Founded in 2004 by a group of business & technology entrepreneurs and inventors, Dolcera is a next generation patent and information analytics company. Our proprietary patent search strategy and domain expertise combined with our game changing AI/ML driven tools like PCS and E-Search enable us to offer top notch patent search and analytics services to our diverse clientele in key decision-making areas of IP strategy & creation, litigation, portfolio analysis, competitive intelligence, product development and licensing. Our AI- driven tools and services include: Dolcera PCS – A deep-learning driven superfast patent search engine that offers domain specific Semantic and taxonomy-oriented search capabilities for Patent portfolio analysis, Prior Art, Invalidation and licensing searches. Dolcera Machine learning for Auto-categorization – The transparent, customizable system solves the problem of human bias or comprehension of a patent when classifying them by leveraging proprietary Dolcera AI/ML. The system gives complete control to the user to deliver highly accurate categorized documents with a quick turn around. Dolcera Enterprise Search (E-Search) – Dolcera Enterprise Search is a machine learning enabled enterprise-wide search engine integrated with various value added features and smart charts. The application uses patent documents, scientific literature, product information or generally other technical documents inside a company. The system lets users have access to all the related files with minimal clicks to generate actionable insights. Dolcera ETSI Dashboard – A collection of SEP from various technologies and various specifications updated regularly + PCS (AI driven patent search tool) links the SEPs declared under the specific technical standards & visualize the information for. Our bespoke services include IP research, technical review, business research and newsletter alerts which help our clients make key strategic decisions in relation to: Choosing the right technology Filing & protecting the right IP Standard mapping and claim charting Product enhancement IP, technical & business landscapes Competitive intelligence Regulatory landscape The amalgamation of Dolcera’s AI driven tools, IP services & market research capabilities have time and again assisted our clients in managing their IP & non-IP assets systematically, while enhancing their overall decision-making process. For further information visit: www.dolcera.com Our Global presence: USA: California, Chicago Europe: Germany, UK Asia: India, China

1. Turnkey Intellectual Property Solutions
from
World’s Leading AI-Driven IP Analytics Company

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Dolcera
Dolcera is a leading patent
analytics provider with 14+
years experience in serving
the needs of prosecution,
licensing & innovation
departments
Dolcera’s AI powered software
tools are used by leading
innovation and licensing teams
for day to day patent related
needs
Dolcera has built unique databases
related to patent, business and
technology data which help our clients
spot salient trends in their industry
Dolcera’s Data Science offerings aid
our clients accelerate their processes
by building custom solutions for their
data processing needs
Software Tools
Data
Warehousing
Patent
analysis
Data Science
We help companies across the world be more tactical and strategic with their patents
Dolcera’s deep understanding of patents and expertise in software helps it provide unique solutions
to the raise the value of corporate patent portfolios
About Us

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Dolcera
Geographical focus
HQ: Bay Area,
4 Offices
US
HQ: Germany
EU
HQ: Shenzhen
China
HQ: Hyderabad
India

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Dolcera
About Dolcera – Our Clients
Our clients include over 100 of the world’s most innovative companies, over 50 of Fortune 500
Healthcare
& Biotech
Consumer
Goods
Food
& Beverage
Technology
& Telecom
Energy
& Chemicals
Law Firms
Manufacturing

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Dolcera
Our AI ML play
Dolcera PCS
• AI-driven patent search engine
• State-of-the-art deep learning platform
• Concept-driven, superfast search
• Over 120M patents, and 10M concepts
ML for autocategorization &
similarity search
• State-of-the-art deep learning platform
• Model training with specific taxonomies
• Customizable concept look-up
• Model recalibration based on testing scores

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Dolcera
Workflow
Dolcera PCS
• Patent search
• Concept searching & analysis
• Clean data- company & legal information, family
• Autocategorization
ML for
autocategorization
• Use proprietary learning to train models
• Autocategorization and similarity exploration
• Need based model refining to improve
outcomes
Dolcera
Dashboard
• Powerful document management
• Customizable analytics, dynamic charts
and visualizations
• Option to rate, comment, attach and
share documents

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Dolcera PCS Tool
– Use cases
– Instant landscape reports
– Portfolio audit
– Prior art searches
– Technology scouting
– Identifying assets for licensing

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Dolcera
§ Empowers users to answer the right questions
§ Synonyms and semantics
§ High quality analytics
§ Map patent disclosures
§ Value added layers
§ Assignee normalization
§ Dolcera_tags, CPC explorer
§ Drug patent, Legal status, Opposition filed,
PTEs etc.
§ User friendly interface
AI in PCS

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Dolcera
dynamic
taxonomy
State of the art
assignee
standardization
semantic
search
capabilities
PCS Advantage § PCS is Dolcera’s flagship patent analytics
platform
§ With a corpus of over 150 million worldwide
patents updated daily, PCS provides answers
to every patent question in detail, and blindingly
fast
§ PCS aggregates data from multiple sources
(patent data, open source knowledge, paid
data sources)
§ Information is then subjected to Natural
Language Processing and machine learning
algorithms to generate relevant content for the
report
Dolcera PCS Tool

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Dolcera
PCS generates instant patent landscape report on any topic
Instant patent landscape report
Typical price of landscape report charged
by patent analytics services
Average Time saved by PCS over
traditional process of landscape report
$20,000
2 weeks
• PCS uses its knowledge base to generate exhaustive but precise query
for any given topic automatically.
• Results fetched by query are analysed to give insights within seconds.
• Patents are classified into intuitive taxonomy that explains distribution
of patents obtained in search results.
• Additionally user is able to create different charts to dissect data as per
requirement
Why create patent landscape report?
• For discovering patents that needs to be acquired/licensed to support your
product
• To assess focus and strength of competitors and guide your own IP strategy
• To discover white spots in technology that can be exploited to create
competitive advantage

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Dolcera
Instant audit of patent portfolio
PCS can analyse any organization’s patent portfolio within
seconds
Typical price charged by patent analytics
services for portfolio analysis report of
bigger organizations like IBM, Samsung
Average Time saved by PCS over
traditional process of landscape report
$10,000
1 week
• PCS uses proprietary company name standardization algorithm to
normalize assignee names in patent databases
• Reassignment, corporate trees of organizations are also takes into
consideration to find ultimate owner of any technology
• This standardized data also allows PCS to generate report on any
company’s patent portfolio
• Like in landscape report users get insights on number of
patents/families, classification of them in taxonomy, filing trends and
variety of filters to analyse data in more details
Why audit patent portfolios?
• To know strengths and weaknesses of your/competitors’ portfolio in
technology
• To check alignment between your products and patents
• To assess innovation growth across organization over time

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Dolcera
PCS uses new age deep learning methods to search for prior-art
Prior-art search
Typical price of prior art search report
Average Time saved by PCS over
traditional process of prior art search
report
$750
1 day
• For given concept or for patent, PCS extracts key concepts
• Using pre-processed patent data, its able to identify all different
variations of expression of same concept
• Consequently its able to identify documents which are semantically very
similar to given concept.
• This heuristic works exceptionally well compared to traditional
document similarity algorithms
• Apart from speed it also provides consistency and accuracy in prior art
searches
Why search for prior-art for patents/concepts?
• To know patentability of inventions i.e. to check if same concept is patented
before
• To check if patent can be invalidated by any previously published literature

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Dolcera
Identifying assets for licensing
PCS has in-built industry proven ranking mechanism which allows users to identify valuable patents in their portfolio
Typical price charged by patent analytics
services for identifying best patents report
using qualitative analysis of bigger
organizations like IBM, Samsung
Average Time saved by PCS over
traditional process of patent ranking
$10,000
1 week
• Dolcera has developed patent ranking module to score patents which
has very high correlation with expert assessment
• This is saving lot of efforts for organizations wanting to choose their best
assets for licensing out to other companies
• On the other note, ranking module also helps in putting dollar value
onto set of patents which can be then used as securities/mortgage
Why search for prior-art for patents/concepts?
• To know patentability of inventions i.e. to check if same concept is patented
before
• To check if patent can be invalidated by any previously published literature

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Dolcera
Salient features
Semantic search
§ 11+ million concepts, semantically linked
§ Enriched semantics from IEEE, Wikipedia and other sources
§ Based on best deep learning principles
Inventor Normalization
§ World class algorithm that takes signals no one has attempted before
§ Signals involve normalized assignee names, technical area of work, name
variations of inventors, time of employment, geography, address, co-
inventors
§ 26 million inventors normalized with very high precision
Assignee Normalization
§ Millions of assignees normalized
§ Built on top of Linked Open Data principles and curated data from S&P
§ Millions of subsidiaries normalized
§ Very high accuracy
§ Re-assignments normalized and adjusted for in asset allocations
§ Re-assignments adjusted for heuristics to avoid considering collateralized
obligations made to banks
§ Re-assignment heuristics to allocate assets appropriately based on
transactions between buyers and sellers (and not just time, where
transactions reporting is delayed and hence leads to noise)
Prior Art Search
§ Combination of deep learning tools with patent ranking principles
§ Learns from old searches
§ AIA friendly to check for double patenting
§ Takes input from text, products, Invention disclosure statements or
patent number

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ML Models
– Use cases
– Patent & non-patent literature analysis
– Landscapes
– Similarity and concept look-up
– Unpublished claim matter/invention disclosures

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Dolcera
•The machine reads the manually
analyzed information(training set)
and identifies key concepts related
to each category
•Relevant information from various
patent/non-patent documents
helps reinforce concept
understanding
Training
•System classifies the testing data
after model development for
assessing the accuracy
•Recall and precision is used to test
the accuracy
Testing •System automatically classifies the
data into the defined meaningful
categories
•Further user feedback helps better
the model
Classification
Principle

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Dolcera
Methodology
Categorization process
Extracting from
Title, Abstract,
Claims
Tag the technical
words/phrases
Train, Validation
and Test Data
Customizing learning
based on client data set
Intrinsic Model
(Trained on
Patents)
Tf-idf weighted
average
Patent Vectors
Build Vectors on tagged
TAC*
Output
Confusion Matrix to
measure recall and
precision
Classification
Report
Category
Prediction
Phase I Phase II Phase III
Optimizing data-
points in each
category
Finalizing-
categories and
underlying
technologies
Pre-classified
training set
Obtaining pre-classified
sample training set
Phase 0
Copyright © 2019 Dolcera Corporation. Circulation and representation without the consent of the author is prohibited

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Dolcera
Model Fitting
For a given Taxonomy prepare Training set by
• Preparing highly targeted Searches
• Manual Analysis
Training Set
• Repeat step 3 for every Quarter to get delta patents
• Fine Tune the model half-yearly by incorporating new
training set for better accuracy.
Periodic Updates
• Train the ML Model using curated Training set
• Validate the model for accuracy
• Fine tune for better accuracy by altering hyper
parameters
Preparing ML Model
Process
• Identify potentially relevant patents to the technology
(Using broad search terms or class codes)
• Fit the trained model on data
• Dolcera Engineers will quickly scan the data
• Visualize the results in Dashboard

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Dolcera
• Optimizing data-points in each
category
• Finalizing-categories and underlying
technologies
Obtain Training set
• Extracting from Title, Abstract,
Claims
• Tag the technical word /phrases
Customized Learning
• Obtains vectors specially trained on
Patents
• Use Attention/Weightage mechanism to
boost relevant phrases
Build Vectors
• Build a Model using Latest NLP
Methodologies
• Validate the test results
• Generate Classification report
Build a Model and Validation
ML Model
Feed additional Training set / Tune hyper parameters for improved accuracy

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Dolcera
Corpus
-- US, EP, WO patents. One per family
giving preference to US grant
Tag Patents
-- Patents are tagged with concepts whose vector distance is below a
certain threshold
Dolcera Tags
-- Entire patent corpus is tagged with by
looking at vector signals/class code signals
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2
3
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Class code to Concept association
-- The model generalizes to associate tags to class codes
Intrinsic Model
-- Model is trained on tagged patents
which helps in building vectors to phrases
as compared to traditional models
Dolcera Advantage
- Vector representation for phrases.
- When text has the phrase “long term
evolution”, traditional models treat them
separately as in “long”, ”term”, “evolution”
which doesn’t capture the true essence of
the phrase.
- Word disambiguation (Contextual mapping)
- Tablet can be used in different contexts say
Tablet (computer) or Tablet (Pharmacy), but
with proprietary Dolcera tagging, we are
able to disambiguate it. Hence, based on
the context of the text associated vector
representation of tablet is chosen.
- This behavior can be seen on PCS by
searching for
- dolcera_tags:”Tablet (computer)”
- dolcera_tags:”Tablet (pharmacy)”
5
Over 1 million Key Concepts
Intrinsic Model

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Dolcera
Document Vector
• The test set (50 patents) vectors are compared with the other patent vectors to determine the most similar ones.
• The patents in same family are ignored.
Weighted average of tf-idf weights
to phrase/word vector
Document Vector
TAC is tagged using Dolcera Tagger
to extract key concepts
Title Abstract Claim Tagging
Each word is weighed based on its
term frequency, inverse document
frequency
Tf-Idf weighing
Copyright © 2019 Dolcera Corporation. Circulation and representation without the consent of the author is prohibited

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Dolcera
Categorization Dashboard (Sample)
Dashboard helps the user to categorize a set of patents based on their taxonomy for
hassle-free analysis.
Click here to model the sample set based on
analysis sheet uploaded
• Upload the Training Patents to the Dashboard
• Upload the respective Taxonomy to the Dashboard that we need to create a model.
• Click on Model to generate the model
Click here to train the sample set
• Once Training got Completed, it will create a model that we can use it on Test
set for auto categorization
• The system can absorb feedback and retrain the model to reflect changes in the
entire dataset

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Dolcera
• Once the Model got generated, Upload new set of patents and click on Auto
categorization.
• New patents got categorized based on trained model and patents are categorized
to respective technology nodes
Click here to enable auto-categorization
of the new set of patents
• Below screenshot shows the new patents are auto categorized
to existing taxonomy nodes.
• Ambiguity patents are moved to Inbox for further manual
analysis
Categorization Dashboard (Sample)

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Dolcera
Performance of an example model
Similarity Analysis
Neural Network
Document Vectors
Input
Level1: Precision: ~86%
Level2: Precision: ~60%
Performance
Classification - Category
Output
Level1 (2): Precision: ~95%
Level2 (3): Precision: ~82%
Choice – Top N
• For each patent top 10 similar patents were
recognized using vectors.
• The categories of each of these 10 similar
patents were observed.
• ~80% of the level 1 categories were
consistent with the given category.
• ~58% of the level 2 categories were
consistent with the given category. This can
also be observed in the prediction
probabilities from neural network with
average max probability of ~0.42. This
indicates a probable overlap of categories.
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4
3
1
Note:
• Dolcera has build models for other companies with complex datasets. The above explains the performance of one of the models on such datasets.

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Dolcera
Keep innovating with
Dolcera
Thank You
Sumair Riyaz
sumair.riyaz@dolcera.com
+49 7621 986 4830
+49 172 8234614
www.dolcera.com