Retail Platform with Natural Interaction: LOAD's Vision

Pedro Colarejo

, Davide Ricardo

, André Fernandes

, Miguel Fonseca

, Pedro Oliveira

Nidhal Cherni

, João Abrantes

, Hélio Guilherme

and António Teixeira

LOAD Interactive, Lda., Aveiro, Portugal

IEETA, Dep. Telecommunications and Information Technology Electronics, University of Aveiro, Aveiro, Portugal

{pedro.colarejo, davide.ricardo, andre.fernandes, miguel.fonseca, pedro.oliveira, nidhal.cherni, joao.abrantes,

Keywords: Retail, Information for Consumers, Virtual Assistants, Dialogue, Natural Language, Image Recognition,

Blockchain.

Abstract: In a context of indirect sales channels, where the product reaches the final consumer through intermediaries, it

is very difficult for consumers to know the origin of the product and identify the origin of problems. In this

paper an innovative technological platform is proposed, oriented to the retail market, and capable of providing

information from the entire product distribution chain to its various stakeholders. The platform is based on 3

pillars: a decentralized blockchain-based information network covering the path of a product from its origin to

the end consumer; extraction of information about users/customers and products from images and video;

interaction using natural language. A first instantiation of the platform is also presented as well as the first

results. In its development, recent technologies were used in the areas of image recognition and dialogue

systems.

1 INTRODUCTION

From the point of view of the global process of selling

products/services by an organization to its customers,

the customer service has always had a great

importance in the ability of these organizations to

generate revenue. This assistance to customers can

consist of a set of services that help customers to

know more information about a product/service they

intend to purchase and help their choice. In this sense,

these organizations have a higher cost to improve

these services, investing in the training of employees,

to understand by interacting with the clients their

opinions and needs regarding the services provided.

A bad provision of these services increases to

probability of losing customers to the competition.

Examples of poor practices in the provision of this

type of services are well known, including excessive

duration in problem solving, rude treatment, lack of

transparency in resolution processes, not being able

to give a concrete answer on the problem to be solved,

promises that have not been fulfilled, etc.

In a context of indirect sales channels, where there

is a value chain, and where the product reaches the

final consumer from intermediaries, the customer

service, during the sales process, becomes more

complicated. The manufacturer who produces the

product or provides the service does not reach the

final consumer directly. There is a set of distributors

and retailers that complete this value chain, where

distributors buy the product and sell it to retailers,

who will make the product available and sell to final

consumers. There are advantages to this type of

distribution channels, but there are also some

limitations. On the one hand, the manufacturer loses

control over the message that is transmitted to the end

customer, as this is done by retailers and resellers, nor

can it control the importance of the product facing its

competitors in retail stores. Furthermore, in situations

where there is a problem with the product (delivery

delay, defects, etc.), it is very difficult for end

consumers to understand the origin of the product and

whether the problem is in the manufacturer, or

elsewhere in the chain.

In recent years, organizations have invested in

automated methods to provide these support services.

Virtual Customer Assistants (VCA), which include

chatbots, are the latest of these methods. VCAs are

applications that are typically available online

capable of establishing a conversation with customers

via voice and/or text. VCA's use has grown rapidly,

as there is great potential in improving the provision

Colarejo, P., Ricardo, D., Fernandes, A., Fonseca, M., Oliveira, P., Cherni, N., Abrantes, J., Guilherme, H. and Teixeira, A.

Retail Platform with Natural Interaction: LOAD’s Vision.

DOI: 10.5220/0010996000003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 293-300

ISBN: 978-989-758-569-2; ISSN: 2184-4992

293

of customer service, both on the organization side and

on the client side. Part of this is justified by the

growing tendency among customers to use text and

instant messaging as their first choices when

communicating with an organization, rather than

using the traditional voice channel (the phone). In

addition, there is a growing tendency for customers to

want to solve their own problems in a direct way,

rather than asking someone else to do so for them.

From the point of view of organizations, the

implementation of VCA, in addition to its greater

efficiency, can result in a considerable reduction in

costs, since the traditional voice channels, using the

phone, are quite expensive, being much cheaper to

keep this service fully available 24 hours a day, than

the same availability with human resources.

However, there are several technological

challenges to implement such assistants, capable of

interacting as if they were human. The interaction

platform has to be able to improve the way it interacts

with customers, not only from the point of view of

usability, but also from the user experience, that is,

the perception with which customers are when

interacting with the system, or the level of

satisfaction/frustration that the customer experiences.

This implementation tends to be sustained by

technological advances in artificial intelligence,

namely Machine Learning (ML), with applications in

Natural Language Processing (NLP) and Image

Recognition.

Thus, the main objective defined for this work,

was the development of an innovative technological

platform, oriented to the retail market, applicable in

different business areas, to provide information of the

entire product distribution chain to its various

stakeholders. It must be based on a decentralized

information network, covering the complete path of a

product from its origin until it reaches the final

consumer.

This article is structured as follows: in section

the work related to the areas most directly related to

the implementation of the platform (Blockchain,

image/video recognition and natural language

dialogue system) is presented. In the third section is

defined the main scenario to be considered, in order

to realize the implementation of the prototype. In

section 4 the general architecture of the system and its

main components is described; in section 5 how the

first prototype was implemented; and, in section 6,

the first results of the integration of the various

modules. The main conclusions from the work

performed and some future paths are presented in

section 7.

2 RELATED WORK

The development of a solution such as the one

proposed in this paper requires the integration of

results from various areas. In the following

subsections is very briefly presented the work related

to the implementation of a blockchain for the retail

sector, object recognition technologies and

technologies associated with conversational

assistants (chatbots). No platforms or Assistants

directly related to what is proposed in this paper were

found.

2.2 Blockchain (for Retail)

Blockchain technology (Yaga, Mell, Roby, &

Scarfone, 2019) has been attracting increasing

attention in a wide range of industries due to its ability

to reliably manage transaction-based applications

without the need for a centralized authority. This was

not possible before blockchain emerged. This

technology ensures trust in transactions in a network

between untrusted nodes, since all of these nodes can

trade, even if they do not trust each other. Blockchain

was initially developed to support secure digital

currency transaction (Bitcoins) in order to prevent

transactions from being improperly duplicated.

However, its potential extends to several domains

using peer-to-peer (P2P) architectures, where

interconnected nodes make transactions with each

other without using a centralized management

system, such as electronic voting, purchase and sale

of intellectual property, distribution of confidential

medical information, or intelligent value chains

applying Internet of Things (IoT), called Smart

Logistics (Uckelmann, 2008; Kawa, 2012).

Great strides have been made in the use of

Blockchain for application areas not related to crypto-

currencies, such as Slock.it, a company that works in

smart electronic lock (Slocks), which can be unlocked

with smartphones that have the appropriate token, and

that can be purchased on Ethereum using Ethers. A

Slock owner can rent his house or car at a price for

timed access, as the door lock is automatic. Anyone

interested in renting can identify the Slock, pay the

requested amount and unlock the door. Another

example are transactive energy companies with

blockchain-based solutions, allowing you to buy and

sell energy (generated by solar panels) automatically,

according to the criteria defined by the user.

In relation to value chains, there is a huge

potential for application of Blockchain technology.

With a network based on Blockchain technology,

product tracking and control is complete, since the

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same information in the database is shared by all

entities, where all updates are cryptographically

verified and propagated over the network

automatically, thus creating a record of the

information history that can be validated by all

entities in the network. There are several initiatives in

the retail areas in pilot phases, some more advanced

than others, as, for example, jewelry (e.g., De Beers

and TrustChain Jewelry

) and food industry (e.g.,

IBM Food Trust

and Norwegian Sea Food

2.2 Object Recognition

A state-of-the-art survey of image recognition was

carried out, and some technologies available on the

market with the potential to integrate a first

implementation of the platform were identified and

characterized, such as:

● Watson - Visual Recognition

- Developed by

IBM, the Watson system offers - among many

other things - the Visual RecognitionAPI, which

allows you to: sort content according to scenarios;

recognize objects, faces, colors, food, text, and

inappropriate content; train models for specific

cases.

● Amazon Rekognition

- The AWS cloud

computing services platform has in its spectrum

this Image Recognition service that allows you to

identify objects, people, text, scenarios, activities,

and explicit/inappropriate content in both image

and video.

● Google Mobile Vision

- Part of the Google ML

Kit, in addition to identifying faces in both

photographs and video, allows to find and observe

facial landmarks (such as eyes, mouth, nose, etc.)

providing information about whether, for

example, the subject is laughing or has his eyes

open. It also allows you to recognize text and

present it according to its structure, thus

maintaining paragraphs, lines, punctuation, etc...

● Google Cloud Vision API

- Is part of the Google

Cloud AIplatform, providing detection of objects,

faces, emotions, scenarios, tags, explicit or

inappropriate content, text and colors.

https://www.jckonline.com/editorial-article/de-beers-

blockchain-platform/

https://www.jckonline.com/editorial-article/richline-

blockchain-trace-gold/

https://www.ledgerinsights.com/blockchain-food-

traceability-gs1-foodlogiq-ibm-food-trust-ripe-io-sap/

https://cointelegraph.com/news/from-sea-to-table-

norways-seafood-industry-hooks-into-ibm-blockchain

https://www.ibm.com/watson/services/visual-

recognition/

● Vize.ai

- This company provides the cloud

solution (Vize Custom Image Recognition API)

and an On-device solution (Vize MobileModels)

to recognize and classify images. Unlike most,

this solution requires initial training according to

categories defined by the user in order to cover

specific problems.

● Microsoft Azure Cognitive Services Vision

- It

is one of the 5 sets of services that Microsoft

provides through its Cognitive Services platform.

● Cloudsight AI

- Provides an API that allows you

to filter and categorize images as well as monitor

explicit/inappropriate content.

● Clarifai

- Clarifai provides a cloud solution that

allows you to tag images and videos, create your

own template that is applied to a specific use case,

search content through visual or tag similarities,

and moderate content by identifying whether the

content is explicit/inappropriate or not.

● Imagga

- It's a PaaS that provides image

recognition APIs that allow companies to build

and monetize image-intensive applications in the

cloud.

● Kairos

- It’s one of the leading companies in the

field of Facial Recognition, providing an API and

SDK that allow you to detect multiple faces,

identify faces (answer the question, "who is

this?"), check faces (answers the question, "this is

..."), detect emotions, detect age groups, detect

facial landmarks, detect levels of attention and

finally detect ethnicities.

A comparison of these solutions is presented in

Table 1. The most promising solutions considering

the objectives were those of IBM and Microsoft.

However, we wanted to ensure full flexibility to

change and adapt to our idea, which was not

guaranteed by these platforms. It was then considered

beneficial to develop solutions tailored to the project,

to gain more knowledge in the area and have more

flexibility to adapt the functionalities. The libraries

Tensorflow, OpenCV and PyZbar were adopted.

https://aws.amazon.com/rekognition/

https://developers.google.com/vision

https://cloud.google.com/vision/

https://vize.ai/

https://azure.microsoft.com/en-us/services/cognitive-

services/directory/vision/

https://cloudsight.ai/

https://www.clarifai.com/

https://imagga.com/

https://www.kairos.com/

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Table 1: Comparison of evaluated solutions for image recognition.

2.3 Conversational Assistants

Dialogue systems, Chabots and Conversational

Assistants are increasingly popular due to their use-

fulness: they provide easier and more versatile access

to large and diverse sets of information. Conversation

assistants perform chatbot-like interactions and allow

speech as input and outputs. Google Assistant and

Amazon Alexa are popular examples.

To allow interaction in a dialog format, a typical

conversation system integrates several modules: (1)

Automatic Speech Recognition (ASR) that converts

speech into text; (2) Natural Language Understanding

(NLU) processes ASR word sequences to identify

important information, such as intentions and entities;

(3) The Dialog Manager (DM) manages dialogue and

context information, taking into account the intention,

entities and previous conversations; (4) Natural

Language Generation (NLG) generates phrases; (5)

Speech synthesis uses text-to-speech (TTS) to

produce synthetic speech.

In recent years, there has been impressive progress

in NLP and NLU technologies, particularly accelerated

by the increased popularity of technologies such as the

Internet of Things (IoT) and artificial intelligence. As

a result, several platforms have emerged providing

sophisticated NLU capabilities, one of the main

components of a dialog system, and tools aimed at

developing dialog/chatbot systems, of which are

representative examples: TrindiKit, OpenDial, IBM's

Watson Conversation Service, RASA, (Bocklisch,

Faulkner, Pawlowski, & Nichol, 2017)PyDial, Dialog

Flow, Alexa Skills, ICECAPS, Emora and Plato. One

example to highlight is Plato (Papangelis, et al., 2020),

a tool available by Uber in 2020 that can be used to

create conversational agents, supporting interactions

through speech, text, or dialogue. An application in

Plato consists of four main components: the dialogue

that defines and implements acts of dialogue and states;

the domain that includes the ontology of the dialogue

and the database that the dialogue system consults; the

controller who orchestrates the conversations; and the

agent that implements different components of each

conversation agent.

3 SCENARIO

The following scenario was defined, depending on

which the prototype was designed, developed:

 A consumer in a supermarket approaches a shelf

with a particular product. The chatbot/Assistant

detects the presence and starts a conversation,

greeting him/her and asking what it can do to help

(using the right gender context).

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Figure 1: General Architecture of the proposed System/Platform, with its 3 main modules.

 The consumer requests information about the

product, such as storage conditions, temperature,

humidity, and also some location.

 The Conversational Assistant interprets the

questions and responds, querying the blockchain

to obtain the information needed.

 The system complements the speech/text output

with a map showing the product path and all

geographic points that the product has passed

through.

4 PROPOSED SYSTEM

Figure 1 schematically presents the architecture

proposed for the system, consisting of 3 main

modules:

• Interaction with the user.

• Image, Speech and Language Processing.

• Blockchain Infrastructure.

The User interaction module is the frontend

responsible for the user's information, from questions

by voice, to his/her image, or even possibly text, but

also for informing the user, through

questions/answers by voice, image (e.g., map) and

written information displayed on the screen.

The Image, Speech, and Language Processing

Module is a system backend module, which includes

the following components:

 Image recognition, responsible for image

processing for facial and product codes

recognition.

 NLP/NLU and Dialogue Management, supporting

interaction by natural language through the

identification of intentions and entities in user

interactions (NLU), natural language generation

and dialogue management.

The third main component of the system is

dedicated to the entire Blockchain Infrastructure,

implemented in EOS.IO. This is where all the

information added to the product (transactions, state

changes, value) is stored throughout its progression in

the distribution chain.

5 PROTOTYPE DEVELOPMENT

5.1 Speech and Language Processing

The simplest way to communicate with the system is

through natural language, whether the text is from the

user or Speech Recognition.

To recognize speech and translate it into text a

speech recognition service is used (from Google). To

transform system’s response to speech a module was

developed in C# using Microsoft Speech SDK.

To implement the determination of users'

intentions, extraction of entities (e.g., topic, day),

language generation and interaction management, the

Plato Research Dialogue System, previously

mentioned in this article, was used.

USER INTERACTION

PROCESSING

BLOCKCHAIN

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297

5.2 Image Processing

The integration of image processing with the user

interface, by integrating the web interface through

Websockets services, is a challenge, because it is

where the harmony between the machine and the

human needs to occur. For this prototype, image

recognition focuses on three actions: face recognition,

user gender recognition, and barcode scanner. Face

recognition informs the assistant regarding the start of

the conversation; gender recognition is needed to

shape the conversation; the barcode scanner allows

product identification, so that the system can obtain

information from the blockchain and answer the

questions posed by the customer.

Specific solutions have been created in Python

using libraries such as Tensorflow, OpenCV and

PyZbar. Tensorflow was used to detect client's gender

and emotions; OpenCV was used to detect a person's

face; and PyZbar was used to read barcodes (Abadi,

et al., 2016).

5.2 Blockchain and Communications

The code base of the EOS blockchain has been

identified as being the most suitable for the targeted

prototype. We worked on the integration of external

libraries (AI) in smart contracts. Focusing on

applying proof of concept to a real solution to real

problems (blockchain for intelligent logistics), the

development of a DAPP (Decentralized App) was

started, to store different states of a supply chain,

from the creation of raw material to assembly, and

until reaching the final consumer.

Support has been developed for a set of

interactions with blockchain, such as:

 Deployment of the initial contracts for basic use of

the blockchain.

 Creation of the default wallet.

 Creation of accounts to deploy contracts.

 Deployment of smart contracts.

 Interaction with the smart contract through

transactions.

Automatic accounts were then created, multi-

indexed tables and the available API (JSON RPC)

were used to communicate with the blockchain and

obtain data from the recorded tables. During this

process, it was concluded that files (such as images)

should be saved off-chain, and then their reference

saved in the blockchain. The IPFS was selected as the

most promising approach.

To integrate the blockchain with the dialog, an

API was developed, which also intervenes in the

interaction with the IoT component (temperature,

humidity, and location sensors). In this way, it is

possible to read and write data to a tag, using the

RFID-RC522 shield, as well as allow the sending of

images captured the assistant to be stored in IPFS (the

distributed system for storing and accessing

applications and data). The API between blockchain

and dialog allows to:

 Register users, companies, devices, and products.

 Relate each device with different types of sensors

and with a company.

 Relate each product to different IDs to be able to

accept different barcodes and RFID.

 Send measurement messages from the IoT

component, which will be stored on the blockchain

(and it was possible to save location, time,

temperature, and humidity for a specific product).

 Get all the values of each registered entity.

 Receive a request for characteristics of a product

(for example: send barcode and receive general

information about a product).

 Receive specific questions about a product for the

different sensors (e.g., product x part at

temperature y on day/hour z).

6 INITIAL RESULTS

This section presents information about results for the

first prototype of the platform, starting with the

Blockchain-based module and ending with the

interaction with the system Assistant, visible face of

the system and, in a certain way. integrator of the

whole system.

Once the blockchain was implemented, creating

the initial contracts to record the transactions to be

recorded along the distribution chain, it was

integrated with a set of sensors used to simulate

temperature, humidity, and location data. After this

definition, it was then possible to extract the

information needed (topic, a date, and a location),

making a call to the blockchain API in which the topic

(temperature / humidity), date and location are passed

as parameters. If the blockchain response is

successful, a response is constructed by Plato with the

entities received and the quantity returned. The

following is an example of using blockchain:

-data

'{"table":"product","scope":"loadtestac

c1","code": "loadtestacc1", "json":

"true"}'

{"rows":[{"key":0,"user_id":"0,""nam

e":"lettuce,""description":"organic

lettuce,""from_product":",""sub_product

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s_list":[]},

{"key":1,"user_id":"0,""name":"artichok

e,""description":"artichoke,""from_prod

uct":",""sub_products_list":[]},{"key":

2,""user_id":"0,""name":"carrot,""descr

iption":"organic-growing

carrot,""from_product":",""sub_products

_list":[]},

more":true,""next_key":"10,""next_key_b

ytes":""}

With the following blockchain blocks produced:

info 2021-05-24T12:44:27.000 nodeos

producer_plugin.cpp:1965 produce_block

] Produced block 08cd1f92adcfd669...

#234949 @ 2021-05-24T12:44:27.000 signed

by eosio [trxs: 0, lib: 234948,

confirmed: 0]

info 2021-05-24T12:44:27.500 nodeos

producer_plugin.cpp:1965 produce_block

] Produced block 77107e01d7396056...

#234950 @ 2021-05-24T12:44:27.500 signed

by eosio [trxs: 0, lib: 234949,

confirmed: 0]

info 2021-05-24T12:44:28.002 nodeos

producer_plugin.cpp:1965 produce_block

] Produced block 45e8c73f7ea32c3...

#234951 @ 2021-05-24T12:44:28.00signed

by eosio [trxs: 0, lib: 234950,

confirmed: 0]

As mentioned in the previous section, the

interaction with the user uses image to sense a human

face approaching, its gender (male, female) and

feeling (positive, negative, neutral). The same camera

is also used for reading the product barcode. The

barcode reading process allows to read most barcode

formats, as shown in Figure 2.

Figure 2: Examples of bar code reading by the developed

system.

Figure 3 presents an example of an interaction

with the system, using the implemented interface.

In the example presented in Figure 3, we can

observe the system locating a user who approaches

the system, evaluating whether it should refer to

verbal forms in the male or female, and his/her feeling

at that moment, to better decide how to greet and

answer the customer.

After the desired information collected, a map

with a selection of the product route is displayed

according to the question that was asked.

Figure 3: Graphical part of the user interface of the

prototype: (top left) dialogue that represents in writing

what is said by voice; (right) images of the client with

Information relating to Image Recognition; (bottom left)

graphic information (map with path and other Information);

The reproduction of two other real interaction

examples is presented next, following the internal flow

of data from the user: NLU processing, request to the

blockchain API, generation of the response, and trans-

mission of the system's global response to the user:

Interaction Example 1

User:

What is the humidity in Aveiro on

March 25, 2021?

NLU:

Topic = humidity

Day = 25

Month = March

Year = 2021

City = Aveiro

Blockchain:

API request:

product/info/0?

sensors=humidity&time=1616630400.0

&location=Aveiro

API Response:

{'error': False, 'code': 9000,

'description': 'Product info sent

with success', 'date': {'inTime': 0,

'date': [{'sensor': 'humidity',

'value': 4, 'time': 1610983085}]}}

System Response:

Humidity on March 25, 2021 in Aveiro

was 4%

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299

Interaction Example 2

User:

What is the temperature in Aveiro on

March 25, 2021

NLU:

Topic = temperature

Day = 25

Month = March

Year = 2021

City = Aveiro

Blockchain:

API request:

product/info/0?

sensors=temperature&time=1616630400.

0&location=Aveiro

API Response:

{'error': False, 'code': 9000,

'description': 'Product info sent

with success', 'date': {'inTime': 0,

'date': [{'sensor':

'temperature','value': 3, 'time':

1610983085}]}}

System Response:

The temperature on March 25, 2021 in

Aveiro was 3ºC

In the first interaction example the user asks a

question regarding the storage temperature of the

product that was recognized through its barcode, the

language processing system identifies the topic

(temperature), which is a question as well as date and

location, uses this information to query the

Blockchain and, based on the API response of this

(value=4), generates a sentence to be transmitted as a

response to the user. The second example is similar,

changing the topic detected in the user's sentence,

now "temperature". Although not integrating the

examples presented, due to space limitations, the

system is equipped with the ability to question the

user if he/she does not immediately provide all the

information necessary to have a complete query to the

blockchain. For example, if you the customer doesn’t

inform the local, the system will ask for it.

7 CONCLUSIONS

This article presents the vision for a platform that

facilitates consumer access to blockchain-based

product information by use of natural language

dialogue supported by image, speech, and language

processing technologies. As a first prototype, a

system capable of interaction by speech with a

customer that approaches the system was obtained,

distinguishing gender and initial "mood" and capable

of receiving requests for information by voice,

distinguishing the different entities from the

expression used, consulting the information on the

blockchain, and responding to the user by voice, text,

and image.

In its current state of development, several aspects

require evolution, such as: greater flexibility in the

extraction of users’ intentions and feelings; greater

ability to convey intentions and emotions using

speech (e.g., timbre adjustment); evolution of the

blockchain to profit from the constant evolution of

this technology. The system should also be subjected

to user use and evaluation in environments gradually

closer to the real, allowing its user-centric evolution.

ACKNOWLEDGEMENTS

This work was co-financed by project no. 38546

under the Incentive System R&D Projects-Individual

Projects of Portugal 2020.

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