AU2018101340A4 - Robotic Property Conveyancing Service - Google Patents

Abstract

Abstract Property Conveyancing is the legal process of transferring a title of real property. The invention establishes an online Robotic Property Conveyancing Service, which combines various Artificial Intelligence technologies with property conveyancing legal knowledge, to accelerate the property conveyancing processes for individual or organisational property vendor and purchaser parties, and other related parties, such as Law Firms, Government, etc.

Description

The invention establishes an online Robotic Property Conveyancing Service, which combines various Artificial Intelligence technologies with property conveyancing legal knowledge, to accelerate the property conveyancing processes for individual or organisational property vendor and purchaser parties, and other related parties, such as Law Firms, Government, etc.

EDITORIAL NOTE

There are 6 pages in the description only.

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Robotic Property Conveyancing Service

The purpose of the invention is to establish an online Robotic Property Conveyancing Service, which combines various Artificial Intelligence technologies with property 5 conveyancing legal knowledge, to accelerate the property conveyancing processes for individual or organisational property vendor and purchaser parties, and other related parties, such as Law Firms, Government, etc.

Property Conveyancing is the legal process of transferring a title of real property.

The new invention of the Robotic Property Conveyancing Service has key offerings include:

• Establishing a neural network machine learning model particular used for Property Conveyancing;

• Over a conveyancing transaction, representing either the property vendor or the property purchaser as a conveyancer, who intelligently generates, modifies and finalises the “Contract for the sale and purchase of land” with standard and special conditions based on the Property Conveyancing machine learning model;

• Over the whole lifecycle of a conveyancing transaction, capturing requirements, explaining and discussing special conditions and explaining law terms with property vendor and purchaser parties, as well as other related parties, using natural languages, such as text/document, voice, graphic, etc.

• Establishing the Property Conveyancing machine deep learning knowledge base;

• Leveraging real-life conveyancers, who are licensed and qualified professionals, who will provide the advice and information to the Robotic Property Conveyancing Service for handling any special scenarios out of the Robotic

Property Conveyancing Service training knowledges. The handling of the new scenarios will be used to enrich the Property Conveyancing machine deep learning knowledge base for future usage.

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DETAILED DESCRIPTION OF THE INVENTION

As shown in Fig 1, the Vendor’s Robotic Conveyancing Service, which is an Al virtual conveyancer, is used to represent the vendor, includes the following stages:

Step F1.01: A vendor or a property sales agent lodge a property information to the 5 Vendor’s Robotic Conveyancing Service, such as property address, property title, property type, what are included for sale etc.

Step FI.02: (Al) Based on the deep learning trained model and knowledge data as well as the property information, the Vendor’s Robotic Conveyancing Service will automatically generate the basic draft “Contract for the sale and purchase of land”.

Step FI.03: (Al) Based on different input data of step FI.01, the Vendor’s Robotic

Conveyancing Service will also automatically generate a list of special conditions to discuss with the vendor or the property sales agent of step F1.01.

Step FI.04: (Al) The Vendor’s Robotic Conveyancing Service will use various natural languages to explain to and discuss with the vendor, the vendor’s conveyancer or the property sales agent for each special condition and then capture the inputs to modify the special conditions.

Step FI.05: The Vendor’s Robotic Conveyancing Service will generate the TODO checklist and a list of attachments by leveraging the vendor, the property sales agent and various third-party systems.

Step FI.06: The Vendor’s Robotic Conveyancing Service will integrate the content from

FI.02, FI.03, FI.04 and FI.05 to generate the first version of Contract, which will be distributed to the vendor, (optional) vendor’s conveyancer and the property sales agent, and then the contract will then be further distributed to the various prospect purchasers via the property sales agent.

Step FI.07: Over the property sales period, the property sales agent can interact with the Vendor’s Robotic Conveyancing Service with the feedbacks from prospect purchasers as reference data, but the Vendor’s Robotic Conveyancing Service will not modify the contract over this period.

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Step FI.08: The formal changes request will be provided by the purchaser’s conveyancer, which will be captured by the Vendor’s Robotic Conveyancing Service and then feedback to the vendor or (optional) Vendor’s conveyancer.

Step FI.09: The Vendor’s Robotic Conveyancing Service will decide and provide the 5 replies to the purchaser’s conveyancer by leveraging the Deep learning model, the existing knowledge base as well as the actual legal experts, following the process defined in Fig 3 Robotic Conveyancing Service Al Process.

Step FI. 10: Once all special condition changes are agreed, the Vendor’s Robotic Conveyancing Service will then generate the final contract for exchange. The content of 10 final contract could be reused for a future selling for the same property.

Step Fl.l 1: The Vendor’s Robotic Conveyancing Service can leverage the digital signature technologies to capture the signature of each parties for contract exchange.

Step FI. 12: Once the contract exchange finalized, the property settlement process will start. The Vendor party’s settlement will be managed and monitored by the Vendor’s

Robotic Conveyancing Service.

Step FI. 13: In some case, an exchange property sales contract can be cancelled over the cool-off period. The cancel contract request will be captured by the Vendor’s Robotic Conveyancing Service to finalize the cancel process.

As shown in Fig 2, the Purchaser’s Robotic Conveyancing Service, which is an Al virtual conveyancer, is used to represent the purchaser, includes the following stages:

Step F2.01: The purchaser or (optional) the purchaser’s conveyancer send the “Contract for the sale and purchase of land” to the Purchaser’s Robotic Conveyancing Service, which will then kick off the process.

Step F2.02: (Al) Purchaser’s Robotic Conveyancing Service will extract the contract information and then analysis and review the contract by leveraging the Deep learning

2018101340 12 Sep 2018 model, the existing knowledge base as well as the actual legal experts, following the process defined in Fig 3 Robotic Conveyancing Service Al Process.

Step F2.03: Based on the analysis and review information, the Purchaser’s Robotic Conveyancing Service will explain the potential risks, special conditions detail to the 5 purchaser or (optional) the purchaser’s conveyancer as well as providing the suggestions of special condition changes.

Step F2.04: The Purchaser’s Robotic Conveyancing Service will use various natural language to discuss the changes and suggestions with the purchaser or (optional) the purchaser’s conveyancer and then provide the feedback to the Vendor’s Conveyancer.

Step F2.05: For any inputs from the Vendor’s Conveyancer, the Purchaser’s Robotic Conveyancing Service will decide and then provide the replies to the Vendor’s Conveyancer by leveraging the Deep learning model, the existing knowledge base as well as the actual legal experts, following the process defined in Fig 3 Robotic Conveyancing Service Al Process.

Step F2.06: Once all discussion complete, the Purchaser’s Robotic Conveyancing Service will agree all the changes with the Vendor’s Conveyancer and also verify the final contract, which will be provided by the Vendor’s Conveyancer.

Step F2.07: The Purchaser’s Robotic Conveyancing Service can leverage the digital signature technologies to capture the signature of each parties for contract exchange.

Step F2.08: Once the contract exchange finalized, the property settlement process will start. The Purchaser party’s settlement will be managed and monitored by the Vendor’s Robotic Conveyancing Service.

Step F2.09: Over the cool-off period (if applicable), if the Purchaser wants to cancel the contract, the Purchaser can send the cancellation request to the Purchaser’s Robotic

Conveyancing Service.

Step F2.10: The Purchaser’s Robotic Conveyancing Service will distribute the contract cancelation to the Vendor’s Conveyancer.

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As shown in Fig 3, Robotic Conveyancing Service AI Process is a general process, which will be reused in different steps, shown in Fig 1 and Fig 2, over the whole conveyancing process across the vendor and purchaser parties. The Robotic service is a combination of a neural network machine learning model particular used for Property Conveyancing,

Property Conveyancing machine deep learning knowledge base, the interactions with legal experts, and various natural languages for inputs and outputs, such as text/document, voice, graphic, etc.

Step F3.01: The Robotic Conveyancing Service captures the inputs via various natural languages for inputs, such as text/document, voice, graphic, etc via various channels, such 10 as Email, Mobile App, or Web Application, etc.

Step F3.02: The Robotic Conveyancing Service uses various recognition technologies, such as hand-writing recognition, image recognition, voice recognition, face recognition to translate the natural language inputs into the inputs of the conveyancing service high dimensional deep learning model.

Step F3.03: The inputs will then go through the neural network hidden layers, which is the property conveyancing high dimensional deep learning model, trained by a large number of historical “Contract for the sale and purchase of land”.

Step F3.04: The Robotic Conveyancing Service calculates over the hidden layers with weight, which determines the training confidence.

Step F3.05: The Robotic Conveyancing Service will make the decisions by the training confidence. If the training confidence is lower than a pre-defined percentage, the Robotic Conveyancing Service will decide not to generate the outputs by the deep learning model.

Step F3.06: Instead of generating the outputs, the Robotic Conveyancing Service will raise a case to a list of registered legal experts via Mobile push notification, email or tasks over a web application.

Step F3.07: One of the registered legal experts will pick up and resolve the case with text as the inputs, which will also be used as the outputs.

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Step F3.08: The legal expert’s inputs for the case will also be used to enrich the property conveyancing knowledge base.

Step F3.09: The Robotic Conveyancing Service will generate the outputs of the outputs of the conveyancing service high dimensional deep learning model.

Step F3.10: The Robotic Conveyancing Service will then translate the outputs of the conveyancing service high dimensional deep learning model into the actual natural language outputs based on the original natural language inputs.

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Claims (5)

1. Claims

   1. The establishing of a neural network machine learning model, which is particularly used for conveyancing to transferr a title of real property.
2. 2. The establishing of the property conveyancing machine deep learning knowledge base. This knowledge base is based on the neural network machine learning model and is trained by many “Contract for the sale and purchase of land”, and will be enriched by the future property conveyancing cases.
3. 3. The establishing of virtual robotic conveyancing service to serve property vendor(s) by leveraging the neural network machine learning model and the property conveyancing machine deep learning knowledge.
4. 4. The establishing of virtual robotic conveyancing service to serve property purchaser(s) by leveraging the neural network machine learning model and the property conveyancing machine deep learning knowledge.
5. 5. The artificial intelligence processing process of taking various nature language inputs, and then leverage the neural network machine learning model, the machine deep learning knowledge base as well as human experts to produce nature language outputs.

   EDITORIAL NOTE

   There are 3 pages in the drawings only.

   Fig. 1 shows the high-level process and functionalities Vendor’s Robotic Conveyancing

   Service.

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   BRIEF DESCRIPTION OF THE DRAWING

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   Fig. 1 - Vendor’s Robotic Conveyancing Service

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   Fig. 2 shows the high-level process and functionalities Purchaser’s Robotic Conveyancing Service.

   I--------------------1

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   Fig. 2 - Purchaser’s Robotic Conveyancing Service

   Fig. 3 shows the high-level process and functionalities that the Robotic Conveyancing

   Service handles the inputs and provide the outputs.

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   Fig. 3 - Robotic Conveyancing Service Al Process