WO2020100158A1

WO2020100158A1 - An automated bargaining method using quantified price signals as monetizable points in a cooperative network

Info

Publication number: WO2020100158A1
Application number: PCT/IN2019/050824
Authority: WO
Inventors: Rajesh BHASKARAN
Original assignee: Bhaskaran Rajesh
Priority date: 2018-11-17
Filing date: 2019-11-07
Publication date: 2020-05-22

Abstract

An automated bargaining method using Quantified Price Signals (QPS) as monetizable points for processing a negotiation between a customer (122) and a merchant (124) in a price signal network (100) (PSN) discloses, a process for receiving "reserve price" and "posted price" for each product (102) along with the access to the historical "Seller Signal Patterns" (108) of the merchant (124) to calculate the Merchant Price Sensitivity Score (PSS(m)) (112). Further, the method discloses a process for receiving interest form customer (114) along with access to the historical "Buyer Signal Patterns" (118) for the customer to calculate Customer Price Sensitivity Score (PSS(c)) (120). In one embodiment, the PSN (100) automatically calculates the "Final posted price" (130) of the product which is in equilibrium between the "posted price" and "reserve price" of the product and based on the PSS(m) (112) and PSS(c) (120).

Description

An automated Bargaining Method Using Quantified Price Signals as Monetizable Points in a Cooperative Network

The present invention relates generally to an automated platform for any distributed merchant networks more particularly to a method and a system for processing a bargaining transaction between buyer and seller by way of negotiation using quantified price signals as monetizable points.

In a typical shopping experience, a customer visits one or more shops for purchase of a product. Only when the customer finds the product, that he or she is looking for at a reasonable price or at good quality, the customer expresses interest in purchase of the product from that particular merchant. This traditional method of shopping requires the customer to visit more than one shop or merchant to determine a reasonable price for the purchase of the product.

In recent times, this disadvantage is overcome by online shopping, wherein products are sold to customers through communication networks such as with online transactions completed through the Internet. E-commerce sales have been growing steadily over the past few years and are expected to continue, because customers are attracted to the ease and convenience of shopping online. For example, a customer can shop online from the comfort of home at any time of day.

One of the most important reasons for such an increase in sales via online channels is the ease of comparison of prices of products from different merchants. For example, an e-commerce platform compiles prices from various sources (e.g. Merchants and/or retail stores that are not online) for various products. As price information becomes more accessible through the e-commerce platforms, customer becomes more price sensitive and demands that products are sold at lower prices.

Present e-commerce trade has dozens of customer-merchant protocols for use, majority of which are merchant driven, that is they focus on the methods and processes available to the merchant allowing them to determine price or package the products more efficiently. In such an environment, most products and/or services sold whereby, the merchant decides the price and the customer decides whether or not to purchase the product at that price.

Typically, there are two types of auctions, the English auction (Ascending price) and the Dutch auction (Descending price). In any types of auctions, there exists a merchant’s reserve price and a customer’s affordable price. In auctions the merchant attracts numerous customers who as a group determine the final selling price. During active negotiations between the parties, when the customer’s affordable price is greater than the merchant’s reserve price, a sale is executed.

Recent advancements, have introduced negotiations in e-commerce platforms. Many e-commerce platforms, however, do not have a two-sided negotiation process which aids both the merchant and the customer. A major disadvantage of having a merchant-sided negotiation process is that when the customer bid is not above the reserve price, the merchant rejects the bid. This may result in the customer losing interest in the transaction and the merchant may potentially lose valuable customers. Also, a purely customer-sided negotiation process may not attract lot of merchants as the merchants do not want to negotiate with their products for those prices which the customer demands.

Presently, technologies and algorithms have been developed, that captures customer buying patterns and merchant sales patterns and match customer and merchant requirements in third party e-commerce platforms. These pattern matching algorithms monitor general customer preferences such as likes and dislikes and buying patterns with the merchant preferences to provide customised products to increase successful sale transactions. These platforms in turn work on commissions from the merchants for their revenues. Due to the fact that these platforms charge a product and price dependent margin over the price offered by the merchant, the platforms have a vested self-interest to offer higher prices, which may not be the best possible option for either the merchant or the customer.

There are many disadvantages to the existing online bilateral negotiation process. For example, in the conventional online bilateral negotiation process the negotiating parties are not given assistance in reaching a successful conclusion. In addition, there are no guarantees on the length of the negotiation and there is no coherent notion of what the current state of the negotiation is that the negotiating party could take in at a glance. Also, these existing systems are time consuming and result in inefficient negotiations between customer and merchant and do not allow the customer to achieve the best value for money and at the same time, do not allow the merchant to provide value in addition to price. Further, these existing systems also do not allow the customer to consider the additional value in the buyer’s decision making process and vice versa.

To overcome these drawbacks, we propose such a system where both the parties i.e. customer and merchant can equally negotiate on the deal by expressing any comments or offers closely resembling the thought of buyer and merchant with certain conditions.

Therefore, which will become apparent to those skilled in the art upon reading and understanding the specification there is a need in the art for a method and system for processing a transaction by way negotiation via algorithms or platforms that specifically work for each unique merchant and each unique customer and help negotiate price more efficiently for the benefit for both the customer and the merchant.

The following presents a simplified summary of one or more embodiments in order to provide a basic understanding of such embodiments. This summary is not an extensive overview of all contemplated embodiments and is intended to neither identify key or critical elements of all embodiments nor delineate the scope of any or all embodiments. Its sole purpose is to present some concepts of one or more embodiments in a simplified form as a prelude to the more detailed description that is presented later.

In accordance with one aspect, the invention is an automated bargaining method using Quantified Price Signals (QPS) as monetizable points for processing a negotiation between a customer (122) and a merchant (124) in a price signal network (100) comprising,
a) Receiving both the reserve price and a posted price (104) of a product (102) from the merchant (124),
b) Calculating the Merchant Price Sensitivity Score (PSS(m)) (112) of the merchant (124) for product(s) (102) based on the merchant propensity of issuing and accepting points (106) and/or merchants’ history of “Seller Signal Patterns” (108) using information gathered and stored in the PSN (110),
c) Receiving an interest from the customer to negotiate (114) on any product (102) that is put to offer for sale from merchant (124) in step (a),
d) Calculating the Customer Price Sensitivity Score (PSS(c)) (120) of the customer (122) for the product(s) (102) based on the customers propensity of accepting and using points (116) and/or customers history of “Buyer Signal Patterns” (118) using information gathered and stored in the PSN (110),
e) Providing an automatic Final Posted Price (FPP) (130) to the customer (122) that is numerically between the merchant’s reserve price and posted price to the customer (122) in the PSN (100) to enable to arrive at a mutually acceptable price and to close the transaction,
f) Enabling the customer to spend the same amount of points (128) in lieu of the discount amount as calculated by the formula
Points Spent By Customer = Discount = Posted Price – Final Posted Price (130), and
g) Enabling the customer to receive and the merchant to issue the same amount of points (126) as calculated by the PSN (100) in lieu of “excess surplus”.

The automated bargaining method according to the present invention provides quantified price signals, such that these quantified price signals are used in calculations to arrive at a negotiating price customised for each of the merchant (124) and customer (122) based on their propensity to accept and issue (and/or) use “points” and/or “buyer signal patterns” (118) of the customer (122) and/or “Seller Signal Patterns” (108) of the merchant (124).

Similarly, the method allows the merchant (124) to determine the acceptance and issuance of points known as Quantified price signals (QPS) in a merchant co-operative, wherein merchant (124) propagate said price signals or points to the merchant co-operative whenever a purchase is made by a customer (122) from that merchant (124).

The method according to the present invention advantageously helps individual merchant (124) to automatically negotiate with the individual customer (122) in offline and online transactions. More advantageously, the method allows flexibility for the merchant (124) to choose the means of negotiation, such as discounts or as an offer of “points” for individual or bundled products and product categories.

For a more complete understanding of the present invention and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings wherein like numbers designate like objects and in which:

Fig. 1

shows a block diagram of an automated bargaining method using quantified price signals as monetizable points in a co-operative network.

Fig. 2

shows a flow diagram depicting an automated bargaining method using quantified price signals as monetizable points in a co-operative network.

Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may have not been drawn to scale. For example the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure. Throughout the drawings it should be noted that like reference numbers are used to depict the same or similar elements features and structures.

The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and / or detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

Before undertaking the detailed description of the invention below it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document: the terms “include” and “comprise ” as well as derivatives thereof mean inclusion without limitation; the term “or ” is inclusive meaning and/or; the phrases “associated with” and “associated therewith ” as well as derivatives thereof may mean to include be included within interconnect with contain be contained within connect to or with couple to or with be communicable with cooperate with interleave juxtapose be proximate to be bound to or with have a property of or the like;

As used herein “Customer” (122) means a person who buys goods or services from an individual and/or shop and/or business and/or e-commerce and is interchangeably used as buyer, consumer, purchaser and its equivalents thereof.

As used herein “Merchant” (124) means a person who sells goods or services from an individual and/or from a shop and/or business and/or e-commerce and is interchangeably used as trader, seller, re-seller, distributor, wholesaler and its equivalents thereof.

As used herein “Reserve Price” is that price below which a product (102) or a service cannot be sold by a merchant (124) to the customer (122).

As used herein “Posted Price” is that price that is offered to the customer (122) or the price for which the customer (122) pays to buy a product (102) or service from the merchant (124).

As used herein “Final Posted Price” (FPP) (130) is that price according this patent application that is offered to the customer (122) taking into consideration discounts and promotions. In this present application, FPP (130) also includes, the price offered to the customer (122) following the process disclosed in the present invention, whereby the Final posted Price (130) is greater than the Reserve price and lesser than or equal to the “Posted Price”. When the FPP (130) is offered to the customer (122) for a particular product (102), it supersedes the offer of the Posted Price by the merchant (124) following which the customer (122) choose to pay the FPP (130) to buy the product (102).

As used herein “Quantified Price Signal” (QPS) is an indicator that quantifies some value that is lost or gained in any transaction (in this patent application related to the bargaining process) during purchase. The value may be understood in terms of pricing information, product quality information and other forms of hidden information not revealed by merchant (124) or customer (122) in the usual purchasing process. It may also be understood as pricing power for merchants and purchasing power for customers. When this value is quantified, it is converted to potentially monetizable points such as “points” or “tokens” which may be used as a form of currency with some associated monetary value in transactions with merchants (124). This term can be interchangeably used as pricing power signal (for merchant) and purchasing power signal (for customer).

As used herein “Quantified Price Signal of the customer” refers to the purchasing behaviour of the customer (122) which is determined by whether the customer (122) paid a full price or a discounted price during purchase of products (102) or services and if so, by what discounted price or at what premium or “excess surplus”.

As used herein “Quantified Price Signal of the merchant” refers to the promotional behaviour of the merchant (124), which is determined by whether and by what price merchant (124) has discounted his products (102) or services or by what price that merchant (124) has set his prices at premium or “excess surplus”.

As used herein, the Price Signal Network (PSN) (100) encapsulates a dependent relationship among merchants (124) and customers (122), which requires them to share QPS data related to purchases made within the network. The PSN (100) Entity in charge of administering this network may be an organization, a centralized or decentralized database, a network with a central server, a blockchain with multiple decentralized nodes or any such entity capable of storing this data on the Internet, or on an intranet or on an extranet. The network may also be any form of non-electronic system for example paper-based documentation system. Also the network may be a mobile service network. An extranet may be a private network connection. The network platform collects, stores and generates quantified price signals from sales records and ”Seller Signal Patterns”/”Buyer Signal Patterns” of the merchant/customer respectively in real time from point of sale systems or from database or purchase history of some or all products exchanged or traded in transactions between merchant (124) and customer (122). This term interchangeably can be used with Price signal database or price signal algorithm. The PSN Entity (100) has the ability to set rules for the participating merchants (124), customer (122) and third parties which enable this network to function in a well-defined manner. The PSN Entity (100) also has the ability using to generate points from the Quantified Price Signals, and distribute these points for a fee or for free to merchants (124), customer (122) and any other third parties participating in the PSN (100). The merchants (124), customer (122) and other third parties acquiring these points may distribute them to propagate the QPS information to the PSN (100).

As used herein the “merchant’s excess surplus” refers to an excess of profit or a measure of goodwill that may be passed on by the merchant (124) to the customer (122) in terms of points. For example, the merchant (124) may have priced a product artificially high due to scarcity conditions and may seek to compensate the customer (122) by offering points in lieu of the excess profit. The excess surplus may be an entirely subjective number decided by the merchant (124) or may be calculated by PSN (100) based on relevant data available with PSN (100).

As used herein, the “merchant surplus” is defined as the absolute difference between the reserve price and the posted price i.e., (Merchant Surplus = Posted Price - Reserve Price).

As used herein, the “customer surplus” is defined as the absolute difference between the final posted price (130) and the posted price i.e., (“Customer Surplus” = Posted Price – Final Posted Price)

As used herein, “Price Sensitivity Score of the customer” (PSS(c)) (120) refers to the Quantified Price Signal of the customer (122) which is aggregated across all products in the history of the customer’s purchases and a score is calculated to give a mathematical representation of the purchasing behaviour or “Buyer Signal Patterns” (118) of the customer. This score is called the Price Sensitivity Score of the customer PSS(c) (120). The PSS(c) is represented as numeral value, textual value, colour codes, tags and/or n-tuples with multiple types and values.

As used herein “Price Sensitivity Score of the merchant” (PSS(m)) (112) refers to the Quantified Price Signal of the merchant (124) which is aggregated across all products in the history of the merchant’s sales and a score is calculated to give a mathematical representation of the “Seller Signal Patterns” (108) of the merchant. This score is called the Price Sensitivity Score of the customer (PSS(m)) (112). The PSS(m) is represented as numeral value, textual value, colour codes, tags and/or n-tuples with multiple types and values.

As used herein “receiving an interest from the customer” (114) refers to any interest which indicates that the customer (122) is willing to buy the product (102), such as not limited to visiting the store, visiting the web page of the merchant (124) or the product (102) or placing the product (102) in the wish list or cart. In simple terms, this comprises both active and passive requests made available at the time in the art.

As used herein “Auction” will be used to refer to any of a number of formats (known and to be developed) for selling goods or services in a competitive bidding environment. In one instance, it refers to the format of transaction between a single customer (122) and single merchant (124). In the present invention, auction is interchangeably used as bargaining process.

As used herein “Negotiation” refers to the set of activities that take place to solicit, receive, analyze and respond to bids for a particular product(s) (102) or service(s). This involves the interactions of several entities including one or more customer (122) one or more merchant (124) and one or more third-party entities.

As used herein “Merchant Co-operative” refers to an arena/channel that facilitates commercial transactions that includes sales or offer to sales of good/services by merchant (124) to customer (122). The transactions in this patent herewith include all offline transactions and online transactions, private or semi-private transactions that may or may not be facilitated by third party vendors. Specifically, the merchant co-operative requires the merchants (124) within the co-operative network to accept and offer points from/to customers (122) as price signals, which is used by other merchants (124) to offer better prices to their customers (122) in the bargaining process in accordance with the present invention.

As used herein “tokens” or “points” are treated as having worth monetary value equivalent to some amount of any applicable currency by using an exchange rate calculation which maps points/tokens or crypto currencies to the applicable currency by the merchant (124) and customer (122) and any third-party platforms. The exchange rate may be a predefined fixed or dynamic rate or may be a rate traded in any sort of currency exchange or in a merchant co-operative.

As used herein “Buyer Signal Patterns” (118) refers, in addition to the price signals gathered from the PSN (100), to all models or statistics that is employed to track and store data related to the customer (122) profile and shopping pattern of individual customer, such as not limited to gender of the customer, age of the customer, demography of the customer, choice of products purchased, time of purchase of products, number of times of visiting pages of product display, time spent in browsing products, choice of purchase between two vendors selling same product, propensity of discount or promotion used. such as the customer persona, product or product category based price signals, customer based price signals, discounts or points taken in the recent past, i.e. short term verses long term behavioural characteristics of the customer, frequency of use of points, other trending characteristics, discounts and point history within merchant coalitions or the like.

As used herein “Seller Signal Patterns” (108) refers, in addition to the price signals gathered from the Price Signal Network (PSN) (100), to all models and statistics that is employed to track and store data related to the merchant profile (124) and selling pattern of individual seller/ merchant (124) such as not limited to type of products sold, mode of sale of products - offline and/or online, quality of products sold, customer review or feedback, promotions offered, purchase probability of each product and/or the supply (availability) and/or demand for each product, product price, product specific discount, discount to product cost ratio, product type, merchant information, merchant type(s), competitor product and price information, brand, product category, the type of discount offered (such as the product specific degree of price discrimination), discounts or points taken in the recent past, i.e. short term verses long term behavioural characteristics of the merchant, point acceptance history and patterns within a merchant co-operative or the like.

As used herein “Regret prices” refers to prices at which sale did not materialize (or) prices at which sales are completed wherein the price is different from what is predicted by the Price Signal Network (PSN) (100) or offered by the merchant (124).

Definitions for certain words and phrases are provided throughout this patent document those of ordinary skill in the art should understand that in many if not most instances such definitions apply to prior as well as future uses of such defined words and phrases.

The present invention relates to an automated bargaining method using Quantified Price Signals (QPS) as monetizable points for processing a transaction between a customer (122) and a merchant (124) in a price signal network (100).

In a merchant environment, for every product (102) the merchant (124) offers to sell, has a “reserve price”, and a “posted price”. Further, the merchant (124) also usually promotes his product (102) in a competitive market using discounts and reduction prices. This final sale price is the “Final posted price” (FPP) (130) for the product (102).

On the contrary, if a merchant (124) cannot offer discounts, the merchant (124) provides “excess surplus”, as points to the customer (122), either as gratification or as a return for the “premium” charged by the merchant (124).

Similarly, points are accepted by merchants (124) from customer (122) on sale of products (102) for which the merchant (124) has offered to provide discount (Typically, in the case of discount, the acceptance of points equal to the difference between the final posted price (130) and the posted price of that product). Here, the merchant (124) acknowledges the monetary value of points and accepts it in lieu of the discounted amount for the product (102).

As disclosed, earlier, the Price signal network (100) captures these “points” issued and accepted across different merchant (124), and pertaining to the individual merchant (124) to calculate the Price Sensitivity Score of the Merchant (PSS(m)) (112) using the algorithm disclosed forthwith.

Let k = i + j be the total number of products (102) sold by a merchant (124) in the captured history of all his transactions in the PSN (100). For all products “k” sold by the merchant (124), let points be offered for i products and be accepted for j products. Let the total points offered = C(i) and total points accepted = C(j).
Price Sensitivity Score of Merchant PSS(m) (112) = C(i) / (C(i) + C(j))

In one embodiment of the present invention the PSS(m) (112) thus captures the discount propensity of the merchant by using price signals captured across the entire merchant co-operative network.

In one embodiment of the present invention, the PSS(m) (112) of the merchant (124) is enhanced and various complex PSS(m) values calculated using various “Seller Signal Patterns” (108) of the merchant (124) defined in this patent application to improve the accuracy of the PSS(m) (112) for determining the Final posted price (130) in the Price Signal Network (PSN) (100).

Similarly, in a customer environment, the customers (122), typically seeks to purchase products of interest from sellers. Some customers (122) buy products/services at the posted price of the seller, while few others buy products at a discounted price or reduced price herein referred to as “Final Posted price” (130) of the product (102). Customers (122) spend points having value equal to the discounted price that the customer (122) wishes to use to buy for the products (102) or services sold by the merchant (124). The merchant (124) accepts the given points in lieu of the actual discounted amount for the product (102) sold by him.

As disclosed, earlier, the Price signal network (100) captures these data of “points” accepted and issued pertaining to the individual customer (122) and calculates the Price Sensitivity Score of the Customer (PSS(c)) (120) using the algorithm disclosed forthwith.

Let t = r + s be the total number of products (102) purchased by a customer (122) in the captured history of all his transactions in the Price Signal Network (PSN) (100). For all products “t” purchased by the customer (122), let points be offered for r products and be accepted for s products. Let the total points spent = C(s) and total points received = C(r).
Price Sensitivity Score of the Customer PSS(c) (120) = C(s) / (C(r) + C(s)).

Thus, the discount seeking propensity of the customer (122) is calculated using price signals received from various merchant (124) from the co-operative network.

In one embodiment of the present invention, the PSS(c) (120) of the customer (122) is enhanced and various complex versions of PSS(c) are calculated using various “Buyer Signal Patterns” (118) defined in this patent application to improve the accuracy of the PSS(c) (120) and the Final posted price (130) in the Price Signal Network (PSN) (100).

In one embodiment, the present invention provides means and methods to automate the process of bargaining, wherein the PSS(m) (112) of the merchant (124) is calculated for each product (102) sold by the merchant (124). Similarly, for the same product, the PSS(c) (120) of the customer (122) is calculated. During the transaction process, the PSN (100) provides a Final posted price (130) based on the PSS(m) (112) and the PSS(c) (120) in the PSN (100) via calculating the Price Sensitivity Score mid-point between the customer (122) and the merchant (124).
Price Sensitivity Score Midpoint PSS(Mid) = (PSS(m) + 2 * PSS(c)) / 2
Final Posted Price (FPP) (130) = (Posted price - Reserve Price) * PSS(Mid) + Reserved price

In one embodiment, of the present invention, the PSN (100) is enabled to find an equilibrium, by automatically arriving at a mid-point of a product price that is mutually acceptable for both the merchant (124) and the customer (122) enabling the success of the negotiation.

In one embodiment, of the present invention, the reserve price and the posted price is not a static component wherein, the Price Signal Network (PSN) (100) enables the merchant (124) to alter the said “Reserve price” and said “Posted price” for each of the merchant’s product with in the Price Signal Network (PSN) (100). The PSN (100) allows at predetermined time interval, where the merchant (124) is allowed to alter the said “Reserve Price” and “Posted Price” for any and/or all of the products (102) within the PSN (100). It is to be noted that the PSN (100) sets the “Final Posted Price” (130) of each product (102) to be equal (or) greater than the “Reserve Price” of the same product (102) irrespective of the “Price Sensitivity Score (PSS) of the customer” (120).

In one embodiment of the present invention, the PSN (100) captures the success of each transaction of product (102) and the price settled in the said transaction. The PSN (100) optionally uses the “Regret Prices” to calculate the FPP (130) of each product (102) to increase the accuracy of the “Final Posted price” (130) and increase the success of transaction. In one aspect of the embodiment, the points can be issued and accepted by the same merchant (124), and/or by one or more individual merchants (124) in a merchant co-operative or by a third-party platform that operates in a merchant co-operative environment. In another aspect, the third party platform may offer to sell the price signals or points for free or a fee as determined by the PSN (100).

In one embodiment, of the present invention, incentive is provided to both the merchant (124) and the customer (122), to disclose to the PSN (100) information pertaining to the merchant’s “Reserve Price” and for the information access to the transaction history of the customer (122) and the merchant (124).

In one embodiment, of the present invention, the PSN (100) enables the collection and storage of data or information (110) related to the customer (122) and merchant (124) within any storage unit known in the art, wherein the data are encrypted with techniques known in the art to ensure privacy and data safety.

In one embodiment of the present invention, the “point” mechanism used here is a scalar count that is used to signify an acceptance of a discount or an acknowledgement of excess surplus by the merchant (124). Similarly, the “point” is used by the customer (122) as partial payment in purchase of a product in case of a discount and is accepted as a credit by the customer (122) when the merchant (124) is unable or unwilling to provide a discount for a product (102). The point credit thus acquired by the customer (122) can be spent for purchasing another product (102) from the same merchant or from other merchants (124) accepting points for discounts in the PSN (100) merchant co-operative environment.

In one embodiment, of the present invention when a merchant (124) is unwilling or unable to provide a discount, PSN (100) enables the merchant (124) to issue “points” which are calculated based on the PSS(m) (112). Similarly, when the merchant (124) is in a position to provide a discount, the merchant (124) accepts “points” equal to the discounted amount for that product (102).

In one embodiment of the present invention, the merchant (124) if and when chooses to accept the Points from the customer (122) within the PSN (100), will accept points equal to the difference of the “Final posted price” (130) and the posted price. The points accepted by the merchant (124) is
Points (accepted) by the Merchant = PP - FPP.

In one embodiment of the present invention, the PSN (100) sets up a cooperative mechanism between merchant (124) to issue and accept “points” and “Quantified Price signal” between them. This cooperative credit between merchant (124) is stored as a ratio known as (sharing ratio) which is further monitored by the PSN to ensure that each merchant (124) also issues points while he accepts points and does not abuse the system by only accepting or issuing points.

In one embodiment, the PSN (100) optionally issues incentive to the merchant (124) for maintaining a healthy sharing ratio with the PSN (100). Further, the PSN (100) optionally charge the merchant (124) a penalty for not honouring the sharing ratios and/or offer additional incentives for honouring the said sharing ratios.

In one embodiment of the present invention, the PSN (100) sets an upper and/or lower limit to the sharing ratio for merchants and/or merchants (124) in a co- operative to accept and issue “points”. For example, in one instance when the sharing ratio goes below a certain number (0.2), the merchant (124) will be unable to issue points and can only accept points and/or for example, in one instance if the ratio goes above (0.8) the merchant (124) will be unable to accept points and can only issue points. The said cooperative rule(s) is set up by the PSN (100) to ensure active participation and co-operation among merchants (124).

In one embodiment, of the present invention, the merchant (124) in the PSN (100) pay a certain price to the PSN (100) for transaction of each point within the PSN (100). This transaction cost may be calculated either dependent or independent of the price of the product (102) in the PSN (100).

In one embodiment, the transaction cost comprises of issuing of points to the customer (122) and/or acceptance of points from the customer (122). In one embodiment, the said points are transferable within the PSN (100), from one customer ((122) to another customer (122) and/or customer (122) to merchant (124).

In one embodiment of the present invention, the PSN (100) enables the merchant (124) to choose between acceptance and issuance of point simultaneously in one transaction or many related transactions, where the PSN (100) calculates that the “amount of point” to be issued and/or the “amount of point to be accepted” for each product which has the net effect of the customer (122) getting both discounts and points.

In one embodiment, the invention is an automated bargaining method using Quantified Price Signals (QPS) as monetizable points for processing a negotiation between a customer (122) and a merchant (124) in a price signal network (100) comprising,
a) Receiving both the reserve price and a posted price (104) of a product (102) from the merchant (124),
b) Calculating the Merchant Price Sensitivity Score (PSS(m)) (112) of the merchant (124) for product(s) (102) based on the merchant propensity of issuing and accepting points (106) and/or merchants history of “Seller Signal Patterns” (108) using information gathered and stored in the PSN (110),
c) Receiving an interest from the customer to negotiate (114) on any product (102) that is put to offer for sale from merchant (124) in step (a),
d) Calculating the Customer Price Sensitivity Score (PSS(c)) (120) of the customer (122) for the product(s) (102) based on the customers propensity of accepting and using points (116) and/or customers history of “Buyer Signal Patterns” (118) using information gathered and stored in the PSN (110),
e) Providing an automatic Final Posted Price (FPP) (130) to the customer (122) that is numerically in between the merchant’s reserve price and posted price to the customer (122) in the PSN (100) to enable arrive at a mutually acceptable price and to close the transaction,
f) Enabling the customer to spend and the same amount of points (128) in lieu of the discount amount as calculated by the formula
Points accepted by merchant = Discount = Posted Price – Final Posted Price (130), and
g) Enabling the customer to receive and the merchant to issue the same amount of points (126) as calculated by the PSN (100) in lieu of “excess surplus”.

In one embodiment, the auction method in the present application is instant, which do not necessarily require both parties i.e., the merchant (124) and the customer (122) to actively participate in an interactive multi-step process in the price negotiation.

In one embodiment, the auction methods comprise an interactive multi-step negotiation to improve performance, user experience and satisfaction between the participating parties. Additionally, the auction method in the present application is based on past history of discounting or premium buying or selling signal patterns.

In one embodiment the present invention, is used in any of the transaction environment, such as in shops or point of sale counters or in an e-commerce site, wherein after “receiving an interest from the customer” (114), the basic details of the customer (122) is captured with permission to access the customer (122) data to calculate the PSS(c) (120) in the PSN (100).

In one embodiment of the present invention, at the time of sale, the PSN (100) automatically, calculates the Final posted price (130) of the merchant (124) and is offered to the customer (122). The way of offering may be as displaying to the customer (122) in the e-commerce site or by way of a software application at the point of sale store or by any other device known in the art for communicating prices to the customer (122).

In one embodiment of the present invention, the “Final posted price” (130) calculated by the PSN (100) for every product (102) is communicated to the customer (122), prior to the sale of the said product (102) and/or after the sale of the said product (102) to the customer (122) via various online and offline communication methods known in the art at that time.

In one embodiment of the present invention, the Price Signal Network (PSN) (100) is used by merchants (124) to create multiple profiles or personas to segregate pricing information and strategies for discounting verses issuing points for one or more group or one or more sub-group of customers (122). Similarly, the PSN (100) provides means and methods for customer (122) to create multiple profiles or personas to indicate and differentiate to the PSN (100) the price optimising behaviour of the customer from quality seeking behaviour of the customer for various products.

In one embodiment, the PSS(c) (120) and PSS(m) (112) are calculated for groups of one or more customer (122) or merchants (124) respectively.

In one of the embodiments of the present invention, although the “Quantified Price Sensitivity Score” and the “Final Posted price” (130) of the product is independent of the product category or type, the PSN (100) is made more accurate with additional and optional parameters such as and not limited to the purchase probability of a product (102) and/or the supply/demand for said product (102). In this scenario, the Final posted price (130) is additionally customized to the individual product (102), for individual merchant (124) and for the individual customer (122).

In one embodiment, PSS(c) (120) and PSS(m) (112) may be calculated at various points of time in history, and may be denoted as PSS(t,c) or PSS(t,m) to identify a PSS at that point of time for the customer (122) or merchant (124). Further, various mathematical combinations of the historical versions of PSS(t,c) and PSS(t, m) may be enhanced along with other mathematical combinations of buyer and seller signal patterns and other additional parameters as listed above, to calculate a (120) or PSS(m) (112) at a given instant. This calculation may use various versions of Markov processes or other methods known in the art to calculate the present PSS from the PSS calculated at a historic time (t)thereby deriving an enhanced PSS.

In one embodiment of the present invention, the PSN (100) uses empirical distributions of points against any of the complex parameters listed in this application, from the QPS history of both the merchant (124) and the customer (122), and an appropriate divergence metric is used to calculate PSS, that is more accurate in establishing an automated bargain mechanism for each customer (122) and merchant (124) and for each product (102) offered for sale.

In one embodiment, the said complex scenarios are captured, executed and/or implemented via present and emerging technologies such as statistical models using artificial intelligence or the like.

In one embodiment the present invention, provides a means for engaging in price discrimination for each product (102) put on offer for sale by the merchant (124) in the PSN (100), wherein the merchant (124) issues or accepts a part of the “merchant surplus” in the form of points based on the individual discount seeking behaviour or price sensitivity of the customer (122). For example, when the customer (122) seeks discount, the merchant (124) accepts points and when the customer (122) or merchant (124) prefers to signal quality over price, the merchant (124) issues points. Hence, the PSN (100) provides qualitative customer categorization and quantitative customer categorization (Customer Price Sensitivity Score (PSS(c)) (120)).

In one embodiment, the automated bargaining method using Quantified Price Signals (QPS) as monetizable points finds application in the field of airlines, crypto-currencies and various types of exchanges (stock, commodity, currency etc) and/or the like, which has a scope of bargain during transaction between two entities.

In one embodiment, of the present invention, the PSN (100) may be implemented as a set of smart contracts in a block chain application, where the merchants (124), the customer (122) are participating entities in the price signal network (100), which is by itself implemented as a smart contract in a suitable block chain.

In particular applicants have recognized that the use of one or more functions in a PSN (100) to transform and personalize the negotiation experience for multiple differently-situated participants to facilitate a competitive and symbiotic environment between these participating merchants (124) and customers (122) resulting in overall rational prices for customers (122) and increased demand for merchant (124).

In one embodiment, the present invention provides the ability to maximise “customer surplus” and the “merchant surplus” simultaneously to the extent possible by seeking to understand the purchasing behaviour of the customer (122) and the promotional behavior customer (122) of the merchant (124) using quantified price signals from the history of purchases across the price signal network (100).

Although the invention has been described in terms of preferred embodiment, it is not limited thereto. Those skilled in this technology can make various alterations and modifications without departing from the scope and spirit of the invention. Therefore the scope of the invention shall be defined and protected by the following claims and their equivalents.

The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein, is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
(100) - Price Signal Network
(102) - Product
(104) - Receive reserve price and a posted price of the product; (106) - Merchant propensity of issuing and accepting points
(108) - Merchants history of “Seller Signal Patterns”
(110) - information gathered and stored in the PSN
(112) - calculated Merchant Price Sensitivity Score (PSS(m))
(114) - Received interest from the customer to negotiate on any product
(116) - customers’ propensity of accepting and using points
(118) - customers’ history of “Buyer Signal Patterns”
(120) - calculated Customer Price Sensitivity Score (PSS(c))
(122) - Customer
(124) - Merchant
(126) - Enabling the customer to receive and the merchant to issue the same amount of points as calculated by the PSN in lieu of “excess surplus”
(128) - Enabling the customer to spend and the merchant to accept same amount of points in lieu of discount
(130) - Final Posted Price (FPP) provided to the customer and Merchant

Claims

An automated bargaining method using Quantified Price Signals (QPS) as monetizable points for processing a negotiation between a customer (122) and a merchant (124) in a price signal network (100) (PSN) comprising:
a) Receiving both the reserve price and a posted price (104) of a product (102) from the merchant (124),
b) Calculating the Merchant Price Sensitivity Score (PSS(m)) (112) of the merchant (124) for product(s) (102) based on the merchant propensity of issuing and accepting points (106) and/or merchant’s history of “Seller Signal Patterns” (108) using information gathered and stored in the PSN (110),
c) Receiving an interest from the customer to negotiate (114) on any product (102) that is put to offer for sale from merchant (124) in step (a),
d) Calculating the Customer Price Sensitivity Score (PSS(c)) (120) of the customer (122) for the product(s) (102) based on the customers propensity of accepting and using points (116) and/or customers history of “Buyer Signal Patterns” (118) using information gathered and stored in the PSN (110),
e) Providing an automatic Final Posted Price (FPP) (130) to the customer (122) that is numerically in between the merchant’s reserve price and posted price to the customer (122) in the PSN (100) to enable arrive at a mutually acceptable price and to close the transaction,
f) Enabling the customer to spend and the merchant to accept the same amount of points (128) in lieu of the discount amount as calculated by the formula
Points accepted by merchant = Discount = Posted Price – Final Posted Price (130), and
g) Enabling the customer to receive and the merchant to issue the same amount of points (126) as calculated by the PSN (100) in lieu of “excess surplus”.
An automated bargaining method according to claim 1, wherein the propensity of issuing and accepting points (106) for each merchant (124) is obtained by the data pertaining to the “Seller Signal Patterns” (108) of the merchant (124) and the propensity of accepting and using points (116) of each customer (122) is calculated by the data pertaining to the “Buyer Signal Patterns” (118) of the customer (122) accessed by the PSN (100).
An automated bargaining method using Quantified Price Signals (QPS) as monetizable points for processing a negotiation between a customer (122) and a merchant (124) in a price signal network (100) (PSN) comprising,
a) Receiving both the reserve price and a posted price (104) of a product (102) from the merchant (124),
b) Calculating the Merchant Price Sensitivity Score (PSS(m)) (112) of the merchant (124) for product(s) (102) based on the merchant propensity of issuing and accepting points (106) using information gathered and stored in the PSN (110),
c) Receiving an interest from the customer to negotiate (114) on any product (102) that is put to offer for sale from merchant (124) in step (a),
d) Calculating the Customer Price Sensitivity Score (PSS(c)) (120) of the customer (122) for the product(s) (102) based on the customers propensity of accepting and using points (116) using information gathered and stored in the PSN (110),
e) Providing an automatic Final Posted Price (FPP) (130) to the customer (122) that is numerically in between the merchant’s reserve price and posted price to the customer (122) in the PSN (100) to enable arrive at a mutually acceptable price and to close the transaction,
f) Enabling the customer to spend and the merchant to accept the same amount of points (128) in lieu of the discount amount as calculated by the formula
Points accepted by merchant = Discount = Posted Price – Final Posted Price (130), and
g) Enabling the customer to receive and the merchant to issue the same amount of points (126) as calculated by the PSN (100) in lieu of “excess surplus”.
An automated bargaining method according to claim 3, wherein the “Final Posted Price” (130) calculated by the PSN (100) comprises optional parameters such as “Buyer Signal Patterns” (118) of the customer (122) and “Seller Signal Patterns” (108) of the merchant (124) such as not limited to the actual product, product type, merchant, merchant type and various product, customer (122) and merchant (124) identification parameters.
An automated bargaining method according to claim 1 and claim 3, wherein receiving both the “reserve price” and a “posted price” are provided by the merchant (124) for each product (102) and at regular intervals of time to the PSN (100).
An automated bargaining method according to claim 1 and claim 3, wherein “Final Posted Price” (130) calculated by the PSN (100) is in equilibrium that is mutually acceptable for both the merchant (124) and the customer (122).
An automated bargaining method according to claim 1 and claim 3, wherein “Final Posted Price” (130) calculated by the PSN (100) is communicated to the customer (122) via offline and/or online communication channels.
An automated bargaining method according to claim 1 and claim 3, wherein the PSN (100) charges transaction cost to customer (122) and/or the merchant (124) for acceptance, issuance and redemption of the points within the PSN (100).
An automated bargaining method according to claim 1 and claim 3, wherein the Merchant Price Sensitivity Score (PSS(m)) (112) and the Customer Price Sensitivity Score (PSS(c)) (120) is represented as numeral value, textual value, colour codes, tags and/or n-tuples with multiple types and values.