Claims:

1. A method for simulation of humans using a human twin, the method comprising a processor implemented steps of:
obtaining, by one or more hardware processors, a first set of information comprising real-time data of humans from a plurality of sources (301);
extracting, based upon the first set of information, a second set of information comprising real-time contextually correlated data corresponding to humans, wherein the second set of information is extracted by abstracting the first set of information and a pattern of pre-defined activities of humans via a data layer (302);
creating, from the second set of information, a plurality of process models via an orchestration layer, wherein each of the plurality of process models comprise at least one pre-defined criteria and at least one computed value for simulating a real-time operating environment to be optimized, and wherein the real-time operating environment comprises humans and a plurality of activities with which humans interact (303);
simulating, by implementing the human twin, each of the plurality of process models and the first set of information for generating a set of simulated information (304); and
optimizing, based upon the simulation, the real-time operating environment by implementing the human twin, wherein the optimizing comprises identifying an optimal alternative of executing a set of real-time activities in the real-time operating environment. (305).

2. The method as claimed in claim 1, wherein the step of simulating comprises simulating behavior and activities of humans via the human twin for optimizing the real-time operating environment.

3. The method as claimed in claim 1, wherein the human twin comprises simulating humans with a physical environment or the real-time environment using a digital twin for optimizing the real-time operating environment.

4. The method as claimed in claim 1, wherein the step of optimizing is preceded by generating, based upon the simulation, a set of recommendations to optimize the real-time operating environment.

5. The method as claimed in claim 4, wherein the step of generating the set of recommendations is preceded by identifying a set of optimal values on behavior and activities of humans by implementing the human twin.

6. The method as claimed in claim 1, wherein the step of simulating is preceded by identifying, from at least one of the plurality of process models, at least one sub-criteria corresponding to the pre-defined criteria by implementing the human twin, for optimizing the real-time operating environment.

7. A system (100) for a simulation of humans with using a human twin, the system (100) comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
obtain a first set of information (201) comprising real-time data of humans from a plurality of sources;
extract, based upon the first set of information (201), a second set of information (202) comprising real-time contextually correlated data corresponding to humans, wherein the second set of information (202) is extracted by abstracting the first set of information (201) and a pattern of pre-defined activities of humans via a data layer;
create, from the second set of information (202), a plurality of process models (203) via an orchestration layer, wherein each of the plurality of process models (203) comprise at least one pre-defined criteria and at least one computed value for simulating a real-time operating environment to be optimized, and wherein the real-time operating environment comprises humans and a plurality of activities with which humans interact;
simulate, by implementing the human twin, each of the plurality of process models (203) and the first set of information (201) for generating a set of simulated information (204); and
optimize, based upon the simulation, the real-time operating environment by implementing the human twin, wherein the optimizing identifying an optimal alternative of executing a set of real-time activities in the real-time operating environment (204).

8. The system (100) as claimed in claim 7, wherein the step of simulating comprises simulating behavior and activities of humans via the human twin for optimizing the real-time operating environment.

9. The system (100) as claimed in claim 7, wherein the human twin comprises simulating humans with a physical environment or the real-time environment using a digital twin for optimizing the real-time operating environment.

10. The system (100) as claimed in claim 7, wherein the one or more hardware processors (104) are configured to optimize the real-time operating environment by generating, based upon the simulation, a set of recommendations (205) to optimize the real-time operating environment.

11. The system (100) as claimed in claim 10, wherein the one or more hardware processors (104) are configured to generate the set of recommendations (205) by identifying a set of optimal values on behavior and activities of humans by implementing the human twin.

12. The system (100) as claimed in claim 7, wherein the one or more hardware processors (104) are configured to identify, from at least one of the plurality of process models (203), at least one sub-criteria corresponding to the pre-defined criteria by implementing the human twin, for optimizing the real-time operating environment.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

SYSTEMS AND METHODS FOR SIMULATION OF HUMANS BY HUMAN TWIN

Applicant

Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.


TECHNICAL FIELD
The disclosure herein generally relates to digital human twin, and, more particularly, to systems and methods for simulation of humans using human twin.

BACKGROUND
Digital twin(s) are virtual replicas of physical devices that data scientists and other professionals can use to run simulations before actual devices are built and deployed. They are also changing how technologies such as Internet of Things (IoT), Artificial Intelligence (AI) and analytics are optimized. In recent times, the technology behind digital twins has expanded to include larger items such as buildings, factories and even cities, and some have said people and processes can have the digital twin(s), expanding the concept even further.
IoT is one of the key driver of the digital twin technology. With the explosive amount of data coming in from sensors and other devices, IoT provides an unprecedented visibility into systems and processes. With the growing adoption of industrial IoT, companies are looking to streamline their processes and cut down the inefficiencies in the system. In addition, going "smart" allows companies to increase their productivity, which, in turn, increases their revenue. With the convergence of IoT in industry, digital twin technology can be incorporated easily and allow companies to have a true digital representation of systems and facilities.
Therefore, as humans and physical objects are becoming increasingly connected, gaining useful insights from the massive amount of data involved becomes extremely important. The digital twin provides useful insights by creating a virtual space where data can be visualized. As industries and businesses look at gaining a competitive edge, adopting smart solutions such as the digital twin becomes imperative to stay ahead in the market. However, traditional systems and methods have so far implemented the digital twin to only above mentioned limited areas and related technologies, and thus, real provisioning of the digital twin not been shown or implemented by any of the traditional systems and methods.

SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for simulation of humans using a human twin is provided, the method comprising: obtaining, by one or more hardware processors, a first set of information comprising real-time data of humans from a plurality of sources; extracting, based upon the first set of information, a second set of information comprising real-time contextually correlated data corresponding to humans, wherein the second set of information is extracted by abstracting the first set of information and a pattern of pre-defined activities of humans via a data layer; creating, from the second set of information, a plurality of process models via an orchestration layer, wherein each of the plurality of process models comprise at least one pre-defined criteria and at least one computed value for simulating a real-time operating environment to be optimized, and wherein the real-time operating environment comprises humans and a plurality of activities with which humans interact; simulating, by implementing the human twin, each of the plurality of process models and the first set of information for generating a set of simulated information; optimizing, based upon the simulation, the real-time operating environment by implementing the human twin, wherein the optimizing comprises identifying an optimal alternative of executing a set of real-time activities in the real-time operating environment; simulating behavior and activities of humans via the human twin for optimizing the real-time operating environment; generating, based upon the simulation, a set of recommendations to optimize the real-time operating environment; identifying a set of optimal values on behavior and activities of humans by implementing the human twin; and identifying, from at least one of the plurality of process models, at least one sub-criteria corresponding to the pre-defined criteria by implementing the human twin, for optimizing the real-time operating environment.
In another aspect, there is provided a system for simulation of humans using a human twin, the system comprising a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: obtain a first set of information comprising real-time data of humans from a plurality of sources; comprising real-time contextually correlated data corresponding to humans, wherein the second set of information is extracted by abstracting the first set of information and a pattern of pre-defined activities of humans via a data layer; create, from the second set of information, a plurality of process models via an orchestration layer, wherein each of the plurality of process models comprise at least one pre-defined criteria and at least one computed value for simulating a real-time operating environment to be optimized, and wherein the real-time operating environment comprises humans and a plurality of activities with which humans interact; simulate, by implementing the human twin, each of the plurality of process models and the first set of information for generating a set of simulated information; optimize, based upon the simulation, the real-time operating environment by implementing the human twin, wherein the optimizing comprises identifying an optimal alternative of executing a set of real-time activities in the real-time operating environment; simulating behavior and activities of humans via the human twin for optimizing the real-time operating environment; optimize the real-time operating environment by generating, based upon the simulation, a set of recommendations to optimize the real-time operating environment; generate the set of recommendations by identifying a set of optimal values on behavior and activities of humans by implementing the human twin; and identify, from at least one of the plurality of process models, at least one sub-criteria corresponding to the pre-defined criteria by implementing the human twin, for optimizing the real-time operating environment.
In yet another aspect, there is provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes the one or more hardware processors to perform a method for simulation of humans using a human twin, the method comprising: obtaining a first set of information comprising real-time data of humans from a plurality of sources; extracting, based upon the first set of information, a second set of information comprising real-time contextually correlated data corresponding to humans, wherein the second set of information is extracted by abstracting the first set of information and a pattern of pre-defined activities of humans via a data layer; creating, from the second set of information, a plurality of process models via an orchestration layer, wherein each of the plurality of process models comprise at least one pre-defined criteria and at least one computed value for simulating a real-time operating environment to be optimized, and wherein the real-time operating environment comprises humans and a plurality of activities with which humans interact; simulating, by implementing the human twin, each of the plurality of process models and the first set of information for generating a set of simulated information; optimizing, based upon the simulation, the real-time operating environment by implementing the human twin, wherein the optimizing comprises identifying an optimal alternative of executing a set of real-time activities in the real-time operating environment; simulating behavior and activities of humans via the human twin for optimizing the real-time operating environment; generating, based upon the simulation, a set of recommendations to optimize the real-time operating environment; identifying a set of optimal values on behavior and activities of humans by implementing the human twin; and identifying, from at least one of the plurality of process models, at least one sub-criteria corresponding to the pre-defined criteria by implementing the human twin, for optimizing the real-time operating environment.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
FIG. 1 illustrates a block diagram of a system for simulation of humans using a human twin, in accordance with some embodiments of the present disclosure.
FIG. 2 is an architectural diagram depicting components and flow of the system for simulation of humans using the human twin, in accordance with some embodiments of the present disclosure. .
FIG. 3 is a flow diagram illustrating the steps involved in the process of simulation of humans using the human twin, in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
Embodiments of the present disclosure provide systems and methods for simulation of humans using a human twin. Digital twins are normally used in manufacturing, energy, transportation and construction. Large, complex items such as aircraft engines, trains, offshore platforms and turbines could be designed and tested digitally before being physically produced. These digital twins could also be used to help with maintenance operations. For example, technicians could use a digital twin to test that a proposed fix for a piece of equipment works before applying the fix the physical twin.
With the explosion of IoT sensors, digital-twin scenarios can include smaller and less complex objects, giving additional benefits to companies. Further, by incorporating multi-physics simulation, data analytics, and machine learning capabilities, the digital twins can also demonstrate the impact of design changes, usage scenarios, environmental conditions, and other endless variables – eliminating the need for physical prototypes, reducing development time, and improving quality of the finalized product or process.
Traditional systems and methods have exploited the digital twin only to manufacturing, aircraft engines etc. One of the most important application of the digital twin could be simulation of humans or citizens. This is called as human twin. While very few of the traditional systems and methods have shown, used and implemented the human twin, but so far it has been restricted to digitization of a human body for medical purposes, creation of information fabric by grouping a digital twin graph and thereby modeling humans as twins and one or two other purposes.
The traditional systems and methods face major challenges for the simulation of humans with real-time environments using the digital twin. The proposed disclosure overcome the limitations of the traditional systems and methods by providing, for example, simulating humans using the human twin, and optimizing the real-time environment using the human twin.
Referring now to the drawings, and more particularly to FIG. 1 through 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 illustrates an exemplary block diagram of a system 100 for simulation of humans using a human twin, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory 102. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
According to an embodiment of the present disclosure, the architecture of the system for the simulation of humans using the human twin may be considered in detail. Referring to FIG. 2, it may be noted that architecture comprises the human twin, wherein the human twin facilitates simulating humans with a physical environment or real-time environment using a digital twin, for optimizing a real-time operating environment.
The architecture further comprises a first set of information 201 obtained from a plurality of sources, a second set of information 202 extracted by abstracting the first set of information 201, a plurality of process models 203 created from the second set of information 202, a set of simulated information 204 generated by simulating each of the plurality of process models 203, wherein the simulation is performed by implanting the human twin, and a set of recommendations 205 for optimizing the real-time operating environment using the human twin.
FIG. 3, with reference to FIG. 1 and FIG. 2, illustrates an exemplary flow diagram of a method for the simulation of humans using the human twin, in accordance with some embodiments of the present disclosure. In an embodiment the system 100 comprises one or more data storage devices of the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to the components of the system 100 as depicted in FIG. 1 and the flow diagram. In the embodiments of the present disclosure, the hardware processors 104 when configured the instructions performs one or more methodologies described herein.
According to an embodiment of the present disclosure, at step 301, the one or more hardware processors 104 obtain the first set of information 201 comprising real-time data of humans from a plurality of sources. As is known, a real-time data is data that is up-to-date and viewable the moment it’s available. There are many common uses for real-time data. Real-time information may be obtained, inter-alia, to draw a description of human actions and interactions through the analysis and understanding of human motion patterns. The first set of information 201 may comprise information / data on one or more actions performed by humans (for example, walking, driving and the like) and may also comprise metadata of data.
In an embodiment, the first set of information 201 is obtained from the plurality of sources. The plurality of sources may comprise IoT devices, enterprise data sources, contextualization sources like Geographic Information System(s) (GIS), human personas, engagement and feedback data etc. The plurality of sources may thus comprise of sensors, smartphones, smart watches, tablets, smart televisions, computers, laptops, smart home system, smart accessories, networked appliances or devices or other devices for monitoring or interacting with or for people and/or places, or any combination thereof. Considering an example scenario, the first set of information 201 (that may be obtained) may comprise of a real-time driving data of a person X driving an automatic car.
According to an embodiment of the present disclosure, at step 302, the one or more hardware processors 104 extract, based upon the first set of information 201, the second set of information 202 comprising real-time contextually correlated data corresponding to humans, wherein the second set of information 202 is extracted by abstracting the first set of information 201 and a pattern of pre-defined activities of humans via a data layer (not shown in the figure). In an embodiment, the term “activity” comprises any activity or event that can be represented as data which may be monitored by any device and which can be processed by a computer based on a signal from an equipment sensor, a report (data entry) by a person, and others, and is typically a “human activity” performed anywhere (for example, in a house, office, road, or a clinic).
Generally, activity pattern discovery comprises finding some unknown patterns directly from low-level sensor data without any predefined models or assumptions. The activity pattern discovery facilitates developing a pervasive system first and then analyzes the sensor data to discover activity patterns. The goal of activity recognition is to recognize common human activities in real life settings. Accurate activity recognition is challenging because human activity is complex and highly diverse. Considering an example scenario, the pattern of pre-defined activities of humans may comprise “Mr. A starting at 09:30 AM from a place X and expected to reach at a place B by 11:00 AM”.
In an embodiment, the one or more hardware processors 104 abstract the first set of information 201 and the pattern of pre-defined activities to extract the second set of information 202. In general, contextual data grouping increases productivity and social relationships for individuals. Moreover, the contextual data grouping and analysis may be used for improving services for corporations by providing, for example, targeted advertising and/or location-based services. Further, the correlation of the contextually grouped data provides various advantages which, inter-alia, may comprise of understanding the relationship between the one or more correlated sets of data, performing normalization and comparison of the one or more correlated sets of data.
Data correlation offers an intelligent way of associating a portion of the datasets with another portion of the datasets. The data correlation may be based on time synchronization, shared social relation (e.g., devices are owned by user accounts in the same social group), shared data dimension (e.g., both devices measures weight), shared data source profile (e.g., location or device-type, etc.), data owner profile (e.g., user profile or user configurations), shared known semantic (e.g., both devices are considered “kitchenware”), shared known context (e.g., both devices are operated in the context of exercising), or any combination thereof. For example, the one or more contextually grouped data sets on exercising patterns of a person may be correlated against pulse rate data of the person.
According to an embodiment of the present disclosure, an example of extracting the second set of information 202 may now be considered in detail. Suppose, a set of data comprising glucose level and one or more exercising patterns of a person is obtained from a smartphone. The set of data may be initially integrated and transformed as:
MOTION: JUMPING
PULSE RATE: HIGH
BLOOD PRESSURE: 210/120 mmHg (millimeter of mercury)
HEART BEAT: 90 per minute
GLUCOSE LEVEL: 60 mg/DL (milligrams per deciliter)
The set of data may then be correlated against a threshold value set by a doctor who has also prescribed a medicine for the blood pressure for the person. Suppose, the threshold values set by the doctor 130/90 mmHg for the blood pressure and 90 mg/DL for the glucose level. The integrated and transformed set of data may be correlated against the prescribed threshold of 130/90 mmHg, which means that the system 100 through the one or more hardware processors 104 may generate an alert “too high blood pressure while exercising” or “high activity level leads to sudden drop in glucose level.” The contextually correlated data may compared with other persons (like a person of similar age and similar medical problems) to guide them with useful exercising tips since the person is administered with blood pressure medicine and based on his body vitals has one or more set patterns.
According to an embodiment of the present disclosure, at step 303, the one or more hardware processors 104 create, from the second set of information 202, the plurality of process models 203 via an orchestration layer (not shown in the figure), wherein each of the plurality of process models 203 comprise at least one pre-defined criteria and at least one computed value, for simulating the real-time operating environment to be optimized.
In an embodiment, each of the plurality of process models 203 is created by implementing the digital twin. The real-time operating environment comprises humans and a plurality of activities with which humans interact. The process of creation of the plurality of process models 203 by understanding the digital twin, the human twin, and implementation of the human twin by the proposed methodology to overcome the technical limitations of the traditional systems and methods may now be considered in detail.
Digital twin- A digital twin is a digital version of a physical world / environment. Once created, the digital twin can be used to represent a digital representation of a real world. Digital Twin concept represents the convergence of the physical and the virtual world where every industrial product will get a dynamic digital representation. Additionally, the digital twin may mirror the status of the physical world / environment within a greater system. The digital twin also facilitates making any changes necessary to maintain its correspondence to the physical twin.
Human twin- The term ‘Human twin’ or ‘Human digital twin’ as applied and implemented by the proposed disclosure comprises a simulation of humans with physical world using the digital twin for optimizing real-time operating environment(s) or real-time physical environment(s). The human twin thus facilitates a platform which digitally represent the physical or the real-time world in which the human interact, overlaying on top of the digital model human persona(s), context and domain information for monitoring, predictions, simulation, optimizing operational environment, personalization etc. The implmentation / application of the human twin for optimizing real-time environments with example implementations has been discussed in detail in subsequent paragraphs.
Application of the human twin and technical improvements over the traditional systems and methods- As mentioned, the human twin (or the human digital twin) facilitates simulation or digital representation of humans with the physical environment or the real-time environment using the digital twin for optimizing the real-time operating environment. The proposed disclosure facilitates simulation of digital human(s) interacting with a set of services which are orchestrated for attaining a specific human goal. Further, the proposed disclosure provides for a modelling of a plurality of pre-defined criteria as one or more processes, and achieving of one or more sub-criteria (defined in terms of quantified values) associated with each of the plurality of pre-defined criteria, and computing one or more optimum values using the digital twin, for optimizing the real-time environment.
Although some of the traditional systems and methods cite the human digital twin, however, real provisioning of the digital twin for human has not been shown or implemented by any of the traditional systems and methods. None of the traditional systems and methods provide for a digital representation of human activity and related patterns, and simulating the real-time environment to predict on activity, location, physiological parameters.
The traditional systems and methods simply provide for digitalizing human interacting with machines, creation of information fabric by grouping a digital twin graph and thereby modeling humans as twins and then recording interaction of all twins as sub-nodes in the digital twin graph, simulation of human body using the digital twin or modelling human systems for cardiovascular simulations.
The process of creation of the plurality of process models 203 by the human twin may now be considered in detail. In an embodiment, each of the plurality of pre-defined criteria (a pre-defined criteria may be considered similar to a pre-defined activity in general) may be modelled as processes in a computing system. Considering an example scenario, Mr. A has at a location L1 has a meeting at another location L2 at time T1. Using the human twin, the pre-defined criteria, that is, time T2 at which Mr. A needs to start from home to reach office before the time T1, that is before the meeting starts, assuming that the distance between the location L1 and the location L2 is D1, may be initially modelled as explained below.
In an embodiment, modelling each of the plurality of pre-defined criteria comprises at least one sub-criteria corresponding to the pre-defined criteria by implementing the human twin. Generally speaking, a sub-criteria may be considered as an interim goal that may be necessary to achieve for completing an activity. Similarly, for modeling each of the plurality of pre-defined criteria as a process, one or more sub-criteria corresponding to each of plurality of pre-defined criteria needs to be identified and achieved.
The proposed disclosure facilitates modelling of the plurality of pre-defined criteria as the one or more processes, and achieving of the one or more sub-criteria associated with each of the plurality of pre-defined criteria by implementing the human twin, wherein the one or more sub-criteria are further modelled and achieved by a series of computations performed by the digital twin.
In an embodiment, the one or more sub-criteria corresponding to the pre-defined criteria discussed above may be identified as below:
Traffic signals at various locations within the distance D1. In this scenario, if there are three traffic signals, the one or more sub-criteria to be achieved may be identified as L11, L12, and L13; and
The parameters that may impact the travel of Mr. A, such as human behavior, vehicle, location, environment factors, and the like.
Based upon the pre-defined criteria and the second set of information 202, the plurality of process models 203 may be created as:
The average speed of Mr. A to L11 is S1, from L11 to L12 is S2, and L12 to L13 is S3;
Aggression score computed based upon a comparison of speed of Mr. A with his overall average speed is A1 and having a negative impact;
Road condition score is computed as P1 with a negative impact;
Rain prediction score computed as P1 with a positive impact; and
Upon reaching L11, there are may be an impact due to aggression.
According to an embodiment of the present disclosure, at step 304, the one or more hardware processors 104 simulate, by implementing the human twin, each of the plurality of process models 203 and the first set of information 201 for generating the set of simulated information 204. Initially, all possible routes to L13 may be displayed in a digitized mode to Mr. A. Upon selection of a route, the above process model created gets instantiated.
The one or more sub-criteria corresponding to the route selected may then be identified and simulation may be performed, wherein the simulation comprises computing impact of traffic congestions, road conditions, identifying parameters that may negatively and positively impact the speed etc. Based upon the sub-criteria amongst the one or more sub-criteria, below parameters may be identified and applied using the process model created referred to in FIG. 4.
In an embodiment, the step of simulating comprises simulating behavior and activities of humans via the human twin for optimizing the real-time operating environment. The embodiment of the present disclosure further facilitates generating the set of recommendations 205 with the set of simulated information 204 for identifying a set of optimal values on behavior and activities changes of humans. In an example implementation of the simulation, using the process model in FIG. 4, a simulation of average speed may be performed as below:
Average Speed of A+(Average Speed×Impact)/10,wherein 10 is a total of all attribute scores
If average speed of Mr. A in L11 is 32.5 and a current average speed of 25, wherein the current average speed has a negative impact, the average speed of Mr. A to reach L13 may be simulated as below:
32.5+(32.5×((32.5-25)/32.5)×(-1)=32.5-7.5=25 ;
when average speed of Mr. A is 25 miles per hour (mph) and an aggression score is 4 with a positive impact, optimal speed (simulated using the human twin) at which Mr. A may be recommended to drive to reach L13 is as below:
25+(25(1)×4))/10=25+10=35 mph
As shown above in the simulation of the average speed, aggression, that is, behavior of Mr. A may be also simulated simultaneously with the activities (for example, driving etc.), and the set of optimal values on behavior and activities (that is, the aggression score and the optimal average speed and the like) may further be generated based upon the simulation. In another example scenario the set of simulation information 204 may be generated in a tabular form showing an impact of a plurality of parameters on speed as shown in Table 1 below:
Table 1
Stages Average Speed in current location Mr. A daily average speed Mr. A current average speed Aggression in driving Rain Road condition Vehicle condition
Weightage Weightage Weightage Weightage Weightage
-1 1 -1 -1 -1
-1 4 2 2 0
Start 0 0 0 0 0 0 0
Stage 1 25 32.5 25 35 28 22.4 22.4
Stage 2 30 39 30 42 33.6 26.88 26.88
Stage 3 25 32.5 25 35 28 22.4 22.4
Stage 4 35 45.5 35 49 39.2 31.6 31.6
Stage 5 30 39 30 42 33.6 26.88 26.88

According to an embodiment, the proposed disclosure facilitates generating the set of recommendations 205 with the set of simulated information 204. Considering an example scenario, the set of recommendations 205 may be generated as:
Mr. A must drive at least at 25 mph at L11 with an aggression score of a minimum of 4; and / or
Mr. A must cross L12 before 09:30 AM as there are a high chances of rain.
According to an embodiment of the present disclosure, at step 305, the one or more hardware processors 104 optimize, based upon the simulation, the real-time operating environment by implementing the human twin. The real-time operating environment may be optimized using the set of simulated information 204 (generated based upon the simulation), the set of optimum values and the generated set of recommendations 205. In an embodiment, the step of optimizing comprises identifying an optimal alternative of executing a set of real-time activities in the real-time operating environment. Thus, the real-time operating environment is optimized by identifying the optimal alternative by identifying an impact / influence of human persona(s) and the physical world parameters on the overall stimulation.
Considering an example scenario, suppose Mr. A is driving his car at 20 mph, and is recommended based upon the simulation that “Mr. A must cross L12 before 09:30 AM as there are a high chances of rain”, by implementing the human twin, the set of recommendations 205 may be generated as:
“Park car at L111 parking slot near L11 and board a train by 09:00 AM to reach to L12”; and
“Current average speed of the train is 30 mph between L111 and L12, the train crosses L12 by 09:15 daily with 500 passengers load capacity”.
Other than the example scenario considered above, the embodiments of the proposed disclosure facilitate optimizing the real-time operating environment by generating one or more new models or by generating information via a tabular representation to a user, or by any other means thereof, based upon the set of simulated information 204, the set of optimum values and the generated set of recommendations 205.
According to an embodiment of the present disclosure, advantages of the proposed disclosure may now be considered. As discussed and shown above, the proposed disclosure facilitates simulation of humans on the digital twin, also referred to as the human twin. Real provisioning of the digital twin for human has not been shown or implemented by any of the traditional systems and methods. The proposed disclosure provides for optimizing the entire real-time operating environment, wherein humans interact with the plurality of activities. Thus, the proposed methodology does not merely focusses on automation like the traditional systems and methods, but also focusses on holistic or personalized criteria (or activities), that humans must achieve in order to optimize environment around them.
The embodiments of the present disclosure leverage integrated and unified data models referring to PAS 182 inter-operability standards. The PAS 182 is aimed at organizations that provide services to communities in cities, and manage the resulting data, as well as decision-makers and policy developers in cities. PAS 182 was established to enable interoperability between silo capabilities. None of the traditional systems and methods has been able to so far implement PAS 182 or create such models referring PAS 182.
The embodiments of the present disclosure further support multi-tenancy. The multi-tenant system may comprise of the systems in which various elements of hardware and software of the database system may be shared by one or more tenants. For example, a given application server may simultaneously process requests for a great number of tenants, and a given database table may store rows for multiple tenants. For example, in the step 302 above, the contextually correlated data may be used by a cardiologist, who may set his alerts or rules for guiding his patient (for example, prescribing a new medicine when pulse rate goes down) and the first set of aggregated data remains isolated from others.
The embodiments of the present disclosure facilitate supporting PaaS (platform-as-a-service) and SaaS (software-as-a-service) models. PaaS offerings typically facilitate deployment of web applications without the cost and complexity of buying and managing the underlying hardware and software and provisioning hosting capabilities, providing all of the facilities required to support the complete life cycle of building and delivering web application and service entirely available from the internet (for example Google App Engine™), while the SaaS platform allows developers to provide software solutions via the mediator server directly to customers, and ensures data availability and data security (for example Google Apps™).
In an example implementation, the proposed human twin platform may be hosted on any cloud platform. Each customer access the platform from the cloud URL, which is provided and controlled by the owner. Being multi-tenant, the platform need not be replicated or redeployed for each and every customer or moved to different cloud location. The human twin platform may serve multiple customer. It is also deployed on an elastic infrastructure, which scales accordingly.
In an embodiment, the memory 102 can be configured to store any data that is associated with the simulation of humans using the human twin. In an embodiment, the information pertaining to the first set of information 201, the second set of information 202, the plurality of process models 203, the generated set of simulated information 204, and the optimized real-time environment using the human twin etc. is stored in the memory 102. Further, all information (inputs, outputs and so on) pertaining to the simulation of humans with real-time world using the human twin.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
The embodiments of present disclosure herein addresses unresolved problem of the simulation of humans using the human twin (or the digital twin). The embodiment, thus provides for the optimization of the real-time operating environment by implementing the human twin, wherein the human twin comprises simulating humans with physical or real-time environment using the digital twin, for optimizing the real-time operating environment. Moreover, the embodiments herein further provides simulating behavior and activities of humans via the human twin for optimizing the real-time operating environment.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.