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 § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-10, 12 and 14-21 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (“Kim”)(US 11,961,281) in view of Piccinini (WO 2024/194080) and Capelli et al. (“Capelli”)(EP 4 764 753).
Kim teaches a system and method for automatically diverting products from a shipping lane, the system comprising:
(re: certain elements of claim 12) a package conveyor system including a shipping lane downstream of a package sorter, the package conveyor configured to transport products to the package sorter (fig. 1 showing package conveyor 130 transporting products from input 150 to robot stations 110 and then to downstream shipping element 160 wherein conveyor region near delivery vehicle can be regarded as shipping lane; col. 3, ln. 65-col. 4, ln. 55 teaching robot stations 110 configured to perform inventory tasks in order fulfillment system, e.g., storing item, picking item and packaging/preparing item for delivery to shipping/delivery element 160); and
one or more computing devices communicatively coupled to a network (fig. 1 showing network and central computer 180), the one or more computing devices configured to:
receive a plurality of digital images of products (col. 4, ln. 2-15 teaching training a computer vision model with images generated from robot stations 110 and camera elements attached thereto);
based on the plurality of digital images, generate a non-anomalous data set (col. 2, ln. 8-col. 3, ln. 25 teaching training machine learning model using one or more semi-supervised learning techniques that can involve labeled data, pseudo-labeled data and unlabeled data, wherein said data is image data from item packages and col. 7, ln. 16-col. 8, ln. 55 and col. 11, ln. 15-60 teaching continuous re-training of computer vision model with image data from robot/sorter stations, wherein machine learning model can classify an item as a package, as not a package, or other suitable classification and can include a confidence score associated with the classification—wherein data used to classify item as a package can be regarded as non-anomalous data);
train a machine learning model using the non-anomalous data set (Id.);
receive from an image capture device a digital image of a target product traveling on the conveyor system (Id.);
prior to the target product reaching the package sorter, determine, via the trained machine learning model, that the second target product is a non-anomalous product (Id.);
(re: claim 14) wherein the one or more computing devices includes a local computing device coupled through a local area network to the image capture device and a remote computing device coupled through a wide area network to the local computing device and wherein the local device is configured to determine, via the trained machine learning model that the target product is an anomalous product and the remote computing device is configured to train the machine learning model (col. 4, ln. 55-col. 5, ln. 50 teaching that robot/sorters including imaging elements can be connected via local portion of network 170 and computer system 180 can also include virtual/remote computer elements configured to train machine-learning model);
(re: claim 17) wherein the one or more computing devices are further configured to:
after diverting the target product from the shipping lane, receive at the computing device a diverting digital image of the target product; and
deliver a command signal to the package sorter to cause the package sorter to direct the target product to the shipping lane (col. 3, ln. 65-col. 4, ln. 55 teaching each robot station 110 configured to generate image of item and to remove/sort an item to holder 140, package/prepare item for delivery and then return said item to shipping lane for transport to shipping/delivery element 160);
(re: claim 18) wherein the one or more computing devices are further configured to:
receive from the image capture device a digital image of a second target product (fig. 1 showing conveying of multiple product types and col. 7, ln. 16-col. 8, ln. 55 and col. 11, ln. 15-60 teaching conveying, sorting, packaging and then return of target/packaged products to shipping lane for transport to shipping/delivery element 160);
prior to the target product reaching the package sorter, determine, via the trained machine learning model, that the target product is a non-anomalous product (Id.);
deliver a command signal to the package sorter to cause the package sorter to divert the non-anomalous product to the shipping lane (Id.);
(re: claim 19) wherein the one or more computing devices are further configured to:
calculate a confidence score representative of a lack of anomalies present on the second target product (Id. also teaching use of confidence score when classifying a product, wherein said score can be regarded as representative as lack of anomalies or defects in product classification).
(re: claims 1, 3-10) The claimed method steps are performed in the normal operation of the combined device described below.
Kim as set forth above teaches all that is claimed except for expressly teaching
(re: certain elements of claim 12) based on the plurality of digital images, generate an anomalous data set;
train a machine learning model using the anomalous data set;
deliver a command signal to the package sorter to cause the package sorter to divert the anomalous product from the shipping lane;
(re: claim 15) wherein the one or more computing devices are configured to determine that target product is an anomalous product within a timeframe of:
a) less than about 2 seconds from the target product reaching the package sorter along the conveyor;
b) less than about 5 seconds from the target product reaching the package sorter along the conveyor;
c) from about 2 seconds to about 5 seconds of the target product reaching the package sorter along the conveyor system;
d) from about 1 second to about 2 seconds of the target product reaching the package sorter along the conveyor system; or e) less than about 1 second from the target product reaching the package sorter along the conveyor;
(re: claim 16) wherein the one or more computing devices are further configured to calculate a confidence score representative of a severity of anomalies present on the target product;
(re: claim 20) wherein the anomalous product is characterized by defective seal;
(re: claim 21) wherein the image capture device is configured to scan the target product for a container ID and capture the digital image of the target product for determining an anomaly status of the target product.
Capelli, however, teaches that it is well-known in the automated material handling/sorting arts
(re: certain elements of claims 12, 20)
-to train a machine learning model with non-anomalous as well as anomalous data sets to improve the efficiency and accuracy of quality control/inspection elements to allow early detection/quality assessment and rejection/divert of unacceptable packages in automated sorting systems (para. 11, 14-18 teaching training machine-learning model with real-time data to identify anomalies in seal quality; para. 38-43 teaching use of data representing acceptable and unacceptable seal quality to train machine learning model that improves quality control and inspection processes in automated sorting systems);
(re: claim 15)
-to use real-time data of packages/products during conveyance to allow the machine learning model to generate real-time quality assessments (para. 12-16 teaching real-time measurements allow for immediate quality assessments and ongoing process optimization); and
(re: claim 21)
-to generate unique identifiers that can be associated with an item that allows scanning of said identifier to provide a quality indicator, i.e., anomaly status of the product, and improve the ability to monitor product quality (para. 16-25 teaching unique identifiers configured to display quality indicator and enhance real-time monitoring of the package/product line).
Piccinini further teaches that it is well-known in the automated material handling/packaging arts
(re: certain elements of claims 12, 20)
-to train a machine learning model with an anomalous data set to allow more accurate identification of defective, i.e., anomalous, packages (fig. 5 showing vision system 10 connected to artificial intelligence 24 and deviation monitoring module 26; p. 10, ln. 28-p. 11, ln. 13 and p. 13, ln. 25-p. 18, ln. 13 teaching use of anomalous data, i.e., images of defective seals, to improve automated defect detection and classification machine learning algorithm, wherein training data includes anomalous data related to “variations, defects, warning, objects of interest, suspicious areas”);
(re: claims 16, 20)
-to train a machine learning model with image data indicating quality measurement to allow the model to indicate the severity of the detected defects as well a confidence rating of the quality/defect measurement, wherein said defects can be defective package seals (col. 14, ln. 10-col. 15, ln. 30 teaching machine learning model configured to provide an array of quality assessments—e.g., severity, location and type of defects—based on image data; see also p. 7, ln. 3-p. 8, ln. 17 teaching vision system generating time evolution images of seal in packages to identify packages or products with defective seals via artificial intelligence algorithms and p. 10, ln. 28-p. 11, ln. 13 and p. 18, ln. 13 teaching use of anomalous data, i.e., images of defective seals).
It would thus be obvious to one with ordinary skill in the art to modify the base reference with these prior art teachings—with a reasonable expectation of success—to arrive at the claimed invention. The rationale for this obviousness determination can be found in the prior art itself.
Further, the prior art discussed and cited demonstrates the level of sophistication of one with ordinary skill in the art and that these modifications are predictable variations that would be within this skill level. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Kim
for the reasons set forth above.
Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, Piccinini and Capelli et al. (“Kim et al.”) as applied to the claims above, and further in view of what is well known in the art.
Kim et al. as set forth above teach all that is claimed except for expressly teaching
(re: claims 2, 13) wherein the conveyor system further includes a conveyor gapper and the one or more computing devices are configured to:
cause the conveyor gapper to cooperate with the image capture device to ensure that the digital image of the target product does not reflect an adjacent package.
The use of a conveyor gapper/singulator to improve image capture, however, is well-known in the automated material handling/conveying arts and Examiner takes Official Notice of such. It would thus be obvious to one with ordinary skill in the art to modify the combination of references with these prior art teachings—with a reasonable expectation of success—to arrive at the claimed invention as these modifications are already well-known and commonly implemented in the conveying arts. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the invention of Kim et al. for the reasons set forth above.
Allowable Subject Matter
Claims 11 and 22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any references not explicitly discussed above but made of record are regarded as helpful in establishing the state of the prior art and are thus considered relevant to the prosecution of the instant application.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH C RODRIGUEZ whose telephone number is 571-272-3692 (M-F, 9 am – 6 pm, PST). The Supervisory Examiner is MICHAEL MCCULLOUGH, 571-272-7805.
Alternatively, to contact the examiner, send an E-mail communication to Joseph.Rodriguez@uspto.gov. Such E-mail communication should be in accordance with provisions of the MPEP (see e.g., 502.03 & 713.04; see also Patent Internet Usage Policy Article 5). E-mail communication must begin with a statement authorizing the E-mail communication and acknowledging that such communication is not secure and may be made of record. Please note that any communications with regards to the merits of an application will be made of record. A suggested format for such authorization is as follows: "Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with me concerning any subject matter of this application by electronic mail. I understand that a copy of these communications will be made of record in the application file”.
Information regarding the status of an application may also be obtained from the Patent Center: https://patentcenter.uspto.gov/
/JOSEPH C RODRIGUEZ/Primary Examiner, Art Unit 3655
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July 28, 2026
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