Source: https://patents.google.com/patent/US20160328660A1/en
Timestamp: 2020-02-29 14:28:20
Document Index: 355165806

Matched Legal Cases: ['§112', '§112', '§112', '§112', '§112', '§112', '§112']

US20160328660A1 - Machine Learning System, Method, and Program Product for Point of Sale Systems - Google Patents
Machine Learning System, Method, and Program Product for Point of Sale Systems Download PDF
US20160328660A1
US20160328660A1 US15/146,043 US201615146043A US2016328660A1 US 20160328660 A1 US20160328660 A1 US 20160328660A1 US 201615146043 A US201615146043 A US 201615146043A US 2016328660 A1 US2016328660 A1 US 2016328660A1
US15/146,043
US10282722B2 (en
2015-05-04 Priority to US201562156848P priority Critical
2016-05-04 Application filed by Yi Sun Huang filed Critical Yi Sun Huang
2016-05-04 Priority to US15/146,043 priority patent/US10282722B2/en
2016-11-10 Publication of US20160328660A1 publication Critical patent/US20160328660A1/en
2019-05-07 Publication of US10282722B2 publication Critical patent/US10282722B2/en
A machine learning innovation for point of sale systems is provided which includes a scanner component, which can scan at least one of the following codes: Barcode, QR code, RFID or any other new code and id, a camera component, which can get image or picture of objects, and a compute component with prediction algorithm to classify the object. The system also includes a method with prediction and learning capability that sends the classified labels to central controller or server. A central controller or server gathers classified labels and analyze and learn from classified labels information, and sends updated a scanner component, which can scan at least one of the codes.
The present Utility patent application claims priority benefit of the U.S. provisional application for patent Ser. No. 62/156,848 entitled “MACHINE LEARNING SYSTEM ON RETAIL SHOPPING” filed 4 May 2015 under 35 U.S.C. 119(e). The contents of this related provisional application are incorporated herein by reference for all purposes to the extent that such subject matter is not inconsistent herewith or limiting hereof.
One or more embodiments of the invention generally relate to machine learning systems. More particularly, certain embodiments of the invention relates to machine learning systems in point of sale systems.
The following is an example of a specific aspect in the prior art that, while expected to be helpful to further educate the reader as to additional aspects of the prior art, is not to be construed as limiting the present invention, or any embodiments thereof, to anything stated or implied therein or inferred thereupon. In a traditional way, retail stores may either label each item with barcode or quick response (QR) code. Sometimes, they may need cashiers to remember an ID of each item or non-labeled items. The ID could be a few letters, digits or combination of letters and digits. The barcode, QR code or ID may be used for connecting to a database to get the price of that item. A point-of-sale (POS) is a place where a retail transaction may be completed. It is the place that a customer makes a payment to the merchant in exchange for goods. At the point of sale, the retailer may calculate the total amount customer needs to pay and provide options for the customer to make a payment. The merchant may also normally issue a receipt for the transaction. There are various POS systems for different retail industries uses. Different retail industries may use their customized hardware and software according to their requirements. Many retailers may use weighing scales, scanners, electronic and manual cash registers, terminals, touch screens and any other wide variety of hardware and software available for us with POS. For example, a grocery store may use a scale at the point of sale, while restaurants may use software to customize service sold when a customer requests for a meal or drink. The point of sale may also be referred to as a point of service. It is because it is not just a point of sale, but also a point of return or customer order. The POS system has many features such as inventory management, CRM, financials and warehousing, etc.
By way of educational background, another aspect of the prior art generally useful to be aware of is a cloud-based POS is a system deployed as software as a service, which may be accessed directly from the Internet by using an internet browser. Cloud-based POS systems may be independent from platform and operating system limitations. It may also be designed to be compatible with a wide range of POS hardware and sometimes compatible with mobile devices. Cloud-based POS systems may store data, and inventory in a remote server. The cloud-based POS system may not run locally, so there may be no installation required in the local store.
FIG. 1 illustrates an exemplary flow diagram for machine learning, in accordance with an embodiment of the present invention;
FIG. 2 illustrates an exemplary flow diagram for machine learning with key entry, in accordance with an embodiment of the present invention;
FIG. 3 illustrates an exemplary prediction, in accordance with an embodiment of the present invention;
FIG. 4 illustrates an exemplary neural network architecture, in accordance with an embodiment of the present invention;
FIG. 5 illustrates an exemplary integrated machine learning system, in accordance with an embodiment of the present invention;
FIG. 6 illustrates an exemplary device with scanner and camera capability, in accordance with an embodiment of the present invention;
FIG. 7 illustrates an exemplary block diagram of a device with scanner and camera capability, in accordance with an embodiment of the present invention;
FIG. 8 illustrates an exemplary block diagram of a surveillance system, in the prior art;
FIG. 9 illustrates an exemplary block diagram of a surveillance system, in accordance with an embodiment of the present invention;
FIG. 10 illustrates an exemplary block diagram of a surveillance system, in accordance with an embodiment of the present invention;
FIG. 11 illustrates an exemplary method for a search, in accordance with an embodiment of the present invention; and
“Configured To.” Various units, circuits, or other components may be described or claimed as “configured to” perform a task or tasks. In such contexts, “configured to” or “operable for” is used to connote structure by indicating that the mechanisms/units/circuits/components include structure (e.g., circuitry and/or mechanisms) that performs the task or tasks during operation. As such, the mechanisms/unit/circuit/component can be said to be configured to (or be operable) for perform(ing) the task even when the specified mechanisms/unit/circuit/component is not currently operational (e.g., is not on). The mechanisms/units/circuits/components used with the “configured to” or “operable for” language include hardware-—for example, mechanisms, structures, electronics, circuits, memory storing program instructions executable to implement the operation, etc. Reciting that a mechanism/unit/circuit/component is “configured to” or “operable for” perform(ing) one or more tasks is expressly intended not to invoke 35 U.S.C. .sctn.112, sixth paragraph, for that mechanism/unit/circuit/component. “Configured to” may also include adapting a manufacturing process to fabricate devices or components that are adapted to implement or perform one or more tasks
Many embodiments, and variations thereof, may provide for machine learning methods and means for learning from, but not limited to, tags, barcodes, human entering IDs, QR codes, radio-frequency identification (RFID)s, etc. In some embodiments, a machine learning system may identify an item associated with a database. The machine learning system may classify the item by obtaining an image or images from, without limitation, a camera. The system may classify the item to match a database by, but not limited to, ID, barcode or QR code etc.
Some embodiments, may be implemented in a component such as, but not limited to, a smart device with a scanner and a camera capability. Through a network connection, the smart device may send barcode and associated images to a cloud computing system. The cloud computing system may train a neural network or machine learning database. In some embodiments, the smart device may be incorporated in a robotic like device. Some embodiments may be implemented for recognizing an ID of a customer.
In some embodiments, a POS system may include, without limitation, a scanner component, which may scan, but not limited to, barcodes, QR codes, RFIDs, IDs, etc., a camera component, which may capture one or more images of objects, and a computing component with a prediction algorithm to classify the object. In some embodiments, the POS system may include one or more database for, but not limited to, a point of sale, a point of return or customer order, aisle number and map of items, inventory management, customer relationship management (CRM), financials and warehousing, etc. In some embodiments, the POS system may include a training database in a server system for training a neural network or artificial intelligence algorithm. In some embodiments, the POS system may include a database for storing a POS database for such as, but not limited to, calculating total price, making a payment in exchange for goods, issuing a receipt for the transaction, inventory management, CRM, financials and warehousing, etc. In some embodiments, the POS system may include an image database for, but not limited to, supervised training. In some embodiments, the POS system may include a database for storing aisle number and floor map of items. In some embodiments, the POS system may function to display the aisle number and floor map of searched items. In some embodiments, the POS system may function to compare an aisle number and floor maps of an object to a database to see whether it is misplaced or not.
Some embodiments may be incorporated with a surveillance system. In some embodiments, the surveillance system may incorporate supervised learning. In some embodiments, the surveillance system may incorporate unsupervised learning. In some embodiments, the surveillance system may include, but not limited to: one or more devices that may include, but not limited to, a camera unit for capturing images, and a computing unit for capturing images/videos to identify names of the people; a server system for training on images/video and uploading neural weights to the one or more devices; and a network for linking the devices and the server system. In some embodiments, the surveillance system may find a similarity of a certain signature or image and report the location.
FIG. 1 illustrates an exemplary flow diagram for machine learning, in accordance with an embodiment of the present invention. In the present embodiment, a system 100 such as, without limitation, a POS system, may have a scanner system 120 for scanning identification means of an item or object such as, but not limited to, tags, barcodes, QR codes, RFIDs, etc. Scanner system 120 may also include an imaging device such as, but not limited to, a camera for capturing one or more images of the item. The scanned identification may be used to obtain an ID 110 for the object from a database 105. The ID for the item and the captured images may then be transferred to training system 115. The images associated with the ID may be stored into an images database. In some embodiments, a supervised training method may be used to train a neural network system. After, the training system 115 obtains enough images for each classification items and runs enough epochs (iterations) for the training. As a non-limiting example, this process may take hours or days. Later, the training system 115 may send trained synaptic weights to prediction module 125 to predict the classification of new objects. An error rate 130 may be determined from comparing the ID 110 from identification means and ID from prediction module 125. When the error rate is at an acceptable level, the prediction module 125 may be used to classify objects instead of scanning identification means to identify the object.
FIG. 2 illustrates an exemplary flow diagram for machine learning with key entry, in accordance with an embodiment of the present invention. In the present embodiment, a system 200 such as, without limitation, a POS system, may have a scanner system 220 for scanning identification means of an item or object such as, but not limited to, tags, barcodes, QR codes, RFIDs, etc. Scanner system 220 may also include an imaging device such as, but not limited to, a camera for capturing one or more images of the item. If the scanner system 220 obtained an identification 222, the scanned identification may be used to obtain an ID 210 for the object from a database 205. If the scanner system 220 did not obtained an identification 222, a manual entry 224 may be used to obtain an ID 210 for the object from a database 205. The ID for the item and the captured images may then be transferred to training system 215. The images associated with the ID may be stored into an images database. In some embodiments, a supervised training method may be used to train a neural network system. After, the training system 215 obtains enough images for each classification items and runs enough epochs for the training. As a non-limiting example, this process may take hours or days. Later, the training system 215 may send trained synaptic weights to prediction module 225 to predict the classification of new objects. An error rate 230 may be determined from comparing the ID 210 from identification means and ID from prediction module 225. When the error rate is at an acceptable level, the prediction module 225 may be used to classify objects instead of scanning identification means to identify the object.
In some embodiments, a supervised learning problem may have access to labeled training examples (x(i), y(i)) where x is an input image and y is a classification ID. A training model may use a softmax regression model (or multinomial logistic regression). A fixed training set may be: {(x(1),y(1), . . . , (x(m),y(m))} of m labeled examples where the input features are: x(i)ε
The present example may use a notational convention of letting the feature vectors x be n+1 dimensional, with x0=1 corresponding to the intercept term. With logistic regression in the binary classification setting, the labels may be:
y(i)ε{0,1}
A hypothesis may take the form:
h θ  ( x ) = 1 1 + exp  ( - θ T  x ) ,
Given a training set of m examples, an overall cost function may be defined to be the model parameters θ were trained to minimize the cost function:
J  ( θ ) = - 1 m  [ ∑ i = 1 m   y ( i )  log   h θ  ( x ( i ) ) + ( 1 - y ( i ) )  log  ( 1 - h θ  ( x ( i ) ) ) ]
One iteration of batch gradient descent may be implemented as follows:
∇ θ   j  J  ( θ ) = - 1 m  ∑ i = 1 m   [ x ( i )  ( 1  { y ( i ) = j } - p  ( y ( i ) = j  x ( i ) ; θ ) ) ]
The cost function may be modified by adding a weight decay term:
λ 2  ∑ i = 1 k  ∑ j = 0 n  θ ij 2
which may penalize large values of the parameters. The cost function is now:
J   θ = - 1 m  [ ∑ i = 1 m   ∑ j = 1 k   1  { y ( i ) = j }  log   θ j T  x ( i ) ∑ i = 1 k   θ i T  x ( i ) ] + λ 2  ∑ i = 1 k   ∑ j = 0 n  θ ij 2
With this weight decay term (for any λ>0), the cost function J(θ) may be now strictly convex, and may be guaranteed to have a unique solution. The Hessian is now invertible, and because J(θ) is convex, algorithms such as gradient descent, L-BFGS, etc. are guaranteed to converge to the global minimum.
To apply an optimization algorithm, a derivative of this new definition of J(θ) may be needed. One may show that the derivative is:
∇ θ   j  J  ( θ ) = - 1 m  ∑ i = 1 m   [ x ( i )  ( 1  { y ( i ) = j } - p  ( y ( i ) = j  x ( i ) ; θ ) ) ] + λθ j
By minimizing J(θ) with respect to θ, one may have a working implementation of softmax regression.
To train the neural network, one may now repeatedly take iterations of gradient descent to reduce the cost function J(θ). After training process, one may reduce the value of cost function after running back propagation for many Enter ID (iterations). When the error rate achieves to an acceptable level, the system may store the neural weights for prediction process.
By using the neural weights in the prediction process, one may predict objects in prediction module 125 or 225. In a non-limiting example, prediction module 125 or 225 may predict it is a gala apple and a kind of apple. Each kind of apple may have an object ID number associated with price, the POS system may calculate the weight and the price.
FIG. 3 illustrates an exemplary prediction, in accordance with an embodiment of the present invention. In the present prediction example 300, a captured image 335 may be predicted by prediction module 125 or 225 and may predict it is a gala apple and a kind of apple 350. Similar images 340 may be stored in training system 115 or 215. Each kind of apple may have an object ID number 345 associated with price, the POS system may calculate the weight and the price.
FIG. 4 illustrates an exemplary neural network architecture, in accordance with an embodiment of the present invention. A neural network 400 may include a plurality of multiple stages of subset neural layers 465, a plurality of stages of fully connected layers 470, and a final stage of a classifier layer 475. Subset neural network 465 may have a convolutional layer 455 and a pooling layer 460. Convolutional layer 455 may compute a forward pass and a backpropagation pass, and may have trainable filters and one trainable bias per feature map. A hyperbolic tangent or rectified function may be applied to activations in this layer. In convolutional layers 455, each map may be connected to all of its preceding feature maps. The purpose of pooling layers 460 may be to achieve spatial invariance by reducing a resolution of the feature maps. Each pooled feature map may correspond to one feature map of the previous layer. The unit of the pooling layer may be an n x n patch size window. The pooling window may be of arbitrary size, and windows may be overlapping.
One may evaluate two different pooling operations: max pooling and subsampling.
The subsampling function:
a j = tanh ( β  ∑ N × N   a i n × n + b )
takes the average over the inputs, multiplies it with a trainable scalar, adds a trainable bias b, and passes the result through the non-linearity. The max pooling function:
a j = max N × N  ( a i n × n  u  ( n , n ) )
applies a window function u(x; y) to the input patch, and computes the maximum in the neighborhood. In both cases, the result may be a feature map of lower resolution.
The training may use the forward propagation and backpropagation algorithm. For error propagation and weight adaption in convolutional layers 455, pooling layers 460, fully connected layers 470, and classifier layer 475 may use a standard procedure of neural networks. After training neural network 400 with the image database, the system may calculate proper weights which may be used to classify the objects.
In some embodiments, the machine learning system may link with store layouts with aisles number, and may cooperate with a mobile robot such as, without limitation, having wheels or legs with camera to navigate aisle by aisle. In the present embodiment, the machine learning system may map items with the layout of the store. In some embodiments, using the map of items, a navigation system may help shoppers find what they want. In some embodiments, the system may be be able to display aisle numbers and floor map for searched items. In some embodiments, the robot with deep learning or machine learning algorithm may identify the misplaced items. In some embodiments, the robot may either put the misplaced items to the right place, or report a record of misplaced items and locations. In some alternate embodiments, the machine learning system may be used in warehouse systems.
In some embodiments, the machine learning system may be utilized to recognize a name of a customer. Some store cards may have at least one, barcode, QR code magnetic strip, or credit card number associated with personal name. As a non-limiting example, whenever the customer may enter the store the system may scan the store card and capture images of the customer. The system may send (image, id) as a pair to a database. Later, the system may have enough data to train a neural network to recognize the ID of the image. The system may use the ID to look up the name in the store-card's database to recognize the name of the customer from an image.
FIG. 5 illustrates an exemplary integrated machine learning system, in accordance with an embodiment of the present invention. In the present embodiment, an integrated system 500 such as, without limitation, a smart device, may have a scanner system 520 for scanning identification means of an item or object such as, but not limited to, tags, barcodes, QR codes, RFIDs, etc. Scanner system 520 may also include an imaging device such as, but not limited to, a camera for capturing one or more images of the item. The scanned identification may be used to obtain an ID 510 for the object from a database such as, without limitation, a POS database using a network connection 507. The ID for the item and the captured images may then be transferred to training system 515. The images associated with the ID may be stored into an images database. Training system 515 may send trained synaptic weights to prediction module 525 to predict the classification of new objects. An error rate 530 may be determined from comparing the ID 510 from identification means and ID from prediction module 525. When the error rate is at an acceptable level, the prediction module 525 may be used to classify objects instead of scanning identification means to identify the object and send the identification to the POS system using network connection 507.
FIG. 6 illustrates an exemplary device with scanner and camera capability, in accordance with an embodiment of the present invention. In the present embodiment, device 600 such as, without limitation, a smart device, may have a display 632 and sensors 624 for scanning identification means of an item or object such as, but not limited to, tags, barcodes, QR codes, RFIDs, etc., and an imaging device such as, but not limited to, a camera for capturing one or more images of the item. Device 600 may communicate with a server system, such as, without limitation, a cloud computing system. In operation, device 600 may send the cloud computing system for example, without limitation, IDs and associated images to the cloud computing system. The cloud computing system may train a neural network or machine learning database for recognition capability by using a supervision training algorithm. After training, the cloud computing system sends back neural weights to device 600 for object recognition. In some embodiments, the machine learning system may link with store layouts or maps with aisles number, and may cooperate with a mobile robot such as, without limitation, having wheels or legs with camera to navigate aisle by aisle. In a non-limiting example, the robot may move around and capture 2D or 3D map and environment. With the function to recognize the IDs of goods and to calculate the aisle location and map, device 600 may compare to the aisle map in the store's database whether it is in the right location or not.
FIG. 7 illustrates an exemplary block diagram of a device with scanner and camera capability, in accordance with an embodiment of the present invention. In the present embodiment, device 700 such as, without limitation, a smart device, may have a network connection 707, a scanner 721 for scanning identification means of an item or object such as, but not limited to, tags, barcodes, QR codes, RFIDs, etc., an imaging device 723 such as, but not limited to, a camera for capturing one or more images of the item, and a prediction module 727. Device 700 may communicate with a server system, such as, without limitation, a cloud computing system. In operation, device 700 may send the cloud computing system for example, without limitation, IDs and associated images to the cloud computing system. The cloud computing system may train a neural network or machine learning database for recognition capability by using a supervision training algorithm. After training, the cloud computing system sends back neural weights to prediction module 727 for object recognition.
FIG. 8 illustrates an exemplary block diagram of a surveillance system, in the prior art. Typically, traditional surveillance systems 800 may use a device 806 to capture images with or without motion detection. Those images may be uploaded to a network server 811 for later analysis by a machine or a human.
FIG. 9 illustrates an exemplary block diagram of a surveillance system, in accordance with an embodiment of the present invention. In the present embodiment, system 900 may include a device 907 to capture images within a surveilled area. Captured images may be sent to a network server system 912. The present embodiment may learn how to classify an object by using a plurality of images from an existing database or capturing images from cameras. Neural weights may be learned from server system 912. System 912 may upload the neural weights to device 907. Then device 907 may classify the objects by using parameters that may be calculated by using neural weights. If device 907 cannot classify an object with higher score, device 907 may ask for instructions from a human or any other help. After giving instruction or help, device 907 may do a small learning in device or send to server 912. If device 907 sends the images with a low probability to server 912, then server 912 may try to learn from the images/video again and update the neural weights to device 907. Thus, the system may increase the accuracy.
Some embodiments may use a supervised learning. As a non-limiting example, the system 900 may provide labeled personal images in a company. The neural network may be trained to classify names of people by using a tagged/labeled database. Company security may check who is in front of camera.
Some embodiments may use an unsupervised training where people may be identified thru unsupervised learning. In some embodiments, the learning may run on device 907. In some embodiments, images may be uploaded to server 912 for unsupervised learning. Because of non-labeled images, device 907 or server 912 may just classify to different people without the proper name. As a non-limiting example, images may be classified into people-0, people-1, etc. In some embodiments, names may also be registered to put names on after the device has classified to people-0 or people-1, etc.
FIG. 10 illustrates an exemplary block diagram of a surveillance system, in accordance with an embodiment of the present invention. In the present embodiment, system 1000 may include a device 1008 to capture images within a surveilled area. Device 1008 may do a partial training. Classified labels may be sent to a network server system 1013 to reduce communication bandwidth.
FIG. 11 illustrates an exemplary method for a search, in accordance with an embodiment of the present invention. Referring to FIG. 9 or FIG. 10, in the present embodiment, a search for, but not limited to, an image or a signature may begin at a step 1102 where network server system 912 or 1013 may send to device 907 or 1008 the image or signature for the search. In a step 1100 device 907 or 1008 may attempt to detect a similarity of the image or signature in a captured image or signature. In a step 1106, if device 907 or 1008 may not find a high probability of detection the process may return to step 1104. If device 907 or 1008 may find a high probability of detection, device 907 or 1008 may report their network location to network server system 912 or 1013. In a non-limiting example, to find a person named “Peter” the system may upload a picture or signature of “Peter” from network server system 912 or 1013 to device 907 or 1008, then the system may ask device 907 or 1008 to find “Peter”. Once the device 907 or 1008 finds “Peter”, the device 907 or 1008 may send a message to network server system 912 or 1013. Network server system 912 or 1013 may know which device and location of the device. The system may identify the location of “Peter”.
Client 1202 may communicate bi-directionally with local network 1206 via a communication channel 1216. Client 1204 may communicate bi-directionally with local network 1208 via a communication channel 1218. Local network 1206 may communicate bi-directionally with global network 1210 via a communication channel 1220. Local network 1208 may communicate bi-directionally with global network 1210 via a communication channel 1222. Global network 1210 may communicate bi-directionally with server 1212 and server 1214 via a communication channel 1224. Server 1212 and server 1214 may communicate bi-directionally with each other via communication channel 1224. Furthermore, clients 1202, 1204, local networks 1206, 1208, global network 1210 and servers 1212, 1214 may each communicate bi-directionally with each other.
In one embodiment, global network 1210 may operate as the Internet. It will be understood by those skilled in the art that communication system 1200 may take many different forms. Non-limiting examples of forms for communication system 1200 include local area networks (LANs), wide area networks (WANs), wired telephone networks, wireless networks, or any other network supporting data communication between respective entities.
Clients 1202 and 1204 may take many different forms. Non-limiting examples of clients 1202 and 1204 include personal computers, personal digital assistants (PDAs), cellular phones and smartphones.
Client 1202 includes a CPU 1226, a pointing device 1228, a keyboard 1230, a microphone 1232, a printer 1234, a memory 1236, a mass memory storage 1238, a GUI 1240, a video camera 1242, an input/output interface 1244 and a network interface 1246.
CPU 1226, pointing device 1228, keyboard 1230, microphone 1232, printer 1234, memory 1236, mass memory storage 1238, GUI 1240, video camera 1242, input/output interface 1244 and network interface 1246 may communicate in a unidirectional manner or a bi-directional manner with each other via a communication channel 1248. Communication channel 1248 may be configured as a single communication channel or a multiplicity of communication channels.
CPU 1226 may be comprised of a single processor or multiple processors. CPU 1226 may be of various types including micro-controllers (e.g., with embedded RAM/ROM) and microprocessors such as programmable devices (e.g., RISC or SISC based, or CPLDs and FPGAs) and devices not capable of being programmed such as gate array ASICs (Application Specific Integrated Circuits) or general purpose microprocessors.
As is well known in the art, memory 1236 is used typically to transfer data and instructions to CPU 1226 in a bi-directional manner. Memory 1236, as discussed previously, may include any suitable computer-readable media, intended for data storage, such as those described above excluding any wired or wireless transmissions unless specifically noted. Mass memory storage 1238 may also be coupled bi-directionally to CPU 1226 and provides additional data storage capacity and may include any of the computer-readable media described above. Mass memory storage 1238 may be used to store programs, data and the like and is typically a secondary storage medium such as a hard disk. It will be appreciated that the information retained within mass memory storage 1238, may, in appropriate cases, be incorporated in standard fashion as part of memory 1236 as virtual memory.
CPU 1226 may be coupled to GUI 1240. GUI 1240 enables a user to view the operation of computer operating system and software. CPU 1226 may be coupled to pointing device 1228. Non-limiting examples of pointing device 1228 include computer mouse, trackball and touchpad. Pointing device 1228 enables a user with the capability to maneuver a computer cursor about the viewing area of GUI 1240 and select areas or features in the viewing area of GUI 1240. CPU 1226 may be coupled to keyboard 1230. Keyboard 1230 enables a user with the capability to input alphanumeric textual information to CPU 1226. CPU 1226 may be coupled to microphone 1232. Microphone 1232 enables audio produced by a user to be recorded, processed and communicated by CPU 1226. CPU 1226 may be connected to printer 1234. Printer 1234 enables a user with the capability to print information to a sheet of paper. CPU 1226 may be connected to video camera 1242. Video camera 1242 enables video produced or captured by user to be recorded, processed and communicated by CPU 1226.
CPU 1226 may also be coupled to input/output interface 1244 that connects to one or more input/output devices such as such as CD-ROM, video monitors, track balls, mice, keyboards, microphones, touch-sensitive displays, transducer card readers, magnetic or paper tape readers, tablets, styluses, voice or handwriting recognizers, or other well-known input devices such as, of course, other computers.
Finally, CPU 1226 optionally may be coupled to network interface 1246 which enables communication with an external device such as a database or a computer or telecommunications or internet network using an external connection shown generally as communication channel 1216, which may be implemented as a hardwired or wireless communications link using suitable conventional technologies. With such a connection, CPU 1226 might receive information from the network, or might output information to a network in the course of performing the method steps described in the teachings of the present invention.
It will be further apparent to those skilled in the art that at least a portion of the novel method steps and/or system components of the present invention may be practiced and/or located in location(s) possibly outside the jurisdiction of the United States of America (USA), whereby it will be accordingly readily recognized that at least a subset of the novel method steps and/or system components in the foregoing embodiments must be practiced within the jurisdiction of the USA for the benefit of an entity therein or to achieve an object of the present invention. Thus, some alternate embodiments of the present invention may be configured to comprise a smaller subset of the foregoing means for and/or steps described that the applications designer will selectively decide, depending upon the practical considerations of the particular implementation, to carry out and/or locate within the jurisdiction of the USA. For example, any of the foregoing described method steps and/or system components which may be performed remotely over a network (e.g., without limitation, a remotely located server) may be performed and/or located outside of the jurisdiction of the USA while the remaining method steps and/or system components (e.g., without limitation, a locally located client) of the forgoing embodiments are typically required to be located/performed in the USA for practical considerations. In client-server architectures, a remotely located server typically generates and transmits required information to a US based client, for use according to the teachings of the present invention. Depending upon the needs of the particular application, it will be readily apparent to those skilled in the art, in light of the teachings of the present invention, which aspects of the present invention can or should be located locally and which can or should be located remotely. Thus, for any claims construction of the following claim limitations that are construed under 35 USC §112 (6) it is intended that the corresponding means for and/or steps for carrying out the claimed function are the ones that are locally implemented within the jurisdiction of the USA, while the remaining aspect(s) performed or located remotely outside the USA are not intended to be construed under 35 USC §112 (6). In some embodiments, the methods and/or system components which may be located and/or performed remotely include, without limitation: It is noted that according to USA law, all claims must be set forth as a coherent, cooperating set of limitations that work in functional combination to achieve a useful result as a whole. Accordingly, for any claim having functional limitations interpreted under 35 USC §112 (6) where the embodiment in question is implemented as a client-server system with a remote server located outside of the USA, each such recited function is intended to mean the function of combining, in a logical manner, the information of that claim limitation with at least one other limitation of the claim. For example, in client-server systems where certain information claimed under 35 USC §112 (6) is/(are) dependent on one or more remote servers located outside the USA, it is intended that each such recited function under 35 USC §112 (6) is to be interpreted as the function of the local system receiving the remotely generated information required by a locally implemented claim limitation, wherein the structures and or steps which enable, and breath life into the expression of such functions claimed under 35 USC §112 (6) are the corresponding steps and/or means located within the jurisdiction of the USA that receive and deliver that information to the client (e.g., without limitation, client-side processing and transmission networks in the USA). When this application is prosecuted or patented under a jurisdiction other than the USA, then “USA” in the foregoing should be replaced with the pertinent country or countries or legal organization(s) having enforceable patent infringement jurisdiction over the present application, and “35 USC §112 (6)” should be replaced with the closest corresponding statute in the patent laws of such pertinent country or countries or legal organization(s).
Having fully described at least one embodiment of the present invention, other equivalent or alternative methods of implementing machine learning systems according to the present invention will be apparent to those skilled in the art. Various aspects of the invention have been described above by way of illustration, and the specific embodiments disclosed are not intended to limit the invention to the particular forms disclosed. The particular implementation of the machine learning systems may vary depending upon the particular context or application. By way of example, and not limitation, the machine learning systems described in the foregoing were principally directed to point of sale systems implementations; however, similar techniques may instead be applied to automated manufacturing systems and warehouse systems, which implementations of the present invention are contemplated as within the scope of the present invention. The invention is thus to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the following claims. It is to be further understood that not all of the disclosed embodiments in the foregoing specification will necessarily satisfy or achieve each of the objects, advantages, or improvements described in the foregoing specification.
a scanner component, which can scan at least one of the following codes:
Barcode, QR code, RFID or any other new code and id.
a camera component, which can get image or picture of objects.
a compute component with prediction algorithm to classify the object;
2. A POS method comprising;
Steps for a machine learning system, the method using
a device with prediction and learning capability sends the classified labels to central controller or server;
a central controller or server gathers classified labels and analyze and learn from classified labels information; and
a central controller or server sends updated
US15/146,043 2015-05-04 2016-05-04 Machine learning system, method, and program product for point of sale systems Active 2036-06-29 US10282722B2 (en)
US201562156848P true 2015-05-04 2015-05-04
US15/146,043 US10282722B2 (en) 2015-05-04 2016-05-04 Machine learning system, method, and program product for point of sale systems
US20160328660A1 true US20160328660A1 (en) 2016-11-10
US10282722B2 US10282722B2 (en) 2019-05-07
ID=57221910
US15/146,043 Active 2036-06-29 US10282722B2 (en) 2015-05-04 2016-05-04 Machine learning system, method, and program product for point of sale systems
US (1) US10282722B2 (en)
CN108241860A (en) * 2018-01-23 2018-07-03 佛山市顺德区中山大学研究院 A kind of QR codes detection localization method under complex environment
WO2019119047A1 (en) * 2017-12-21 2019-06-27 Tiliter Pty Ltd A retail checkout terminal fresh produce identification system
EP3537340A1 (en) * 2018-03-09 2019-09-11 Ricoh Company, Ltd. User interface for object detection and labeling
US7130834B2 (en) * 1997-12-12 2006-10-31 Idaho Potato Commission Identification system and method for determining the geographic origin of a fresh commodity
JP5535508B2 (en) * 2009-03-31 2014-07-02 Ｎｅｃインフロンティア株式会社 Self-POS device and operation method thereof
US9053430B2 (en) * 2012-11-19 2015-06-09 Qualcomm Incorporated Method and apparatus for inferring logical dependencies between random processes
JP6177068B2 (en) * 2013-09-24 2017-08-09 株式会社ブレイン Store system
2016-05-04 US US15/146,043 patent/US10282722B2/en active Active
US10282722B2 (en) 2019-05-07
Hofmann et al. 2016 RapidMiner: Data mining use cases and business analytics applications
Tan et al. 2016 Improved recurrent neural networks for session-based recommendations
Chu et al. 2017 GARCH modelling of cryptocurrencies
US20120150790A1 (en) 2012-06-14 Generating a recommendation to add a member to a receptivity cohort
CN103098079A (en) 2013-05-08 Personalized program selection system and method