DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-15 all recite subject matter falling within one of the four categories of invention (Step 1).
Claims 1-11 recite:
1. A method implemented by a computing system storing a model for determining the similarity of images and text from customer order information, using data on the verification product name, verification image, and the unique code registered by the entity assigning the unique code to the corresponding product, where the unique code, verification product name, and verification image are stored on an external server and copied and stored, comprising:extracting data from a target site;filtering the extracted data to display the desired information to the customer;extracting and storing the order product name, which is the name of the product displayed on the order screen, and the order image, which is the image of the product displayed;scanning and storing the unique code displayed on the received product;determining whether the verification product name and verification image corresponding to the unique code are data learned by the model, and if they are not learned data, downloading the verification product name and verification image corresponding to the unique code.
2. The method of claim 1, further comprising: predicting an order date based on a received product's arrival date, which is determined based on the time when the unique code is scanned and stored.
3. The method of claim 1, further comprising: searching for the verification product name and verification image corresponding to the unique code in the computing system's storage device or an external server, when the scanned unique code is already part of the learned data; andsearching for the order product name and order image corresponding to the verification product name and verification image based on the order date.
4. The method of claim 2, further comprising: when the order product name and order image corresponding to the verification product name and verification image do not exist, determining that the accuracy of the model that teamed the verification product name and verification image corresponding to the scanned unique code is low, and setting the verification product name and verification image corresponding to the unique code as a subject for learning.
5. The method of claim 1, further comprising: when the verification product name and verification image corresponding to the unique code match the order product name and order image, commanding the shipment of the received product; and designating a storage location for the received product in the order of the fastest order.
6. The method of claim 5, further comprising: displaying a storage location indication and a relocation command message for the received product.
7. The method of claim 1, further comprising: extracting GTIN information from the scanned unique code.
8. The method of claim 7, wherein the verification product name and verification image are information that matches the extracted GTIN information.
9. The method of claim 2, further comprising: counting the order quantity based on at least one of the order product name corresponding to the verification product name or the order product image corresponding to the verification image for the predicted order date.
10. The method of claim 9, further comprising: if the predicted order quantity matches the actual received quantity, labeling the remaining information in at least one of the order product name, order image, verification product name, or verification image, and classifying it into a high-accuracy data set.
11. The method of claim 9, further comprising: if the predicted order quantity and the actual received quantity do not match, labeling the remaining information in at least one of the order product name, order image, verification product name, or verification image, and classifying it into a low-accuracy data set.
But for the recitation of the underlined elements claims 1-11 recite concepts for relating customer order information and received product information. A person could mentally review details on a website, received product information and model output for matches. As such claims 1-11 recite a mental process (Step 2A_1).
Claims 1-11 contain the additional elements:
1) computing system, external server, storage device
2) model
3) displaying (claim 6)
4) scanning
5) downloading
The computing system, external server and storage device, and (machine learning) model are recited at a high degree of generality such that they amount to mere instructions to implement an abstract idea, which per MPEP 2106.05(f) means they do not provide a practical application or significantly more.
Displaying, scanning and downloading amount to data gathering or output, which per MPEP 2106.05(g) means those elements would be considered extra-solution activity that does not provide a practical application or significantly more. Additionally, those elements would not provide significantly more because they are well-understood, routine and conventional. Regarding downloading see case citations in MPEP 2106.05(d)(II)(i in first block of citations). Regarding scanning see case citations in MPEP 2106.05(d)(II)(v in first block of citations). Regarding displaying see case citations in MPEP 2106.05(d)(II)((iv in third block of citations).
Based on the above the additional elements alone and in combination do not provide a practical application or significantly more (Step 2A_2 and Step 2B) and claims 1-11 are ineligible.
Claims 12-14 recite:
12. A method implemented by a computer, comprising: extracting data from a target site; filtering the extracted data to display desired information to the customer; extracting and saving the order product name and order image corresponding to the product displayed when the customer places an order; scanning and saving the unique code displayed on the received product; verifying whether the verification product name and verification image corresponding to the unique code match the order product name and order image; and determining the arrival date of the received product based on the time the unique code is scanned and saved, and predicting the order date based on the arrival date.
13. The method of claim 12, further comprising: if the number of order product names and verification product names match based on the predicted order date, labeling the remaining information in at least one of the order product name, order image, verification product name, or verification image.
14. The method of claim 12, further comprising: if the number of order product names and verification product names do not match based on the predicted order date, labeling the remaining information in at least one of the order product name, order image, verification product name, or verification image; and performing ensemble learning using the matching and non-matching information.
But for the recitation of the underlined elements claims 12-14 recite concepts for relating customer order information and received product information. A person could mentally review details on a website and received product information and make determinations based on the information. As such claims 12-14 recite a mental process (Step 2A_1).
Claims 12-14 contain the additional elements:
1) computer
2) performing ensemble learning
3) scanning
The computer, and ensemble learning are recited at a high degree of generality such that they amount to mere instructions to implement an abstract idea, which per MPEP 2106.05(f) means they do not provide a practical application or significantly more.
Scanning amount to data gathering, which per MPEP 2106.05(g) means those elements would be considered extra-solution activity that does not provide a practical application or significantly more. Additionally, those elements would not provide significantly more because they are well-understood, routine and conventional. Regarding scanning see case citations in MPEP 2106.05(d)(II)(v in first block of citations).
Based on the above the additional elements alone and in combination do not provide a practical application or significantly more (Step 2A_2 and Step 2B) and claims 12-14 are ineligible.
Claim 15 recites:
15. A computer-implemented method comprising: extracting and storing the order product name, which is the name of the product displayed on the customer's device, and the order image, which is the image of the product displayed on the customer's device, when the customer places an order through a website;scanning and storing the unique code displayed on the product when the product arrives at the logistics warehouse;determining whether the verification product name and verification image corresponding to the unique code are learned data, and if they are not learned data, downloading the verification product name and verification image corresponding to the unique code;if the order product name and order image corresponding to the verification product name and verification image do not exist, setting the unique code as a learning target;wherein the verification product name is the product name registered by the entity that assigned the unique code,the verification image is the product image registered by the entity that assigned the unique code,the downloading is performed by a computing system that copies and stores data from an external server where the unique code, verification image, and verification product name are stored, andthe computing system learns the information of the verification product name and verification image upon input of the unique code and determines the similarity between the image and text from the customer's order information.
But for the recitation of the underlined elements claims 1-11 recite concepts for relating customer order information and received product information. A person could mentally review details on a website, received product information and model output for matches. As such claims 1-11 recite a mental process (Step 2A_1).
Claims 1-11 contain the additional elements:
1) computing system, external server, computer, customer device
2) scanning
3) downloading
The computing system, customer deivce, external server and computer are recited at a high degree of generality such that they amount to mere instructions to implement an abstract idea, which per MPEP 2106.05(f) means they do not provide a practical application or significantly more.
Scanning and downloading amount to data gathering or output, which per MPEP 2106.05(g) means those elements would be considered extra-solution activity that does not provide a practical application or significantly more. Additionally, those elements would not provide significantly more because they are well-understood, routine and conventional. Regarding downloading see case citations in MPEP 2106.05(d)(II)(i in first block of citations). Regarding scanning see case citations in MPEP 2106.05(d)(II)(v in first block of citations).
Based on the above the additional elements alone and in combination do not provide a practical application or significantly more (Step 2A_2 and Step 2B) and claim 15 is ineligible.
Claim Status
Claims 1-15 are considered to distinguish over the cited art. Naish (US 20020156706 A1) discloses matching incoming shipments to customer purchase orders. Heitner (US 20040193502 A1) discloses matching inbound and outbound inventory. Shi (US 20140100992 A1) discloses matching inbound shipments with pending customer orders. Arun Singhal (US 9336509 B1) discloses crossdocking inbound shipments. Birsan (US 20230031992 A1) discloses automatically printing labels based on receiving inbound shipments corresponding to orders awaiting fulfillment. Logiotatidis (US 20240354710 A1) discloses a technology for extracting information from a document related to an order. Sundaresan (US 20250024977 A1) discloses a technology for parsing order confirmations. None of the cited art discloses the subject matter of claims 1-15.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN A MITCHELL whose telephone number is (571)270-3117. The examiner can normally be reached M-F 9-5.
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/NATHAN A MITCHELL/ Primary Examiner, Art Unit 3627
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