Claims:CLAIMS
1. A system for displaying a plurality of consumer products on an e-commerce platform, the system comprising:
a demand computation module configured to compute a first set of consumer products based on a demand index for the plurality of consumer products; wherein the demand index is computed using a plurality of variables based on consumer interaction monitored on the e-commerce platform;
a probability module configured to identify a second set of consumer products with a high potential of being traded on the e-commerce platform;
an integration module configured to select and generate a store-front viewing set of consumer products; wherein the store-front viewing set of consumer products comprises elements from the first set of consumer products and the second set of consumer products selected in a pre-determined ratio;
a store-front module configured to create a plurality of unique store-fronts to a plurality of consumers; and
an interface module configured display a plurality of unique store-fronts to the plurality of consumers; wherein each unique store-front comprise the elements from store- front viewing set of consumer products; wherein each unique store-front is distinct for a corresponding sets of consumers.

2. The system of claim 1, wherein the plurality of variables comprises sales data, sales velocity data, consumer preference data, product visibility data, or combinations thereof.

3. The system of claim 1, wherein the probability module is configured to create a consumer pattern based on the sales data, the demand index and the product visibility data; wherein the consumer pattern is applied to identify the second set of consumer products.

4. The system of claim 1, further comprising a personalization module configured to compile consumer preference data for each consumer; wherein the consumer preference data is computed based on the consumer’s browsing history and/or purchase history.
5. The system of claim 4, wherein, for each registered consumer, the interface module is configured to create a unique store-front based on elements selected from store-front viewing set of consumer products matching with the consumer’s preference data.

6. The system of claim 1, further comprising an inventory management module configured to index the plurality of consumer products based on the tracked inventory levels, order history, sales data, deliveries of consumer products, low sales velocity or combinations thereof.

7. The system of claim 6, wherein the inventory management module is configured to identify a third set of consumer products based on the inventory levels.

8. The system of claim 7, wherein the integration module is configured to generate the store-front viewing set of consumer products using the first set, the second set and the third set of consumer products.

9. The system of claim 1, wherein each unique store-front is created at least three times in an interval of 24 hours.

10. A system for efficient management of inventory for an e-commerce trading platform, the system comprising:
a demand computation module configured to compute a first set of fashion goods based on a demand index for the plurality of fashion goods; wherein the demand index is computed using a plurality of variables based on consumer interaction monitored on the e-commerce platform;
a probability module configured to identify a second set of fashion goods with a high potential of being traded on the e-commerce platform;
an inventory management module configured to index the plurality of fashion goods based on a plurality of inventory data and to flag a third set of fashion goods based on the plurality of inventory data;
an integration module configured to select and generate a store-front viewing set of fashion goods; wherein the store-front viewing set of fashion goods comprises elements from the first set, the second set and the third set of fashion goods;
a store-front module configured to create a plurality of unique store-fronts to a plurality of consumers; and
an interface module configured to display a plurality of unique store-fronts to a plurality of consumers; wherein each unique store-front comprises the elements from store-front viewing set of fashion goods; wherein each unique store-front is distinct for different sets of consumers.

11. The system of claim 10, further comprising a personalization module configured to compile a consumer preference data for the plurality of consumers; wherein the preference data is compiled based on a consumer’s gender, consumer’s location, a season or combinations thereof.

12. The system of claim 10, wherein the probability module is configured to generate the second set of consumer products by selecting one or more elements from the third set of consumer products.

13. The system of claim 10, wherein the store-front viewing set is configured to be displayed on a hand-held device.

14. A method for managing an inventory for an e-commerce trading platform, the method comprising:
computing a first set of fashion goods based on a demand index for the plurality of fashion goods; wherein the demand index is computed using a plurality of variables based on consumer interaction monitored on the e-commerce platform;
identifying a second set of fashion goods with a high potential of being traded on the e-commerce platform;
indexing the plurality of fashion goods to flag a third set of fashion goods based on low sales velocity, tracking inventory levels, order history, sales data, deliveries of consumer products or combination thereof;
selecting and generating a store-front viewing set of fashion goods in a pre-determined ratio comprising elements from the first set of fashion goods and the second set of fashion goods and third set of fashion goods; and
creating and displaying a plurality of unique store-fronts to a plurality of consumers; wherein each unique store-front comprise the elements from store-front viewing set of fashion goods; wherein each unique store-front is distinct for different sets of consumers.

15. The method of claim 14, wherein the plurality of variables comprises sales data, sales velocity data, consumer preference data, product views data, or combinations thereof.

, Description:BACKGROUND
[0001] The invention relates generally to inventory management systems, and more particularly to a method and a system for effective management of inventory in organizations within the e-commerce sector.
[0002] With the expanding use of the Internet, consumer products are being extensively bought and sold by different e-commerce operators over e-commerce platforms that are either web based or available as applications that execute on hand-held devices such as smartphones, tablets and the like. Such e-commerce platforms, generally referred by the term ‘e-commerce marketplace’ or ‘multichannel e-commerce’ have enabled retailers, manufacturers, and independent sellers to display and sell different products and services to a wide range of consumers. It may be noted that in an e-commerce marketplace, consumer transactions are generally processed by the ‘marketplace operator’ and then delivered and fulfilled by the participating retailers, manufacturers or independent sellers. Examples of marketplace operators include Flipkart, Snapdeal and the like.
[0003] Similarly, well-established retailers have also begun to use e-commerce platforms to provide online retailing of their products for their customers. In such a scenario, the products available on the e-commerce platform are usually exclusive to a specific retail outlet. Examples of such online retail outlets include GAP, Titan and the like.
[0004] There are several advantages of purchasing products and services electronically from retailers including being provided by a wide selection of products to choose from, the convenience of shopping for products independent of geographic locations, reduced prices and the like. However, one problem that is generally associated with e-commerce marketplace and/or online retailers, specifically for the retailer or seller, is the ineffective display and management of the large number of consumer products that are being offered for sale.
[0005] It is often difficult to ensure that high selling items attract the attention of customers. In addition, the new products and services are sometimes lost amongst the large number of pre-existing items being offered for sale. Also, it is critical for marketplace operators and online retailers to ensure that the consumer products are effectively displayed to ensure an effective rotation of inventory.
[0006] The present invention is directed to an inventory management system and method that provide e-commerce marketplace operators and/or online retailers the ability to effectively rotate inventory by applying unique display mechanisms.
SUMMARY
[0007] The following summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
[0008] According to some examples of the present disclosure, a system for displaying a plurality of consumer products on an e-commerce platform is provided. The system includes a demand computation module configured to compute a first set of consumer products based on a demand index for the plurality of consumer products. The demand index is computed using a plurality of variables based on consumer interaction monitored on the e-commerce platform. The system also includes a probability module configured to identify a second set of consumer products with a high potential of being traded on the e-commerce platform. Moreover, the system includes an integration module configured to select and generate a store-front viewing set of consumer products. The store-front viewing set of consumer products comprises elements from the first set of consumer products and the second set of consumer products selected in a pre-determined ratio. In addition, the system includes a store-front module configured to create a plurality of unique store-fronts to a plurality of consumers. Each unique store-front comprise the elements from store-front viewing set of consumer products. The system includes an interface module configured to display a plurality of unique store-fronts to the plurality of consumers. Moreover, each unique store-front is distinct for a corresponding sets of consumers.
[0009] According to additional examples of the present disclosure a system for efficient management of inventory for an e-commerce trading platform is provided. The system includes a demand computation module configured to compute a first set of fashion goods based on a demand index for the plurality of fashion goods. The demand index is computed using a plurality of variables based on consumer interaction monitored on the e-commerce platform. Moreover, the system includes a probability module configured to identify a second set of fashion goods with a high potential of being traded on the e-commerce platform. In addition, the system includes an inventory management module configured to index the plurality of fashion goods based on a plurality of inventory data and to flag a third set of fashion goods based on the plurality of inventory data. Further, the system includes an integration module configured to select and generate a store-front viewing set of fashion goods. The store-front viewing set of fashion goods comprises elements from the first set, the second set and the third set of fashion goods. The system includes a store-front module configured to create a plurality of unique store-fronts to a plurality of consumers and an interface module configured to display a plurality of unique store-fronts to a plurality of consumers. Each unique store-front comprises the elements from store-front viewing set of fashion goods and each unique store-front is distinct for different sets of consumers.
[0010] According to additional examples of the present disclosure a method for managing an inventory for an e-commerce trading platform is provided. The method includes computing a first set of fashion goods based on a demand index for the plurality of fashion goods. The demand index is computed using a plurality of variables based on consumer interaction monitored on the e-commerce platform. The method also includes identifying a second set of fashion goods with a high potential of being traded on the e-commerce platform. In addition, the method includes indexing the plurality of fashion goods to flag a third set of fashion goods based on low sales velocity, tracking inventory levels, order history, sales data, deliveries of consumer products or combination thereof. Further, the method includes selecting and generating a store-front viewing set of fashion goods in a pre-determined ratio comprising elements from the first set of fashion goods, the second set of fashion goods and third set of fashion goods. The method includes creating and displaying a plurality of unique store-fronts to a plurality of consumers. Each unique store-front comprise the elements from store-front viewing set of fashion goods and each unique store-front is distinct for different sets of consumers.
BRIEF DESCRIPTION OF THE FIGURES
[0011] FIG. 1 is a block diagram of an example embodiment of a system for displaying a plurality of consumer products on an e-commerce platform implemented according to aspects of the present technique;
[0012] FIG. 2 is a flow chart illustrating one method by which a unique store-front is created and displayed to a plurality of consumers on an e-commerce platform implemented according to aspects of the present technique;
[0013] FIG. 3 is a graphical representation of an example user interface for displaying a plurality of unique store-fronts to a plurality of consumers implemented according to aspects of the present technique;
[0014] FIG. 4 is a graphical representation of an example screenshot of a screen that displays linear regression mechanism implemented for identifying the consumer products with a high potential of being traded on the e-commerce platform according to aspects of the present technique; and
[0015] FIG. 5 is graphical representation of an example user interface of unique store-fronts created using the regression mechanism of FIG. 4 and displayed to a plurality of consumers.
DETAILED DESCRIPTION
[0016] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the Figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein.
[0017] FIG. 1 is a block diagram of an example embodiment of a system for displaying a plurality of consumer products on an e-commerce platform implemented according to aspects of the present technique. The system 10 includes consumers 12-A through 12-N, store-fronts 14-A through 14-N, and an e-commerce platform 30. The e-commerce platform 30 includes an interface module 16, a personalization module 18, a store-front module 20, an integration module 22, a demand computation module 24, a probability module 26, and an inventory module 28. Each component is described in further detail below.
[0018] The discovery of consumer products plays an important aspect in any typical e-commerce transaction. The discovery of consumer products may be an activity or set of activities leading to consumers 12-A through 12-N to browse through a catalogue of consumer products. In one embodiment, the store-fronts 14-A through 14-N are plurality of listing pages which catalogues a plurality of consumer products based on a number of preset criteria such as gender, age, type of goods, etc. The listing pages are used as a mechanism to discover products, banners, shop-in-shop pages, email communications, search campaigns, affiliate partner links, product recommendations, product offers and/or combinations thereof.
[0019] On the e-commerce platform 30, the aisle management or list page organization plays an important role for discovery of consumer products. To enhance the visibility of consumer products, the store-front module 20 is implemented in the e-commerce platform 30. The store-front module 20 is responsible for spreading visibility of consumer products by analyzing customer patterns, sales data and other such parameters available on the consumer products. The store-front module creates plurality of unique store-fronts 14-A through 14-N to a plurality of consumers 12-A through 12-N.
[0020] Demand computation module 24 is configured to compute a first set of consumer products based on a demand index for the plurality of consumer products. In one embodiment, the demand index is computed using a plurality of variables based on consumer interaction monitored on the e-commerce platform 30. The plurality of variables include, for example sales data, sales velocity data, consumer preference data, product visibility data, or combinations thereof. In an example embodiment, the first set of consumer products are selected based on sales of consumer products, sales velocity, listings and product pages seen by the consumers 12-A through 12-N, catalogue inventory depth, product margin and/or combination thereof.
[0021] Probability module 26 is configured to identify a second set of consumer products with a high potential of being traded on the e-commerce platform 30. In particular, the probability module is configured to create a consumer pattern based on the sales data, the demand index and the product visibility data. In one embodiment, the consumer pattern is applied to identify the second set of consumer products.
[0022] In one embodiment, the second set of consumer products, i.e. the consumer products with high potential are identified and selected using a linear regression technique. Linear regression techniques attempts to model the relationship between two variables by determining a linear equation to the observed data. In this embodiment, the variables used for identifying the high potential consumer products are the visibility of the consumer products and the revenue generated by the trade of the consumer products on the e-commerce platform 30. In one embodiment, consumer activity as tracked on the e-commerce platform 30 is processed to generate the list views. In one embodiment, the revenue numbers are received from an order database. The example embodiments explaining the regression mechanism are further described in FIG. 4 and FIG. 5.
[0023] Continuing with FIG.1, inventory management module 28 is configured to identify a third set of consumer products based on tracked inventory levels, order history, sales data, deliveries of consumer products, low sales velocity or combinations thereof. In one embodiment, the third set of consumer products comprises products that have not been viewed and/or purchased by the consumers.
[0024] Integration module 22 is configured to select and generate a store-front viewing set of consumer products. The store-front viewing set of consumer products include elements from the first set of consumer products, second set of consumer products and third set of consumer products selected in a pre-determined ratio. For example, the store-front viewing set includes a few elements from the first set of the consumer products, second set of consumer products and third set of consumer products in an X, Y and Z ratio. In this example, the first set of consumer products are represented as “X” and are a fixed set of consumer products. The second set of consumer products are represented as “Y” and the third set of consumer products represented by “Z” are a variable set of consumer products. The higher value of “Y” or “Z” means more variability of consumer products amongst several store-fronts 14-A through 14-N. A higher value of “X” means more similarity of consumer products amongst several store-fronts 14-A through 14-N.
[0025] In an example embodiment, store-fronts 14-A through 14-N are created for a category of products. For example, a store-front 14-A through 14-G may be created for men’s apparel, a store-front 14-H through 14-J may be created for footwear and bags, a store-front 14-K through 14-N may be created for accessories, and/or combinations thereof. The consumer is allocated a store-front upon access to a particular category. Store-fronts may be distinct for different sets of consumers. For example, a store-front visible to a customer based out of Delhi will be different to a store-front visible to a customer in Bangalore.
[0026] In an alternate embodiment, the probability module is configured to generate the second set of consumer products by selecting one or more elements from the third set of consumer products. Thus, the second set of consumer products includes elements identified by the probabilty module and the inventory module.
[0027] Interface module 16 is configured to display the store-fronts 14-A through 14-N to a plurality of consumers 12-A through 12-N. Each store-front comprise the elements from store-front viewing set of consumer products and each store-front is distinct for a corresponding sets of consumers 12-A through 12-N. In one embodiment, each unique store-front is created multiple times in an interval of 24 hours. In a further embodiment, the store-front is created once every 24 hours. In another embodiment, the store-front is created thrice in an interval of 24 hours.
[0028] Personalization module 18 is configured to compile consumer preference data for each consumer 12-A through 12-N. The consumer preference data is computed based on the consumer’s browsing history and/or purchase history. In one example embodiment, the store-fronts 14-A through 14-N are showcased to different "sets" of consumers. For example, a Delhi T-shirt store-front is showcased to Delhi users. In addition, preference data of a single consumer i.e. personalization is also complied. Further, the interface module 16 is configured to create a store-front based on elements selected from store-front viewing set of consumer products matching with the consumer’s preference data.
[0029] The above described system may be presented to consumers as a website or as an application installed on various hand-held devices such as smartphones, tablets and the like. In particular, the store- fronts created using the techniques described herein are distinct for each consumer. The manner in which a unique store-front is created and displayed to a plurality of consumers on an e-commerce platform is described in further detail below.
[0030] FIG. 2 is a flow chart illustrating one method by which a unique store-front is created and displayed to a plurality of consumers on an e-commerce platform implemented according to aspects of the present technique. In particular, the process 40 is used for managing an inventory for the e-commerce trading platform. Each step is described in further detail below.
[0031] At step 42, a first set of fashion goods is computed based on a demand index for the plurality of fashion goods. Examples of the fashion goods include clothing, footwear, beauty-care products, lingerie and sleepwear, grooming accessories and the like. In one embodiment, the demand index is computed using a plurality of variables based on consumer interaction monitored on the e-commerce platform. Examples of the plurality of variables include sales data, sales velocity data, consumer preference data, product views data, or combinations thereof.
[0032] At step 44, a second set of fashion goods with a high potential of being traded on the e-commerce platform are identified. Fashion goods with a high potential of being traded is determined based on sales data, demand index and product visibility data. The consumer pattern is applied to identify the second set of fashion goods.
[0033] At step 46, a third set of fashion goods are generated based on inventory data. Examples of inventory data includes low sales velocity, inventory levels for each consumer product, order history, sales data, or combination thereof.
[0034] At step 48, a store-front viewing set of fashion goods are selected in a pre-determined ratio comprising elements from the first set of fashion goods, the second set of fashion goods and third set of fashion goods. At step 50, a plurality of unique store-fronts are created and displayed to a plurality of consumers. In one embodiment, each unique store-front comprise the elements from store-front viewing set of fashion goods. Moreover, each unique store-front is distinct for different sets of consumers.
[0035] The above described system and method for displaying a plurality of consumer products on an e-commerce trading platform implements several user interfaces to enable the user to view the consumer products and/or fashion goods. Some of the relevant interfaces are described in further detail below.
[0036] FIG. 3 is graphical representation of an example user interface for displaying a plurality of unique store-fronts to a plurality of consumers. FIG. 3 shows an example screenshot 60 of a screen that displays the store-front 14-A (represented by reference numeral 62) and store-front 14-B (represented by reference numeral 64).
[0037] As mentioned above in FIG. 1 the store-front 14-A comprise the elements from store-front viewing set of consumer products. Each unique store-front is distinct for a corresponding sets of consumers. In one embodiment, an integration module 22 is configured to generate store-front viewing set of consumer products. The store-front viewing set of consumer products comprises elements from the first set of consumer products, the second set of consumer products and third set of consumer products selected in a pre-determined ratio. It can be seen from FIG.3 that the consumer product 66 and consumer product 68 as displayed on the store-front 14-A is different as compared to consumer product 70 and consumer product 72 as displayed on the store-front 14-B. The different consumer products 66 and 68 displayed on the store-front 14-A and consumer products 70 and 72 as displayed on the store-front 14-B for the casual shirts store-fronts are created and shown to different consumers at the same time.
[0038] FIG. 4 is a graphical representation of an example screenshot of a screen that displays linear regression mechanism implemented for identifying the second set of consumer products implemented according to aspects of the present technique. The second set of consumer products correspond to products that have high potential sale. The screen 80 provides an option to view and classify the consumer products based on the potential of being traded on the e-commerce platform.
[0039] In one example embodiment, the regression mechanism is implemented for displaying and classifying T-shirts based on its potential of visibility on the e-commerce platform. The screen 80 represents the graph depicting the individual consumer products. In addition, the screen 80 represents the regression mechanism implemented (list on x-axis, revenue on-y axis) to classify the consumer products in various segments.
[0040] For example, the T- shirts with high potential are shown as ‘stud’ (represented by reference numeral 102) in the graph, the T-shirts with mid potential are shown as ‘bud’ (represented by reference numeral 92) in the graph. In this example embodiment, the T-shirts with high potential means the ‘most’ promising consumer products being traded of on the e-commerce platform. The consumer product (e.g., T-Shirts) with high potential are identified by the linear regression mechanism and are as represented with reference numeral 82, 84, 86 and 88 in the graph of FIG.4.
[0041] Similarly, the T-shirts with mid potential means the ‘most likely’ promising consumer products being traded on the e-commerce platform are also identified and categorized. Likewise, the screen 80 represents T-shirts with low list view and are depicted as ‘bud’ (represented by reference numeral 94) in the graph. As can be seen, the visibility of bud category of T-shirts are not shown enough in the graph. Similarly, the T-shirts having performance scales with higher visibility are depicted as ‘stud high list view’ (represented by reference numeral 104) in the graph. Moreover, the graph depicts, that the performance goes down with more visibility and is shown as ‘dud high list view’ (represented by reference numeral 98) in the graph. The store-fronts created based on the regression mechanism are described in FIG. 5 in detail below.
[0042] FIG. 5 is graphical representation of an example user interface of unique store-fronts created using the regression mechanism and displayed to a plurality of consumers. As described in FIG. 4, the consumer products identified by the regression mechanism are selected and displayed on the unique store-fronts to a plurality of consumers. In this example, the store-front 100-A displays the consumer products with high potential being traded on e-commerce platform. These consumer products are T- shirts identified and classified as high potential (for example ‘stud’ represented by reference numeral 102) in the graph of FIG. 4. The store-front 100-A displays the T-shirts with high potential and are represented with reference numeral 82 and 84. On the other hand the store-front 100-B also displays the T-shirts with high potential but different than the T-shirts displayed on store-front 100-B and are represented with reference numeral 86 and 88.
[0043] Thus, multiple store-fronts facilitate the display of plurality of consumer products which results in the improvement of the visibility of the consumer products. As a result, sales of these products are substantially increased thereby leading to more number of distinct products being sold thus resulting in effective inventory management.
[0044] The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims.
[0045] The present disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is to be understood that this disclosure is not limited to particular methods, reagents, compounds compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
[0046] With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.
[0047] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present.
[0048] For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.
[0049] In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “ a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “ a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.).
[0050] It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”
[0051] As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc.
[0052] As will also be understood by one skilled in the art all language such as “up to,” “at least,” “greater than,” “less than,” and the like include the number recited and refer to ranges which can be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells, and so forth.
[0053] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.