Patent Application
20220245703
This disclosure relates generally to a system and/or method for personalizing item recommendations.
Providing item recommendations is a popular technique in e-commerce to make it easier for customers to explore similar items and also to boost sales of complementary items. However, existing recommender systems fail to identify a user's purpose of viewing an anchor item and present item recommendations for the anchor item accordingly. It thus can be desired to have a system and/or method for determining a personalized item recommendation strategy according to the user's purpose and the anchor item.
To facilitate further description of the embodiments, the following drawings are provided in which:
For simplicity and clarity of illustration, the drawing figures illustrate the general manner of construction, and descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the present disclosure. Additionally, elements in the drawing figures are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of embodiments of the present disclosure. The same reference numerals in different figures denote the same elements.
The terms “first,” “second,” “third,” “fourth,” and the like in the description and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms “include,” and “have,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, device, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, system, article, device, or apparatus.
The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,” “under,” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the apparatus, methods, and/or articles of manufacture described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein.
The terms “couple,” “coupled,” “couples,” “coupling,” and the like should be broadly understood and refer to connecting two or more elements mechanically and/or otherwise. Two or more electrical elements may be electrically coupled together, but not be mechanically or otherwise coupled together. Coupling may be for any length of time, e.g., permanent or semi-permanent or only for an instant. “Electrical coupling” and the like should be broadly understood and include electrical coupling of all types. The absence of the word “removably,” “removable,” and the like near the word “coupled,” and the like does not mean that the coupling, etc. in question is or is not removable.
As defined herein, two or more elements are “integral” if they are comprised of the same piece of material. As defined herein, two or more elements are “non-integral” if each is comprised of a different piece of material.
As defined herein, “approximately” can, in some embodiments, mean within plus or minus ten percent of the stated value. In other embodiments, “approximately” can mean within plus or minus five percent of the stated value. In further embodiments, “approximately” can mean within plus or minus three percent of the stated value. In yet other embodiments, “approximately” can mean within plus or minus one percent of the stated value.
As defined herein, “real-time” can, in some embodiments, be defined with respect to operations carried out as soon as practically possible upon occurrence of a triggering event. A triggering event can include receipt of data necessary to execute a task or to otherwise process information. Because of delays inherent in transmission and/or in computing speeds, the term “real-time” encompasses operations that occur in “near” real-time or somewhat delayed from a triggering event. In a number of embodiments, “real-time” can mean real-time less a time delay for processing (e.g., determining) and/or transmitting data. The particular time delay can vary depending on the type and/or amount of the data, the processing speeds of the hardware, the transmission capability of the communication hardware, the transmission distance, etc. However, in many embodiments, the time delay can be less than approximately one second, five seconds, ten seconds, thirty seconds, one minute, five minutes, ten minutes, or fifteen minutes.
Turning to the drawings,
Continuing with
As used herein, “processor” and/or “processing module” means any type of computational circuit, such as but not limited to a microprocessor, a microcontroller, a controller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor, or any other type of processor or processing circuit capable of performing the desired functions. In some examples, the one or more processors of the various embodiments disclosed herein can comprise CPU 210.
In the depicted embodiment of
In some embodiments, network adapter 220 can comprise and/or be implemented as a WNIC (wireless network interface controller) card (not shown) plugged or coupled to an expansion port (not shown) in computer system 100 (
Although many other components of computer system 100 (
When computer system 100 in
Although computer system 100 is illustrated as a desktop computer in
Turning ahead in the drawings,
In many embodiments, system 300 can include a system 310, one or more machine learning models 320, a recommender system 330, a front end system 340, and/or a database 350. In many embodiments, system 300, system 310, one or more machine learning models 320, recommender system 330, and/or front end system 340 each can be modules of computing instructions (e.g., software modules) stored at non-transitory computer readable media that operate on one or more processors. In other embodiments, system 300, system 310, one or more machine learning models 320, recommender system 330, and/or front end system 340 each can be implemented in hardware. System 300, system 310, one or more machine learning models 320, recommender system 330, and/or front end system 340 each can be a computer system, such as computer system 100 (
In some embodiments, system 300, system 310, one or more machine learning models 320, recommender system 330, and/or front end system 340 each can be in data communication directly or through Internet 370 with one or more user computers, such as user device 380. In some embodiments, user device 380 can be used by users, such as a user 381. In many embodiments, system 300, system 310, or front end system 340 can host one or more websites and/or mobile application servers. For example, front end system 340 can host a website, or provide a server that interfaces with a mobile application on user device 380, which can allow user 381 to browse and/or search for items (e.g., products, grocery items), to add items to an electronic cart, to purchase items, and/or request grocery delivery, in addition to other suitable activities.
In certain embodiments, user computers (e.g., user device 380) can be desktop computers, laptop computers, a mobile device, and/or other endpoint devices used by users (e.g., user 381). A mobile device can refer to a portable electronic device (e.g., an electronic device easily conveyable by hand by a person of average size) with the capability to present audio and/or visual data (e.g., text, images, videos, music, etc.). For example, a mobile device can include at least one of a digital media player, a cellular telephone (e.g., a smartphone), a personal digital assistant, a handheld digital computer device (e.g., a tablet personal computer device), a laptop computer device (e.g., a notebook computer device, a netbook computer device), a wearable user computer device, or another portable computer device with the capability to present audio and/or visual data (e.g., images, videos, music, etc.). Thus, in many examples, a mobile device can include a volume and/or weight sufficiently small as to permit the mobile device to be easily conveyable by hand. For examples, in some embodiments, a mobile device can occupy a volume of less than or equal to approximately 1790 cubic centimeters, 2434 cubic centimeters, 2876 cubic centimeters, 4056 cubic centimeters, and/or 5752 cubic centimeters. Further, in these embodiments, a mobile device can weigh less than or equal to 15.6 Newtons, 17.8 Newtons, 22.3 Newtons, 31.2 Newtons, and/or 44.5 Newtons.
Exemplary mobile devices can include (i) an iPod®, iPhone®, iTouch®, iPad®, MacBook® or similar product by Apple Inc. of Cupertino, California, United States of America, (ii) a Blackberry® or similar product by Research in Motion (RIM) of Waterloo, Ontario, Canada, (iii) a Lumia® or similar product by the Nokia Corporation of Keilaniemi, Espoo, Finland, and/or (iv) a Galaxy™ or similar product by the Samsung Group of Samsung Town, Seoul, South Korea. Further, in the same or different embodiments, a mobile device can include an electronic device configured to implement one or more of (i) the iPhone® operating system by Apple Inc. of Cupertino, California, United States of America, (ii) the Blackberry® operating system by Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) the Palm® operating system by Palm, Inc. of Sunnyvale, California, United States, (iv) the Android™ operating system developed by the Open Handset Alliance, (v) the Windows Mobile™ operating system by Microsoft Corp. of Redmond, Washington, United States of America, or (vi) the Symbian™ operating system by Nokia Corp. of Keilaniemi, Espoo, Finland.
Further still, the term “wearable user computer device” as used herein can refer to an electronic device with the capability to present audio and/or visual data (e.g., text, images, videos, music, etc.) that is configured to be worn by a user and/or mountable (e.g., fixed) on the user of the wearable user computer device (e.g., sometimes under or over clothing; and/or sometimes integrated with and/or as clothing and/or another accessory, such as, for example, a hat, eyeglasses, a wrist watch, shoes, etc.). In many examples, a wearable user computer device can include a mobile device, and vice versa. However, a wearable user computer device does not necessarily include a mobile device, and vice versa. In specific examples, a wearable user computer device can include a head mountable wearable user computer device (e.g., one or more head mountable displays, one or more eyeglasses, one or more contact lenses, one or more retinal displays, etc.) or a limb mountable wearable user computer device (e.g., a smart watch). In these examples, a head mountable wearable user computer device can be mountable in close proximity to one or both eyes of a user of the head mountable wearable user computer device and/or vectored in alignment with a field of view of the user.
In more specific examples, a head mountable wearable user computer device can include (i) Google Glass™ product or a similar product by Google Inc. of Menlo Park, California, United States of America; (ii) the Eye Tap™ product, the Laser Eye Tap™ product, or a similar product by ePI Lab of Toronto, Ontario, Canada, and/or (iii) the Raptyr™ product, the STAR 1200™ product, the Vuzix Smart Glasses M100™ product, or a similar product by Vuzix Corporation of Rochester, New York, United States of America. In other specific examples, a head mountable wearable user computer device can include the Virtual Retinal Display™ product, or similar product by the University of Washington of Seattle, Washington, United States of America. Meanwhile, in further specific examples, a limb mountable wearable user computer device can include the iWatchTM product, or similar product by Apple Inc. of Cupertino, California, United States of America, the Galaxy Gear or similar product of Samsung Group of Samsung Town, Seoul, South Korea, the Moto 360 product or similar product of Motorola of Schaumburg, Illinois, United States of America, and/or the Zip™ product, One™ product, Flex™ product, Charge™ product, Surge™ product, or similar product by Fitbit Inc. of San Francisco, California, United States of America.
In many embodiments, system 300, system 310, one or more machine learning models 320, recommender system 330, and/or front end system 340 each can include one or more input devices (e.g., one or more keyboards, one or more keypads, one or more pointing devices such as a computer mouse or computer mice, one or more touchscreen displays, a microphone, etc.), and/or can include one or more display devices (e.g., one or more monitors, one or more touch screen displays, projectors, etc.). In these or other embodiments, one or more of the input device(s) can be similar or identical to keyboard 104 (
Meanwhile, in many embodiments, system 300, system 310, one or more machine learning models 320, recommender system 330, and/or front end system 340 also can be configured to communicate with and/or include one or more databases, such database 350, and/or other suitable databases. The one or more databases can include a product database that contains information about products, items, or SKUs (stock keeping units), for example, among other data as described herein, such as described herein in further detail. The one or more databases can be stored on one or more memory storage units (e.g., non-transitory computer readable media), which can be similar or identical to the one or more memory storage units (e.g., non-transitory computer readable media) described above with respect to computer system 100 (
The one or more databases can each include a structured (e.g., indexed) collection of data and can be managed by any suitable database management systems configured to define, create, query, organize, update, and manage database(s). Exemplary database management systems can include MySQL (Structured Query Language) Database, PostgreSQL Database, Microsoft SQL Server Database, Oracle Database, SAP (Systems, Applications, & Products) Database, and IBM DB2 Database.
Still referring to
Further, the wired and/or wireless communication can be implemented using any one or any combination of wired and/or wireless communication network topologies (e.g., ring, line, tree, bus, mesh, star, daisy chain, hybrid, etc.) and/or protocols (e.g., personal area network (PAN) protocol(s), local area network (LAN) protocol(s), wide area network (WAN) protocol(s), cellular network protocol(s), powerline network protocol(s), etc.). Exemplary PAN protocol(s) can include Bluetooth, Zigbee, Wireless Universal Serial Bus (USB), Z-Wave, etc.; exemplary LAN and/or WAN protocol(s) can include Institute of Electrical and Electronic Engineers (IEEE) 802.3 (also known as Ethernet), IEEE 802.11 (also known as WiFi), etc.; and exemplary wireless cellular network protocol(s) can include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Evolution-Data Optimized (EV-DO), Enhanced Data Rates for GSM Evolution (EDGE), Universal Mobile Telecommunications System (UMTS), Digital Enhanced Cordless Telecommunications (DECT), Digital AMPS (IS-136/Time Division Multiple Access (TDMA)), Integrated Digital Enhanced Network (iDEN), Evolved High-Speed Packet Access (HSPA+), Long-Term Evolution (LTE), WiMAX, etc. The specific communication software and/or hardware implemented can depend on the network topologies and/or protocols implemented, and vice versa. In many embodiments, exemplary communication hardware can include wired communication hardware including, for example, one or more data buses, such as, for example, universal serial bus(es), one or more networking cables, such as, for example, coaxial cable(s), optical fiber cable(s), and/or twisted pair cable(s), any other suitable data cable, etc. Further exemplary communication hardware can include wireless communication hardware including, for example, one or more radio transceivers, one or more infrared transceivers, etc. Additional exemplary communication hardware can include one or more networking components (e.g., modulator-demodulator components, gateway components, etc.).
Meanwhile, in many embodiments, one or more machine learning models 320 can include a semi-supervised learning model 321 and/or a supervised learning model 322. In many embodiments, one or more machine learning models 320 each can be implemented by any suitable software components, hardware components, and/or various combinations thereof, of system 300. For example, in some embodiments, one or more machine learning models 320 each can adopt any suitable algorithms, such as decision tree, logistic regression, random forest, support vector machines, clustering, and so forth. In some embodiments, one or more machine learning models 320 each can be implemented through any suitable software development platforms or open source software packages, such as, TensorFlow, Theano, PyTorch, PySpark, or Scikit-learn, and written in any suitable languages, such as Python, C++, and/or CUDA.
Turning ahead in the drawings,
In many embodiments, system 300 (
In some embodiments, method 400 and other blocks in method 400 can include using a distributed network including distributed memory architecture to perform the associated activity. This distributed architecture can reduce the impact on the network and system resources to reduce congestion in bottlenecks while still allowing data to be accessible from a central location.
Referring to
In a number of embodiments, method 400 further can include a block 420 of determining an anchor label for the anchor item based at least in part on an anchor category of the anchor item. In many embodiments, the anchor label for the anchor item can be upsell or cross-sell. In some embodiments, one or more categories can include the anchor category, and each of the one or more categories can be associated with a single respective label (e.g., upsell or cross-sell, but not both). Examples of categories with an upsell label (i.e., upsell categories) can include: fresh & frozen vegetables, juices, cheeses, packaged meals, video games, over-the-counter medicines, board games, books, and/or movies, etc. Examples of categories with a cross-sell label (i.e., cross-sell categories) can include televisions, vacuum cleaners, air fryer, cell phones, exercise bikes, and/or mattresses, etc.
In many embodiments, each of the one or more categories can include one or more respective features. In some embodiments, block 420 can determine the one or more respective features of each of the one or more categories based at least in part on one or more item attributes (e.g., product descriptions, images, brands, etc.) of items within the each of the one or more categories. In similar or different embodiments, block 420 additionally can determine the one or more respective features of each of the one or more categories based at least in part on historical transactions associated with the each of the one or more categories during a period of time (e.g., 3 months, 5 months, 1 year, 2 years, or any other suitable periods of time). Examples of the one or more respective features of a category based on historical transactions can include one or more of: (a) a number of customers that bought at least 2 items from the category in the same transaction; (b) a number of items in the category that are part of the transactions in (a), and/or (c) a ratio of customers who bought at least 2 items from the category to customers who bought any item from the category.
In a number of embodiments, block 420 can determine the anchor label for the anchor item by a pre-trained machine learning model. In several embodiments, the pre-trained machine learning model can be trained offline based at least in part on historical input data (e.g., historical transactions during a predetermined period of time, and/or one or more respective features of each of the one or more categories, etc.) and historical output data (e.g., a respective verified label for the each of the one or more categories). In many embodiments, the pre-trained machine learning model can include one or more machine learning models (e.g., one or more machine learning models 320 (
Turning ahead in the drawings,
In many embodiments, system 300 (
In some embodiments, method 500 and other blocks in method 500 can include using a distributed network including distributed memory architecture to perform the associated activity. This distributed architecture can reduce the impact on the network and system resources to reduce congestion in bottlenecks while still allowing data to be accessible from a central location.
Referring to
In many embodiments, block 510 further or alternatively can include training the semi-supervised learning model iteratively to generate the respective pseudo label for each of respective untrained portions of the unlabeled categories. The historical input data can include one or more respective features of each of labeled categories, historical transactions during the predetermined time period; and/or the one or more respective features of each of respective trained portions of the unlabeled categories. The historical output data can include a respective predetermined label for each of the labeled categories and/or the respective pseudo label for each of the respective trained portions of the unlabeled categories. The unlabeled categories can be divided into any suitable number of untrained portions (e.g., 2, 3, 5, or 10), and the untrained portions can be of the same or different sizes.
In a number of embodiments, block 510 can further include a block 511 of training the semi-supervised learning model with the respective predetermined label for the each of labeled categories. In some embodiments, an operator or administrator can provide the respective predetermined label for the each of the labeled categories by one or more input device(s) (e.g., keyboard 104 (
In a number of embodiments, block 510 can further include a block 512 of using the semi-supervised learning model as trained to generate a respective pseudo label for each of respective untrained portions of unlabeled categories. In many embodiments where the unlabeled categories can be divided into at least 3 portions, block 512 can include: (a) using the semi-supervised learning model as trained in block 511 to generate the respective pseudo label for each of a first untrained portion (e.g., the first 10%, 20%, 30%, or 50% of categories) of the unlabeled categories; (b) retraining the semi-supervised learning model as trained in block 511 with the respective predetermined label for the each of the labeled categories and the respective pseudo label for the each of the first untrained portion generated in part (a); (c) using the semi-supervised learning model as trained in part (b) to generate the respective pseudo label for each of a second untrained portion of the unlabeled categories; (d) retraining the semi-supervised learning model as trained in part (b) with the respective predetermined label for the each of labeled categories, the respective pseudo label for the each of the first untrained portion generated in part (a), and the respective pseudo label for the each of the second untrained portion generated in part (c); (e) using the semi-supervised learning model as trained in part (d) to generate the respective pseudo label for a third untrained portion of the unlabeled categories, and so forth, until there are no untrained portions of the unlabeled categories.
In some embodiments, block 510 can further include a block 513 of re-training the semi-supervised learning model with the predetermined label for the each of labeled categories and the respective pseudo label for the each of the unlabeled categories. In many embodiments, block 513 can include procedures, processes, and/or activities similar or identical to some or all of the procedures, processes, and/or activities of block 511 and/or block 512.
In several embodiments, block 510 can further include a block 514 of determining whether a predetermined confidence level (e.g., 80%, 85%, 90%, 95%, 99%, etc.) is reached. If the confidence level for the pseudo label data is not at least as great as the predetermined confidence level, block 510 can repeat by going back to block 513 to further train the semi-supervised learning model. In some embodiments, block 510 can repeat blocks 511, 512, and/or 513 until reaching the predetermined confidence level.
In a number of embodiments, method 500 further can include a block 520 of obtaining known categories including the unlabeled categories and the labeled categories and a respective verified label for each of the known categories. In certain embodiments, block 520 can obtain the known categories including the unlabeled categories and the labeled categories and the respective verified label for each of the known categories from one or more databases (e.g., database 350 (
In a number of embodiments, method 500 additionally can include a block 530 of training a supervised learning model (e.g., supervised learning model 322 (
Referring back to
In a number of embodiments, method 400 further can include a block 440 of determining a personalized recommendation strategy for the user (e.g., user 381 (
In a number of embodiments, method 400 additionally can include a block 450 of re-ranking the item recommendations based at least in part on the personalized recommendation strategy. In many embodiments, block 450 further can include, when the personalized recommendation is upsell (e.g., when the user mode is discovery and the anchor label is upsell), giving at least one similar recommendation of the item recommendations a high ranking; and/or, when the personalized recommendation is cross-sell (e.g., when the user mode is repurchase, and the anchor label is cross-sell), giving at least one complimentary recommendation of the item recommendations the high ranking. In some embodiments, a high ranking can refer to top 1, top 3, top 10, or top 50%, etc., among the item recommendations. In other or similar embodiments, a high ranking can refer to an improved ranking than the original ranking for an item (e.g., changing the ranking from N to N-1 or N-2, etc.).
In certain embodiments, block 450 additionally can include re-ranking the item recommendations further based at least in part on the respective recommendation basis for each of the item recommendations. For example, in embodiments where each of the item recommendations is associated with a respective recommendation basis, when the personalized recommendation strategy determined in block 440 is upsell, block 450 can re-rank the item recommendations by moving at least one recommendation whose respective recommendation basis is similarity-based (e.g., personalized similarity-based or common similarity-based) to the top 10% or at least one ranking higher than its original ranking, if possible, among the item recommendations. In similar or other embodiments, when the personalized recommendation strategy determined is cross-sell, block 450 can re-rank the item recommendations by moving at least one recommendation whose respective recommendation basis is complementary-based (e.g., personalized complementary-based or common complementary-based) to at least the top 30%, among the item recommendations
In a number of embodiments, method 400 further can include block 460 of transmitting the item recommendations re-ranked to be displayed with the anchor item on the user interface. In some embodiments, block 460 can transmit, directly or indirectly through the computer network (e.g., Internet 370 (
In many embodiments, the techniques described herein can provide several technological improvements. In many embodiments, the techniques described herein can improve the personalization of item recommendations by automatically determining a personalized item recommendation strategy based in part on the anchor category of the anchor item and the user mode for the user in real-time. In addition, in some embodiments, the techniques described herein can improve the personalization of item recommendations by using a machine learning model to automatically identify the nature of each item category based at least in part on item attributes and transaction histories for the each item category and label the each item category accordingly.
In many embodiments, the techniques described herein can be used continuously at a scale that cannot be handled using manual techniques. For example, the number of users concurrently browsing or searching items on an e-commerce site can be at least hundreds or thousands, and the automatic determination of the personalized item recommendation strategy and the re-ranking of the item recommendations accordingly for the hundreds or thousands of users cannot be handled manually in real-time.
In a number of embodiments, the techniques described herein can solve a technical problem that arises only within the realm of computer networks, as e-commerce does not exist outside the realm of computer networks. Moreover, the techniques described herein can solve a technical problem that cannot be solved outside the context of computer networks. Specifically, the techniques described herein cannot be used outside the context of computer networks, in view of a lack of data and because the machine learning models cannot be performed without a computer.
Various embodiments can include a system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform certain acts. The acts can include obtaining item recommendations associated with an anchor item chosen by a user via a user interface executed on a user device of the user. The acts further can include determining an anchor label for the anchor item. In some embodiments, the anchor label can be determined based at least in part on one or more features of an anchor category of the anchor item. The acts additionally can include determining a personalized recommendation strategy for the user based at least in part on a user mode for the user and the anchor label. The acts also can include re-ranking the item recommendations based at least in part on the personalized recommendation strategy. The acts further can include transmitting the item recommendations re-ranked to be displayed with the anchor item on the user interface.
A number of embodiments can include a method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include obtaining item recommendations associated with an anchor item chosen by a user via a user interface executed on a user device of the user. The method further can include determining an anchor label for the anchor item. In a number of embodiments, the anchor label can be determined based at least in part on one or more features of an anchor category of the anchor item. The method additionally can include determining a personalized recommendation strategy for the user based at least in part on a user mode for the user and the anchor label. The method further can include re-ranking the item recommendations based at least in part on the personalized recommendation strategy. The method also can include transmitting the item recommendations re-ranked to be displayed with the anchor item on the user interface.
Although determining a personalized recommendation strategy and re-ranking item recommendations based at least in part on the personalized recommendation strategy has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes may be made without departing from the spirit or scope of the disclosure. Accordingly, the disclosure of embodiments is intended to be illustrative of the scope of the disclosure and is not intended to be limiting. It is intended that the scope of the disclosure shall be limited only to the extent required by the appended claims. For example, to one of ordinary skill in the art, it will be readily apparent that any element of
Replacement of one or more claimed elements constitutes reconstruction and not repair. Additionally, benefits, other advantages, and solutions to problems have been described with regard to specific embodiments. The benefits, advantages, solutions to problems, and any element or elements that may cause any benefit, advantage, or solution to occur or become more pronounced, however, are not to be construed as critical, required, or essential features or elements of any or all of the claims, unless such benefits, advantages, solutions, or elements are stated in such claim.
Moreover, embodiments and limitations disclosed herein are not dedicated to the public under the doctrine of dedication if the embodiments and/or limitations: (1) are not expressly claimed in the claims; and (2) are or are potentially equivalents of express elements and/or limitations in the claims under the doctrine of equivalents.
