DESC:FORM 2

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

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

METHOD AND SYSTEM PROVIDING ANALYTICS PLATFORM FOR DERIVING ENTERPRISE COLLABORATION METRICS

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.
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY

[001] The present application claims priority from Indian patent application no. 201821021396, filed on June 07th, 2018 the complete disclosure of which, in its entirety is herein incorporated by reference.

TECHNICAL FIELD
[002] The disclosure herein relates to field of enterprise collaboration, and, more particularly, relates to analytics platform for enterprise level collaboration to automatically curate and deliver enterprise level collaboration metrics.

BACKGROUND
[003] Enterprise collaboration platforms gather a lot of information related to user activity based on user collaboration, interactions, messages, calls, emojis and so on. To get insights from the user activity there is need to run jobs on daily, weekly, quarterly, monthly and yearly basis. These jobs gather statistical information of user interactions, their behavioral patterns, most interacted and least interaction teams or members etc. It is required to curate the statistical information to deliver enterprise level metric, alternatively referred as enterprise collaboration metrics. The curation task today requires manual intervention where the statistical information is collected, collated, arranged and presented to a user with intelligence and experience added by the manual intervention. The user, interested in the enterprise level metric may belong to one among the multiple hierarchical levels in an organization, thus giving rise to plurality of user types. The information providing enterprise level metric needs to be curated to the needs and hierarchical level of the requesting user type, so that the user receives the required analytical data that is simple to understand and interpret, while providing the required analytical details

SUMMARY
[004] 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 processor implemented method for deriving enterprise collaboration metrics in an analytics platform is provided. The processor implemented method comprising (a) acquiring, by an artificial agent via one or more hardware processors, input data from one or more sources at a pre-defined frequency; (b) ingesting, by the artificial agent via the one or more hardware processors, the acquired input data, into a specific unique format by utilizing an auto scaling technique; (c) deriving, by the artificial agent via the one or more hardware processors, statistical information from the ingested data; (d) analyzing, by the artificial agent via the one or more hardware processors, the derived statistical information to obtain one or more collaboration metrics; (e) provisioning, by the artificial agent via the one or more hardware processors, one or more collaboration metrics to one or more applications based on associated requests thereof via an unified application programming interface layer; and (f) generating, by the artificial agent via the one or more hardware processors, at least one of one more reports, graphs, questionnaire using the one or more collaboration metrics using an interactive communication interface.
[005] In an embodiment, the artificial agent may be implemented as a cloud based solution. In one embodiment, the artificial agent may be configured to calculate a user engagement quotient may be calculated based on the activity level of a user in a particular group. In an embodiment, the one or more collaboration metrics may be provisioned based on role based access associated with one or more users. In an embodiment, the step of provisioning may be performed based on on-demand user collaboration and activity trends. In an embodiment, the step of ingesting may be performed based on file size of the input data. In an embodiment, the statistical information may include user interactions, behavioral patterns, most interacted and least interacted user groups.
[006] In one aspect, a processor implemented system to derive enterprise collaboration metrics in an analytics platform is provided. The system includes 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: (a) acquire, input data from one or more sources at a pre-defined frequency; (b) ingest, the acquired input data, into a specific unique format by utilizing an auto scaling technique; (c) derive, statistical information from the ingested data; (d) analyze, the derived statistical information to obtain one or more collaboration metrics; (e) provision, one or more collaboration metrics to one or more applications based on associated requests thereof via an unified application programming interface layer; and (f) generate, at least one of one more reports, graphs, questionnaire using the one or more collaboration metrics using an interactive communication interface.
[007] In an embodiment, the artificial agent may be implemented as a cloud based solution. In one embodiment, the artificial agent may be configured to calculate a user engagement quotient may be calculated based on the activity level of a user in a particular group. In an embodiment, the one or more collaboration metrics may be provisioned based on role based access associated with one or more users. In an embodiment, the step of provisioning may be performed based on on-demand user collaboration and activity trends. In an embodiment, the step of ingesting may be performed based on file size of the input data. In an embodiment, the statistical information may include user interactions, behavioral patterns, most interacted and least interacted user groups.
[008] In yet another aspect, there are 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 (a) acquiring, by an artificial agent via one or more hardware processors, input data from one or more sources at a pre-defined frequency; (b) ingesting, by the artificial agent via the one or more hardware processors, the acquired input data, into a specific unique format by utilizing an auto scaling technique; (c) deriving, by the artificial agent via the one or more hardware processors, statistical information from the ingested data; (d) analyzing, by the artificial agent via the one or more hardware processors, the derived statistical information to obtain one or more collaboration metrics; (e)provisioning, by the artificial agent via the one or more hardware processors, one or more collaboration metrics to one or more applications based on associated requests thereof via an unified application programming interface layer; and (f) generating, by the artificial agent via the one or more hardware processors, at least one of one more reports, graphs, questionnaire using the one or more collaboration metrics using an interactive communication interface.
[009] In an embodiment, the artificial agent may be implemented as a cloud based solution. In one embodiment, the artificial agent may be configured to calculate a user engagement quotient may be calculated based on the activity level of a user in a particular group. In an embodiment, the one or more collaboration metrics may be provisioned based on role based access associated with one or more users. In an embodiment, the step of provisioning may be performed based on on-demand user collaboration and activity trends. In an embodiment, the step of ingesting may be performed based on file size of the input data. In an embodiment, the statistical information may include user interactions, behavioral patterns, most interacted and least interacted user groups.
[010] 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
[011] 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:
[012] FIG. 1a is a block diagram illustrates a system for automatically deriving enterprise collaboration metrics in an analytics platform, according to some embodiments of the present disclosure.
[013] FIG. 1b illustrates an architecture of the system of FIG. 1 providing analytics platform for enterprise level collaboration to automatically curate and deliver enterprise level metrics, according to embodiments of the present disclosure.
[014] FIG.1c illustrates data pipeline of the system, according to embodiments of the present disclosure.
[015] FIG. 2 illustrates a reception bay of the analytic platform provided by the system of FIG. 1b according to some embodiments of the present disclosure.
[016] FIG. 3 illustrates a parking bay of the analytic platform provided by the system of FIG. 1a, according to some embodiments of the present disclosure.
[017] FIG. 4 illustrates a processing bay of the analytic platform provided by the system of FIG. 1b, according to some embodiments of the present disclosure.
[018] FIG. 5 illustrates a storage bay of the analytic platform provided by the system of FIG. 1b, according to some embodiments of the present disclosure.
[019] FIG. 6 illustrates a serving bay and a consumers’ bay of the analytic platform provided by the system of FIG. 1b, according to some embodiments of the present disclosure.
[020] FIG. 7 is a flow diagram illustrating a method for automatically deriving enterprise collaboration metrics in an analytics platform using system of FIG. 1, according to some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[021] 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 scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims.
[022] The embodiments herein provide a method and system analytic platform providing an Artificial Agent (AA) residing in an orchestration bay to automate tasks, provision servers, schedule and monitor events, provide insights in the form of metrics and graphs. The terms “Artificial Agent” and “AA” have been used interchangeably in the present disclosure.
[023] FIG. 1a is a block diagram illustrates a system 100 for automatically deriving enterprise collaboration metrics in an analytics platform, according to embodiments 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 memory 102 comprises a database 108. 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. 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.
[024] 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.
[025] 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.
[026] The database 108 may store information but are not limited to, information associated with an analytics platform. Further, the database 108 stores information pertaining to inputs fed to the system 100 and/or outputs generated by the system (e.g., at each stage), specific to the methodology described herein. More specifically, the database 108 stores information being processed at each step of the proposed methodology.
[027] FIG. 1b illustrates an architecture of a system for providing analytics platform for enterprise level collaboration to curate and deliver enterprise level metrics and FIG. 1c provides data pipeline of the system, according to some embodiments of the present disclosure. The architecture comprises a reception bay, a parking bay, a processing bay, a storage bay, a serving bay and a consumers bay managed by the AA in the orchestration bay. The analytics platform can be replicated for multiple enterprises or entities.
[028] The functions of AA include:
1. Collection of input data, alternatively referred as enterprise collaboration data, through a reception bay, from input sources such as enterprise collaboration systems or platforms, at pre-defined frequency.
2. Ingesting gathered input data, alternatively referred as enterprise collaboration data, into a parking bay, in a specific unique format and using auto scaling or provisioning of ingestion servers to avoid under provisioning or overprovisioning.
3. Deriving statistical information of the ingested data using a processing bay.
4. Computing various collaboration metrics by analyzing the gathered statistical information using the processing bay and storing the various computed metric in a storing bay.
5. Providing a unified API layer a serving bay for front-end applications to consume the metrics from the storage bay; and
6. Providing a natural language-based chat interface in a consumer bay to deliver metrics in the form of reports, graphs, question answer mechanism etc., wherein the collaboration metrics details provided are dependent on role based access. Thus the proposed AA provides these metrics and insights to the user in a sleek natural language based chat interface which has sophisticated understanding of context and context switching.
[029] In one embodiment, the analytics platform can be implemented as a cloud based solution. The proposed analytics platform provides the enterprise collaboration metrics and insights to a user in a sleek natural language based chat interface which has sophisticated understanding of context and context switching.
[030] As depicted in FIG. 2 the reception bay collects input data, alternatively referred as enterprise collaboration data, from various sources such as enterprise collaboration systems of an organization into reception bay by automating scheduling of jobs through on one or more servers in the cloud wherein scheduling the jobs (job runs) is at predefined frequency such at hourly, daily, weekly, quarterly, monthly and yearly basis, till date and so on. The gathered data correspond to information related to user collaboration, interactions, messages, calls, emojis etc. The input data collected provides information on teams’ collaboration, document collaboration, app footage, associates and affiliation collaboration, call and meetings collaboration, learner’s foot print, interactions with chatbots, employee demographics, social collaboration and feedback interactions.
[031] The analytic platform is scalable as the AA automatically provisions additional servers to ingest data based on the amount of data available for ingestion. Existing methods have challenge in collecting enterprise collaboration data seamlessly and at such a large scale. With large scale enterprise collaboration data collected seamlessly, the enterprise collaboration metric derived by the analytic platform from the collected data provides more accurate insights.
[032] The job frequency can be set based on business demand for respective enterprise. Also, the parameters that are considered for job scheduling include type of the source, data latency, data criticality, data quantity etc. For example the data could be ingested from various enterprise collaboration platforms like enterprise messaging systems, conference systems, learning portals etc. Each of these system data can have their criticality level and has to be analyzed with due care. Scheduling also depends on the quantity of the data. If the amount of input data is more, then more servers are spun and frequency of job runs will also be increased. Data latency also effects the number of time the jobs are run. Some enterprise collaboration servers will be busy throughout the day and pushing of enterprise collaboration data can be delayed to the times where the load on the source systems is less. Thus, job scheduling provided is optimal. Further, the AA enables monitoring the scheduled jobs, performing the data gathering, through cloud-based tools and notifying the failure of jobs to the development teams of the enterprise. At times the jobs fail because of out of memory errors due to insufficient RAM requirements for the amount of data being processed. In such cases this kind of notifications will alert the development team about the root cause of the issue. Complete error log can be captured at the time of the failure which helps the development team to diagnose the problem. The job scheduling is based on load, time and frequency of jobs, which provides simple and easy to implement solution, effectively reducing complexity.
[033] Built in Fault tolerance and retry capabilities are incorporated by the AA. As the data is huge, lot of servers are spun to run jobs. Data is replicated on multiple servers so that in case of any server failure due to hardware or software configurations, the jobs will not stop abruptly. In case of job failures due to code, a retry mechanism, for example up to 4 times, is used to ensure that issue is with user code and nothing is due to the hardware/server configurations.
[034] Once the data is gathered in the reception bay, the AA enables ingesting data gathered by one or more ingestion servers (component of the parking bay and may be hosted in cloud as well), wherein data ingestion happens using a unified data ingestion format, for example, (JSON). This brings uniformity in types of data gathered from various data sources as some sources deal with user messages and others deal with conference calls/screenshares of user. The AA enables ingestion of the input user data by the data ingestion servers multiple times of the day to reduce burden on data ingestion servers. Additionally, the data is pushed in the parking bay by the Artificial Agent in a specific file structure so that retrieval is easy and fast. The file structure becomes like a standard for all the data ingested from the source systems and brings uniformity. Downstream systems can then rely on the same set of data format irrespective of the type of data ingested from different sources. The data collection jobs are evenly divided throughout the day on hourly basis. Every hour data gets collected and pushed to the Parking bay illustrated in FIG. 3. The Parking bay has three major sub sections as below:
1) Access Methods which have different kinds of formats of input data. Readonly schema files, JSON documents, Native logs from collaboration systems and data feeds from API’s are some formats which will be ingested.
2) Process Evaluation: This deals with identification of type of data, methods to use for frequency selection of jobs runs, calculation of size/capacity of data, configuring the parameters required for job runs etc.
3) Staging area: This provides the output staging area for the files in the Parking bay. Once the data is ingested it gets stored in a persistent cloud storage for future processing. All the output files follow a standard convention which stays same across the further life cycle of the process.
[035] The AA checks the size of input data to be ingested from reception bay to parking bay and auto scaling the ingestion servers based on the input data to be ingested. Where there is more input data (data size/ size threshold can be configured in the AA), automatic partitioning of data is done and new servers can be spun to ingest additional data. Data is partitioned by the file size. For example, for every 1000 records the split happens and this count can be configured at the Artificial Agent level. When the data gets ingested it is better to take a window of records (here 1000 records) which are chunked as manageable data packets and will be sent to the parking bay. By doing so, avoids the unevenness in data sizes and will form a uniformity in data processing. Apart from regular data ingestion the AA also monitors the data ingestion and alerts corresponding users incase data does not arrive. There will be internal coordination between functions of AA to plan and schedule jobs based on the data ingested
[036] From the ingested data in the parking bay, the AA enables deriving statistical information of the ingested data using processing bay as illustrated in FIG. 4. The AA provides intercommunication between multiple bays and utilizes the information from Parking bay and Reception bay to plan and schedule servers in the Processing bay. Information such as size of data ingested, capacity of data, number of files to be processed etc. is be used as indication for launching servers with bigger/smaller capacities. Incase if there is no data accumulated for a particular day, the AA can cancel the job run for that particular day, hence saving on the cost incurred on provisioning hardware for job run. This sophisticated understanding of available data quantities and intelligently scheduling and managing servers is what is unique about our solution. Thus, the proposed solution is resource efficient, cost efficient and provides a holistic approach rather than a mere ingestion part of it.
[037] The derived information comprises stats of user interactions, their behavioral patterns, most interacted and least interaction teams or members etc. Standard statistical methods like mean, count, average etc. are used to calculate metrics. Normal and Standard deviation curves are utilized for data distribution. Percentiles and Quartiles are used to identify most and least active users. The derived Statistical information, for example, can be:
1. Number of interactions between the team, top collaborators for a given time period (daily, weekly, quarterly, yearly etc.).
2. Number of messages shared within the team and outside the team.
3. Identifying frequently used topics within the team so that we understand what team is currently discussing mostly on.
4. Number of reaction a particular user has used
5. Number of files/documents shared by the team
6. Number of polls conducted by the fellow team members and the team response for the poll.
7. Number of conference calls made by the team member and how many participated in the calls, average duration of the calls etc.
8. Computing various collaboration metrics by analyzing the gathered statistical information.
[038] Apart from regular collaboration metrics, patterns of user behaviors like which users have tendency to engage with which users/groups can be gathered. How do they behave in the interactions, how their engagement affects the profitability of business like sales/new deal wins etc.
[039] User engagement quotient is calculated based on the activity level of the user in a particular group or team or within a unit of work. These user engagement quotients could be useful for HR team to gauge the wellbeing of the employee with-in the organizations. These metrics could also become key parameters to identify employee attrition, performance appraisal and other professional growth attributes.
[040] User who have a high engagement quotient or activity quotient will tend to act like influencers in the organization. As they have most connections and interactions with other users in the group. Thus, disclosed system 100 can identify such users or individuals, which can then be be used by an organization for dissipating critical information to their contacts more effectively.
[041] The required installation of software and libraries for the jobs which compute the metrics is performed automatically. On timely basis AA provisions servers and bootstraps required softwares in the cloud servers. AA monitors the jobs runs and after successful computation of the metrics terminates the cloud servers automatically. These cloud servers do not need to run all day long and are provisioned and configured by AA on adhoc basis, thus saving huge costs during processing stage.
[042] All computed metrics are be stored in a persistent storage for future retrieval in the storage bay as illustrated in FIG. 5. Collaboration Metrics comprises of user interaction stats, group level stats, stats at the account level, Industry unit level and whole enterprise level. Stats include metrics about teams’ collaboration, document collaboration, app footage, associates and affiliation collaboration, call and meetings collaboration, learner’s foot print, interactions with chatbots, employee demographics, social collaboration and feedback interactions. Metrics are stored in multiple tables like message metrics, conference metrics, bot stats, usage stats etc. When the metrics is partitioned into their own entity groups, it enables easy and faster data retrieval from the front end applications. Further, it enables easy comparison of known trends.
[043] Further, as illustrated in FIG. 6 the serving bay provides a unified API layer for the front-end applications to consume the metrics from storage bay. All Dashboards, Natural Language based chat interfaces and CSV based metrics downloads may access this unique serving layer. Provided is a natural language-based chat interface in a consumer bay to deliver metrics in the form of reports, graphs, question answer mechanism etc., wherein access to the metrics is role based access, provided by security bay. It utilizes NLP for understanding the questions and context of the conversation.
[044] Artificial agent is built with a sophisticated understanding of context to deliver appropriate stats and insights to the users. Context switching is inbuilt so users can ask multiple stats or insights without restarting their conversation all over again.
[045] For example, a manager can ask below sample questions
• Who sent more messages in the team last week?
• Who has performed more conference calls in last month?
• Who is the most active and least active member in the team?
• What are the frequently discussed topics in the team (these could be pain areas or issues which team is struggling on)?
• Team usage for last week (stats/graph)
• Call usage for last week, comparative stats with the previous weeks/months. How users are collaborating for idea generation
• How many users are interacting with chatbots and what are the interaction pattern
• What are the main topics that are discussed in a team, week, month, year etc.
• Behavioral insights of users interacting in public channels, private channels, direct messages.
• Usages of type and number of emojis.
[046] In the orchestration bay the agent acts as an interface between multiple bays for transferring data, provisioning servers, store results and serve metrics to end users.
[047] In the orchestration bay, Agent also act as monitoring agent to track the execution of jobs and detect any errors/job terminations. In case of any errors, the agent notifies the development team about the errors. Respective error logs can also be posted in the enterprise collaboration platform for defect analysis.
[048] Security Bay deals with providing role-based access to Account Managers, Project Managers, Team leads etc. with different functionalities.
[049] For example, an Account manager is able to look at stats for various projects within his account where as a project manager can only be able to view stats for his/her team. Senior level executives may have more access to view organizational level metrics like health of the entire organization, trending issues within the organization, productivity levels of associates within different industry units, learning patterns of the associates within different industry units etc.
[050] Role level access is provided by existing systems, but the existing systems do not provide enterprise collaboration metrics with such granular level of access methods.
[051] In an embodiment, the system 100 comprises one or more data storage devices or the memory 102 operatively coupled to the processor 104 and is configured to store instructions for execution of steps of the method by the processor 104. The steps of the method of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1 and the steps of flow diagram as depicted in FIG. 7. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
[052] FIG. 7 is a flow diagram illustrating an exemplary method for deriving enterprise collaboration metrics in an analytics platform, according to an embodiment of the present disclosure.
[053] At Step 702, input data from one or more sources at a pre-defined frequency is acquired by an artificial agent via one or more hardware processors.
[054] At Step 704, the acquired input data is ingested into a specific unique format by utilizing an auto scaling technique by the artificial agent via the one or more hardware processors.
[055] At Step 706, the statistical information from the ingested data is derived by the artificial agent via the one or more hardware processors.
[056] At Step 708, the derived statistical information is analyzed to obtain one or more collaboration metrics by the artificial agent via the one or more hardware processors.
[057] At Step 710, one or more collaboration metrics to one or more applications is provisioned based on associated requests thereof via an unified application programming interface layer provisioning, by the artificial agent via the one or more hardware processors.
[058] At Step 712, at least one of one more reports, graphs, questionnaire is generated using the one or more collaboration metrics using an interactive communication interface by the artificial agent via the one or more hardware processors.
[059] 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.
[060] 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 processing components 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.
[061] 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 components described herein may be implemented in other components or combinations of other components. 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.
[062] 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 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.
[063] 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.
[064] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.
,CLAIMS:1. A processor implemented method for deriving enterprise collaboration metrics in an analytics platform comprising :
acquiring, by an artificial agent via one or more hardware processors, input data from one or more sources at a pre-defined frequency;
ingesting, by the artificial agent via the one or more hardware processors, the acquired input data, into a specific unique format by utilizing an auto scaling technique;
deriving, by the artificial agent via the one or more hardware processors, statistical information from the ingested data;
analyzing, by the artificial agent via the one or more hardware processors, the derived statistical information to obtain one or more collaboration metrics;
provisioning, by the artificial agent via the one or more hardware processors, one or more collaboration metrics to one or more applications based on associated requests thereof via an unified application programming interface layer; and
generating, by the artificial agent via the one or more hardware processors, at least one of one more reports, graphs, questionnaire using the one or more collaboration metrics using an interactive communication interface.

2. The processor implemented method of claim 1, wherein the artificial agent is implemented as a cloud based solution.

3. The processor implemented method of claim 1, wherein the artificial agent is configured to calculate a user engagement quotient for deriving collaboration metric based on the activity level of a user in a particular group.

4. The processor implemented method of claim 1, wherein the one or more collaboration metrics are provisioned based on role based access associated with one or more users.

5. The processor implemented method of claim 1, wherein the step of provisioning is performed based on on-demand user collaboration and activity trends.

6. The processor implemented method of claim 1, wherein the step of ingesting is performed based on file size of the input data.

7. The processor implemented method of claim 1, wherein the statistical information comprises user interactions, behavioral patterns, most interacted and least interacted user groups.

8. A system (100) to derive enterprise collaboration metrics in an analytics platform, the system comprising:
one or more hardware processors (104);
one or more data storage devices (102) operatively coupled to the one or more hardware processors and configured to store instructions configured for execution by the one or more hardware processors to:
acquire, input data from one or more sources at a pre-defined frequency;
ingest, the acquired input data, into a specific unique format by utilizing an auto scaling technique;
derive, statistical information from the ingested data;
analyze, the derived statistical information to obtain one or more collaboration metrics;
provision, one or more collaboration metrics to one or more applications based on associated requests thereof via an unified application programming interface layer; and
generate, at least one of one more reports, graphs, questionnaire using the one or more collaboration metrics using an interactive communication interface.

9. The system of claim 8, wherein the artificial agent is implemented as a cloud based solution.

10. The system of claim 8, wherein the artificial agent is configured to calculate a user engagement quotient for deriving collaboration metric based on the activity level of a user in a particular group.

11. The system of claim 8, wherein the one or more collaboration metrics are provisioned based on role based access associated with one or more users.

12. The system of claim 8, wherein the step of ingesting is performed based on file size of the input data.

13. The system of claim 8, wherein the step of provisioning is performed based on on-demand user collaboration and activity trends.

14. The system of claim 8, wherein the statistical information comprises user interactions, behavioral patterns, most interacted and least interacted user groups.