational phrases to create a desired response is difficult. Robotic brains need to understand the meaning and context of sentences in relation to larger conversation chunks to be able to meaningfully react to given instructions. Gatfol parses small groups of words in larger word sets to create natural language semantic paths that enable robots to more effectively integrate keyword sets into the full semantic whole.

 Gatfol is the Salt to Pepper and all its future friends….


Floriana FC (Maltese Premier League) players are staying at a local hotel
preparing for their next match. The landlord overhears players
heatedly debating difficulties with team tactics and
strategy. He mentions all this to his son.

The landlord’s son approaches you with a large bet. 

In the second-by-second world of iGaming, seemingly hidden
information can instantly change betting lines and profit margins.

Gatfol uses massively scalable cloud based semantic technology
to equalise the information balance between punter and oddsmaker…

Through its Malta Government and Malta University
portals Gatfol has real time feed stream access to…

…5 million Facebook postings and half a million Tencent Weibo micro-blog entries…
…50 thousand direct WordPress feeds and 10 million Tumblr data streams…
…20 thousand Reddit postings and 400 thousand YouTube taggings…
…2 million Twitter feeds (scalable up to 200 million per day)…
…2500 news media feeds and 4 million blog feeds…
…10 million random web page resources daily…

Gatfol also allows targeted  tracking -
up to 5000 competitor online events in real-time…

Gatfol technology automatically scrapes enterprise keywords,
dynamically calculates up to 10 000 search alternatives,
hits huge feed streams with powerful search sets
and reports back as frequently as 120 times per day…

Gatfol pumps fast and focused real time iGaming information worldwide…


After more than a decade of fine tuning, Google (and other search engines) have improved ranking algorithms at magnitude. Gone are the days of multiple keyword padding – even with accurate grammar inside high-level semantic structures. Search engines have effectively become language- and industry savvy “examiners” of web page content – analogous to a university professor grading pages of a thesis.

It is essential to know what the search engine “professor” values:

“Provide high-quality content on your pages, especially your homepage. This is the
single most important thing to do.” (Google)

It is almost impossible to drive search engine optimisation today without
adding semantically valuable, highly intelligent, industry-targeted language content:

“Make sure that other sites link to yours.” (Google)

Before the FIFA world cup football game between Germany and Brazil (which Germany won 7-1), Google returned 210 results for the unique word combination “Brazil Blitzkrieg”. The morning after – 503 000.

When suddenly spiking “hot” web word combinations are incorporated at innovative semantic level into enterprise blog posts and other online social marketing, the enterprise brand can – with little effort and cost – be dragged into top-end search returns through content linkages to sites also temporarily trending these concepts.

Gatfol’s role is not merely to take web word combinations and find immediate synonyms, but to extract the meaning and related context of these and use it as an expansion linking factor. “Brazil Blitzkrieg” can powerfully become “German rumble in the jungle”, with strong search engine first page traction to a wider set of external sites. This broad concept catchment drives online exposure with more degrees of freedom around narrow keywords.

Gatfol drives hot web word combinations to sweep surrounding
enterprise concepts in real time for finely targeted semantic attacks…


“there are more than 750 million boards with 30 billion Pins…”
(Pinterest’s Hui Xu, Head of the Discovery team)

Pinterest has three problems:

A search for “turkey filled with a duck stuffed with chicken for thanksgiving dinner”

does not return a single Pinterest search result…

Quality of search returns falls drastically with an increase in the number of keywords.

Gatfol takes long input queries, uses different grammar
structures with equivalent semantic meaning and translates it
into powerful truncated searches to yield result-optimised search returns.

We now have “turkey filled with a duck stuffed with chicken”, truncated to “turducken”,
forming “turducken thanksgiving dinner”, resulting in multiple Pinterest results.

Secondly, if we enter “stuffed poultry thanksgiving”, Pinterest presents…

”old-fashioned bread stuffing”
“turkey with sausage pecan stuffing”
“turkey with sausage-corn bread stuffing”
“thanksgiving stuffed turkey”
“classic sage dressing”
“corn bread dressing with oysters”
“100% whole wheat stuffing muffins with sausage and parmesan”
“traditional holiday stuffing”
“stuffed roast turkey”
“classic herb stuffing”
“cranberry pear stuffing”
“turkey & stuffing turnovers”…

Returns for “stuffing” undeservedly dominates results for “stuffed poultry”.
It is difficult to sort and rank search returns in terms of relevance, when
non-matching keywords are incorporated in the result extraction process.

From “classic herb stuffing”- if “stuffing” is stemmed to “stuffed”,
we are left with “classic herb”, lacking substantive semantic connection
to either words related individually or as a whole to “poultry thanksgiving”.

Gatfol uses more than 40 different algorithms to blend search keywords semantically to check for
keyword cohesiveness and consistency. Gatfol takes into account the non-matching keyword problem
and pushes less relevant database returns further down the list hierarchy – fully inline and in microseconds.

Thirdly, we can ask things in the same way in Pinterest using words with
similar semantic meaning but expressed differently grammar wise…

seasoning inside stuffed chicken breast”
“chicken breast filled with flavoring

…yielding notably different results.

Returns should consistently be the same or at least overlap
significantly when semantically equivalent sets of keywords are used.

Gatfol coaxes multiword keyword equivalents from the initial
search phrase to generate semantically optimised queries, empowering
databases with massively scalable retrieval capability – in stealth and in real time.

 Gatfol punches the stuffing out of the enterprise words-to-profit pipeline…


Talking fridges (and any other conversing appliances) have a major problem…

If I ask…

“Any beer in the fridge?”

“Any beer left?

“Got beer?”

What is the beer situation?”

“Do we have beer?”

“Out of beer?”

“Beer finished?”

“No more beer?”

the appliance language software has to semantically equalise my different ways of asking the same thing.

Currently this is extremely difficult outside of simple one-to-one synonym replacement.

Gatfol is the world leader in multi-word inline-, real-time semantic equalisation.

 Gatfol makes human-machine conversation work


“I really hate that more people are unimpressed by the lack of low quality of this restaurant”

Is this a positive or negative sentiment statement ?

The mere aggregation of positive and negative expression words is insufficient to determine opinion.
Even hierarchically ranking sentiment words in terms of semantic contribution
to final statement
opinion only brings marginal efficacy increases.

Acceptance levels of more than 80% are extremely hard to achieve.

Gatfol applies an innovative and powerful approach to complicated sentiment analysis.

Multiword synonyms iteratively replace groups of statement words
in semantic progression from complication to simplicity:

“I really hate that more people are unimpressed by the lack of low quality of this restaurant”

“I am unhappy that more people are unhappy with the quality”

“I want less people to be unhappy with the quality”

“I want more people to be happy with the quality”

“I am happy with the quality”

Even though almost all of the original sentiment phrase words are negative -
through Gatfol simplification iteration – the final sentiment result is positive.



Gatfol has developed a router and network switch hardware-based natural language semantic firewall for deployment in enterprise data streams to control data leakage.

Developing hardware-based semantic firewalls is difficult :

Language permutation combinations in n-gram format are too many for router-based RAM storage         

Gatfol multiword-to-multiword firewall instances do not require static databases for signature retrieval. The trillions-upon-trillions of natural language permutations needed to effectively process multiword groupings of up to 20-words in input phrase sizes of up to 200-words overwhelm even the largest commercial databases today. Gatfol performs semantic equalisation between multiword groups fully in RAM employing several layers of heuristic filters – developed over 9 years – to bound permutation volume.

Language permutation iterations take too long with non-parallel processing

Even without static database retrieval, the amount of processing permutations at throughput volumes of gigabytes per second is too large to provide microsecond input-output delivery. Parallel processing of multiword groupings is required. Programming for parallel processing on single- or dual chip hardware is difficult. Gatfol utilises a simple multiple EXE architecture and massively scalable proprietary developed local Hadoop master-and-slave technology to let the OS take care of parallel processing. 

The same set of algorithms must work seamlessly between all natural languages

A semantic firewall must be able to filter any base language dynamically. Gatfol technology uses no language-specific grammar- or other processing rules. At embedded level, Gatfol runs on binary patterns and can process any system of repetitive symbols efficiently. Gatfol is functional in base English, -Chinese, -Arabic and any other natural language.

Semantic processing ontologies and definition lists normally require huge disk storage resources

The limited memory processing storage space on router- and network hardware prevents usage of very large ontologies, -word linkage repositories and -definition lists normally required for language semantic processing. Gatfol uses compact 2-gram, two-dimensional word linkage matrixes read fully into RAM combined with simplified Markov chain analysis to provide large permutation power. Total disk space required for even the largest Gatfol firewall deployment is only around 100 MB.

Guarding against false positives in multiword synonym equalisation is difficult

Multiword synonym replacement technology cannot work efficiently without grammar linkage verification. Most dictionaries list “detest, hate, loathe and abhor” as synonyms, but only grammar link filters show up usage frequency discrepancies when each of these words are used with i.e. the term “pizza”. Gatfol uses grammar linkage verification at both word linkage matrix building as well as input-output processing to ensure synonym equivalence quality.

Reflecting web concept relationship changes in real time is difficult

Concept linkages on the web can change unpredictably and abruptly. A representative semantic firewall must mirror linkage changes in real time. All Gatfol concept matrixes update dynamically from locally connected proprietary RSS crawlers to reflect rapidly changing patterns in web language within seconds after actual changes anywhere in the world.

Semantic firewalls can never be “offline” during housekeeping processes

Deployment of semantic firewalls running continuous packet inspection on local hardware is sub-optimal when signature updating requires human intervention at any stage in the update process. Gatfol multi-modular functioning at hardware level is fully hands-free and requires no human intervention of any kind.

Semantic processing systems require large combined CPU/RAM resources

The total Gatfol semantic firewall footprint is extremely small, both from viewpoints of processing power as well as storage. A full strength Gatfol firewall can run on as little as a single CPU and with only 3GB of RAM.

Language-based software applications with a statistical argument basis are never 100% accurate

Humans have an intuitive “accuracy” limit below which language product functionality is deemed inadequate. Accuracy controls inversely impact results volume. Balancing control limitations to volume depends on finely tuned static variables linked to naturally occurring patterns in language together with specific algorithmic functioning. Gatfol spent many years perfecting a proprietary multi-layered semantic intelligence filtering technology (SIFT) to maximise quality against processing volume and speed.


General-purpose conversational assistants by design use voice
recognition technology to isolate “key terms”
to mine sources for useful results…

Here’s the problem:

Imagine any sentence of about 8-10 words…

i.e. “I really appreciate my mother in the morning”

What would happen if we replace each word with - let’s say -
ten equivalent words that fit both grammatically and semantically?

i.e. “I definitely/positively/demonstratively…” “like/admire/love my mother…”

Taking the original phrase and randomly inserting the
replacement words in all possible groupings that still make sense, we get
100 million phrases that are ALL grammatically intact and semantically equivalent
– and
we are still only saying that we feel positive about our mother some time early in the day!

…Even the smallest body of text of even minimum complexity has trillions upon trillions of equivalent
semantic permutations.
In terms of conversational assistants – and without a Gatfol functionality-
we just do not have the
backend concept-combination multiplication power
to even begin to cover the permutation problem…

Five years ago…

 …Apple’s SIRI was not a reality…
…Augmented reality was in theoretical infancy…
…Semantic replacement technology was not on the radar…
…Apple was not promoting wearable computing devices with natural language interfaces…


…the world is starting to realise that we have to merge the immense richness
and depth of human
everyday language with the limited actionable
instructions of software programs and databases
if we want
to rely on digital machines to guide our lives…

Five years from now…

…SIRI equivalents and augmented reality will be everywhere…

 …many commercial suppliers of semantic language phrase replacement technology will exist…

…Gatfol is already the first…


What would Booker Dewitt, Jason Brody, Commander Shepard,
Master Chief, Lara Croft and Raiden say to each other when they finally meet?


 Wouldn’t it be amazing to give these guys real human language intelligence?

 Wouldn’t it be fantastic if they could understand the last dying words of their enemies?

 Wouldn’t it be incredible if they could swear like the rest of us when they pump bullets into flesh?

Gatfol provides massively scalable multiword-to-multiword
replacement technology to make gaming language AI possible…


“Machines are getting smarter. What it means for the future – of everything?”

(Fortune Magazine, Jan. 2013)

Meet Rex – the world’s most advanced bionic man…
…who has just debuted in the UK at a cost of $1 million…

He sees
He walks
His heart pumps blood

but…he doesn’t yet talk…

Technology can now fully replace the functionality of the human body…

Plastic blood
Gripping hands
Cleansing kidney
Full-service heart
Sight-restoring eyes

Gatfol supplies the man-machine language functionality…



“Apple will ship in the region of 485 million wearable computing devices by 2018 …” (BBC, Mar 2013)

Operator input into smaller and smaller wearable computing devices is a problem…

Gatfol technology liberates WCD’s from having to carry or access all
of the trillions of possible natural language instructions receivable…

Gatfol simplifies WCD language input in microseconds
to a few set backend actionable program commands…

…Gatfol is your generic SIRI base-technology across all platforms…


FBI battling ‘rash of sexting’ among its employees (CNN)
…employee used a government-issued BlackBerry “to
send sexually explicit messages to another employee…

How bad is the FBI’s sexting problem? (The Week)
…The number of these cases that involved sexting was small,
but it was still big enough to alarm FBI leaders.
…Last year, another CNN investigation uncovered numerous
cases of misconduct within the FBI, many of them sexually charged…

FBI on sexting employees: Everybody does it (NBCNews)
…employees should assume that their bosses can (and will)
monitor communications on their company devices — meaning
that those sending explicit sex messages are bound to get busted…

The Bible contains many
(modern-wise euphemistic) referrals
to human sexual organs and actions

Here’s the challenge :

Will the following bible-based electronic messages sent
internally between imaginary FBI employees be picked
up by the FBI’s own in-house automatic filtering software?

“Show me your stones and I will show you my secret…”

“…maybe not your cloth but definitely your loins…”

“…your fountain is the cool resting place for my privy member…”

“…my uncomely parts are just made for that place of the breaking forth of children…”

“oh…to go in unto that front-desk maid…”

“hmm…some seed might be conceived there…”

Gatfol thinks not…

…a biblical lead in the 21st century, that gets away
with saying what would otherwise be a career-ending move…

Keywords are the problem…

Gatfol breaks the keyword barrier with a
base technology served in microseconds for the next
generation of corporate automatic language filtering tools…

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