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:
METHOD AND SYSTEM FOR MULTI-OBJECTIVE MAXIMIZATION FOR ENTITY OR ENTITY COHORTS WITH RECOMMENDATIONS

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

Preamble to the description:
The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
[001] The disclosure herein generally relates to objective maximization and, more particularly, to a method and system for generating recommendation for objective maximization for an entity.

BACKGROUND
[002] An entity or entity cohort adopts different actions to maximize objective(s). The success of these actions is influenced by concurrent actions of its peers and its current state. It is difficult to objectively identify the key set of actions and interventions that will help maximize the entity’s objectives. This becomes increasingly difficult and complex when the entity is desirous on multiple objective maximization. For example, consider an enterprise which launches numerous initiatives across various industry segments and spanning across multitude of business functions. As the business is spanning across multiple segments and the business functions, it becomes difficult to reach a uniform level of technology maturity. The level of competition also varies for different industry segments, creating disproportionate challenges. These often make it challenging for the enterprise to identify the priority areas of technology intervention for improved business results.
[003] The state of the art approaches to maximize single or multiple objectives are often subject to prior-knowledge, competitiveness, and judgement and time-sensitive single entity-system variance. In the context of the aforementioned enterprise example; maturity models in the existing approaches assess enterprises on a standalone basis, often taking into consideration internal information which can only be accessed through enterprise commissioned engagements. Limitations of such assessment is that they focus on a particular point in time, generating results for as-is business environment and not considering longitudinal progression of new initiatives in the future.

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 multi-objective maximization is provided. The method includes receiving as input, via one or more hardware processors, a) at least one focus entity, b) a plurality of attributes of the at least one focus entity, c) a set of objective parameters and action parameters corresponding to the at least one focus entity, d) a set of secondary entities, e) a plurality of attributes of the set of secondary entities, f) a plurality of action parameters and objective parameters of the secondary entities, and g) association of entities. Further, a set of entities from among the set of secondary entities as comparable entities is identified via the one or more hardware processors, based on proximity of the attributes and the objective parameters of the entities in the set of secondary entities with the attributes and objectives of the at least one focus entity. Further, an entity from among the comparable entities is determined as a performing entity, based on extent of difference of value of the objective parameters of the comparable entities with value of the objective parameters of the at least one focus entity. Further, an action parameter matrix is generated for the at least one focus entity and the comparable entities, in each time period, wherein the action parameter matrix comprises a plurality of intensity scores. Further, it is determined if the association of entities is association of collaboration or association of competition. Further, a difference matrix and a direction matrix are computed from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of competition, wherein a recommendation set for convergence, and a recommendation set for divergence are determined from the difference matrix and the direction matrix. Further, a summation matrix and a direction matrix are computed from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of collaboration, wherein a recommendation set for collaboration is generated from the summation matrix and the direction matrix. Further, an impact score of each of the plurality of action parameters of the at least one focus entity and the secondary entities is determined, based on progression of each of the plurality of action parameters in the direction matrix of the performing entity over multiple time periods. Further, a relative impact each of the plurality of action parameters of the at least one focus entity and the secondary entities with respect to a) addition of one or more new action parameters, or b) change in the comparable entities, is determined, wherein information on the relative impact is used to generate a simulation of outcome for the multi-objective maximization.
[005] In another aspect, a system for multi-objective maximization is provided. The system includes one or more hardware processors, a communication interface, and a memory storing a plurality of instructions, wherein the plurality of instructions when executed, cause the one or more hardware processors to receive as input, a) at least one focus entity, b) a plurality of attributes of the at least one focus entity, c) a set of objective parameters and action parameters corresponding to the at least one focus entity, d) a set of secondary entities, e) a plurality of attributes of the set of secondary entities, f) a plurality of action parameters and objective parameters of the secondary entities, and g) association of entities. Further, a set of entities from among the set of secondary entities as comparable entities is identified via the one or more hardware processors, based on proximity of the attributes and the objective parameters of the entities in the set of secondary entities with the attributes and objectives of the at least one focus entity. Further, an entity from among the comparable entities is determined as a performing entity, based on extent of difference of value of the objective parameters of the comparable entities with value of the objective parameters of the at least one focus entity. Further, an action parameter matrix is generated for the at least one focus entity and the comparable entities, in each time period, wherein the action parameter matrix comprises a plurality of intensity scores. Further, it is determined if the association of entities is association of collaboration or association of competition. Further, a difference matrix and a direction matrix are computed from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of competition, wherein a recommendation set for convergence, and a recommendation set for divergence are determined from the difference matrix and the direction matrix. Further, a summation matrix and a direction matrix are computed from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of collaboration, wherein a recommendation set for collaboration is generated from the summation matrix and the direction matrix. Further, an impact score of each of the plurality of action parameters of the at least one focus entity and the secondary entities is determined, based on progression of each of the plurality of action parameters in the direction matrix of the performing entity over multiple time periods. Further, a relative impact each of the plurality of action parameters of the at least one focus entity and the secondary entities with respect to a) addition of one or more new action parameters, or b) change in the comparable entities, is determined, wherein information on the relative impact is used to generate a simulation of outcome for the multi-objective maximization.
[006] In yet another aspect, a non-transitory computer readable medium for multi-objective maximization is provided. The non-transitory computer readable medium includes a plurality of instructions which when executed, cause one or more hardware processors to perform the following steps for the multi-objective maximization. The method includes receiving as input, via one or more hardware processors, a) at least one focus entity, b) a plurality of attributes of the at least one focus entity, c) a set of objective parameters and action parameters corresponding to the at least one focus entity, d) a set of secondary entities, e) a plurality of attributes of the set of secondary entities, f) a plurality of action parameters and objective parameters of the secondary entities, and g) association of entities. Further, a set of entities from among the set of secondary entities as comparable entities is identified via the one or more hardware processors, based on proximity of the attributes and the objective parameters of the entities in the set of secondary entities with the attributes and objectives of the at least one focus entity. Further, an entity from among the comparable entities is determined as a performing entity, based on extent of difference of value of the objective parameters of the comparable entities with value of the objective parameters of the at least one focus entity. Further, an action parameter matrix is generated for the at least one focus entity and the comparable entities, in each time period, wherein the action parameter matrix comprises a plurality of intensity scores. Further, it is determined if the association of entities is association of collaboration or association of competition. Further, a difference matrix and a direction matrix are computed from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of competition, wherein a recommendation set for convergence, and a recommendation set for divergence are determined from the difference matrix and the direction matrix. Further, a summation matrix and a direction matrix are computed from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of collaboration, wherein a recommendation set for collaboration is generated from the summation matrix and the direction matrix. Further, an impact score of each of the plurality of action parameters of the at least one focus entity and the secondary entities is determined, based on progression of each of the plurality of action parameters in the direction matrix of the performing entity over multiple time periods. Further, a relative impact each of the plurality of action parameters of the at least one focus entity and the secondary entities with respect to a) addition of one or more new action parameters, or b) change in the comparable entities, is determined, wherein information on the relative impact is used to generate a simulation of outcome for the multi-objective maximization.
[007] 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
[008] 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:
[009] FIG. 1 illustrates an exemplary system for multi-objective maximization, according to some embodiments of the present disclosure.
[010] FIGS. 2A and 2B (collectively referred to as FIG. 2) is a flow diagram illustrating steps involved in the multi-objective maximization using the system of FIG. 1, according to some embodiments of the present disclosure.
[011] FIG. 3 is a flow diagram illustrating steps involved in the process of generating an action parameter matrix, using the system of FIG. 1, according to some embodiments of the present disclosure.
[012] FIG. 4 is a flow diagram illustrating steps involved in the process of generating the recommendation set for convergence for the association of competition, using the system of FIG. 1, according to some embodiments of the present disclosure.
[013] FIG. 5 is a flow diagram illustrating steps involved in the process of generating the recommendation set for divergence for the association of competition, using the system of FIG. 1, according to some embodiments of the present disclosure.
[014] FIG. 6 is a flow diagram illustrating steps involved in the process of generating the recommendation set for divergence for the association of collaboration, using the system of FIG. 1, according to some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[015] 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.
[016] State of the art approaches to maximize single or multiple objectives are often subject to prior-knowledge, competitiveness, and judgement and time-sensitive single entity-system variance. Maturity models being used by the state of the art systems assess an enterprise on a standalone basis, often taking into consideration internal information which can only be accessed through enterprise commissioned engagements. Limitations of such assessment is that they focus on a particular point in time, generating results for as-is business environment and not considering longitudinal progression of new initiatives in the future.
[017] Referring now to the drawings, and more particularly to FIG. 1 through FIG. 6, 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.
[018] FIG. 1 illustrates an exemplary system for multi-objective maximization, according to some embodiments of the present disclosure. The system 100 includes or is otherwise in communication with hardware processors 102, at least one memory such as a memory 104, an I/O interface 112. The hardware processors 102, memory 104, and the Input /Output (I/O) interface 112 may be coupled by a system bus such as a system bus 108 or a similar mechanism. In an embodiment, the hardware processors 102 can be one or more hardware processors.
[019] The I/O interface 112 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 112 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a printer and the like. Further, the I/O interface 112 may enable the system 100 to communicate with other devices, such as web servers, and external databases.
[020] The I/O interface 112 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the I/O interface 112 may include one or more ports for connecting several computing systems with one another or to another server computer. The I/O interface 112 may include one or more ports for connecting several devices to one another or to another server.
[021] The one or more hardware processors 102 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, node machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more hardware processors 102 is configured to fetch and execute computer-readable instructions stored in the memory 104.
[022] The memory 104 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. In an embodiment, the memory 104 includes a plurality of modules 106.
[023] The plurality of modules 106 include programs or coded instructions that supplement applications or functions performed by the system 100 for executing different steps involved in the process of multi-objective maximization, being performed by the system 100. The plurality of modules 106, amongst other things, can include routines, programs, objects, components, and data structures, which performs particular tasks or implement particular abstract data types. The plurality of modules 106 may also be used as, signal processor(s), node machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the plurality of modules 106 can be used by hardware, by computer-readable instructions executed by the one or more hardware processors 102, or by a combination thereof. The plurality of modules 106 can include various sub-modules (not shown). The plurality of modules 106 may include computer-readable instructions that supplement applications or functions performed by the system 100 for the multi-objective maximization.
[024] The data repository (or repository) 110 may include a plurality of abstracted piece of code for refinement and data that is processed, received, or generated as a result of the execution of the plurality of modules in the module(s) 106.
[025] Although the data repository 110 is shown internal to the system 100, it will be noted that, in alternate embodiments, the data repository 110 can also be implemented external to the system 100, where the data repository 110 may be stored within a database (repository 110) communicatively coupled to the system 100. The data contained within such external database may be periodically updated. For example, new data may be added into the database (not shown in FIG. 1) and/or existing data may be modified and/or non-useful data may be deleted from the database. In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS). Functions of the components of the system 100 are now explained with reference to steps in flow diagrams in FIG. 2 through FIG. 5.
[026] FIGS. 2A and 2B (collectively referred to as FIG. 2) is a flow diagram illustrating steps involved in the multi-objective maximization using the system of FIG. 1, according to some embodiments of the present disclosure.
[027] At step 202 of a method 200 in FIG. 2, the system 100 receives as input, via one or more hardware processors, a) at least one focus entity, b) a plurality of attributes of the at least one focus entity, c) a set of objective parameters and action parameters corresponding to the at least one focus entity, d) a set of secondary entities, e) a plurality of attributes of the set of secondary entities, f) a plurality of action parameters and objective parameters of the secondary entities, and g) association of entities. In various embodiments, only one entity or a group of entities may be selected as the focus entity at any instance. Accordingly, one focus entity or a group of focus entities may be compared with the secondary entities in subsequent steps.
[028] Further, at step 204 of the method 200, the system 100 identifies, via the one or more hardware processors, a set of entities from among the set of secondary entities as comparable entities, based on proximity of the attributes and the objective parameters of the entities in the set of secondary entities with the attributes and objectives of the at least one focus entity. For example, consider that the attributes of the focus entity are being compared with attributes of 7 secondary entities (Entity 1, Entity 2, Entity 3, Entity 4, Entity 5, Entity 6, and Entity 7), as in Table. 1.
Attribute Attribute 1 Attribute 2 Attribute 3 Attribute 4
Entity
Focus Entity 1 A a @
Entity 1 2 B d #
Entity 2 1 D a @
Entity 3 3 B c $
Entity 4 1 A a @
Entity 5 2 C b #
Entity 6 1 D b $
Entity 7 1 A a @
Table. 1
[029] In Table. 1, values of the attributes of Entity 4 and Entity 7 are matching the values of the attributes of the focus entity, hence they are identified as the comparable entities. In an embodiment, number of entities to be identified as the comparable entities may be pre-configured with the system. If the number of entities to be identified as the comparable entities is two, then in the above example Entity 4 and Entity 7 are selected as the comparable entities. In Table. 1, Entity 4 and Entity 7 have values of attributes exactly matching that of the focus entity. However, if a third comparable entity is to be identified, next closest match is identified, even if values of all the attributes are not exactly matching that of the focus entity.
[030] Further, at step 206 of the method 200, the system 100 determines an entity from among the comparable entities as a performing entity, based on extent of difference of value of the objective parameters of the comparable entities with value of the objective parameters of the at least one focus entity. Periodicity of objective parameters determines time-period block for assessment through frequency distribution. Time period blocks start from the time of first reported data in objective parameters and continue till the latest reported data in the objective parameters, which are stored in a pre-filled database in the memory 104. The system 100 performs appropriate matrix operation(s) to identify the performing entity from among the comparable entities.
Time Period Block Time Period Block 1 Time Period Block 2 Time Period Block 3
Objective Parameter Objective Parameter 1 Objective Parameter 2 Objective Parameter 3 Objective Parameter 1 Objective Parameter 2 Objective Parameter 3 Objective Parameter 1 Objective Parameter 2 Objective Parameter 3
Entity
Focus Entity 2 4 3 5 7 8 10 12 11
Comparable Entity 1 8 10 12 16 20 22 12 14 13
Comparable Entity 2 5 7 9 10 12 14 16 20 24
Table. 2

[031] Based on the matrix operations, it is identified that the comparable entity 1 has attribute values that are best performing in objective parameters (in terms of extent of difference as explained at step 206) with that of the focus entity in maximum number of Time period blocks, i.e. Time Period Block 1 and Time period block 2performing entity. Whereas, in the latest time period block, i.e. Time Period Block 3, the comparable entity 2 has objective parameter values that are best performing with that of the focus entity performing entity
[032] Further, at step 208 of the method 200, the system 100 generates an action parameter matrix for the at least one focus entity and the comparable entities, in each time period, wherein the action parameter matrix comprises a plurality of intensity scores. The intensity scores are calculated based on repeated occurrence and change of state of same initiatives (also referred to as ‘actions’) till the latest time period block. The intensity score for an action parameter or intersection of more than one action parameters can be represented as below:
I = SXiYj --- (1)
Where I is the intensity score, Xi is the ith unique initiative and Yj is the jth maturity level. Xi can take the value of 1, in case of any new unique initiative. The value of i represents the number of unique initiatives and can vary from 1 to any finite integer. The value of Yj equals to j, where j can vary from 1 to m, where m is the highest defined maturity level score.
[033] Steps involved in the process of generating the action parameter matrix are depicted in method 300 in FIG. 3. At step 302 of the method 300, the system 100 assesses any action for checking similarity with previous actions, by comparing the plurality of action parameters of a current time period with a plurality of action parameters at a previous time period, using natural language processing. If the actions are found to be similar/same, then existing intensity score is retained at step 304 of the method 300. If the actions are identified to be different, then at step 306 of the method 300, an incremental action intensity score is added to an existing intensity score to update the intensity score. The intensity scores may be in the form of n-dimensional arrays for each time period block.
[034] Examples of the action parameter matrix are given below:
a) Action Parameter Matrix for Focus Entity for Time Period 1 (M1)

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 1 2 3
2B 4 5 6
2C 7 8 9
Table. 3

b) Action Parameter Matrix for Focus Entity for Time Period 2 (M2)

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 2 4 6
2B 4 7 9
2C 8 8 10
Table. 4

[035] Further, at step 210, the system 100 determines if the association of entities is association of competition or association of collaboration, based on the input data collected.
[036] If the association of entities is the association of competition, then at step 212 of the method 200, the system 100 computes a difference matrix and a direction matrix from the performing entity and the action parameter matrix, at each time period. The difference matrix represents difference of the current time period action parameter matrix of the performing entity and the at least one focus entity. The direction matrix comprises difference between the action parameter matrix between two consecutive time periods, for the at least one performing entity. For example, consider the following action parameter matrices.
A. Action Parameter Matrix for Focus Entity for Time Period Block 3
M3
Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 4 5 8
2B 6 6 10
2C 9 9 12
Table. 5

B. Action Parameter Matrix for Comparable Entity 1 for Time Period Block 3
M13
Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 18 20 19
2B 22 22 25
2C 19 12 12
Table. 6

C. Action Parameter Matrix for Comparable Entity 2 for Time Period Block 2
M22
Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 13 13 12
2B 16 15 11
2C 15 9 10
Table. 7

D. Action Parameter Matrix for Comparable Entity 2 for Time Period Block 3
M23
Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 19 20 21
2B 22 23 23
2C 20 12 14
Table. 8

[037] From these action parameter metrices, the difference matrix and the direction matrix are computed as:
• Difference matrix (M13-M3): M13 is the latest action parameter matrix of the comparable entity 1. Hence difference of M13 and M3 is the Difference matrix in this example scenario.

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 14 15 11
2B 16 16 15
2C 10 3 0
Table. 9
• Direction matrix (M23-M22): M23 is the latest action parameter matrix of Comparable Entity 2 which was the best entity in latest time period block. Hence the system 100 determines the direction of change in action parameters by measuring difference with previous time period block.
Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 6 7 9
2B 6 8 12
2C 5 3 4
Table.10

[038] Further, the system 100 determines a) a recommendation set for convergence, and b) a recommendation set for divergence, from the difference matrix and the direction matrix. Steps in generating the recommendation set for convergence are depicted in FIG. 4, and are explained hereafter. At step 402 of method 400, the system 100 identifies a pre-defined number of highest and lowest scores in the difference matrix, using a plurality of matrix operations. The pre-defined number may be represented as ‘n’, and value of ‘n’ may be configured as per requirements. Further, at step 404, the system 100 identifies a pre-defined number of highest scores of the direction matrix. Further, at step 406, the system 100 identifies all overlapping action parameters between the pre-defined number of highest scores of the difference matrix and the pre-defined number of highest scores of the direction matrix, to generate the recommendation set for convergence. Similarly, Steps in generating the recommendation set for divergence are depicted in FIG. 5, and are explained hereafter. At step 502 of method 500, the system 100 identifies a pre-defined number of highest and lowest scores in the difference matrix, using a plurality of matrix operations. The pre-defined number may be represented as ‘n’, and value of ‘n’ may be configured as per requirements. Further, at step 504, the system 100 identifies a pre-defined number of lowest scores of the direction matrix. Further, at step 506, the system 100 identifies all overlapping action parameters between the pre-defined number of lowest scores of the difference matrix and the pre-defined number of lowest scores of the direction matrix, to generate the recommendation set for divergence. Consider the example below:
Difference matrix (M13-M3) used as input to step 212 is:

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 14 15 11
2B 16 16 15
2C 10 3 0

Table. 11

Direction matrix (M23-M22) used as input to step 212 is

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 6 7 9
2B 6 8 12
2C 5 3 4
Table. 12

[039] In this example, to generate the recommendation set for divergence and the recommendation set for convergence, the system 100 checks for n=1 to m/2 when m is even; and m/2-1 when m is odd where m is the total number of cells in the difference matrix or the direction matrix.
[040] The system 100 checks for n=4. Here m=9 and hence the system is checking for 9/2 – 1 = 4.

Top ‘n’ numbers of the difference matrix are highlighted in the table below:

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 14 15 11
2B 16 16 15
2C 10 3 0
Table. 13

Top ’n’ numbers of the direction matrix are highlighted in the table below:

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 6 7 9
2B 6 8 12
2C 5 3 4

Table. 14

Bottom ‘n’ numbers of the difference matrix are highlighted in the table below:

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 14 15 11
2B 16 16 15
2C 10 3 0
Table. 15

[041] Then the recommendation for convergence is given in the table below: overlapping top ‘n’ score parameters of both sets (where the highlighted cells highlight the focus areas for convergence)
Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A
2B
2C
Table. 16
[042] Similarly the recommendation for divergence is given in the table below: Bottom ‘n’ score parameters of difference set overlapping with top ‘n’ score parameters of direction set overlapping top ‘n’ score parameters of both sets (where the highlighted cells highlight the focus areas for divergence)
Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A
2B
2C
Table. 17
[043] If the association of entities is association of collaboration, then at step 214 of the method 200, the system 100 computes a summation matrix and a direction matrix from the performing entity and the action parameter matrix, at each time period, wherein a recommendation set for collaboration is generated from the summation matrix and the direction matrix.
[044] For example, for the aforementioned action parameter metrices, the summation matrix and the direction matrix are computed as:

• Summation matrix (M13+M3): M13 is the latest action parameter matrix of the comparable entity 1. Hence summation of M13 and M3 is the summation matrix in this example scenario.

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 22 25 27
2B 28 28 35
2C 28 21 24
Table. 18
• Direction matrix (M23-M22): M23 is the latest action parameter matrix of Comparable Entity 2 which was the performing entity in latest time period block. Hence the system 100 determines the direction of change in action parameters by measuring difference with previous time period block.

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 6 7 9
2B 6 8 12
2C 5 3 4
[045] Table. 19Further, a recommendation set for collaboration is determined from the summation matrix and the direction matrix. Steps involved in the process of generating the recommendation set for collaboration are depicted in FIG. 6 (method 600), and are explained hereafter. At step 602 of the method 600, a pre-defined number of highest scores in the summation matrix are identified, using a plurality of matrix operations. Further, at step 604 of the method 600, a pre-defined number of highest scores of the direction matrix are identified. Further, at step 606 of the method 600, all overlapping action parameters between the pre-defined number of highest scores of the summation matrix and the pre-defined number of highest scores of the direction matrix are identified to generate a recommendation set for collaboration. As process is similar to the steps of determining the recommendation set for convergence, and the recommendation set for divergence, the aforementioned example can be considered, except that the inputs are the summation matrix and the direction matrix.
[046] In this example, to generate the recommendation set for collaboration, the system 100 checks for n=1 to m/2 when m is even; and m/2-1 when m is odd where m is the total number of cells in the difference matrix or the direction matrix.
[047] The system 100 checks for n=4. Here m=9 and hence the system is checking for 9/2 – 1 = 4.

Top ‘n’ numbers of the summation matrix are highlighted in the table below:

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 22 25 27
2B 28 28 35
2C 28 21 24
Table. 20

Top ’n’ numbers of the direction matrix are highlighted in the table below:

Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A 6 7 9
2B 6 8 12
2C 5 3 4
Table. 21

[048] Then the recommendation for collaboration is given in the table below: overlapping top ‘n’ score parameters of both sets (where the highlighted cells highlight the focus areas for collaboration)
Action Parameter Type 1
Action Parameter Type 2 1A 1B 1C
2A
2B
2C
Table. 22
[049] In an embodiment, the received input data on the association of entities may indicate that the association of entities is both association of competition and association of collaboration i.e. the system 100 can be configured to process the input data for both types of association of entities, simultaneously, and the processing for different types of association of entities may happen in parallel or in a sequential manner. In another embodiment, the received input data on the association of entities may indicate that the association of entities as one of association of competition and association of collaboration, and in this case the system 100 processes the input data only for the identified association of entities.
[050] Further, at step 216 of the method 200, the system 100 determines an impact score of each of the plurality of action parameters of the at least one focus entity and the secondary entities, based on progression of each of the plurality of action parameters in the direction matrix of the performing entity over multiple time periods. The ‘n’ highest scores in action parameters in the direction matrix are selected over ‘m’ time period blocks. The action parameter(s) with maximum occurrence in n1, n2..nm will have highest impact score, and the impact score will gradually decrease based on decreasing order of occurrence. The system 100 generates the impact scores, and stores it in media for simulation of future outcome. Further, at step 218 of the method 200, the system 100 determines a relative impact each of the plurality of action parameters of the at least one focus entity and the secondary entities with respect to a) addition of one or more new action parameters, or b) change in the comparable entities, wherein information on the relative impact is used to generate a simulation of outcome for the multi-objective maximization using cognitive operations and sensitivity analysis.
[051] The method 200 enables a longitudinal comparative assessment of initiative maturity of entities or entity sets in related proximity at a granular level, thereby enabling focus on multiple entities at a time. For example, in case of business organizations, the system 100 and the method 200 can assess their respective technology maturity at the level of their respective industry segments and business functions and assess the progression across multiple time periods.
[052] The method 200 and the system 100 facilitates identification of high impact areas for intervention, by automated assessment of initiative impact in comparison with respective objective parameters. In case of business organizations, the method 200 and the system 100 can identify organization(s) which maximized objective parameters like revenue/profit growth, in comparison to relevant peers, in a particular time period and identify the progression in technology maturity by the same organization(s) in the same time period. The respective technology action areas for best performing organizations across multiple time periods help in identifying the key impact areas for most impactful technology interventions.
[053] The method 200 and the system 100 utilize natural language processing to assess the maturity level of initiatives and tensor operations to quantify the magnitude and direction of change at a granular level for each entity or entity sets. This helps in objective assessment of maturity at a granular level through difference tensor between the comparable best entity and the focus entity. The comparative difference helps in identifying the key areas of convergence and divergence, helping create the recommendation set of initiatives which can maximize impact on objective parameters. For a business organization, the method 200 and the system 100 help in identifying the major gap areas with comparable best organization as well as the areas of differentiation.
[054] The automated system with objective assessment based on tensors; and development of impact scores of action areas help minimize the inherent bias associated with human driven assessments. It is also repeatable and scalable, and hence can be used for an entire organization, its specific industry or geography units as per requirement. It can also be used to assess the current state of technology maturity in industry segments where the focus organization is yet to enter.
[055] 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.
[056] The embodiments of present disclosure herein address unresolved problem of multi-objective maximization. The embodiment, thus provides a mechanism to assess match between different entities based on similarity or differences between attributes and objective parameters. Moreover, the embodiments herein further provide Determining a relative impact of each of the plurality of action parameters of the at least one focus entity and the secondary entities with respect to a) addition of one or more action parameters, or b) change in the comparable entities, wherein information on the relative impact is used to generate a simulation of outcome for multi-objective maximization.
[057] 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.
[058] 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.
[059] 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.
[060] 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.
[061] 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: We Claim:

1. A processor implemented method (200) for multi-objective maximization, comprising:
receiving as input (202), via one or more hardware processors, a) at least one focus entity, b) a plurality of attributes of the at least one focus entity, c) a set of objective parameters and action parameters corresponding to the at least one focus entity, d) a set of secondary entities, e) a plurality of attributes of the set of secondary entities, f) a plurality of action parameters and objective parameters of the secondary entities, and g) association of entities;
identifying (204), via the one or more hardware processors, a set of entities from among the set of secondary entities as comparable entities, based on proximity of the attributes and the objective parameters of the entities in the set of secondary entities with the attributes and objectives of the at least one focus entity;
determining (206), via the one or more hardware processors, an entity from among the comparable entities as a performing entity, based on extent of difference of value of the objective parameters of the comparable entities with value of the objective parameters of the at least one focus entity;
generating (208), via the one or more hardware processors, an action parameter matrix for the at least one focus entity and the comparable entities, in each time period, wherein the action parameter matrix comprises a plurality of intensity scores;
determining (210), via the one or more hardware processors, if the association of entities is association of collaboration or association of competition;
computing (212), via the one or more hardware processors, a difference matrix and a direction matrix from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of competition, wherein a recommendation set for convergence, and a recommendation set for divergence are determined from the difference matrix and the direction matrix;
computing (214), via the one or more hardware processors, a summation matrix and a direction matrix from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of collaboration, wherein a recommendation set for collaboration is generated from the summation matrix and the direction matrix;
determining (216), via the one or more hardware processors, an impact score of each of the plurality of action parameters of the at least one focus entity and the secondary entities, based on progression of each of the plurality of action parameters in the direction matrix of the performing entity over multiple time periods; and
determining (218), via the one or more hardware processors, a relative impact each of the plurality of action parameters of the at least one focus entity and the secondary entities with respect to a) addition of one or more new action parameters, or b) change in the comparable entities, wherein information on the relative impact is used to generate a simulation of outcome for the multi-objective maximization.

2. The method as claimed in claim 1, wherein generating the action parameter matrix comprises:
comparing (302) the plurality of action parameters, of a current time period, with a plurality of action parameters existed at a previous time period;
retaining (304) an existing intensity score if the plurality of action parameters of the current time period are same as the plurality of action parameters existed at the previous time period; and
adding (306) an incremental intensity score to the existing intensity score if the plurality of action parameters are not same as the plurality of action parameters existed at the previous time period.

3. The method as claimed in claim 1, wherein
the difference matrix represents difference of a current time period action parameter matrix of the performing entity and the at least one focus entity, and
the direction matrix comprises difference between the action parameter matrix between two consecutive time periods, for the performing entity.

4. The method as claimed in claim 1, wherein generating the recommendation set for convergence comprises:
identifying (402) a pre-defined number of highest and lowest scores in the difference matrix, using a plurality of matrix operations;
identifying (404) a pre-defined number of highest scores of the direction matrix; and
identifying (406) all overlapping action parameters between the pre-defined number of highest scores of the difference matrix and the pre-defined number of highest scores of the direction matrix, to generate a recommendation set for convergence.

5. The method as claimed in claim 1, wherein generating the recommendation set for divergence comprises:
identifying (502) a pre-defined number of highest and lowest scores in the difference matrix, using a plurality of matrix operations;
identifying (504) a pre-defined number of lowest scores of the direction matrix; and
identifying (506) all overlapping action parameters between the predefined lowest scores of the difference matrix and the predefined number of lowest scores of the direction matrix, to generate recommendation set for divergence.

6. The method as claimed in claim 1, wherein generating the recommendation set for collaboration comprises:
identifying (602) a pre-defined number of highest scores in the summation matrix, using a plurality of matrix operations;
identifying (604) a pre-defined number of highest scores of the direction matrix; and
identifying (606) all overlapping action parameters between the predefined highest scores of the summation matrix and the predefined number of highest scores of the direction matrix, to generate the recommendation set for collaboration.

7. The method as claimed in claim 1, wherein feedback is collected in response to a determined impact of one or more of the action parameters on the multi-objective maximization, further wherein the feedback is used to fine-tune the step of determining the impact of the one or more action parameters, in subsequent iterations.

8. A system (100) for multi-objective maximization, comprising:

one or more hardware processors (102);
a communication interface (112); and
a memory (104) storing a plurality of instructions, wherein the plurality of instructions when executed, cause the one or more hardware processors to:
receive as input, a) at least one focus entity, b) a plurality of attributes of the at least one focus entity, c) a set of objective parameters and action parameters corresponding to the at least one focus entity, d) a set of secondary entities, e) a plurality of attributes of the set of secondary entities, f) a plurality of action parameters and objective parameters of the secondary entities, and g) association of entities;
identify, a set of entities from among the set of secondary entities as comparable entities, based on proximity of the attributes and the objective parameters of the entities in the set of secondary entities with the attributes and objectives of the at least one focus entity;
determine an entity from among the comparable entities as a performing entity, based on extent of difference of value of the objective parameters of the comparable entities with value of the objective parameters of the at least one focus entity;
generate an action parameter matrix for the at least one focus entity and the comparable entities, in each time period, wherein the action parameter matrix comprises a plurality of intensity scores;
determine if the association of entities is association of collaboration or association of competition;
compute a difference matrix and a direction matrix from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of competition, wherein a recommendation set for convergence, and a recommendation set for divergence are determined from the difference matrix and the direction matrix;
compute a summation matrix and a direction matrix from the performing entity and the action parameter matrix, at each time period, if the association of entity is association of collaboration, wherein a recommendation set for collaboration is generated from the summation matrix and the direction matrix;
determine an impact score of each of the plurality of action parameters of the at least one focus entity and the secondary entities, based on progression of each of the plurality of action parameters in the direction matrix of the performing entity over multiple time periods; and
determine a relative impact each of the plurality of action parameters of the at least one focus entity and the secondary entities with respect to a) addition of one or more new action parameters, or b) change in the comparable entities, wherein information on the relative impact is used to generate a simulation of outcome for the multi-objective maximization.

9. The system as claimed in claim 8, wherein the one or more hardware processors are configured to generate the action parameter matrix by:
comparing the plurality of action parameters, of a current time period, with a plurality of action parameters existed at a previous time period;
retaining an existing intensity score if the plurality of action parameters of the current time period are same as the plurality of action parameters existed at the previous time period; and
adding an incremental intensity score to the existing intensity score if the plurality of action parameters are not same as the plurality of action parameters existed at the previous time period.

10. The system as claimed in claim 8, wherein the difference matrix represents difference of the current time period action parameter matrix of the performing entity and the at least one focus entity, and the direction matrix comprises difference between the action parameter matrix between two consecutive time periods, for the performing entity.

11. The system as claimed in claim 8, wherein the one or more hardware processors are configured to generate the recommendation set for convergence by:
identifying a pre-defined number of highest and lowest scores in the difference matrix, using a plurality of matrix operations;
identifying a pre-defined number of highest scores of the direction matrix; and
identifying all overlapping action parameters between the pre-defined number of highest scores of the difference matrix and the pre-defined number of highest scores of the direction matrix, to generate a recommendation set for convergence.

12. The system as claimed in claim 8, wherein the one or more hardware processors are configured to generate the recommendation set for divergence by:
identifying a pre-defined number of highest and lowest scores in the difference matrix, using a plurality of matrix operations;
identifying a pre-defined number of lowest scores of the direction matrix; and
identifying all overlapping action parameters between the predefined lowest scores of the difference matrix and the predefined number of lowest scores of the direction matrix, to generate recommendation set for divergence.

13. The system as claimed in claim 8, wherein the one or more hardware processors are configured to generate the recommendation set for collaboration by:
identifying a pre-defined number of highest scores in the summation matrix, using a plurality of matrix operations;
identifying a pre-defined number of highest scores of the direction matrix; and
identifying all overlapping action parameters between the predefined highest scores of the summation matrix and the predefined number of highest scores of the direction matrix, to generate the recommendation set for collaboration.

14. The system as claimed in claim 8, wherein the one or more hardware processors are configured to collect a feedback in response to a determined impact of one or more of the action parameters on the multi-objective maximization, further wherein the feedback is used to fine-tune the step of determining the impact of the one or more action parameters, in subsequent iterations.

Dated this 22nd day of April 2022

Tata Consultancy Services Limited
By their Agent & Attorney

(Adheesh Nargolkar)
of Khaitan & Co
Reg No IN-PA-1086