PATENT-READY TECHNICAL
DESIGN
AI–IoT–BIM–EVM
Integrated Closed-Loop Project Performance Monitoring, Prediction &
Optimization System
Enhancement Supplement —
Novel Modules, Compliance Layer, Claims Expansion & Commercialization
Roadmap
Prepared for: Vimal Noble
M.Tech — Project Engineering & Management (2024–26)
Jharkhand University of Technology (JUT) Ranchi,
affiliated to Jharkhand University of Technology (JUT), Ranchi
August 2026
1. Overview | उद्देश्य
यह दस्तावेज़ पहले से तैयार AI–IoT–BIM–EVM Closed-Loop architecture को
patent-strength, compliance-readiness और commercialization-readiness की दृष्टि से
आगे मजबूत करता है। नीचे जोड़े गए modules और sections मौजूदा 5-module ढांचे
(A–E) को प्रतिस्थापित नहीं करते, बल्कि उसे विस्तार देते हैं।
The base architecture (Modules A–E: Physical Data Acquisition,
Spatial-Temporal Fusion, Predictive Engine, Optimization Engine, Feedback
Controller) remains the technical core. This supplement adds three new
technical modules, a prior-art differentiation matrix, expanded dependent
claims, a regulatory/compliance layer, ESG integration, a TRL roadmap, an IP
risk register, and a validation plan — the elements patent examiners and
licensing partners typically look for next.
2.
Three New Technical Modules | नए तकनीकी मॉड्यूल
Module F — Cybersecurity
& Data Integrity Layer (साइबर सुरक्षा
एवं डेटा अखंडता)
Physical-to-digital IoT systems बनते ही एक नया attack surface खोलते हैं
(sensor spoofing, GPS jamming, data tampering)। बिना security layer के, कोई भी
investor या standards body इसे field-deployable नहीं मानेगा — इसलिए यह module
patent और commercialization दोनों के लिए अनिवार्य है।
•
End-to-end
encryption (TLS 1.3 / DTLS) between sensor → edge gateway → cloud
•
Blockchain-anchored
audit trail for EVM baseline changes and corrective-action approvals (immutable
ledger, not just “optional”)
•
Sensor
authentication via device-certificate + RFID cross-check to prevent spoofed
physical-progress data
•
Anomaly-based
intrusion detection on the edge gateway (lightweight ML model, separate from
the prediction engine)
•
Data integrity
hashing (SHA-256) at each fusion checkpoint so tampering between Module A and
Module B is detectable and becomes a defensible technical claim
Module G — Explainable AI
& Human-in-the-Loop Governance (व्याख्येय
AI एवं मानव-पर्यवेक्षित निर्णय)
पूर्ण रूप से autonomous corrective action risky और non-adoptable है —
construction/EPC managers legally accountable होते हैं। Explainability इसे
“black-box AI” के patent-रिजेक्शन जोखिम से भी बचाता है।
•
SHAP/LIME-based
explanation layer attached to every CPI_pred / SPI_pred deviation alert,
showing which sensor/equipment feature drove the prediction
•
Tiered autonomy:
Advisory mode (recommend only) → Supervised mode (manager approves) →
Autonomous mode (pre-approved low-risk actions only)
•
Decision audit
log linking each corrective action to the explanation, approver ID, and outcome
— this closes the loop with accountability, which is a distinct technical +
commercial differentiator
Module H — Digital Twin
Synchronization & Standards Compliance (डिजिटल ट्विन तुल्यकालन एवं मानक अनुपालन)
यह module architecture को actual industry standards से जोड़ता है, जिससे
pilot projects और licensing बातचीत तेज़ होती है।
•
IFC (ISO 19650)
compliant BIM component tagging so Module B's ID-mapping is interoperable with
any standard BIM authoring tool (Revit, ArchiCAD, Tekla)
•
OGC SensorThings
API for sensor data interoperability instead of a proprietary format
•
MQTT/CoAP for
lightweight edge-to-cloud messaging under constrained bandwidth (rural
Jharkhand sites, weak connectivity)
•
Offline-first
edge buffering: system continues local prediction and stores queued updates
when connectivity drops — addresses a real field condition our earlier design
didn't cover
3.
Prior-Art Differentiation Matrix | पूर्व-कला विभेदीकरण
Patent examiners reject combination-inventions fastest when they can't
see a specific technical delta from existing art. This table should be refined
after an actual Espacenet/Google Patents search, but frames the argument
structure clearly.
|
Existing
Art Category |
Typical
Coverage |
Gap
Our System Closes |
|
AI+IoT
construction monitoring (e.g. US20250265520A1-type) |
Sensor
data → dashboard alerts |
No
predictive-EVM transformation; no BIM Work-Package binding of raw sensor data |
|
BIM
+ IoT integration patents (various CN filings) |
Static
or near-real-time BIM sync from sensors |
No
closed-loop multi-objective optimization tied to cost/schedule/risk
simultaneously |
|
Standalone
EVM software |
Historical,
manually-entered progress |
No
physical-sensor-derived EV; purely retrospective, not predictive |
|
Construction
digital twins |
Visualization
+ progress tracking |
No
automated corrective-action generation or explainability/audit layer |
|
Generic
PM AI-prediction tools |
Cost/schedule
risk scoring only |
No
hardware binding — stays an abstract algorithm, weak for patent eligibility |
मुख्य तर्क (core argument): नयापन किसी एक तकनीक में नहीं, बल्कि
Sensor→BIM→EVM→Optimization के specific data-fusion mechanism और उसके
closed-loop, auditable, explainable कार्यान्वयन में है।
4.
Expanded Draft Claims | विस्तारित दावे (Claims)
Independent Claim 1 पहले ही draft किया जा चुका है। नीचे उससे जुड़े
dependent claims हैं जो examiner को novelty की गहराई दिखाते हैं (attorney से
final भाषा तय करवाएँ):
1.
The system of
Claim 1, wherein the data-fusion algorithm cryptographically hashes sensor data
at each fusion checkpoint to produce a verifiable, tamper-evident chain of
custody for physical progress data.
2.
The system of
Claim 1, further comprising an explainability module that generates a
feature-attribution report for each predicted CPI/SPI deviation, identifying
the sensor or equipment source contributing most to the deviation.
3.
The system of
Claim 1, wherein corrective actions are classified into advisory, supervised,
and autonomous tiers based on a pre-configured risk threshold, and autonomous
execution is restricted to actions below said threshold.
4.
The system of
Claim 1, wherein the edge-computing gateway operates in an offline-buffered
mode during network disconnection, queuing fused data and continuing local
predictive inference using a cached model.
5.
The system of
Claim 1, wherein the BIM component ID mapping conforms to the IFC/ISO 19650
schema, enabling interoperability across multiple BIM authoring platforms.
6.
The system of
Claim 1, wherein the multi-objective optimization engine outputs a ranked set
of at least two corrective-action alternatives with associated cost, schedule,
and risk trade-off scores prior to action selection.
7.
The system of
Claim 1, further comprising a blockchain-anchored ledger recording each EVM
baseline change, corrective action, and human approval event as an immutable
audit record.
8.
The system of
Claim 1, wherein sensor authenticity is verified via a device-certificate check
cross-referenced against an RFID/GPS location constraint prior to acceptance
into the fusion pipeline.
5.
Regulatory & Compliance Layer | विनियामक अनुपालन
भारत में field-deployable IoT/AI systems के लिए यह compliance mapping
investor/licensing due-diligence में जल्दी पूछी जाती है।
|
Standard
/ Law |
Relevance |
|
DPDP
Act 2023 (India) |
Governs
any personal/labour data captured via site cameras or mobile field apps —
consent + data-minimization design needed |
|
ISO/IEC
27001 |
Information
security management baseline for the cloud/edge data layer |
|
ISO
19650 (BIM) |
Standard
for managing information over the lifecycle of a built asset using BIM |
|
IEEE
2413 |
Architectural
framework for IoT interoperability |
|
Indian
Patents Act 1970, S.3(k) |
Excludes
“algorithm/software per se” — hardware-bound, technical-effect framing (as
used throughout this design) is essential to eligibility |
6.
Sustainability & ESG KPI Integration | सतत विकास
एवं ESG सूचक
चूंकि आपका broader research profile Jharkhand rural development और
policy से भी जुड़ा है, ESG metrics जोड़ने से यह system government/EPC tenders में
अतिरिक्त वरीयता (preference) पा सकता है।
•
Embodied-carbon
tracking per work package (linking material/equipment sensor data to emissions
factors)
•
Equipment
idle-time reduction as a fuel/energy-saving KPI, auto-computed from Module A
sensor streams
•
Worker safety
index derived from CCTV/computer-vision near-miss detection, feeding into the
Project Success Index (PSI)
•
Waste/material-overrun
tracking via load-cell and RFID variance, supporting circular-construction
reporting
7.
Technology Readiness Level (TRL) Roadmap | प्रौद्योगिकी
तत्परता रोडमैप
|
TRL |
Milestone |
Approx.
Timeline |
|
TRL
1–2 |
Concept
+ architecture finalized (this document) |
Completed |
|
TRL
3 |
Simulated
PoC — synthetic sensor data + AI model + dashboard (thesis-stage) |
3–4
months |
|
TRL
4 |
Lab-scale
demonstrator — ESP32/Arduino sensors + BIM viewer + EVM engine integrated |
4–6
months |
|
TRL
5 |
Field
validation in relevant environment — one instrumented work package on a
live/pilot site |
6–10
months |
|
TRL
6 |
Prototype
demonstrated in operational environment — full closed loop on a small project |
10–16
months |
|
TRL
7–8 |
System
qualified, industry pilot with EPC/government partner |
16–30
months |
|
TRL
9 |
Commercial
deployment / licensing |
30+
months |
8.
IP & Commercialization Risk Register | बौद्धिक
संपदा जोखिम रजिस्टर
|
Risk |
Likelihood |
Mitigation |
|
Prior
art overlap in AI+IoT construction space |
High |
Narrow
claims to the specific fusion/audit/explainability mechanism, not the general
concept |
|
S.3(k)
software-per-se rejection (India) |
Medium |
Keep
claims hardware-bound; emphasize sensor–gateway–actuator technical effect |
|
High
prototype cost delaying filing |
Medium |
File
provisional early with simulated PoC; complete specification within 12 months
as prototype matures |
|
Data
privacy non-compliance (site cameras, worker data) |
Medium |
Build
DPDP-compliant consent/anonymization into Module F from the start, not
retrofitted |
|
Licensing
partner reluctance without field data |
High |
Target
one instrumented pilot work package before approaching EPC licensing partners |
9.
Experimental Validation Plan | प्रयोगात्मक सत्यापन योजना
थीसिस के लिए defensible empirical section बनाने हेतु न्यूनतम KPI
targets:
|
Metric |
Target |
Method |
|
Schedule-delay
prediction accuracy |
≥85% |
Compare
AI-predicted SPI_pred vs actual SPI over pilot duration |
|
Cost-overrun
prediction accuracy |
≥80% |
Compare
CPI_pred vs actual CPI |
|
End-to-end
latency (sensor → dashboard alert) |
<5
seconds (edge), <60 seconds (cloud fallback) |
Timestamp
logging across Modules A–D |
|
False-positive
rate on deviation alerts |
<15% |
Manager-reviewed
alert log over pilot period |
|
Data-integrity
verification success |
100%
of fusion checkpoints hashed & verifiable |
Automated
integrity-check script on stored ledger |
10.
अगले कदम | Next
Steps
9.
Espacenet +
Google Patents + Indian Patent Office पर संशोधित keyword search:
("predictive EVM" OR "earned value") AND "IoT"
AND "BIM" AND ("explainable" OR "blockchain")
10. JUT Ranchi / BIT Sindri के IP Cell से प्रारंभिक
patentability opinion लें
11. Module F–H को thesis Chapter 3
(Methodology) में तकनीकी architecture के हिस्से के रूप में जोड़ें
12. Simulated PoC (Python + synthetic sensor
data + open BIM viewer) पर काम शुरू करें — TRL 3 लक्ष्य
13. Provisional patent application draft तैयार
करें, hardware-bound claims पर केंद्रित रखते हुए
नोट: यह एक technical planning document है, कानूनी सलाह नहीं।
Patent filing से पहले registered patent attorney से patentability opinion अवश्य
लें।
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