WO2020226565A1 - Method and apparatus for a purchase using individualized article information readable from the article to determine an individual price - Google Patents

Method and apparatus for a purchase using individualized article information readable from the article to determine an individual price Download PDF

Info

Publication number
WO2020226565A1
WO2020226565A1 PCT/SE2020/050472 SE2020050472W WO2020226565A1 WO 2020226565 A1 WO2020226565 A1 WO 2020226565A1 SE 2020050472 W SE2020050472 W SE 2020050472W WO 2020226565 A1 WO2020226565 A1 WO 2020226565A1
Authority
WO
WIPO (PCT)
Prior art keywords
article
data
purchased
discount
individual
Prior art date
Application number
PCT/SE2020/050472
Other languages
French (fr)
Inventor
Joachim Samuelsson
Original Assignee
Crunchfish Proximity Ab
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Crunchfish Proximity Ab filed Critical Crunchfish Proximity Ab
Publication of WO2020226565A1 publication Critical patent/WO2020226565A1/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/20—Point-of-sale [POS] network systems
    • G06Q20/201—Price look-up processing, e.g. updating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/20—Point-of-sale [POS] network systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
    • G07G1/00—Cash registers
    • G07G1/0009—Details of the software in the checkout register, electronic cash register [ECR] or point of sale terminal [POS]
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
    • G07G1/00—Cash registers
    • G07G1/12—Cash registers electronically operated
    • G07G1/14—Systems including one or more distant stations co-operating with a central processing unit

Definitions

  • the present invention generally relates to computerized equipment and functionality for improved handling of articles in physical stores in terms of
  • the invention relates to a computerized method of processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store.
  • the invention also relates to an associated computerized system.
  • a similar problem may arises for certain batches or other subgroups of articles of a certain type.
  • a certain production batch may be subject to a certain minor quality impairment such as, for instance, slight discoloration, finish artefacts, mounting misalignment, etc.
  • a mildly impaired batch of articles may still represent a potential usability, but nevertheless be rejected by customers in favor of articles from normal batches and are therefore likely to remain unsold.
  • the present inventor has identified both the need for and the benefits of a computerized manner of processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store. This serves to offer improvements in sustainability and/or traceability of articles and to mitigate the drawbacks of remaining unsold articles.
  • a first aspect of the present invention is a computerized method of processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store, wherein the method comprises:
  • identifying individualized article information in the data read from the data carrier wherein the individualized article information pertains to the individual article to be purchased and is potentially different from individualized article information of other individual articles of said same article type; making one or more computer resource enquiries to determine an individual price for the article to be purchased based on data in the identified collective article information as well as data in the identified individualized article information; and causing performance of a payment transaction for which a buyer of the article to be purchased is charged the determined individual price.
  • the computerized method more specifically involves making a first computer resource enquiry to retrieve a nominal price for the article to be purchased based on the data in the identified collective article information, which may comprise an article number for the article type.
  • the computerized method further involves making a second computer resource enquiry to determine a possible deviation from the nominal price based on the data in the identified individualized article information, which for instance may comprise one or more of a manufacturing date, a manufacturing time, a best-before date, a best-before time, a batch number for articles manufactured in the same batch, and a subgroup identifier which is common to a subgroup of all articles of said type.
  • the computerized method determines the individual price for the article to be purchased based on the retrieved nominal price, as modified, when applicable, by the determined deviation.
  • the flexibility is provided by the second computer resource enquiry; since it operates on individualized article information in contrast to collective article information, the deviation from the nominal (collective) price can be defined in a virtually unlimited number of different ways to take into account factors such as article age or article subgroup (e.g. production batch) to obtain an improved effect in sustainability and/or traceability.
  • article age or article subgroup e.g. production batch
  • the data carrier is a machine-readable optical code, such as for instance a QR (Quick Response) code.
  • QR Quick Response
  • the individual price for the article to be purchased may be determined as a nominal price subject to a discount, the discount being based on the data in the identified individualized article information.
  • Use cases that are believed to be particularly beneficial for improved sustainability by reducing the fraction of articles that remain unsold include applying the computerized method such that a discount is made available for older articles, possibly such that the discount grows bigger as the articles grow older, or such that a discount is made available for articles that are close to their best-before date, possibly such that the discount grows bigger the closer to the best-before date.
  • Still other use cases that are believed to be particularly beneficial for improved sustainability in this regard involve applying the computerized method such that a certain discount is made available close to the best-before date, whereas a bigger and better discount is made available after the best-before date.
  • Use cases that are believed to be particularly beneficial for improved traceability by reducing the fraction of articles that remain unsold include applying the computerized method such that it comprises analyzing the individualized article information to determine whether it identifies the article to be purchased as belonging to a certain subgroup of articles of the same article type. It is recalled that the analyzed individualized article information may comprise at least one of a batch number or lot number for articles manufactured in the same batch or lot, and a subgroup identifier which is common to a subgroup of all articles of the article type in question. If the article to be purchased is identified as belonging to the certain subgroup, the
  • computerized method determines a discount associated with the certain subgroup, and applies the determined discount when determining the individual price for the article to be purchased.
  • embodiments referred to above further causes performance of a compensation transaction for which a manufacturer or distributor of the article to be purchased financially compensates a merchant of the physical store for at least a part of the determined discount.
  • This makes the approach even more attractive in terms of sustainability and/or traceability, since it introduces motivation not only for the end customer (the buyer of the article in question) but also for the store merchant.
  • the computerized method according to any of the embodiments referred to above may further involve presenting the determined individual price for the article to be purchased to the buyer prior to causing performance of the payment transaction. This is beneficial for several reasons. It will make store customers aware of the existence of the approach according to the computerized method, which in turn will boost the approach in terms of sustainability and/or traceability.
  • the computerized method is performed in or by a computerized point-of-sale system in the physical store. In other embodiments, without limitation, the computerized method is performed in or by a mobile communication device.
  • a second inventive aspect is a computerized apparatus for processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store.
  • the apparatus comprises a reader device and a processing device.
  • the processing device is configured for performing the method according to the first aspect as referred to above, including any or all of its embodiments.
  • locution“a data carrier provided on the article to be purchased” shall be understood to include any of the following options, without limitation: the data carrier being printed onto the article, the data carrier being affixed as a label, tag or other unit onto the article, the data carrier being printed onto a packaging of the article, the data carrier being affixed as a label, tag or other unit onto or within the packaging of the article, and the data carrier being attached as a label, tag or other unit in a string to the article or its packaging.
  • a reference to an entity being“designed for” doing something, or“capable of’ doing something in this document is intended to mean the same as the entity being “arranged for”,“configured for” or“adapted for” doing this very something, and vice versa.
  • Fig 1 illustrates an embodiment of a computerized apparatus for processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store.
  • Fig 2 illustrates an alternative embodiment of the computerized apparatus.
  • Fig 3 illustrates another embodiment of the computerized apparatus.
  • Fig 4 illustrates yet another embodiment of the computerized apparatus.
  • Fig 5 illustrates still another embodiment of the computerized apparatus.
  • Fig 6 illustrates an embodiment of a computerized method of processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store.
  • Fig 7 illustrates another embodiment of the computerized method.
  • Fig 8 illustrates yet another embodiment of the computerized method.
  • Fig 1 illustrates a computerized apparatus 100 for processing a purchase of an individual article 132 in a physical store 140.
  • the individual article 132 is selected or selectable by a potential buyer 102 among a plurality of articles 130 of a same article type.
  • the articles may, for instance, be dairy products, bakery products, meat, vegetables, fruit, snacks, drinks, deli products, etc, as well as products that are not related to food or drinks.
  • More samples of the articles 130 of the same article type may be stored external to the store 140, such as in a remote warehouse 142 operated by the store merchant, or by a distributor or a manufacturer of the article type in question.
  • the computerized apparatus 100 has a reader device 110 and a processing device 120.
  • the processing device 120 is configured for receiving, from the reader device 110, data 152 that has been read 151 from a data carrier 150 provided on the article 132 to be purchased, and for identifying collective article information 160 in the data 152 read from the data carrier.
  • respective data carriers like data carrier 150 for the individual article 132 will be provided not only on the individual article 132 but rather on all articles 130.
  • the collective article information 160 is common to articles of the same article type, i.e. to all articles 130 in the situation shown in Fig 1, and comprises article number information but no price information.
  • the data carriers of all the articles 130 will all contain the same collective article information 160, which may typically comprise data 162 in the form of an article number for the article type in question.
  • the article number may, for instance, be provided in European Article Number (EAN) or Global Trade Item Number (GTIN) format.
  • EAN European Article Number
  • GTIN Global Trade Item Number
  • the processing device 120 of the computerized apparatus 100 is further configured for identifying individualized article information 170 in the data 152 read from the data carrier at 151, wherein the individualized article information pertains to the individual article 132 to be purchased and is potentially different from
  • Data 172 in the identified individualized article information 170 may typically comprise one or more of a manufacturing date, a manufacturing time, a best-before date (or expiry date), a best-before time (or expiry time), a batch number or lot number for articles manufactured in the same batch or lot, and a subgroup identifier which is common to a subgroup of all articles 130 of said type (i.e. less than all articles 130).
  • the data carrier 150 is a machine-readable optical code, such as for instance a QR (Quick Response) code.
  • QR Quick Response
  • the machine-readable optical code may be provided on or at the article 132 in any suitable manner, such as printed onto the article 132, affixed as a label onto the article 132, printed onto a packaging of the article 132, affixed as a label onto the packaging of the article 132, attached as a label in a string to the article 132 or its packaging, etc.
  • the data carrier 150 may be an RFID tag, an NFC chip, etc., again suitably provided on or at the article 132.
  • the data on the data carrier 150 may, for instance, be in the form of a Uniform Resource Identifier (URI), Uniform Resource Name (URN) or Uniform Resource Locator (URL).
  • URI Uniform Resource Identifier
  • URN Uniform Resource Name
  • URL Uniform Resource Locator
  • the GS1 Digital Link URI format may be used in advantageous embodiments, wherein the collective article information 160 and the individualized article information 170 are attributes of a GS1 Digital Link URI, encoded in for instance a GS1 QR Code, GS1 DataMatrix or GS1 DataBar that make up the data carrier 150.
  • the processing device 120 makes a computer resource enquiry 180 to determine an individual price 138 for the article 132 to be purchased based on the data 162 in the identified collective article information 160, as well as data 172 in the identified individualized article information 170.
  • the computer resource enquiry 180 is referred to as GetPrice in Fig 1, has the data 162 in the identified collective article information 160 as well as the data 172 in the identified individualized article information 170 as arguments, and is directed at a computer resource 181.
  • the GetPrice retrieval 180 returns the individual price 138 for the article 132 to be purchased.
  • the computer resource 181 is local to the store 140; it may however alternatively be an external computer resource 181’, as is indicated by hatched lines for computer resource enquiry 180’ in Fig 1.
  • the external computer resource 181’ may for instance be a web server resource or a cloud server resource.
  • the computer resource enquiry 180’ may be made by invoking a GS1 Digital Link URI encoded or stored in the data carrier 150, the GS1 Digital Link URI thus pointing at the web server resource or cloud server resource in question.
  • the processing device 120 then causes performance of a payment transaction
  • Fig 2 illustrates an alternative embodiment of the computerized apparatus 100.
  • the processing device 120 is configured for making a first computer resource enquiry 182 to a first computer resource 183 so as to retrieve a nominal price 134 for the article 132 to be purchased based on the data 162 in the identified collective article information 160 (e.g. article number).
  • the first computer resource enquiry 182 is made as a GetNomPrice retrieval 182, taking the data 162 in the identified collective article information 160 as argument and returning the nominal (collective) price 134 of the article 132 to be purchased.
  • the processing device 120 is furthermore configured for making a second computer resource enquiry 184 to a second computer resource 185 so as to determine a possible deviation 136 from the nominal price 134 based on the data 172 in the identified individualized article information 170 (e.g., manufacturing date/time, best- before date/time (or expiry date/time), batch number/subgroup identifier, etc).
  • the second computer resource enquiry 184 is made as a GetDeviation retrieval 184, taking the data 172 in the identified individualized article information 170 and possibly also the data 162 in the identified collective article information 160 as arguments (attributes), and returning the deviation 136 to be applied to the nominal price 134 of the article 132 to be purchased.
  • the processing device 120 is configured for determining the individual price 138 for the article 132 to be purchased based on the retrieved nominal price 134, as modified, when applicable, by the determined deviation 136.
  • the processing device 120 is configured for making the second computer resource enquiry 184 by sending a request to the remote server resource 185 via a communication interface 122 of the computerized apparatus 100 (see Fig 3) and the broadband communication network 110.
  • the request is a first part of the GetDeviation retrieval 184 and includes the data 172 in the identified individualized article information 170 and possibly also the data 162 in the identified collective article information 160.
  • the processing device 120 is further configured for receiving a response to the request from the remote server resource 185 via the communication interface 122.
  • the response is a second part of the GetDeviation retrieval 184 and includes the deviation 136 from the nominal price 134, when applicable.
  • the processing device 120 may be configured for enquiring a local store database 183 using the data 162 in the identified collective article information 160.
  • the enquiry is a first part of the GetNomPrice retrieval 182.
  • the processing device 120 is further configured, in response, for receiving the nominal price 134 for the article to be purchased from the local store database 183.
  • the receiving is a second part of the GetNomPrice retrieval 182 and includes the nominal price 134.
  • the computerized apparatus 100 may advantageously be comprised in a computerized point-of-sale system 310 in the physical store 140.
  • the buyer 102 will bring the selected individual article 132 to be purchased from the spot where it is marketed (together with the other articles 130 of the same article type) in the store 140, to the computerized point-of-sale system 310 typically being located at a cash register or checkout area of the store 140.
  • the reader device 110 may, when appropriate, be implemented by an existing scanner device capable of scanning, for instance, QR codes from printed coupons or the display screens of mobile devices.
  • the processing device 120 may be implemented by an existing processing unit in the computerized point-of-sale system 310, being appropriately (re-)programmed to perform its technical functionality as described in this document.
  • Such a processing unit may, for instance, be a microcontroller, CPU, DSP, FPGA, ASIC, etc.
  • the computerized apparatus 100 may alternatively be comprised in or as a mobile communication device 320.
  • the mobile computing device 320 may, for instance, be a mobile phone, tablet computer, personal digital assistant, smart glasses, smart watch or smart bracelet.
  • the mobile communication device 320 may implement the reader device 110 as well as the processing device 120, similar to the description above for Fig 4. For instance, a camera and an appropriately
  • a programmed image scanner application program in the mobile communication device 320 may implement the reader device 110, whereas a processing unit in the form of, for instance, a microcontroller, CPU, DSP, FPGA or ASIC together with an appropriately programmed application program may implement the processing device 120.
  • Fig 6 illustrates an embodiment of a computerized method 200 of processing a purchase of an individual article 132, selected among a plurality of articles 130 of a same article type, in a physical store 140.
  • the computerized method 200 comprises reading data 152 from a data carrier 150 provided on the article 132 to be purchased.
  • the computerized method 200 comprises identifying collective article information 160 in the data 152 read from the data carrier 150, as seen at 151 in Figs 1, 2, 4 and 5. It is recalled that the collective article information 160 is common to articles 130 of the same article type and comprises article number information but no price information.
  • the computerized method 200 comprises identifying individualized article information 170 in the data 152 read from the data carrier 150.
  • the individualized article information 170 pertains to the individual article 132 to be purchased and is potentially different from individualized article information of other individual articles 130 of the same article type.
  • the computerized method 200 then comprises making 240 one or more computer resource enquiries (cf. 180-180’ in Fig 1 and 182-184 in Fig 2) to determine 250 an individual price 138 for the article 132 to be purchased based on data 162 in the identified collective article information 160 as well as data 172 in the identified individualized article information 170.
  • the computerized method 200 finally comprises causing 260 performance of a payment transaction (cf. 190 in Fig 1 and Fig 2), for which a buyer 102 of the article 132 to be purchased is charged the determined individual price 138.
  • some embodiments involve invocation of a GS1 Digital Link URI which is encoded or stored in the data carrier 150 and points at for instance a web server resource or a cloud server resource (cf. external computer resource 181’ in Fig 1).
  • the GS1 Digital Link URI may contain both the collective article information 160 and the individualized article information 170 as attributes. This means that the step 220 of identifying the collective article information 160 and the step 230 of identifying the individualized article information 170 may be performed by the external computer resource 181’ when a GS1 Digital Link URI has been read by a local reader device (cf. 110 in Fig 1) from the data carrier 150 in step 210 and invoked by a local processing device (cf. 120 in Fig 1).
  • the external computer resource 181’ may identify the collective article information 160 and the individualized article information 170 in the attributes of the invoked GS1 Digital Link URI and use them to make 240 one or one or more computer resource enquiries (locally within the external computer resource 181’, or to another external computer resource) in order to determine 250 the individual price 138 for the article 132 to be purchased.
  • Fig 7 illustrates a refined embodiment of the computerized method 200.
  • the computerized method 200 in Fig 7 also comprises a step 255 of presenting at least either the determined individual price 138 or the deviation/discount 136 for the article 132 to be purchased to the buyer 102, prior to causing performance 260 of the payment transaction 190.
  • Advantages of this refined embodiment have been referred to in the summary section of this document.
  • a corresponding refined embodiment 100’ of the computerized apparatus further comprises a display device 128 (see Fig 3), wherein the processing device 120 is further configured for causing the display device 128 to present at least either the determined individual price 138 or the deviation 136 for the article 132 to be purchased to the buyer 102, prior to causing performance of the payment transaction 190.
  • Fig 8 illustrates another refined embodiment of the computerized method 200.
  • the computerized method 200 in Fig 8 also comprises a step 270 of causing performance of a compensation transaction 192 (see Fig 2) for which a manufacturer or distributor of the article 132 to be purchased financially compensates a merchant of the physical store 140 for at least a part of the determined discount 136.
  • Advantages of this refined embodiment have been referred to in the summary section of this document.
  • the processing device 120 is further configured for performing the functionality of step 270 by making a CompMerch transaction request 191 at the payment service 192 (or another payment service, refund service or crediting service), taking the deviation/discount 136 as argument. This is seen in Fig 2.
  • the processing device 120 is configured for determining 250 the individual price 138 for the article 132 to be purchased as a nominal price 134 subject to a discount 136, wherein the discount 136 is based on the data 172 in the identified individualized article information 170.
  • the discount 136 is a special form of the aforementioned deviation 136.
  • a first use case that is believed to be particularly beneficial for improved sustainability by reducing the fraction of articles that remain unsold involves applying the computerized apparatus 100 and method 200 such that a discount is made available for older articles, possibly such that the discount grows bigger as the articles grow older, or such that a discount is made available for articles that are close to their best- before date, possibly such that the discount grows bigger the closer to the best-before date.
  • the discount 136 increases as the difference increases between a current date or time and a manufacturing date or time for the article 132 to be purchased, wherein the manufacturing date or time is indicated by the data 172 in the identified individualized article information 170.
  • a second use case that is believed to be particularly beneficial for improved sustainability involves applying the computerized apparatus 100 and method 200 such that a discount is made available for articles for which the best-before date has expired, possibly such that the discount grows bigger as time goes past the best-before date.
  • the discount 136 increases as the difference decreases between a best-before date or time for the article 132 to be purchased and a current date or time, wherein the best-before date or time is indicated by the data 172 in the identified individualized article information 170, and wherein the current date or time precedes the best-before date or time.
  • the discount 136 may increase as the difference increases between a current date or time and a best-before date or time for the article 132 to be purchased, wherein the best-before date or time is indicated by the data 172 in the identified individualized article information 170, and wherein the best-before date or time precedes the current date or time.
  • a third use case that is believed to be particularly beneficial for improved sustainability involves applying the computerized apparatus 100 and method 200 such that a certain discount is made available close to the best-before date, whereas a bigger discount is made available after the best-before date.
  • the discount 136 is determined as a function of the difference between a current date or time and a best-before date or time for the article 132 to be purchased, wherein the best-before date or time is indicated by the data 172 in the identified individualized article information 170.
  • the discount function defines a first discount or discount increase when the current date or time precedes the best-before date or time, and a second discount or discount increase when the best- before date or time precedes the current date or time.
  • the second discount or discount increase is greater than the first discount or discount increase.
  • a fourth use case that is believed to be particularly beneficial for improved traceability by reducing the fraction of articles that remain unsold involves applying the computerized apparatus 100 and method 200 such that they comprise analyzing the individualized article information 170 to determine whether or not it identifies the article 132 to be purchased as belonging to a certain subgroup of articles of the same article type. It is recalled that the analyzed individualized article information 170 may comprise at least one of a batch number for articles manufactured in the same production batch, and a subgroup identifier which is common to a subgroup of all articles of the article type in question. If the article 132 to be purchased is identified as belonging to the certain subgroup, the computerized apparatus 100 and method 200 determine a discount associated with the certain subgroup, and apply the determined discount when determining the individual price 138 for the article 132 to be purchased.
  • a fifth use case involves adding an extra dimension to the individualized price determination.
  • the extra dimension involves taking also personal data about the buyer 102 into account when determining the individual price.
  • personal data may for instance be any of the following:
  • a celebration date (such as a birthdate) of the buyer 102;
  • a current mood or condition of the buyer 102 (as expressed for instance in a social media account of the buyer 102, or as determined by a health or physical exercise software application executed by the buyer 102);
  • a current circumstance of the buyer 102 for instance weather, traffic or other environmental conditions.
  • the personal data about the buyer 102 will have to be conveyed to the computerized apparatus 100 for processing the purchase of the individual article 132, to any of the entities 181, 18 , 183, 185 being enquired in the process of determining the individual price, or to the entity 192 that is involved in the performance of the payment transaction 190.
  • the computerized method of processing a purchase of an individual article as described in this document, and in the
  • the personal data can be retrieved in different ways.
  • the buyer 102 may enter the personal data manually at the computerized apparatus 100, or an operator (merchant) of the computerized apparatus 100 may ask the buyer 102 for the personal data and then enter them.
  • the personal data may be conveyed from a mobile communication device of the buyer 102 to the computerized apparatus 100 over short-range wireless data communication, such as Bluetooth or NFC, or from the mobile communication device of the buyer 102 to the device 100 or any of the entities 181, 18G, 183, 185 or 192 by broadband data communication over the network 110.
  • short-range wireless data communication such as Bluetooth or NFC
  • the broadband communication network 110 as referred to in this document may, for instance, be compliant with WCDMA, HSPA, GSM, UTRAN, UMTS, LTE or LTE Advanced, or alternatively wired data communication based, for instance, on TCP/IP.
  • the computer resources described in this document may be implemented by server computers, clusters of such computer devices, or cloud computing resources or services, having processing units in the form of, for instance, CPUs and/or DSPs, and being programmed to perform the respective functionalities as described in this document by the processing unit executing program instructions of computer programs.

Landscapes

  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • Development Economics (AREA)
  • Theoretical Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A computerized apparatus (100) is provided for processing a purchase of an individual article (132), selected among a plurality of articles (130) of a same article type, in a physical store (140). The apparatus has a reader device (110) and a processing device (120). The processing device (120) is configured for receiving, from the reader device (110), data (152) that has been read from a data carrier (150) provided on the article (132) to be purchased, and for identifying collective article information (160) in the data (152) read from the data carrier. The collective article information is common to articles of said same article type and comprises article number information but no price information. The processing device (120) is further configured for identifying individualized article information (170) in the data (152) read from the data carrier, wherein the individualized article information pertains to the individual article to be purchased and is potentially different from individualized article information of other individual articles of said same article type. The processing device (120) makes one or more computer resource enquiries (180; 182-184) to determine an individual price (138) for the article (132) to be purchased based on data (162) in the identified collective article information (160) as well as data (172) in the identified individualized article information (170). The processing device (120) then causes performance of a payment transaction (190) for which a buyer (102) of the article (132) to be purchased is charged the determined individual price (138).

Description

METHOD AND APPARATUS FOR A PURCHASE USING
INDIVIDUALIZED ARTICLE INFORMATION READABLE FROM THE ARTICLE TO DETERMINE AN INDIVIDUAL
PRICE
TECHNICAL FIELD
The present invention generally relates to computerized equipment and functionality for improved handling of articles in physical stores in terms of
sustainability or traceability. More specifically, the invention relates to a computerized method of processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store. The invention also relates to an associated computerized system.
BACKGROUND
Since the resources of our planet are limited and because we are facing increasing challenges to cope with over-usage of these limited resources and other environmental problems, the general society has directed much attention to
sustainability and traceability of articles in and for physical stores in recent times.
Laws and regulations require that many kinds of articles be associated with a manufacturing date, best-before date, or both. This is because product quality and safety may degrade with time. Examples of such kinds of articles are dairy products, bakery products, meat, vegetables and fruit. As is commonly known, products with a limited lifespan are commonly marked with a best-before date. This will allow customers to estimate the freshness or expected remaining usability of the products before deciding to buy them. The present inventor has identified quite severe problems and shortcomings in this regard.
For instance, if articles of the same type (such as 1 -litre packages of milk of a certain brand) have different best-before dates in the store, then many customers have a tendency of always looking for and choosing individual articles of a later best-before date over articles of the same type which have an earlier best-before date. As a result, relatively older products in the store get more and more hard to sell, and will ultimately have to be disposed of, or returned to the manufacturer/distributor.
Moreover, imposing age limits on products is a rather imprecise tool. In many cases, an article is far from useless even if the best-before date is approaching or has even lapsed. Still, since customers generally favor younger articles over older ones judging from the best-before date, the older products will be perceived as being relatively speaking over-priced compared to the younger products of the same type, and therefore remain unsold in the store.
A similar problem may arises for certain batches or other subgroups of articles of a certain type. For instance, a certain production batch may be subject to a certain minor quality impairment such as, for instance, slight discoloration, finish artefacts, mounting misalignment, etc. Again, such a mildly impaired batch of articles may still represent a potential usability, but nevertheless be rejected by customers in favor of articles from normal batches and are therefore likely to remain unsold.
For every unsold article there will be an environmental penalty due to the fact that the resources that were used to produce the article will not result in actual use of the product and therefore represent a waste of resources. In addition, every unsold article will have to be logistically handled, therefore adding on to the environmental penalty in terms of transport and storage resources.
In line with the observations above, the present inventor has identified both the need for and the benefits of a computerized manner of processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store. This serves to offer improvements in sustainability and/or traceability of articles and to mitigate the drawbacks of remaining unsold articles.
SUMMARY
It is accordingly an object of the invention to solve, eliminate, alleviate, mitigate or reduce at least some of the problems and shortcomings referred to above.
A first aspect of the present invention is a computerized method of processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store, wherein the method comprises:
reading data from a data carrier provided on the article to be purchased;
identifying collective article information in the data read from the data carrier, wherein the collective article information is common to articles of said same article type and comprises article number information but no price information;
identifying individualized article information in the data read from the data carrier, wherein the individualized article information pertains to the individual article to be purchased and is potentially different from individualized article information of other individual articles of said same article type; making one or more computer resource enquiries to determine an individual price for the article to be purchased based on data in the identified collective article information as well as data in the identified individualized article information; and causing performance of a payment transaction for which a buyer of the article to be purchased is charged the determined individual price.
The provision of such a computerized method will solve or at least mitigate one or more of the problems or drawbacks identified in the background section of this document, as will be clear from the following detailed description section and the drawings.
Advantageously, the computerized method more specifically involves making a first computer resource enquiry to retrieve a nominal price for the article to be purchased based on the data in the identified collective article information, which may comprise an article number for the article type. The computerized method further involves making a second computer resource enquiry to determine a possible deviation from the nominal price based on the data in the identified individualized article information, which for instance may comprise one or more of a manufacturing date, a manufacturing time, a best-before date, a best-before time, a batch number for articles manufactured in the same batch, and a subgroup identifier which is common to a subgroup of all articles of said type.
The computerized method then determines the individual price for the article to be purchased based on the retrieved nominal price, as modified, when applicable, by the determined deviation. This represents a highly flexible approach, which is still backwards compatible with existing solutions where the price of an article of a certain article type is based solely on collective article information, such as article number. The flexibility is provided by the second computer resource enquiry; since it operates on individualized article information in contrast to collective article information, the deviation from the nominal (collective) price can be defined in a virtually unlimited number of different ways to take into account factors such as article age or article subgroup (e.g. production batch) to obtain an improved effect in sustainability and/or traceability. The benefits of this approach will appear more clearly from the various use cases that are described in the detailed description section of this document.
In advantageous embodiments, the data carrier is a machine-readable optical code, such as for instance a QR (Quick Response) code.
Advantageously, as already briefly touched upon, the individual price for the article to be purchased may be determined as a nominal price subject to a discount, the discount being based on the data in the identified individualized article information. Use cases that are believed to be particularly beneficial for improved sustainability by reducing the fraction of articles that remain unsold include applying the computerized method such that a discount is made available for older articles, possibly such that the discount grows bigger as the articles grow older, or such that a discount is made available for articles that are close to their best-before date, possibly such that the discount grows bigger the closer to the best-before date.
Other use cases that are believed to be particularly beneficial for improved sustainability in this regard involve applying the computerized method such that a discount is made available for articles for which the best-before date has expired, possibly such that the discount grows bigger as time goes past the best-before date.
Still other use cases that are believed to be particularly beneficial for improved sustainability in this regard involve applying the computerized method such that a certain discount is made available close to the best-before date, whereas a bigger and better discount is made available after the best-before date.
Use cases that are believed to be particularly beneficial for improved traceability by reducing the fraction of articles that remain unsold include applying the computerized method such that it comprises analyzing the individualized article information to determine whether it identifies the article to be purchased as belonging to a certain subgroup of articles of the same article type. It is recalled that the analyzed individualized article information may comprise at least one of a batch number or lot number for articles manufactured in the same batch or lot, and a subgroup identifier which is common to a subgroup of all articles of the article type in question. If the article to be purchased is identified as belonging to the certain subgroup, the
computerized method determines a discount associated with the certain subgroup, and applies the determined discount when determining the individual price for the article to be purchased.
Advantageously, the computerized method according to any of the
embodiments referred to above further causes performance of a compensation transaction for which a manufacturer or distributor of the article to be purchased financially compensates a merchant of the physical store for at least a part of the determined discount. This makes the approach even more attractive in terms of sustainability and/or traceability, since it introduces motivation not only for the end customer (the buyer of the article in question) but also for the store merchant. The computerized method according to any of the embodiments referred to above may further involve presenting the determined individual price for the article to be purchased to the buyer prior to causing performance of the payment transaction. This is beneficial for several reasons. It will make store customers aware of the existence of the approach according to the computerized method, which in turn will boost the approach in terms of sustainability and/or traceability. Moreover, it gives the potential buyer of an article the option to accept or decline the purchase based on the determined individual price. In situations where the individual price includes a discount, seeing the discount in advance will likely increase the potential buyer’s motivation to actually proceed with the purchase of the article, even when it is about to expire (or has in fact already expired), or belongs to an impaired production batch or article subgroup.
In some embodiments, without limitation, the computerized method is performed in or by a computerized point-of-sale system in the physical store. In other embodiments, without limitation, the computerized method is performed in or by a mobile communication device.
A second inventive aspect is a computerized apparatus for processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store. The apparatus comprises a reader device and a processing device. The processing device is configured for performing the method according to the first aspect as referred to above, including any or all of its embodiments.
The provision of such a computerized apparatus will solve or at least mitigate one or more of the problems or drawbacks identified in the background section of this document, as will be clear from the following detailed description section and the drawings.
Other aspects, objectives, features and advantages of the disclosed embodi ments will appear from the following detailed disclosure, from the attached dependent claims as well as from the drawings. Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein.
The locution“a data carrier provided on the article to be purchased” shall be understood to include any of the following options, without limitation: the data carrier being printed onto the article, the data carrier being affixed as a label, tag or other unit onto the article, the data carrier being printed onto a packaging of the article, the data carrier being affixed as a label, tag or other unit onto or within the packaging of the article, and the data carrier being attached as a label, tag or other unit in a string to the article or its packaging.
All references to "a/an/the [element, device, component, means, step, etc]" are to be interpreted openly as referring to at least one instance of the element, device, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
A reference to an entity being“designed for” doing something, or“capable of’ doing something in this document is intended to mean the same as the entity being “arranged for”,“configured for” or“adapted for” doing this very something, and vice versa.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig 1 illustrates an embodiment of a computerized apparatus for processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store.
Fig 2 illustrates an alternative embodiment of the computerized apparatus.
Fig 3 illustrates another embodiment of the computerized apparatus.
Fig 4 illustrates yet another embodiment of the computerized apparatus.
Fig 5 illustrates still another embodiment of the computerized apparatus.
Fig 6 illustrates an embodiment of a computerized method of processing a purchase of an individual article, selected among a plurality of articles of a same article type, in a physical store.
Fig 7 illustrates another embodiment of the computerized method.
Fig 8 illustrates yet another embodiment of the computerized method.
DETAILED DESCRIPTION
The disclosed embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which certain embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout. Fig 1 illustrates a computerized apparatus 100 for processing a purchase of an individual article 132 in a physical store 140. The individual article 132 is selected or selectable by a potential buyer 102 among a plurality of articles 130 of a same article type. The articles may, for instance, be dairy products, bakery products, meat, vegetables, fruit, snacks, drinks, deli products, etc, as well as products that are not related to food or drinks.
More samples of the articles 130 of the same article type may be stored external to the store 140, such as in a remote warehouse 142 operated by the store merchant, or by a distributor or a manufacturer of the article type in question.
The computerized apparatus 100 has a reader device 110 and a processing device 120. The processing device 120 is configured for receiving, from the reader device 110, data 152 that has been read 151 from a data carrier 150 provided on the article 132 to be purchased, and for identifying collective article information 160 in the data 152 read from the data carrier. Even though not shown in Fig 1, respective data carriers like data carrier 150 for the individual article 132 will be provided not only on the individual article 132 but rather on all articles 130. The collective article information 160 is common to articles of the same article type, i.e. to all articles 130 in the situation shown in Fig 1, and comprises article number information but no price information. Accordingly, the data carriers of all the articles 130 will all contain the same collective article information 160, which may typically comprise data 162 in the form of an article number for the article type in question. The article number may, for instance, be provided in European Article Number (EAN) or Global Trade Item Number (GTIN) format.
The processing device 120 of the computerized apparatus 100 is further configured for identifying individualized article information 170 in the data 152 read from the data carrier at 151, wherein the individualized article information pertains to the individual article 132 to be purchased and is potentially different from
individualized article information of other individual articles 130 of said same article type. Data 172 in the identified individualized article information 170 may typically comprise one or more of a manufacturing date, a manufacturing time, a best-before date (or expiry date), a best-before time (or expiry time), a batch number or lot number for articles manufactured in the same batch or lot, and a subgroup identifier which is common to a subgroup of all articles 130 of said type (i.e. less than all articles 130).
In the disclosed embodiment, the data carrier 150 is a machine-readable optical code, such as for instance a QR (Quick Response) code. Other types of machine- readable optical codes are however also conceivable. The machine-readable optical code may be provided on or at the article 132 in any suitable manner, such as printed onto the article 132, affixed as a label onto the article 132, printed onto a packaging of the article 132, affixed as a label onto the packaging of the article 132, attached as a label in a string to the article 132 or its packaging, etc. In other embodiments, the data carrier 150 may be an RFID tag, an NFC chip, etc., again suitably provided on or at the article 132.
The data on the data carrier 150 may, for instance, be in the form of a Uniform Resource Identifier (URI), Uniform Resource Name (URN) or Uniform Resource Locator (URL). The GS1 Digital Link URI format may be used in advantageous embodiments, wherein the collective article information 160 and the individualized article information 170 are attributes of a GS1 Digital Link URI, encoded in for instance a GS1 QR Code, GS1 DataMatrix or GS1 DataBar that make up the data carrier 150.
In the embodiment in Fig 1, the processing device 120 makes a computer resource enquiry 180 to determine an individual price 138 for the article 132 to be purchased based on the data 162 in the identified collective article information 160, as well as data 172 in the identified individualized article information 170. The computer resource enquiry 180 is referred to as GetPrice in Fig 1, has the data 162 in the identified collective article information 160 as well as the data 172 in the identified individualized article information 170 as arguments, and is directed at a computer resource 181. The GetPrice retrieval 180 returns the individual price 138 for the article 132 to be purchased.
In the embodiment in Fig 1, the computer resource 181 is local to the store 140; it may however alternatively be an external computer resource 181’, as is indicated by hatched lines for computer resource enquiry 180’ in Fig 1. In the latter case, the external computer resource 181’ may for instance be a web server resource or a cloud server resource. The computer resource enquiry 180’ may be made by invoking a GS1 Digital Link URI encoded or stored in the data carrier 150, the GS1 Digital Link URI thus pointing at the web server resource or cloud server resource in question.
The processing device 120 then causes performance of a payment transaction
190 for which the buyer 102 of the article 132 to be purchased is charged the
determined individual price 138. This may involve invoking a ChargeBuyer transaction 190 at a payment service 192, wherein the ChargeBuyer transaction 190 takes the determined individual price 138 as argument. As is seen in Fig 1, the payment service 192 is typically an external service in Fig 1, accessible via a broadband communication network 110. Other alternatives are however conceivable. Fig 2 illustrates an alternative embodiment of the computerized apparatus 100. Here, the processing device 120 is configured for making a first computer resource enquiry 182 to a first computer resource 183 so as to retrieve a nominal price 134 for the article 132 to be purchased based on the data 162 in the identified collective article information 160 (e.g. article number). In Fig 2, the first computer resource enquiry 182 is made as a GetNomPrice retrieval 182, taking the data 162 in the identified collective article information 160 as argument and returning the nominal (collective) price 134 of the article 132 to be purchased.
The processing device 120 is furthermore configured for making a second computer resource enquiry 184 to a second computer resource 185 so as to determine a possible deviation 136 from the nominal price 134 based on the data 172 in the identified individualized article information 170 (e.g., manufacturing date/time, best- before date/time (or expiry date/time), batch number/subgroup identifier, etc). In Fig 2, the second computer resource enquiry 184 is made as a GetDeviation retrieval 184, taking the data 172 in the identified individualized article information 170 and possibly also the data 162 in the identified collective article information 160 as arguments (attributes), and returning the deviation 136 to be applied to the nominal price 134 of the article 132 to be purchased.
Then, the processing device 120 is configured for determining the individual price 138 for the article 132 to be purchased based on the retrieved nominal price 134, as modified, when applicable, by the determined deviation 136.
In the embodiment shown in Fig 2, the processing device 120 is configured for making the second computer resource enquiry 184 by sending a request to the remote server resource 185 via a communication interface 122 of the computerized apparatus 100 (see Fig 3) and the broadband communication network 110. The request is a first part of the GetDeviation retrieval 184 and includes the data 172 in the identified individualized article information 170 and possibly also the data 162 in the identified collective article information 160. The processing device 120 is further configured for receiving a response to the request from the remote server resource 185 via the communication interface 122. The response is a second part of the GetDeviation retrieval 184 and includes the deviation 136 from the nominal price 134, when applicable.
As further seen for the embodiment of Fig 2, the processing device 120 may be configured for enquiring a local store database 183 using the data 162 in the identified collective article information 160. The enquiry is a first part of the GetNomPrice retrieval 182. The processing device 120 is further configured, in response, for receiving the nominal price 134 for the article to be purchased from the local store database 183. The receiving is a second part of the GetNomPrice retrieval 182 and includes the nominal price 134.
Advantages of the embodiment in Fig 2 have been referred to in the summary section of this document.
As can be seen in Fig 4, the computerized apparatus 100 may advantageously be comprised in a computerized point-of-sale system 310 in the physical store 140. As seen at 312, the buyer 102 will bring the selected individual article 132 to be purchased from the spot where it is marketed (together with the other articles 130 of the same article type) in the store 140, to the computerized point-of-sale system 310 typically being located at a cash register or checkout area of the store 140.
The reader device 110 may, when appropriate, be implemented by an existing scanner device capable of scanning, for instance, QR codes from printed coupons or the display screens of mobile devices. The processing device 120 may be implemented by an existing processing unit in the computerized point-of-sale system 310, being appropriately (re-)programmed to perform its technical functionality as described in this document. Such a processing unit may, for instance, be a microcontroller, CPU, DSP, FPGA, ASIC, etc.
As can be seen in Fig 5, the computerized apparatus 100 may alternatively be comprised in or as a mobile communication device 320. The mobile computing device 320 may, for instance, be a mobile phone, tablet computer, personal digital assistant, smart glasses, smart watch or smart bracelet. The mobile communication device 320 may implement the reader device 110 as well as the processing device 120, similar to the description above for Fig 4. For instance, a camera and an appropriately
programmed image scanner application program in the mobile communication device 320 may implement the reader device 110, whereas a processing unit in the form of, for instance, a microcontroller, CPU, DSP, FPGA or ASIC together with an appropriately programmed application program may implement the processing device 120.
Further alternatives than those described for Fig 4 and Fig 5 are conceivable.
Fig 6 illustrates an embodiment of a computerized method 200 of processing a purchase of an individual article 132, selected among a plurality of articles 130 of a same article type, in a physical store 140. In a first step 210, the computerized method 200 comprises reading data 152 from a data carrier 150 provided on the article 132 to be purchased.
Then, in a second step 220, the computerized method 200 comprises identifying collective article information 160 in the data 152 read from the data carrier 150, as seen at 151 in Figs 1, 2, 4 and 5. It is recalled that the collective article information 160 is common to articles 130 of the same article type and comprises article number information but no price information.
In a third step 230, the computerized method 200 comprises identifying individualized article information 170 in the data 152 read from the data carrier 150. The individualized article information 170 pertains to the individual article 132 to be purchased and is potentially different from individualized article information of other individual articles 130 of the same article type.
The computerized method 200 then comprises making 240 one or more computer resource enquiries (cf. 180-180’ in Fig 1 and 182-184 in Fig 2) to determine 250 an individual price 138 for the article 132 to be purchased based on data 162 in the identified collective article information 160 as well as data 172 in the identified individualized article information 170.
The computerized method 200 finally comprises causing 260 performance of a payment transaction (cf. 190 in Fig 1 and Fig 2), for which a buyer 102 of the article 132 to be purchased is charged the determined individual price 138.
As was explained above for Fig 1, some embodiments involve invocation of a GS1 Digital Link URI which is encoded or stored in the data carrier 150 and points at for instance a web server resource or a cloud server resource (cf. external computer resource 181’ in Fig 1). The GS1 Digital Link URI may contain both the collective article information 160 and the individualized article information 170 as attributes. This means that the step 220 of identifying the collective article information 160 and the step 230 of identifying the individualized article information 170 may be performed by the external computer resource 181’ when a GS1 Digital Link URI has been read by a local reader device (cf. 110 in Fig 1) from the data carrier 150 in step 210 and invoked by a local processing device (cf. 120 in Fig 1). The external computer resource 181’ may identify the collective article information 160 and the individualized article information 170 in the attributes of the invoked GS1 Digital Link URI and use them to make 240 one or one or more computer resource enquiries (locally within the external computer resource 181’, or to another external computer resource) in order to determine 250 the individual price 138 for the article 132 to be purchased. Fig 7 illustrates a refined embodiment of the computerized method 200. In addition to the steps described for Fig 6, the computerized method 200 in Fig 7 also comprises a step 255 of presenting at least either the determined individual price 138 or the deviation/discount 136 for the article 132 to be purchased to the buyer 102, prior to causing performance 260 of the payment transaction 190. Advantages of this refined embodiment have been referred to in the summary section of this document.
Accordingly, a corresponding refined embodiment 100’ of the computerized apparatus further comprises a display device 128 (see Fig 3), wherein the processing device 120 is further configured for causing the display device 128 to present at least either the determined individual price 138 or the deviation 136 for the article 132 to be purchased to the buyer 102, prior to causing performance of the payment transaction 190.
Fig 8 illustrates another refined embodiment of the computerized method 200. In addition to the steps described for Fig 6, the computerized method 200 in Fig 8 also comprises a step 270 of causing performance of a compensation transaction 192 (see Fig 2) for which a manufacturer or distributor of the article 132 to be purchased financially compensates a merchant of the physical store 140 for at least a part of the determined discount 136. Advantages of this refined embodiment have been referred to in the summary section of this document.
In a corresponding refined embodiment of the computerized apparatus 100, the processing device 120 is further configured for performing the functionality of step 270 by making a CompMerch transaction request 191 at the payment service 192 (or another payment service, refund service or crediting service), taking the deviation/discount 136 as argument. This is seen in Fig 2.
A number of use cases for the computerized apparatus 100 and method 200 in their intended operational environment will now be described. These use cases are considered as being particularly advantageous but shall nevertheless be understood as being exemplifying and non-limiting to the general scope of the present invention.
In these use cases, the processing device 120 is configured for determining 250 the individual price 138 for the article 132 to be purchased as a nominal price 134 subject to a discount 136, wherein the discount 136 is based on the data 172 in the identified individualized article information 170. Hence, the discount 136 is a special form of the aforementioned deviation 136.
A first use case that is believed to be particularly beneficial for improved sustainability by reducing the fraction of articles that remain unsold involves applying the computerized apparatus 100 and method 200 such that a discount is made available for older articles, possibly such that the discount grows bigger as the articles grow older, or such that a discount is made available for articles that are close to their best- before date, possibly such that the discount grows bigger the closer to the best-before date.
Accordingly, in the first use case, the discount 136 increases as the difference increases between a current date or time and a manufacturing date or time for the article 132 to be purchased, wherein the manufacturing date or time is indicated by the data 172 in the identified individualized article information 170.
A second use case that is believed to be particularly beneficial for improved sustainability involves applying the computerized apparatus 100 and method 200 such that a discount is made available for articles for which the best-before date has expired, possibly such that the discount grows bigger as time goes past the best-before date.
Accordingly, in the second use case, the discount 136 increases as the difference decreases between a best-before date or time for the article 132 to be purchased and a current date or time, wherein the best-before date or time is indicated by the data 172 in the identified individualized article information 170, and wherein the current date or time precedes the best-before date or time.
Moreover, the discount 136 may increase as the difference increases between a current date or time and a best-before date or time for the article 132 to be purchased, wherein the best-before date or time is indicated by the data 172 in the identified individualized article information 170, and wherein the best-before date or time precedes the current date or time.
A third use case that is believed to be particularly beneficial for improved sustainability involves applying the computerized apparatus 100 and method 200 such that a certain discount is made available close to the best-before date, whereas a bigger discount is made available after the best-before date.
Accordingly, in the third use case, the discount 136 is determined as a function of the difference between a current date or time and a best-before date or time for the article 132 to be purchased, wherein the best-before date or time is indicated by the data 172 in the identified individualized article information 170. The discount function defines a first discount or discount increase when the current date or time precedes the best-before date or time, and a second discount or discount increase when the best- before date or time precedes the current date or time. The second discount or discount increase is greater than the first discount or discount increase. A fourth use case that is believed to be particularly beneficial for improved traceability by reducing the fraction of articles that remain unsold involves applying the computerized apparatus 100 and method 200 such that they comprise analyzing the individualized article information 170 to determine whether or not it identifies the article 132 to be purchased as belonging to a certain subgroup of articles of the same article type. It is recalled that the analyzed individualized article information 170 may comprise at least one of a batch number for articles manufactured in the same production batch, and a subgroup identifier which is common to a subgroup of all articles of the article type in question. If the article 132 to be purchased is identified as belonging to the certain subgroup, the computerized apparatus 100 and method 200 determine a discount associated with the certain subgroup, and apply the determined discount when determining the individual price 138 for the article 132 to be purchased.
A fifth use case involves adding an extra dimension to the individualized price determination. The extra dimension involves taking also personal data about the buyer 102 into account when determining the individual price. Such personal data may for instance be any of the following:
an age of the buyer 102;
a celebration date (such as a birthdate) of the buyer 102;
a current location of the buyer 102;
a current mood or condition of the buyer 102 (as expressed for instance in a social media account of the buyer 102, or as determined by a health or physical exercise software application executed by the buyer 102); and
a current circumstance of the buyer 102, for instance weather, traffic or other environmental conditions.
In the fifth use case, the personal data about the buyer 102 will have to be conveyed to the computerized apparatus 100 for processing the purchase of the individual article 132, to any of the entities 181, 18 , 183, 185 being enquired in the process of determining the individual price, or to the entity 192 that is involved in the performance of the payment transaction 190. In the computerized method of processing a purchase of an individual article as described in this document, and in the
computerized apparatus 100, the personal data can be retrieved in different ways.
For instance, the buyer 102 may enter the personal data manually at the computerized apparatus 100, or an operator (merchant) of the computerized apparatus 100 may ask the buyer 102 for the personal data and then enter them. Alternatively, the personal data may be conveyed from a mobile communication device of the buyer 102 to the computerized apparatus 100 over short-range wireless data communication, such as Bluetooth or NFC, or from the mobile communication device of the buyer 102 to the device 100 or any of the entities 181, 18G, 183, 185 or 192 by broadband data communication over the network 110.
The broadband communication network 110 as referred to in this document may, for instance, be compliant with WCDMA, HSPA, GSM, UTRAN, UMTS, LTE or LTE Advanced, or alternatively wired data communication based, for instance, on TCP/IP.
The computer resources described in this document, particularly the ones that are external to the physical store 140, may be implemented by server computers, clusters of such computer devices, or cloud computing resources or services, having processing units in the form of, for instance, CPUs and/or DSPs, and being programmed to perform the respective functionalities as described in this document by the processing unit executing program instructions of computer programs.
The invention has mainly been described above with reference to a few embodiments. However, as is readily appreciated by a person skilled in the art, other embodiments than the ones disclosed above are equally possible within the scope of the invention, as defined by the appended patent claims.

Claims

1. A computerized method (200) of processing a purchase of an individual article (132), selected among a plurality of articles (130) of a same article type, in a physical store (140), the method comprising:
reading (210) data (152) from a data carrier (150) provided on the article (132) to be purchased;
identifying (220) collective article information (160) in the data (152) read from the data carrier (150), wherein the collective article information is common to articles of said same article type and comprises article number information but no price information;
identifying (230) individualized article information (170) in the data (152) read from the data carrier, wherein the individualized article information pertains to the individual article to be purchased and is potentially different from individualized article information of other individual articles of said same article type;
making (240) one or more computer resource enquiries (180; 182-184) to determine (250) an individual price (138) for the article (132) to be purchased based on data (162) in the identified collective article information (160) as well as data (172) in the identified individualized article information (170); and
causing (260) performance of a payment transaction (190) for which a buyer (102) of the article (132) to be purchased is charged the determined individual price (138).
2. The computerized method (200) of processing a purchase of an individual article as defined in claim 1, wherein making said one or more computer resource enquiries (180; 182-184)) to determine the individual price (138) for the article (132) to be purchased involves:
making a first computer resource enquiry (182) to retrieve a nominal price (134) for the article (132) to be purchased based on the data (162) in the identified collective article information (160);
making a second computer resource enquiry (184) to determine a possible deviation (136) from the nominal price (134) based on the data (172) in the identified individualized article information (170); and determining (250) the individual price (138) for the article to be purchased based on the retrieved nominal price (134), as modified, when applicable, by the determined deviation (136). 3. The computerized method (200) of processing a purchase of an individual article as defined in any preceding claim, wherein the data carrier (150) is a machine- readable optical code.
4. The computerized method of processing a purchase of an individual article as defined in any preceding claim, wherein the data (162) in the identified collective article information (160) comprises an article number for said article type.
5. The computerized method of processing a purchase of an individual article as defined in any preceding claim, wherein the data (172) in the identified
individualized article information (170) comprises one or more of:
a manufacturing date;
a manufacturing time;
a best-before date;
a best-before time;
a batch number for articles manufactured in the same batch; and
a subgroup identifier which is common to a subgroup of all articles of said type.
6. The computerized method of processing a purchase of an individual article as defined in any preceding claim, wherein the individual price (138) for the article to be purchased is determined (250) as a nominal price (134) subject to a discount (136), the discount (136) being based on the data (172) in the identified individualized article information (170). 7. The computerized method of processing a purchase of an individual article as defined in claim 6, wherein the discount (136) increases as the difference increases between a current date or time and a manufacturing date or time for the article to be purchased, the manufacturing date or time being indicated by said data (172) in the identified individualized article information (170). 8. The computerized method of processing a purchase of an individual article as defined in claim 6, wherein the discount (136) increases as the difference decreases between a best-before date or time for the article to be purchased and a current date or time, the best-before date or time being indicated by said data (172) in the identified individualized article information (170), the current date or time preceding the best- before date or time.
9. The computerized method of processing a purchase of an individual article as defined in claim as defined in claim 6, wherein the discount (136) increases as the difference increases between a current date or time and a best-before date or time for the article to be purchased, the best-before date or time being indicated by said data (172) in the identified individualized article information (170), the best-before date or time preceding the current date or time. 10. The computerized method of processing a purchase of an individual article as defined in claim 6, wherein the discount (136) is determined as a function of the difference between a current date or time and a best-before date or time for the article to be purchased, the best-before date or time being indicated by said data (172) in the identified individualized article information (170),
wherein the discount function defines a first discount or discount increase when the current date or time precedes the best-before date or time, and a second discount or discount increase when the best-before date or time precedes the current date or time, and
wherein the second discount or discount increase is greater than the first discount or discount increase.
11. The computerized method of processing a purchase of an individual article as defined in any of claims 1-5, further comprising:
analyzing the individualized article information (170) to determine whether or not it identifies the article (132) to be purchased as belonging to a certain subgroup of articles of said same article type; and
if the article (132) to be purchased is identified as belonging to the certain subgroup:
determining a discount associated with the certain subgroup; and applying the determined discount when determining (250) the individual price (138) for the article (132) to be purchased.
12. The computerized method of processing a purchase of an individual article as defined in claim 11, wherein the analyzed individualized article information (170) comprises at least one of a batch number for articles manufactured in the same batch, and a subgroup identifier which is common to a subgroup of all articles of said article type. 13. The computerized method of processing a purchase of an individual article as defined in any preceding claim, wherein the individual price (138) for the article to be purchased is determined (250) as a nominal price (134) subject to a discount (136), the discount (136) being based on the data (172) in the identified individualized article information (170), the method further comprising:
causing (270) performance of a compensation transaction (192) for which a manufacturer or distributor of the article to be purchased financially compensates a merchant of the physical store (140) for at least a part of the determined discount (136).
14. The computerized method of processing a purchase of an individual article as defined in any preceding claim, further comprising:
presenting (255) at least either the determined individual price (138) or the deviation (136) for the article (132) to be purchased to the buyer (102) prior to causing performance (260) of the payment transaction (190). 15. The computerized method of processing a purchase of an individual article as defined in claim 2, wherein making the second computer resource enquiry (184) comprises:
sending a request to a remote server resource (185) via a communication interface (122), the request including the data (172) in the identified individualized article information (170); and
receiving a response to the request from the remote server resource (185) via the communication interface (122), wherein the response includes the possible deviation (136) from the nominal price (134). 16. The computerized method of processing a purchase of an individual article as defined in claim 15, wherein making the first computer resource enquiry (182) comprises:
enquiring a local store database (183) using the data (162) in the identified collective article information (160); and
in response receiving the nominal price (134) for the article to be purchased from the local store database (183).
17. The computerized method of processing a purchase of an individual article as defined in any preceding claim, performed by a computerized point-of-sale system
(310).
18. The computerized method of processing a purchase of an individual article as defined in any of claims 1-16, performed by a mobile communication device (320).
19. A computerized apparatus (100) for processing a purchase of an individual article (132), selected among a plurality of articles (130) of a same article type, in a physical store (140), the apparatus comprising:
a reader device (110); and
a processing device (120), wherein the processing device (120) is configured for:
receiving, from the reader device (110), data (152) that has been read from a data carrier (150) provided on the article (132) to be purchased;
identifying collective article information (160) in the data (152) read from the data carrier, wherein the collective article information is common to articles of said same article type and comprises article number information but no price information; identifying individualized article information (170) in the data (152) read from the data carrier, wherein the individualized article information pertains to the individual article to be purchased and is potentially different from individualized article information of other individual articles of said same article type;
making one or more computer resource enquiries (180; 182-184) to determine an individual price (138) for the article (132) to be purchased based on data (162) in the identified collective article information (160) as well as data (172) in the identified individualized article information (170); and causing performance of a payment transaction (190) for which a buyer (102) of the article (132) to be purchased is charged the determined individual price (138).
20. The computerized apparatus (100) as defined in claim 19, wherein the processing device (120) is configured for making said one or more computer resource enquiries (180; 182-184) to determine the individual price (138) for the article (132) to be purchased by:
making a first computer resource enquiry (182) to retrieve a nominal price (134) for the article (132) to be purchased based on the data (162) in the identified collective article information (160);
making a second computer resource enquiry (184) to determine a possible deviation (136) from the nominal price (134) based on the data (172) in the identified individualized article information (170); and
determining the individual price (138) for the article to be purchased based on the retrieved nominal price (134), as modified, when applicable, by the determined deviation (136).
21. The computerized apparatus (100) as defined in claim 19 or 20, wherein the data carrier (150) is a machine-readable optical code.
22. The computerized apparatus (100) as defined in any of claims 19-21, wherein the data (162) in the identified collective article information (160) comprises an article number for said article type. 23. The computerized apparatus (100) as defined in any of claims 19-21, wherein the data (172) in the identified individualized article information (170) comprises one or more of:
a manufacturing date;
a manufacturing time;
a best-before date;
a best-before time;
a batch number for articles manufactured in the same batch; and
a subgroup identifier which is common to a subgroup of all articles of said type. 24. The computerized apparatus (100) as defined in any of claims 19-23, wherein the individual price (138) for the article (132) to be purchased is determined (250) as a nominal price (134) subject to a discount (136), the discount (136) being based on the data (172) in the identified individualized article information (170).
25. The computerized apparatus (100) as defined in claim 24, wherein the discount (136) increases as the difference increases between a current date or time and a manufacturing date or time for the article (132) to be purchased, the manufacturing date or time being indicated by said data (172) in the identified individualized article information (170).
26. The computerized apparatus (100) as defined in claim 24, wherein the discount (136) increases as the difference decreases between a best-before date or time for the article (132) to be purchased and a current date or time, the best-before date or time being indicated by said data (172) in the identified individualized article information (170), the current date or time preceding the best-before date or time.
27. The computerized apparatus (100) as defined in claim 24, wherein the discount (136) increases as the difference increases between a current date or time and a best-before date or time for the article (132) to be purchased, the best-before date or time being indicated by said data (172) in the identified individualized article information (170), the best-before date or time preceding the current date or time.
28. The computerized apparatus (100) as defined in claim 24, wherein the discount (136) is determined as a function of the difference between a current date or time and a best-before date or time for the article (132) to be purchased, the best-before date or time being indicated by said data (172) in the identified individualized article information (170),
wherein the discount function defines a first discount or discount increase when the current date or time precedes the best-before date or time, and a second discount or discount increase when the best-before date or time precedes the current date or time, and
wherein the second discount or discount increase is greater than the first discount or discount increase. 29. The computerized apparatus (100) as defined in claim 24, wherein the processing device (120) is further configured for:
analyzing the individualized article information (170) to determine whether or not it identifies the article (132) to be purchased as belonging to a certain subgroup of articles of said same article type; and
if the article (132) to be purchased is identified as belonging to the certain subgroup:
determining a discount associated with the certain subgroup; and
applying the determined discount when determining (250) the individual price (138) for the article (132) to be purchased.
30. The computerized apparatus (100) as defined in claim 29, wherein the analyzed individualized article information (170) comprises at least one of a batch number for articles manufactured in the same batch, and a subgroup identifier which is common to a subgroup of all articles of said type.
31. The computerized apparatus (100) as defined in any of claims 19-30, wherein the individual price (138) for the article to be purchased is determined (250) as a nominal price (134) subject to a discount (136), the discount (136) being based on the data (172) in the identified individualized article information (170), and wherein the processing device (120) is further configured for:
causing performance of a compensation transaction (192) for which a manufacturer or distributor of the article to be purchased financially compensates a merchant of the physical store (140) for at least a part of the determined discount (136).
32. The computerized apparatus (100) as defined in any of claims 19-31, further comprising a display device (128), wherein the processing device (120) is further configured for:
causing the display device (128) to present at least either the determined individual price (138) or the deviation (136) for the article (132) to be purchased to the buyer (102), prior to causing performance (260) of the payment transaction (190).
33. The computerized apparatus (100) as defined in claim 20, further comprising a communication interface (122), wherein the processing device (120) is configured for making the second computer resource enquiry (184) by: sending a request to a remote server resource (185) via a communication interface (122), the request including the data (172) in the identified individualized article information (170); and
receiving a response to the request from the remote server resource (185) via the communication interface (122), wherein the response includes the possible deviation (136) from the nominal price (134).
34. The computerized apparatus (100) as defined in claim 33, wherein the processing device (120) is configured for making the first computer resource enquiry (182) by:
enquiring a local store database (183) using the data (162) in the identified collective article information (160); and
in response receiving the nominal price (134) for the article to be purchased from the local store database (183).
35. The computerized apparatus (100) as defined in any of claims 19-34, comprised in a computerized point-of-sale system (310).
36. The computerized apparatus (100) as defined in any of claims 19-34, comprised in a mobile communication device (320).
37. The computerized apparatus (100) as defined in any of claims 19-35, wherein the processing device (120) is further configured for:
retrieving personal data about the buyer (102); and
taking also the personal data into account when determining the individual price (138).
38. The computerized apparatus (100) as defined in claim 37, wherein the personal data about the buyer (102) comprises any of the following:
an age of the buyer (102);
a celebration date of the buyer (102);
a current location of the buyer (102);
a current mood of the buyer (102); and
a current circumstance of the buyer (102). 39. The computerized method of processing a purchase of an individual article as defined in any of claims 1-18, further comprising:
retrieving personal data about the buyer (102); and
taking also the personal data into account when determining the individual price (138).
40. The computerized method of processing a purchase of an individual article as defined in claim 39, wherein the personal data about the buyer (102) comprises any of the following:
an age of the buyer (102);
a celebration date of the buyer (102);
a current location of the buyer (102);
a current mood of the buyer (102); and
a current circumstance of the buyer (102).
PCT/SE2020/050472 2019-05-07 2020-05-07 Method and apparatus for a purchase using individualized article information readable from the article to determine an individual price WO2020226565A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
SE1950539-5 2019-05-07
SE1950539A SE1950539A1 (en) 2019-05-07 2019-05-07 Method and apparatus for processing a purchase using individualized article information readable from an article to determine an individual price

Publications (1)

Publication Number Publication Date
WO2020226565A1 true WO2020226565A1 (en) 2020-11-12

Family

ID=72916460

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/SE2020/050472 WO2020226565A1 (en) 2019-05-07 2020-05-07 Method and apparatus for a purchase using individualized article information readable from the article to determine an individual price

Country Status (2)

Country Link
SE (1) SE1950539A1 (en)
WO (1) WO2020226565A1 (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7283830B1 (en) 2022-05-30 2023-05-30 株式会社すなおネット Food product sales system
JP2023165155A (en) * 2022-05-02 2023-11-15 株式会社すなおネット Food product, providing method of food product and sales system of food product

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020147649A1 (en) * 2001-04-04 2002-10-10 White Daniel F. System and method of managing time-sensitive items
US7885855B2 (en) * 2005-06-06 2011-02-08 International Business Machines Corporation Cost based dynamic pricing of goods
US20150371375A1 (en) * 2014-06-24 2015-12-24 Kabushiki Kaisha Toshiba Merchandise identification apparatus, method of recognizing discount of merchandise, and freshness degree label
US20160110750A1 (en) * 2014-10-21 2016-04-21 Yuan Ze University Real-time price management system
US20160217447A1 (en) * 2015-01-23 2016-07-28 Shyamal K. Sarkar Dynamic Price Change Management
US20190057409A1 (en) * 2017-08-15 2019-02-21 WasteLess LTD Method and system to reduce food waste and control prices in retail

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020147649A1 (en) * 2001-04-04 2002-10-10 White Daniel F. System and method of managing time-sensitive items
US7885855B2 (en) * 2005-06-06 2011-02-08 International Business Machines Corporation Cost based dynamic pricing of goods
US20150371375A1 (en) * 2014-06-24 2015-12-24 Kabushiki Kaisha Toshiba Merchandise identification apparatus, method of recognizing discount of merchandise, and freshness degree label
US20160110750A1 (en) * 2014-10-21 2016-04-21 Yuan Ze University Real-time price management system
US20160217447A1 (en) * 2015-01-23 2016-07-28 Shyamal K. Sarkar Dynamic Price Change Management
US20190057409A1 (en) * 2017-08-15 2019-02-21 WasteLess LTD Method and system to reduce food waste and control prices in retail

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2023165155A (en) * 2022-05-02 2023-11-15 株式会社すなおネット Food product, providing method of food product and sales system of food product
JP7283830B1 (en) 2022-05-30 2023-05-30 株式会社すなおネット Food product sales system
JP7289583B1 (en) 2022-05-30 2023-06-12 株式会社すなおネット Food product sales system
JP2023175597A (en) * 2022-05-30 2023-12-12 株式会社すなおネット Food products, food product provision method, and food product sales system
JP2023175615A (en) * 2022-05-30 2023-12-12 株式会社すなおネット Food product sales system
JP2023175619A (en) * 2022-05-30 2023-12-12 株式会社すなおネット Food product sales system
JP2023175598A (en) * 2022-05-30 2023-12-12 株式会社すなおネット Food products and food product sales system
JP2023175601A (en) * 2022-05-30 2023-12-12 株式会社すなおネット Food products and food product provision method
JP7416469B2 (en) 2022-05-30 2024-01-17 株式会社すなおネット Food products and food product sales systems

Also Published As

Publication number Publication date
SE543238C2 (en) 2020-10-27
SE1950539A1 (en) 2020-10-27

Similar Documents

Publication Publication Date Title
US10825011B2 (en) Method and system for interrogating and processing codes
US20070162350A1 (en) Method and apparatus for retrieving remote data based on local indicia
US20150324831A1 (en) Method for processing customer loyalty information
US20100235250A1 (en) Tracking items in a distribution channel
US9062999B2 (en) System and method of encoding item information from a scale in a self-service scanning solution
US20130256402A1 (en) Method and apparatus for dynamically presenting content in response to successive scans of a static code
US20070192215A1 (en) Computer-implemented registration for providing inventory fulfillment services to merchants
US20030050862A1 (en) Supplier/reseller interaction
US20030050848A1 (en) Supplier/reseller interaction
McCathie The advantages and disadvantages of barcodes and radio frequency identification in supply chain management
US20180225623A1 (en) System and method for improved inventory management of returned retail items
US20210256464A1 (en) System for generating and maintaining unique identifier data
WO2020226565A1 (en) Method and apparatus for a purchase using individualized article information readable from the article to determine an individual price
US20030050849A1 (en) Supplier/reseller interaction
Ahmad et al. Role of technologies in revamping the supply chain management of kirana stores
US20030050958A1 (en) Supplier/reseller interaction
JP2023529350A (en) Systems and methods for increasing data migration and accessibility
WO2017035532A1 (en) Methods and systems for processing return items
KR20160032647A (en) Product information tracing method utilizing an electronic shelf label
US20030050847A1 (en) Supplier/reseller interaction
Johansson Identification of the main factors influencing an RFID implementation in the automotive and pharmaceutical industries
Kirkness Tools and solutions–internal traceability
VanVactor RFID tags and healthcare supply chain management
JP2023107185A (en) Information processing system and information processing method
WO2014077780A1 (en) Method for processing customer loyalty information

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20801567

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20801567

Country of ref document: EP

Kind code of ref document: A1