[mk_page_section bg_position=”center center” full_width=”true” padding_bottom=”0″ sidebar=”sidebar-1″ first_page=”false” last_page=”true”][vc_column][mk_edge_slider first_el=”true” swiper_bg=”#000000″ parallax=”true” order=”ASC” height=”200″ animation_effect=”horizontal_curtain” slideshow_speed=”3000″ direction_nav=”slit” pagination=”small_dot_stroke” slides=”8728,8732,8734,8737″][/vc_column][/mk_page_section][mk_page_section bg_color=”#27ea8f” attachment=”fixed” speed_factor=”4″ padding_top=”70″ padding_bottom=”60″ first_page=”true” last_page=”true” sidebar=”sidebar-1″][vc_column][mk_fancy_title color=”#252525″ size=”50″ font_weight=”300″ txt_transform=”uppercase” margin_bottom=”18″ font_family=”none” responsive_align=”left”]Difference between ordinary
and extraordinary results[/mk_fancy_title][vc_row_inner][vc_column_inner width=”1/12″][/vc_column_inner][vc_column_inner width=”11/12″][mk_fancy_title color=”#252525″ size=”24″ font_weight=”300″ txt_transform=”none” margin_bottom=”18″ font_family=”none” responsive_align=”left”]Welcome NEO, a deep learning system developed by GreyB.
Simply put, imagine the role of M. Jordan’s coach in his exceptional performance. That is exactly what NEO does.
It helps achieving peak performance in searching patents.
Imagine you found best prior art used key word “Control System” instead of “Transistor”. Seemingly unrelated words, but describing the same technology.
Remember, anyone can find related words – it is easier. NEO connects unrelated words describing the same technology.
Who do you bet on?
Jordan minus his coach or Jordan plus NEO.[/mk_fancy_title][/vc_column_inner][/vc_row_inner][/vc_column][/mk_page_section][vc_row][vc_column][mk_padding_divider size=”30″][/vc_column][/vc_row][vc_row][vc_column][mk_icon_box icon=”mk-moon-lamp-2″ title=”History of Neo” text_size=”26″ style=”simple_ultimate” icon_size=”large” rounded_circle=”true” icon_location=”top” icon_color=”#27ea8f” title_color=”#252525″ txt_color=”#666666″ margin=”10″ animation=”scale-up” box_blur=”false” width=”1/1″ el_position=”first”][/mk_icon_box][vc_row_inner attached=”true” padding=”10″][vc_column_inner width=”1/12″][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”5/6″][vc_wp_text]Invalidity searches are laborious and frustrating as these require one to review 4000-5000 references manually to find ONE relevant reference that had powers to invalidate a patent; however, at the same time, prone to be skipped by human eye as it describes the concept in the most complex way possible and in a completely different manner.
And after running thousand of such validity studies, we decided to build an expeditious system. Our strong background in machine learning helped us conclude that computers, using the strong algorithms, could learn a thing or two from the brain and understand at a basic level how it manages information and process it, while analyzing patents.
The core approach to any deep learning problem is to represent the structure and constraints of the problem in a way that is computable & this is generally harder for some problems than others, and as we already see that there are certain areas where deep learning is already superior to a human. A system that can work with humans to deliver better performance than that of an individual. Our system analyzes the patent data-set to learn and discover relationships among entities in a similar way as humans try to do.
[/vc_wp_text][mk_padding_divider][vc_video link=”https://youtu.be/sTA1ZD8xPvI”][mk_padding_divider][/vc_column_inner][vc_column_inner width=”1/12″][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][mk_animated_columns border_color=”#27ea8f” bg_hover_color=”#27ea8f” icon_size=”48″ icon_color=”#27ea8f” icon_hover_color=”#f6f6f6″ animation=”fade-in”][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space][mk_icon_box icon=”mk-moon-binoculars-2″ text_size=”26″ style=”simple_ultimate” icon_size=”large” rounded_circle=”true” icon_location=”top” icon_color=”#27ea8f” title_color=”#252525″ txt_color=”#666666″ margin=”10″ animation=”scale-up” box_blur=”false” width=”1/1″ el_position=”first”][/mk_icon_box][mk_fancy_title color=”#393836″ size=”26″ font_style=”inhert” txt_transform=”none” margin_bottom=”18″ font_family=”none” align=”center”]DERIVING TRENDS BY SIMULTANEOUSLY REVIEWING HUNDREDS OF PARAMETERS[/mk_fancy_title][vc_row_inner attached=”true” padding=”10″][vc_column_inner width=”1/12″][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”5/6″][vc_wp_text]It’s a methodology for learning high-level concepts about data, frequently through models that have multiple layers of non-linear transformations. It is about learning from what is already lying there. It is about learning about how one technology can be described in various ways by thousands of attorneys while filing patents.
Neo is a system that can predict the way in which a hidden prior art might have been described. If we talk about portfolio analysis, it can identify the hidden insight within the portfolio of a company’s patents. It can tell you where the technology is heading in the future. While that’s far off from the kind of AI found in science-fiction movies, but its close to a place where it is actually useful.[/vc_wp_text][/vc_column_inner][vc_column_inner width=”1/12″][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][mk_page_section bg_image=”https://greybnew.dev.zippisite.com/wp-content/uploads/2015/02/tumblr_myebwtELvb1st5lhmo1_1280-2.jpg” bg_color=”#eaebeb” attachment=”fixed” bg_position=”center center” bg_repeat=”no-repeat” bg_stretch=”true” enable_3d=”true” speed_factor=”0.2″ video_mask=”true” video_opacity=”0.4″ min_height=”400″ padding_top=”70″ sidebar=”sidebar-1″ first_page=”false” last_page=”false”][vc_column][mk_title_box color=”#ffffff” size=”30″ margin_top=”18″ margin_bottom=”18″ font_family=”none” align=”center”]What can Neo do that others can’t?[/mk_title_box][mk_fancy_title color=”#ffffff” size=”18″ font_style=”normal” txt_transform=”none” margin_bottom=”18″ font_family=”none” align=”center”]There are various tools available in the market, that try to give you the results automatically, but the industry has rejected most of them as they were not accurate. A lot of them tried to generate automatic landscape reports, but failed miserably. Therefore, we put our heads to solve this problem and used our experience of working on thousands of invalidation searches to train a system that can think faster than a human. A system that can correlate 50 charts in 2 seconds while a human can only relate a maximum of 5 graphs at one go.[/mk_fancy_title][/vc_column][/mk_page_section][mk_page_section bg_image=”https://greybnew.dev.zippisite.com/wp-content/uploads/2015/02/DeathtoStock_Medium4-e1424067313569.jpg” bg_color=”#4d4f57″ attachment=”fixed” bg_position=”center center” bg_stretch=”true” enable_3d=”true” speed_factor=”0.5″ video_mask=”true” video_opacity=”0.4″ min_height=”375″ padding_top=”50″ sidebar=”sidebar-1″ first_page=”false” last_page=”false”][vc_column][mk_title_box color=”#ffffff” size=”30″ margin_bottom=”18″ font_family=”none” align=”center”]Discover what Matters[/mk_title_box][mk_fancy_title color=”#ffffff” size=”18″ font_style=”inhert” txt_transform=”none” margin_bottom=”18″ font_family=”none” align=”center”]Neo can help find the hidden concepts that are described in a completely different manner and are difficult to be captured by any existing systems. Neo leverages the power of deep learning and contextual pattern matching. It is the first intelligent tool that can read patents as human.[/mk_fancy_title][/vc_column][/mk_page_section][mk_page_section bg_image=”https://greybnew.dev.zippisite.com/wp-content/uploads/2015/02/abacus_hires-e1424866506275.jpg” bg_color=”#d1e5e8″ attachment=”fixed” bg_position=”center bottom” bg_stretch=”true” enable_3d=”true” speed_factor=”0.5″ video_mask=”true” video_opacity=”0.4″ min_height=”400″ padding_top=”70″ sidebar=”sidebar-1″ first_page=”false” last_page=”false”][vc_column][mk_title_box color=”#ffffff” size=”30″ margin_bottom=”18″ font_family=”none” align=”center”]How it works?[/mk_title_box][mk_fancy_title color=”#ffffff” size=”18″ font_style=”inhert” txt_transform=”none” margin_bottom=”18″ font_family=”none” align=”center”]Till now patent tools were more artificial than intelligent. We ingested human brain power in the system to make it work intelligently with patents.
For example, through permutation and combination, Neo has the capability and knowledge to apply 1.35 million logics on the patent data to derive insights which may be difficult to formulate by a human researcher. Landscape projects can now be executed in a completely different way.[/mk_fancy_title][/vc_column][/mk_page_section][mk_page_section bg_image=”https://greybnew.dev.zippisite.com/wp-content/uploads/2015/02/deathtostock_desk4-e1424067784583.jpg” bg_color=”#edf1f4″ attachment=”fixed” bg_position=”center bottom” bg_repeat=”no-repeat” bg_stretch=”true” enable_3d=”true” speed_factor=”0.5″ video_mask=”true” video_opacity=”0.4″ min_height=”400″ padding_top=”40″ sidebar=”sidebar-1″ first_page=”false” last_page=”false”][vc_column][mk_title_box color=”#ffffff” size=”30″ margin_top=”30″ margin_bottom=”18″ font_family=”none” align=”center”]Churning more than a human brain?[/mk_title_box][mk_fancy_title color=”#ffffff” size=”18″ font_style=”inhert” txt_transform=”none” margin_bottom=”18″ font_family=”none” align=”center”]Neo has gone through millions of patents and built its understanding by studying the conceptual relationships on how the technologies have been described. So Neo can build a timeline describing the different ways how a particular technology has been described in last 50 years. It has built the capability to link expected and unexpected connections between various technology descriptions.[/mk_fancy_title][/vc_column][/mk_page_section][mk_page_section bg_image=”https://greybnew.dev.zippisite.com/wp-content/uploads/2015/02/2048-7.jpg” bg_color=”#eaebeb” attachment=”fixed” bg_position=”center top” bg_repeat=”no-repeat” bg_stretch=”true” enable_3d=”true” speed_factor=”0.5″ video_mask=”true” video_opacity=”0.4″ min_height=”400″ padding_top=”50″ sidebar=”sidebar-1″ first_page=”false” last_page=”false”][vc_column][mk_title_box color=”#ffffff” size=”30″ margin_bottom=”18″ font_family=”none” align=”center”]How does it understand patents?[/mk_title_box][mk_fancy_title color=”#ffffff” size=”18″ font_style=”inhert” txt_transform=”none” margin_bottom=”18″ font_family=”none”]Neo is the first and the only Neuro-Linguistic platform that uses the true power of the human brain for the analysis of patents. We can separate the information from the intelligence. After executing thousands of searches, we could see a pattern develop whereby a human is able to spot a difference, but a tool misses. With our strong interest in Artificial Intelligence, we used this human capability and rooted it into an engine to build a deep learning capability.[/mk_fancy_title][/vc_column][/mk_page_section][mk_page_section bg_image=”https://greybnew.dev.zippisite.com/wp-content/uploads/2013/10/contact-bg1.jpg” video_opacity=”0.4″ sidebar=”sidebar-1″ first_page=”false” last_page=”false”][vc_column][mk_padding_divider size=”20″][mk_icon_box icon=”mk-moon-contact-add” title=”Got queries. Fill in your contact details and we’ll get back to you ASAP.” text_size=”20″ style=”simple_ultimate” icon_size=”large” icon_location=”top” icon_color=”#181926″ title_color=”#181926″ box_blur=”false”][/mk_icon_box][vc_row_inner][vc_column_inner width=”1/3″][/vc_column_inner][vc_column_inner width=”1/3″][vc_column_text css=”.vc_custom_1546838308975{margin-bottom: 0px !important;}”]
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