Claims:WE CLAIM:
1. A system (100) for product and service recommendation by automated matching of product and service features to purchaser requirements comprising:
a user input extraction subsystem (110) configured to extract a first set of input, corresponding to at least one of a product and a service requirement received through an interactive series of questions shared via an electronic device associated with a user for a predefined time interval;
a profile generation subsystem (120) operatively coupled to the user input extraction subsystem (110), wherein the profile generation subsystem (120) is configured to generate at least one profile dynamically, within a predefined range, corresponding to at least one of a required product and a required service based on an analysis of a first set of extracted input;
an optimal profile generation subsystem (130) operatively coupled to the profile generation subsystem (120) and configured to generate an optimal profile dynamically, corresponding to the at least one of the required product and the required service based on the analysis of at least one generated profile selected by the user;
a product extraction subsystem (140) operatively coupled to the optimal profile generation subsystem (130) and configured to:
match an optimal generated profile corresponding to the at least one of the required product and the service with a list of one or more products and services available in a marketplace;
recommend at least one of a closely matched product and a service among the list of the one or more products and services available in the marketplace, to the user.
2. The system (100) as claimed in claim 1, wherein the first set of input comprises at least one of a name of the user, gender of the user, city of the user, income level of the user, country of the user, a category of the at least one of the product and the service, a reason of purchasing the at least one product and the service and a use purpose of buying the at least one of the product and the service.
3. The system (100) as claimed in claim 1, wherein the at least one profile corresponding to the required product and the required service comprises a second set of input, wherein the second set of input comprises a plurality of attributes corresponding to the at least one of the required product and the service.
4. The system (100) as claimed in claim 1, wherein the profile generation subsystem (120) is configured to generate the at least one profile corresponding to the at least one of the required product and the required service based on creation of a constrained attribute level space for assignment of an attribute level to the plurality of attributes, computation of a number of profiles generated within the predefined range and creation of a set of initial number of profiles varying within the constrained attribute level space.
5. The system (100) as claimed in claim 1, wherein the optimal profile generation subsystem (130) is configured to generate the optimal profile based on computation of a ratio of number of times the attribute level is chosen by the user divided by total number of times the attribute level appeared during generation of the at least one profile corresponding to the at least one of the required product and the required service.
6. The system (100) as claimed in claim 1, wherein the product extraction subsystem (140) is configured to match the optimal generated profile with the list of the one or more products and services available in the marketplace based on:
computation of a matrix of values corresponding to the attribute level of at least one of an optimal product and the service,
determination of a difference in score across the attribute level corresponding to the list of the one or more products and the services available in the marketplace and;
selection of predefined number of the list of the one or more products and services with a computed difference score.
7. The system (100) as claimed in claim 1, further comprising a product trading subsystem (150) operatively coupled to the product extraction subsystem (140) and configured to provide trading of at least one of a recommended product and a service to the user, by redirecting the user to an external marketplace.
8. The system (100) as claimed in claim 1, further comprising a feedback retrieval subsystem (160) configured to receive a feedback from the user corresponding to the at least one purchased product and the service.
9. A method (600) comprising:
extracting, by a user input extraction subsystem, a first set of input, corresponding to at least one of a product and a service requirement received through an interactive series of questions shared via an electronic device associated with a user for a predefined time interval (610);
generating, by a profile generation subsystem, at least one profile dynamically, within a predefined range, corresponding to at least one of a required product and a required service based on an analysis of a first set of extracted input (620);
generating, by an optimal profile generation subsystem, an optimal profile dynamically, corresponding to the at least one of the required product and the required service based on the analysis of at least one generated profile selected by the user (630);
matching, by a product extraction subsystem, an optimal generated profile corresponding to the at least one of the required product and the service with a list of one or more products and services available in a marketplace (640); and
recommending, by the product extraction subsystem, at least one of a closely matched product and a service among the list of the one or more products and services available in the marketplace, to the user (650).
10. The method (600) as claimed in claim 9, further comprising providing, by a product or service trading subsystem, trading of at least one of a recommended product and a service to the user, by redirecting the user to an external marketplace.
11. The method (600) as claimed in claim 9, further comprising receiving, by a feedback retrieval subsystem, a feedback corresponding to the at least one purchased product and the service.
, Description:BACKGROUND
[0001] Embodiments of the present disclosure relate to a system for generating, and processing purchaser requirements and more particularly to a system and a method for product and service recommendation by automated matching of product and service features to purchaser requirements.
[0002] A recommendation system as defined today is a subclass of an information filtering and processing system which seeks to predict user preferences for a product and a service. The existing recommendation systems are utilised in one or more areas such as at least one of movies, music, books, news, search queries, and shopping. The existing recommendation systems are designed to filter out data using one or more methods and recommend a relevant product or the service to the user. The existing recommendation systems rely on past behaviour of the users. Also, the existing recommendation systems recommend the product or the service which brings maximum profit to the business irrespective of the needs and interests of the user. Various existing systems collect data from various sources and filter out the data by using one or more filtering techniques in order to provide recommendation for the relevant product or the service to the user, which can be suboptimal to the user requirements.
[0003] Conventionally, one such type of existing system available for providing recommendation for relevant item or product and service to the user includes utilisation of a collaborative filtering technique. However, such conventional system for employing the collaborative filtering technique requires actual existing purchase history or data from all past purchasers or the users in order to dynamically construct the relevant product or the service recommendations. Also, such existing systems are therefore fully dependent on past purchases and past purchasers. Moreover, such conventional systems assume that past users have significant amount of knowledge about the functionality and importance of one or more features of the relevant product or the service. Also, such conventional systems are limited in scope of the relevant product or the service features and often ignore critically important features of the relevant product or the service and as a result makes the purchasers dependent on other people. In addition to, such systems require the purchasers to build own respective subjective assumptions and own subjective trade-offs without a lot of information and explicit decision criteria as it applies to them on an individual basis. Furthermore, such systems are unable to help the purchasers to find and act upon pertinent information and as a result the purchasers are misled by one or more seller intermediaries who are driven by commercial interests.
[0004] Hence, there is a need for an improved system and a method for a product and a service recommendation by automated matching of product and service features to purchaser requirements in order to address the aforementioned issues.
BRIEF DESCRIPTION
[0005] In accordance with an embodiment of the present disclosure, a system for a product and a service recommendation by automated matching of product and service features to purchaser requirements is disclosed. The system includes a user input extraction subsystem configured to extract a first set of input, corresponding to at least one of a product and a service requirement received through an interactive series of questions shared via an electronic device associated with a user for a predefined time interval. The system also includes a profile generation subsystem operatively coupled to the user input extraction subsystem. The profile generation subsystem is configured to generate at least one profile dynamically, within a predefined range, corresponding to at least one of a required product and a required service based on an analysis of a first set of extracted input. The system also includes an optimal profile generation subsystem operatively coupled to the profile generation subsystem. The optimal profile generation subsystem is configured to generate an optimal profile dynamically, corresponding to the at least one of the required product and the required service based on the analysis of at least one generated profile selected by the user. The system also includes a product extraction subsystem operatively coupled to the optimal profile generation subsystem. The product extraction subsystem is configured to match an optimal generated profile corresponding to the at least one of the required product and the service with a list of one or more products and services available in a marketplace. The product extraction subsystem is also configured to recommend at least one of a closely matched product and a service among the list of the one or more products and services available in the marketplace, to the user.
[0006] In accordance with another embodiment of the present disclosure, a method for a product and a service recommendation by automated matching of product and service features to purchaser requirements is disclosed. The method includes extracting, by a user input extraction subsystem, a first set of input, corresponding to at least one of a product and a service requirement received through an interactive series of questions shared via an electronic device associated with a user for a predefined time interval. The method also includes generating, by a profile generation subsystem, at least one profile dynamically, within a predefined range, corresponding to at least one of a required product and a required service based on an analysis of a first set of extracted input. The method also includes generating, by an optimal profile generation subsystem, an optimal profile dynamically, corresponding to the at least one of the required product and the required service based on the analysis of at least one generated profile selected by the user. The method also includes matching, by a product extraction subsystem, an optimal generated profile corresponding to the at least one of the required product and the service with a list of one or more products and services available in a marketplace. The method also includes recommending, by the product extraction subsystem, at least one of a closely matched product and a service among the list of the one or more products and services available in the marketplace, to the user.
[0007] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0008] FIG. 1 is a block diagram of a system (100) for a product and a service recommendation by automated matching of product and service features to purchaser requirements in accordance with an embodiment of the present disclosure;
[0009] FIG. 2 illustrates a schematic representation of a technical architecture (200) of the client-side and the remote server-side communication of a system for a product and a service recommendation by automated matching of product and service features to purchaser requirements of FIG.1 in accordance with an embodiment of the present disclosure;
[0010] FIG. 3 illustrates a schematic representation of a server-side architecture (300) of a system for a product and a service recommendation by automated matching of product and service features to purchaser requirements of FIG.1 in accordance with an embodiment of the present disclosure;
[0011] FIG. 4 illustrates a schematic representation of an exemplary embodiment (400) of a system for a product and a service recommendation by automated matching of product and service features to purchaser requirements of FIG. 1 in accordance with an embodiment of the present disclosure;
[0012] FIG. 5 is a block diagram of a computer or a server (500) in accordance with an embodiment of the present disclosure; and
[0013] FIG. 6 is a flow chart representing the steps involved in a method (600) for a product and a service recommendation by automated matching of product and service features to purchaser requirements in accordance with the embodiment of the present disclosure.
[0014] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0015] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0016] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0018] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0019] Embodiments of the present disclosure relate to a system and a method for a product and a service recommendation by automated interactive matching of product and service features to purchaser requirements. The system includes a user input extraction subsystem configured to extract a first set of input, corresponding to at least one of a product and a service requirement received through an interactive series of questions shared via an electronic device associated with a user for a predefined time interval. The system also includes a profile generation subsystem operatively coupled to the user input extraction subsystem. The profile generation subsystem is configured to generate at least one profile dynamically, within a predefined range, corresponding to at least one of a required product and a required service based on an analysis of a first set of extracted input. The system also includes an optimal profile generation subsystem operatively coupled to the profile generation subsystem. The optimal profile generation subsystem is configured to generate an optimal profile dynamically, corresponding to the at least one of the required product and the required service based on the analysis of at least one generated profile selected by the user. The system also includes a product extraction subsystem operatively coupled to the optimal profile generation subsystem. The product extraction subsystem is configured to match an optimal generated profile corresponding to the at least one of the required product and the service with a list of one or more products and services available in a marketplace. The product extraction subsystem is also configured to recommend at least one of a closely matched product and a service among the list of the one or more products and services available in the marketplace, to the user.
[0020] FIG. 1 is a block diagram of a system (100) for a product and a service recommendation by automated interactive matching of product and service features to purchaser requirements in accordance with an embodiment of the present disclosure. The system (100) includes a user input extraction subsystem (110) configured to extract a first set of input, corresponding to at least one of a product and a service requirement received through an interactive series of questions shared via an electronic device associated with a user for a predefined time interval. As used herein, the term ‘first set of input’ refers to one or more user inputs corresponding to at least one of a product and a service.
[0021] In one embodiment, the first set of input may include at least one of a name of the user, gender of the user, city of the user, income level of the user, country of the user, a category of the at least one of the product and a service, and usage reason and usage purpose of purchasing the product and the service. In such embodiment, the first set of input may be received in one or more formats such as a numeric format, a text format, an image format, an audio format or a video format. In some embodiment, the interactive series of questions may be shared as multiple screens on the electronic device associated with the user. In one embodiment, the at least one of the product may include, but not limited to, a digital camera, a laptop, a mobile phone, a music system, a television, a refrigerator, a microwave, a printer, a car, a washing machine, a dryer, a water purifier, an oven, a lawn mower, and a vacuum cleaner. In another embodiment, the at least one of the required service may include selection of a bank, selection of a restaurant, selection of a hospital for treatment or selection of an educational institution.
[0022] The first set of input which is extracted from the user or from a client side in real-time is sent to a remote server for processing extracted user inputs and a technical architecture of the client-side and the remote server-side communication is shown in FIG.2 (200). Here, the input from the client-side is received via a model (210) through JavaScript event handlers by using RelayJS, Angular JS, VueJS or Immutable JS. The input from the model is converted into a view (220) for user interface (UI) build. The view for the user interface is sent via a controller (230) from the client-side to the server-side script servlets (240). The client-side and the server-side interactions are stored in a database (250). Also, the client-side and the server-side interactions are utilised for building one or more models and applications (260) which is implemented in a computer language such as Python, or Java. The remote server components and the client-side components work together using Representational State Transfer Application programming interface (REST API) and JavaScript Object Notation (JSONs) format of communication. As used herein, the term ‘REST API’ refers to a format of the communication for data transmission from client-side application to server-side application. Similarly, the term ‘JSON format’ refers to a data-interchange format for the data transmission over a communication network from the server-side application to the client-side application and vice-versa. In one embodiment, the remote server may include one or more prediction servers (310), one or more load balancers (315), one or more search servers (320), one or more web servers (330), one or more application servers (340) and one or more quad tree servers (350). In such embodiment, the prediction server and the server-side architecture with a plurality of servers is shown in FIG. 3. In one embodiment, the profile generation subsystem and the optimal profile generation subsystem may reside on the one or more prediction servers. In another embodiment, the product extraction subsystem may reside on the one or more search servers.
[0023] In a specific embodiment shown in Fig. 4 (400), the system (100) may include an information storage subsystem (not shown in FIG. 1) configured to store one or more factors of the first set of input extracted from the user. In some embodiment, the information storage subsystem may include a first database (not shown in FIG.1) configured to store information about at least one of the product and the service. In another embodiment, the information storage subsystem may also include a second database (not shown in FIG. 1) configured to store interactions between the user and the system through the interactive series of questions. In one embodiment, the first database and the second database may include at least one of a Mongo Database program (MongoDB), MySQL, Couchbase, NoSQL, SQL Server, Oracle, DB2 and other databases.
[0024] The system (100) also includes a profile generation subsystem (120) operatively coupled to the user input extraction subsystem (110). The profile generation subsystem (120) is configured to generate at least one profile dynamically, within a predefined range, corresponding to at least one of a required product and a required service based on an analysis of a first set of extracted input. In some embodiment, the at least one profile corresponding to the at least one of the required product and the service may include a second set of input, wherein the second set of input may include a plurality of attributes corresponding to the at least one of the required product and the service. In such embodiment, the profile generation subsystem (120) is configured to generate the at least one profile corresponding to the at least one of the required product and the required service based on creation of a constrained attribute level space for assignment of an attribute level to the plurality of attributes, computation of a number of profiles generated within the predefined range and creation of a set of initial number of profiles varying within the constrained attribute level space.
[0025] The profile generation subsystem (120) for computation of the number of profiles generated within the predefined range includes computation of number of profiles theoretically possible for generation within the predefined range. In one embodiment, the predefined range may include a limit which ranges between a minimum number of profiles to a maximum number of profiles. In some embodiment, the constrained attribute level space may include a limited space which includes levels for the plurality of attributes. The profile generation subsystem (120) also considers counts of one or more attribute levels within the constrained attribute level space, construct the minimum number of profiles within the predefined range to ensure that attribute levels appeared nearly equal number of times.
[0026] The system (100) also includes an optimal profile generation subsystem (130) operatively coupled to the profile generation subsystem (120). The optimal profile generation subsystem (130) is configured to generate an optimal profile dynamically, corresponding to the at least one of the required product and the required service based on the analysis of at least one generated profile selected by the user. In one embodiment, the optimal profile generation subsystem (130) is configured to generate the optimal profile based on computation of a ratio of number of times the attribute level is chosen by the user divided by total number of times the attribute level appeared during generation of the at least one profile corresponding to the at least one of the required product and the required service. The optimal profile generation (130) subsystem selects the attribute level with a highest proportion as a "winning attribute level" and such attribute level becomes part of the generation of the optimal profile corresponding to the at least one of the required product and the required service. In one embodiment, the optimal profile generation subsystem (130) may include artificial intelligence and a machine learning technique to generate the optimal profile corresponding to the at least one of the required product and the service.
[0027] The system (100) also includes a product extraction subsystem (140) operatively coupled to the optimal profile generation subsystem (130). The product extraction subsystem (140) is configured to match an optimal generated profile corresponding to the at least one of the required product and the service with a list of one or more products and services available in a marketplace. In one embodiment, the product extraction subsystem (140) is configured to match the optimal generated profile corresponding to the at least one of the required product and the service with the list of the one or products and services available in the marketplace based on matching of a matrix of values corresponding to the attribute level of at least one of an optimal product and the service with attribute levels of the one or more products and services available in the marketplace. In such embodiment, the product extraction subsystem (140) is configured to determine a difference in score which reflects the difference in the attribute levels between the optimal profile and the list of the one or more products and services available in the marketplace.
[0028] The product extraction subsystem (140) is also configured to match the optimal generated profile with the list of the one or more products and services available in the marketplace based on selection of a predefined number of the list of the one or more products and services with a computed difference score, wherein the predefined number of the list of the one or more products and the services includes selection of top ‘n’ number of the list of the one or more products and the services. In one embodiment, the computed difference score may include a lowest difference score to select a closely matched product and the service. The product extraction subsystem (140) is also configured to recommend at least one of the closely matched product and the service among the list of the one or more products and services available in the marketplace, to the user. In one embodiment, the product extraction subsystem (140) may include input from social media circles of the user. In some embodiment, the product extraction subsystem (140) for recommendation of the at least one closely matched product and the service may utilize artificial intelligence and a machine learning technique to use current data as well as leverage relevant past data of the users.
[0029] In a specific embodiment, the system (100) further includes a product trading subsystem (not shown in FIG. 1) operatively coupled to the product extraction subsystem (140). The product trading subsystem is configured to provide trading of at least one of a recommended product and a service to the user, by redirecting the user to at least one of an external marketplace. The product trading subsystem provides at least one link to purchase the optimal product and the optimal service on one or more of the external marketplaces to enable the registered user to purchase the recommended product and the service. In one embodiment, the external marketplace may include one or more of an electronic commerce (e-commerce) websites.
[0030] In a preferred embodiment, the system (100) further includes a feedback retrieval subsystem (not shown in FIG.1) configured to receive a feedback from the user corresponding to the at least one purchased product and the service. The feedback retrieval subsystem receives the feedback from the user upon purchasing of the recommended product and the service. In some embodiment, the feedback corresponding to the at least one recommended and purchased product and the service may include at least one of a purchase experience rating by the user, a comment, suggestion and a complaint. The feedback retrieval subsystem is also configured to receive a plurality of details from the user which may include, but not limited to, registration receipt corresponding to the at least one purchased product and the service, purchase receipt corresponding to the at least one purchased product and the service, product and user manuals corresponding to the at least one purchased product and the service, warranty cards corresponding to the at least one purchased product and the service, and maintenance service and repair work details corresponding to the at least one purchased product and the service.
[0031] FIG. 4 illustrates a schematic representation of an exemplary embodiment (400) of a system (100) for a product and a service recommendation by automated matching of product and service features to purchaser requirements of FIG. 1 in accordance with an embodiment of the present disclosure. The system (100) helps the user (105) in buying complex, valuable and relevant product and the service profitable for the user by providing most relevant recommendation. The system (100) provides recommendation for a plurality of products which may include, but not limited to, digital camera, laptop, mobile phone, music system, television, microwave, printer, car, washing machine and vacuum cleaner. Also, the system (100) provides recommendation for the service such as selection of a bank, selection of a hospital for treatment, selection of a restaurant or selection of an educational institution. For example, let us assume, the user (105) is a layman and wants to buy a laptop from an ecommerce website. In such scenario, the system (100) provides recommendation as per requirement of the user (105) and helps the user (105) in purchasing a relevant laptop, without assuming much user knowledge about laptops, instead interactively constructing the recommendation through a series of interactive information sharing (108) and data processing using backend technical solutions using pertinent information about the laptop and the user (105) in question.
[0032] Here, a first set of input corresponding to at least one of the products such as the laptop is extracted from the user (105) through a user input extraction subsystem (110). The user input extraction subsystem (110) extracts the first set of input from the user (105) through an interactive series of questions shared via an electronic device associated with the user (105) for a predefined time interval. For example, the electronic device associated with the user (105) may include a laptop, a mobile phone, a tablet, a surface computer, a desktop, a smart home device, a personal assistant device, or another type of stationary or handheld electronic device. The interactive series of questions are shared via multiple number of screens (108) of the electronic device associated with the user (105). Here, the first set of input may include one or more user inputs corresponding to at least one of the product and the service such as at least one of a name of the user, gender of the user, city of the user, income level of the user, country of the user, a category of the at least one of the product and a service, usage reason and usage purpose of buying the laptop such as for office use reason vs. personal use reason, and for programming use purpose vs. gaming and entertainment use purpose. Information about at least one of the product and the service is stored in a first database (115) of an information storage subsystem (125). Similarly, interaction between the user (105) and the system (100) is stored in a second database (118) of the information storage subsystem (125).
[0033] After, the first set of the input is extracted from the user (105), such first set of extracted input is sent from a client-side application viz, from the user (105) side application to server-side application through a Representational State Transfer Application programming interface (REST API). The first set of extracted input is utilised by a profile generation subsystem (120) to generate at least one profile dynamically, within a predefined range, corresponding to at least one of a required product and a required service based on an analysis of a first set of extracted input. Here, the profile generation subsystem (120) generates the at least one profile dynamically, wherein the at least one profile corresponding to the at least one of the required product and the service may include a second set of input which includes a plurality of attributes corresponding to the at least one of the required product and the required service. For example, the plurality of attributes corresponding to the laptop profile shared with the user (105) may include at least one of a type of operating system (OS), a screen size, a storage disk capacity, a touch screen, colour of the laptop, brand of the laptop, price of the laptop, size of random access memory (RAM), and size of hard disk drive (HDD). Here, the plurality of attributes corresponding to the laptop is shared to the user (105) through the multiple screens (108) on the electronic device associated with the user (105).
[0034] Let us assume, the at least one profile corresponding to the laptop with the plurality of attributes such as profile 1, profile 2 and profile 3 is shared with the user (105) wherein, the profile 1 corresponds to ‘product A’, profile 2 corresponds to ‘product B’ and profile 3 corresponds to ‘product C’. Now, for the product A, the plurality of attributes are the HDD, the type of OS- Mac OS, the RAM – 8 GB, the Screen size – 12-14 inch, the storage disk capacity – 500 GB and the touch screen – Yes. Similarly, for the ‘product B’ the plurality of attributes are SDD, the type of OS- Windows 10 Pro, the RAM – 16 GB, the screen size - 12-14 inch, the storage disk capacity – 250 GB and the touch screen – Yes. Again, for the ‘product C’, the plurality of attributes are the HDD, the type of OS- Mac OS, the RAM – 32 GB, the screen size- 12-14 inch, the storage disk capacity – 1 TB and the touch screen – No.
[0035] Here, the profile generation subsystem (120), generates the at least one profile such as the abovementioned profile A, the profile B and the profile C, corresponding to the at least one of the required product and the service based on creation of a constrained attribute level space for assignment of an attribute level to the plurality of attributes, computation of a number of profiles generated within the predefined range and creation of a set of initial number of profiles varying within the constrained attribute level space. For example, the computation of the number of the profiles generated within the predefined range includes computation of number of profiles theoretically possible for generation within the predefined range such as a limit which ranges between a minimum number of profiles to a maximum number of profiles.
[0036] After, a series of selections of at least one generated profiles, via multiple interactions (108) by the user (105), the system (100) includes an optimal profile generation subsystem (130) configured to generate an optimal profile dynamically, corresponding to the at least one of the required product and the required service based on the analysis of at least one generated profile selected by the user (105). Here, the optimal profile generation subsystem (130) generates an ideal or the optimal profile corresponding to the at least one of the product and the service based on the user (105) preferences and requirements. In one embodiment, the optimal generation subsystem (130) includes generation of the optimal profile based on computation of a ratio of number of times the attribute level is chosen by the user divided by total number of times the attribute level appeared during generation of the at least one profile corresponding to the at least one of the required product and the required service. Here, in a specific embodiment, the optimal profile generation (130) subsystem selects the attribute level with a highest proportion as a "winning attribute level" and such attribute level becomes part of the generation of the optimal profile corresponding to the at least one of the required product and the required service. Also, in other embodiments, the optimal profile generation subsystem may utilise a machine learning data processing technique such as decision trees, gradient boosting machines or other technique to generate the optimal profile corresponding to the at least one of the required product and the service. For example, the optimal profile upon analysis of the at least one generated profile, which is shared to the user (105) has the plurality of attributes such as the HDD, the type of OS- Windows 10 Pro, the RAM – 8GB, the screen size - 15 inch, the storage disk capacity – 1 TB, the touch screen – No, the brand – LenovoTM, price – between $500-$700 and the colour – black.
[0037] The system (100) also includes a product extraction subsystem (140) configured to match an optimal generated profile corresponding to the at least one of the required product and the service with a list of one or more products and services available in a marketplace. Here, upon generation of the optimal profile corresponding to the desired laptop, the product extraction subsystem (140) matches such optimal profile corresponding to an optimal laptop with the list of the one or more laptops available in the marketplace. Also, the product extraction subsystem (140) is configured to recommend at least one of the closely matched product and the service among the list of the one or more products and services available in the marketplace, to the user (105). In one embodiment, the product and the service recommendation is based on the closest matches based on lowest difference scores computed across all attribute levels between the optimal profile and the available products and services in the marketplace. In another embodiment, matching may be driven by machine learning data processing technique such as natural language processing, image processing, and matrix factorization techniques. For example, the product extraction subsystem upon matching of the ideal profile with laptops available in the marketplace shows the most closely matched laptops determined and displayed such as the Lenovo ThinkPadTM E-480 and HPTM 15 Core i5, displaying the attribute level profile of the two closely matched brands.
[0038] After, providing the recommendation to the user (105), the system (100) also includes a product trading subsystem (150) to enable the user (105) to purchase the closely matched products and services from a suitable and a convenient marketplace such as an external ecommerce website. The product trading subsystem (150) provides one or more links to purchase the selected optimal product and service on one or more of the external marketplaces to the user (105) in order to purchase at least one of the recommended product such as the laptop as per convenience. For example, the product trading subsystem upon selection by the user (150) of the one most relevant laptop from the list of closely matched laptops from product extraction subsystem shows the links to purchase the laptop from at least one or more of the e-commerce site such as Amazon, displaying the link to purchase the laptop.
[0039] Once, the at least one of the recommended product is purchased by the user (105) from the external marketplace, a feedback regarding the purchased product is received from the user (105) through a feedback retrieval subsystem (160). Here, the feedback corresponding to the at least one purchased recommended product may include at least one of a purchase experience rating by the user, a comment, suggestion and a complaint. The feedback received from the user (105) benefits a user, a seller, a retailer, as well as a manufacturer. Also, the feedback retrieval subsystem (160) channelizes repair or maintenance service requirements of the at least one purchased product for the user (105). The feedback retrieval subsystem (160) is also configured to receive a plurality of details from the user (105) corresponding to the at least one purchased product. Here, the plurality of details may include, but not limited to, purchase or registration receipt corresponding to the at least one purchased product and the service, product and user manuals corresponding to the at least one purchased product and the service, maintenance service and repair work details corresponding to the at least one purchased product and the service, and warranty cards and information corresponding to the at least one recommended product and the service. Here, the plurality of details helps the user (105) in case of future repair work or maintenance work for the at least one purchased product and the service and also provides product defect data for future product improvements to the manufacturer or the service provider. For example, the feedback retrieval subsystem upon purchase by the user (150) of the one most relevant laptop from among the shopping links to purchase the laptop from e-commerce sites such as Lenovo ThinkPadTM E-480 captures the purchase receipt, warranty registration information, laptop user manual, laptop product manual, laptop service centre information, and user feedback with comments, suggestions, and complaints pertaining to the laptop at defined time intervals such as a week after the purchase, a month after, a year after or anytime a complaint arises. The laptop feedback received from the user (115) is valuable to the LenovoTM laptop manufacturer, the seller, and the retailer.
[0040] Also, the feedback received from the user (105) corresponding to the at least one purchased product helps the manufacturer (105) learn about the strengths and weaknesses of the product, and more importantly about the defects in the product discovered and shared by the user (105). This direct feedback from the user results in making necessary product improvements by the manufacturer in a timely manner, thus reducing instances of defective products thereby benefitting the user as well as the manufacturer and the intermediaries such as retailers and the sellers. Same thing applies to a service and the service providers.
[0041] FIG. 5 is a block diagram of a computer or a server (500) in accordance with an embodiment of the present disclosure. The server (500) includes processor(s) (530), and memory (510) operatively coupled to the bus (520).
[0042] The processor(s) (530), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0043] The memory (510) includes a plurality of modules stored in the form of executable program which instructs the processor (530) to perform the method steps illustrated in FIG. 1. The memory (510) is substantially similar to the system (100) of FIG.1. The memory (510) has following subsystems: a user input extraction subsystem (110), a profile generation subsystem (120), an optimal profile generation subsystem (130), and a product extraction subsystem (140).
[0044] The user input extraction subsystem (110) is configured to extract a first set of input, corresponding to at least one of a product and a service requirement received through an interactive series of questions shared via an electronic device associated with a user for a predefined time interval. The profile generation subsystem (120) is configured to generate at least one profile dynamically, within a predefined range, corresponding to at least one of a required product and a required service based on an analysis of a first set of extracted input. The optimal profile generation subsystem (130) is configured to generate an optimal profile dynamically, corresponding to the at least one of the required product and the required service based on the analysis of at least one generated profile selected by the user. The product extraction subsystem (140) is configured to match an optimal generated profile corresponding to the at least one of the required product and the service with a list of one or more products and services available in a marketplace. The product extraction subsystem (140) is also configured to recommend at least one of a closely matched product and a service among the list of the one or more products and services available in the marketplace, to the user.
[0045] FIG. 6 is a flow chart representing the steps involved in a method (600) for a product and a service recommendation by automated matching of product and service features to purchaser requirements in accordance with the embodiment of the present disclosure. The method (600) includes extracting, by a user input extraction subsystem, a first set of input, corresponding to at least one of a product and a service requirement received through an interactive series of questions shared via an electronic device associated with a user for a predefined time interval in step 610. In one embodiment, extracting the first set of input, corresponding to the at least one of the product and the service requirement through the interactive series of questions shared via the electronic device associated with the user for the predetermined level may include extracting the first set of input such as at least one of a name of the user, gender of the user, city of the user, income level of the user, country of the user, a category of the at least one of the product and a service, usage reason and usage purpose of buying the product and the service.
[0046] The method (600) also includes generating, by a profile generation subsystem, at least one profile dynamically, within a predefined range, corresponding to at least one of a required product and a required service based on an analysis of a first set of extracted input in step 620. In some embodiment, generating the at least one profile dynamically, within the predefined range, corresponding to the at least one of the required product and the service may include generating the at least one profile which may include a second set of input corresponding to the at least one of the required product and the service. In such embodiment, the second set of input may include a plurality of attributes corresponding to the at least one of the required product and the service.
[0047] The method (600) also includes generating, by an optimal profile generation subsystem, an optimal profile dynamically, corresponding to the at least one of the required product and the required service based on the analysis of at least one generated profile selected by the user in step 630. In one embodiment, generating the optimal profile dynamically, corresponding to the at least one of the required product and the required service may include generating the optimal profile dynamically based on computation of a ratio of number of times the attribute level is chosen by the user divided by total number of times an attribute level appeared during generation of the at least one profile corresponding to the at least one of the required product and the required service. In some embodiment, generating the optimal profile dynamically, corresponding to the at least one of the required product and the required service may include generating the optimal profile corresponding to the at least one of the required product and the required service by using an artificial intelligence and a machine learning technique. In such embodiment, the machine learning technique may include a data analysis technique.
[0048] The method (600) also includes matching, by a product extraction subsystem, an optimal generated profile corresponding to the at least one of the required product and the service with a list of one or more products and services available in a marketplace in step 640. In one embodiment, matching the optimal generated profile corresponding to the at least one of the required product and the service with the list of the one or more products and services available in the marketplace may include matching the optimal generated profile based on computation of a matrix of values corresponding to the attribute level of at least one of an optimal product and the service, determination of a difference in score across the attribute level corresponding to the list of the one or more products available in the marketplace. In such embodiment, matching the optimal generated profile corresponding to the at least one of the required product and the service with the list of the one or more products and services available in the marketplace may also include matching the optimal generated profile based on selection of predefined number of the list of the one or more products and services with a computed difference score.
[0049] The method (600) also includes recommending, by the product extraction subsystem, at least one of a closely matched product and a service among the list of the one or more products and services available in the marketplace, to the user in step 650. In one embodiment, recommending the at least one of the closely matched product and the service among the list of the one or more products and the services available in the marketplace, may include recommending the at least one of the closely matched product by using input from social media circles of the user. In some embodiment, the product extraction subsystem (140) for recommendation of the at least one closely matched product and the service may utilize current data as well as leverage relevant past data of the users.
[0050] In a specific embodiment, the method (600) further includes providing, by a product trading subsystem, trading of at least one of a recommended product and a service to the user, by redirecting the user to at least one of an external marketplace. In one embodiment, the external marketplace may include one or more of an electronic commerce (e-commerce) websites. In one embodiment, the method (600) further includes receiving, by a feedback retrieval subsystem, a feedback corresponding to the at least one purchased product and the service.
[0051] Various embodiments of the present disclosure enable the user to buy a complex, valuable and relevant products and services easily from the marketplace based on specific recommendation provided by the system.
[0052] Moreover, the present disclosed system utilises novel and non-obvious technical data interchange, novel and non-obvious algorithmic data processing, novel and non-obvious server hardware component configuration to construct and discover the industrial problem of technically and interactively constructing in real time an ideal product and the service based on individual users requirements and preferences and the product and the service features objectively and then enabling the user to buy from among closest matching available products and services using the electronic device associated with the user.
[0053] Furthermore, the present disclosed system receives feedback from the user corresponding to the at least one of the recommended and purchased product and the service and as a result helps in creating a complete end-to-end integrated eco-system for the benefit of end users, sellers, retailers, and manufacturers.
[0054] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
[0055] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[0056] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.