FIELD OF THE INVENTION
This invention relates to Smart monthly grocery recommendation System using Machine Learning algorithm for the optimized family budget.
BACKGROUND OF THE INVENTION
US20160232546A1 discloses Current information on transactions that occur in accounts of consumers of financial products that are kept with providers of financial products is routinely received through a communication network from providers of financial goods, an aggregator, or both. The current transaction information is saved in a database of consumer information. Machine learning is used to develop model profiles of transactions in accounts of corresponding categories of customers for corresponding financial products using stored transaction information and other information about the consumers in the database. Transactions that have happened in the accounts of financial product consumers are examined using the model profiles for the applicable groups of customers and financial products as new information about transactions is obtained. A communication network alerts each of the consumers who had transactions that did not conform to the relevant model profile. In our proposed Idea we are proposing the machine learning-based recommender system for the home not for any providers of financial goods in which the grocery list is to be recommended which will be based on the consumption but it will recommend the whole price to reduce the monthly family budget also.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. Present invention is Smart monthly grocery recommendation System using Machine Learning algorithm for the optimized family budget.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
Disclosed herein a Smart monthly grocery recommendation system using machine learning algorithm for the optimized family budget comprises Input data unit (100), Pre-processing Unit (102), Training Unit (103) and Recommendation Unit (104); wherein the input data contains heterogenous information such as item_details, month_special_days, increased numbers of person per day.
In another embodiment, the next component of the system is pre-processing unit of the dynamic data input received from the previous phase; wherein in this segment, the total number of persons that may consume the particular item is calculated based on the different input parameters received from the input data set; and this phase determines the necessary attributes to be selected.
In another embodiment, Training Unit (103) is responsible for the training of the data items and further analysis of the recommended items along with the brand name.
In another embodiment, the machine learning algorithm (SVM) is applied to the processed attributes and the obtained result is again filtered against the previous month's recommendations.
In another embodiment, Recommendation unit is the final component that displays the final recommendation in the form of a list of grocery items along with the total quantity.
In another embodiment, First information item_details contain all possible grocery items along with different brands, prices, quantity.
In another embodiment, the prices of each item in the list for different brands are retrieved from different e-commerce sites dynamically before the pre-processing phase; and the next information is about calculating the total no. of days and special days in which grocery items’ consumption increases; and for these special days, consumption increased is calculated based on the increased number of persons. Information regarding the increased number of people for special days.
In another embodiment, this data is used as a primary input for subsequent phases and a copy of this data is fetched into the next phase and a log is maintained at the backend to see the difference from the updated versions for next month.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
A huge amount of time is indulged to jot down the grocery list for each month in every household. There are multiple options (different brands and prices) available (online and offline both) for a single item of the monthly grocery list and the price and quality of each of them vary. A grocery list is not fixed for each of the months and the price for an item of the specific brand may vary from time to time. Additionally, in some months the number of grocery items may increase due to some festivals or functions. Therefore, We propose a “smart monthly grocery recommendation System using Machine Learning algorithm” that will not only recommend the appropriate amount of any grocery item based on the consumption but will recommend the price (brand) to reduce the monthly family budget.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Fig 1 – System architecture of grocery recommendation system.
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
These and other advantages of the present subject matter would be described in greater detail with reference to the following figures. It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
Components of the system: The invention is primarily composed of four main components named Input, Pre-processing, training and recommendation. Details of each of the components are as below.
(i) Input [100] (Sample):- The first and the most important component of the system is its input data[100]. This input data contains heterogenous information such as item_details, month_special_days, increased no. of person per day etc. First information item_details contains all possible grocery items along with different brands, prices, quantity. The prices of each item in the list for different brands are retrieved from different e-commerce sites dynamically before the pre-processing phase. The next information is about calculating the total no. of days and special days in which grocery items’ consumption increases. For these special days, consumption increased is calculated based on the increased number of persons. Information regarding the increased number of people for special days is provided in table 3. This data is used as a primary input for subsequent phases and a copy of this data is fetched into the next phase and a log is maintained at the backend to see the difference from the updated versions for next month. A sample of the possible data input is listed below.

Table 1:- Sample data input (item_details)
Item Name Item_Code Quantity
Brand 1 Price 1 (INR) Brand 2 Price 2 (INR)
Turmeric Powder AA11 250gm X 30 Y 28
Sugar AA12 2 kg Z 80 Q 78

Rice (Basmati) AB25 5 kg A 370 B 350
- - - - - -
- - - - - -

Table 2:- Sample data input (month_special_days)
Month Name Month_Code Total No. of Days Special Days
January 001 31 5
February 002 28 3
- - - -

Table 3:- Sample data input (month_special_days)
Special Day # Day_code Total No. of person increased
1 INC01 3
2 INC02 5
- - -
Each time a user asks for the prediction, he or she has to identify the possible special days from the given month details. Apart from identifying those special days, the user has to tell an approximate number of guests for each of the special days. This input is directly stored in the input data sets.
(ii) Pre-processing [102]:- The next component of the system is pre-processing of the dynamic data input received from the previous phase. In this segment, the total number of persons that may consume the particular item is calculated based on the different input parameters received from the input data set. This phase determines the necessary attributes to be selected.
(iii) Training [103]: - This is the major component of the system that is responsible for the training of the data items and further analysis of the recommended items along with the brand name. The machine learning algorithm (SVM) is applied to the processed attributes and the obtained result is again filtered against the previous month's recommendations.
(iv) Recommendation [103]: - This is the final component that displays the final recommendation in the form of a list of grocery items along with the total quantity.
Best Method of working:
? The Proposed invention will be capable to automate the grocery list creation process for every month.
? The proposed system will save the user’s time for listing all the grocery items after the end of the month.
? Due to automatic price comparison for a specific item, it is a budget-friendly system.
? This system will automatically calculate increased consumption and will automatically recommend the appropriate quantity for a particular grocery item in advance.
Novel Features of the Invention
1. The Proposed system will automate the grocery list for every month.
2. It will automatically calculate the total consumption of each of the grocery items recommended.
3. It will provide a grocery consumption pattern for each month after the first iteration.
4. It will provide the reduced price available online, for each grocery item recommended.

WE CLAIM:

1. A Smart monthly grocery recommendation system using machine learning algorithm for the optimized family budget comprises Input data unit (100), Pre-processing Unit (102), Training Unit (103) and Recommendation Unit (104); wherein the input data contains heterogenous information such as item_details, month_special_days, increased numbers of person per day.
2. The system as claimed in claim 1, wherein the next component of the system is pre-processing unit of the dynamic data input received from the previous phase; wherein in this segment, the total number of persons that may consume the particular item is calculated based on the different input parameters received from the input data set; and this phase determines the necessary attributes to be selected.
3. The system as claimed in claim 1, wherein Training Unit (103) is responsible for the training of the data items and further analysis of the recommended items along with the brand name.
4. The system as claimed in claim 1, wherein the machine learning algorithm (SVM) is applied to the processed attributes and the obtained result is again filtered against the previous month's recommendations.
5. The system as claimed in claim 1, wherein Recommendation unit is the final component that displays the final recommendation in the form of a list of grocery items along with the total quantity.
6. The system as claimed in claim 1, wherein First information item_details contain all possible grocery items along with different brands, prices, quantity.
7. The system as claimed in claim 1, wherein the prices of each item in the list for different brands are retrieved from different e-commerce sites dynamically before the pre-processing phase; and the next information is about calculating the total no. of days and special days in which grocery items’ consumption increases; and for these special days, consumption increased is calculated based on the increased number of persons. Information regarding the increased number of people for special days.
8. The system as claimed in claim 1, wherein this data is used as a primary input for subsequent phases and a copy of this data is fetched into the next phase and a log is maintained at the backend to see the difference from the updated versions for next month.