This document discusses using artificial intelligence techniques for auto parts data and e-commerce solutions. It outlines using natural language processing to parse vehicle descriptions and create a vehicle identification automata. Pattern analysis and statistical reasoning can be used for field mapping, SKU pattern analysis, and automatic brand mapping. AI can also power a natural language chatting robot for search and an expert system for personalized shopping guides based on customer tracking and purchase history. The current status is between prototyping and coding these solutions with the goal of launching integrated AI applications.

1. Chulho Jo
August 25, 2017
http://AutoParts911.com

2. Overview
• What is Artificial Intelligence (AI)?
• AI Techniques for Auto Parts Data
• AI Techniques for e-Commerce Solutions
• Current Status and Timeline

3. Artificial Intelligence (AI)?
• AI is the study of how to make software
algorithms do things better than human beings do
in real-time.
• AI includes Machine Learning, Natural Language
Processing, Neural Networks, Expert Systems,
and Big Data.
• AI algorithms are becoming more practical and
feasible in the Auto Parts industry due to
innovative hardware improvement.

4. AI Techniques for Auto Parts Data
• Natural Language Processing
o Create Automata using Lexical Analyzer & CFG (Context-Free Grammar) Parser by defining
a variety of vehicle information patterns in the product or fitment description.
Ex) Incomplete or arbitrary vehicle descriptions are parsed with specific Vehicle Id.
2017 Toyota Camry LE 2.4L
2005-2017 Camry LE
Camry LE 2005-17

Vehicle Id

5. AI Techniques for Auto Parts Data
o Implementation
By defining a proto-type model with 3 makes (Hyundai, Toyota,
Honda) using Lex/Yacc, the vehicle automata successfully returns
Vehicle Id for arbitrary
vehicle descirptions.
o Technical Consideration
• Generating Automata
from ACES VCDB
• Adding Categories and Brands
into Automata Parser
o Goal
Ex) Camry 2017 2.4L K&N Cabin Air Filter

2017 Toyota Camry LE / 2.4L
(Vehicle Id, Engine Id)
Accessories and fluids/Filters/Air Filter
(Category, Subcategory, Terminology)
K&N
(Brand)

6. AI Techniques for Auto Parts Data
• Pattern Analysis & Statistical Reasoning
o Field Mapping Control – Loading data in CSV or Text into Database Table
by selecting the input columns or adding in-line PHP codes.
o SKU Pattern Analysis
Click #1 (the first row) to see what SKUs are there.

7. AI Techniques for Auto Parts Data
• Pattern Analysis & Statistical Reasoning
o Automatic Brand Mapper Using Statistical Data
3rd Party data brands matched against ACES (Our Side) brands
(In this test, 70% of brands automatically detected)

8. AI Techniques for Auto Parts e-Commerce
• Natural Language Processing in Search Text or Chatting Robot
o Create Chatting Parser generated from Automata using Lex/Yacc
o Be integrated with the Vehicle/Category/Brand automata mentioned previously)
o Implementation
Proto-type Syntax Analyzer (Yacc)
worked.
Real dialog parser in progress.
o Goal
Less User Input  More Relevant Search Results

9. AI Techniques for Auto Parts e-Commerce
• Shopping Guide Expert Systems
o Visitor Tracking (Unique Cookie Id or User Id if logged in)
o Recording all Visitor’s behaviors (Navigation, Search Keywords, Selection) into knowledge database
(Knowledge Acquisition)
o Adding more data (Ex, Sales history if exists) into knowledge database
o Running Inference Engine (Reasoning with the knowledge)
Ex) Fact data AB, BC Inference data A C (Knowledge Inference)
o Implementation
Proto-type model in progress
o Goal
Customized User Experience with Customer Knowledge database
Search UI & Results are dynamically changed based on customer
Ex) If customer A has purchased Front Brake Pads a month ago and is now browsing in the Brake
category
 Rear Brake Pad should be displayed with higher priority (Assuming Rear Brake Pads worn out)
 For the same data, the systems respond differently based on customer knowledge (Like human
beings).

10. Current Status and Timeline
• Current Status
Between Proto-Type and Coding
• Timeline
Proto-
Type
Coding
Integra
tion
Launch