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 .
Status of Claims
Applicant’s submission filed 7/14/25 has been entered. Claims 1-17 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7/14/25 and 7/2/26 have been considered by the examiner.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims disclose the abstract idea of helping a worker find the best empty container to use in a factory or warehouse, by first acquiring worker positioning information and a storage-area image, then analyzing the image to identify which empty-container types are present and where all containers are located. It uses the overlap between empty-container attributes and container classification labels to filter candidate empty containers. It then combines candidate container positions with the worker’s location to choose the target empty container that is most convenient to determine the shortest route.
STEP 1
Are the claims directed to a process, machine, manufacture or composition of matter?
The claims are all directed to a statutory category (e.g., a process, machine, manufacture, or composition of matter). The answer is YES.
STEP 2A. Prong 1
Exemplary claim 9 recites the following abstract concepts that are found to include “abstract idea”:
“acquire worker positioning information and a storage area image of loading containers;
identify an empty container attribute set in the storage area image;
determine, based on the storage area image, a container classification label corresponding to each loading container and container position information corresponding to each loading container; and
determine a target empty container based on the empty container attribute set, the container classification label corresponding to each loading container, the container position information corresponding to each loading container and the worker positioning information..”
The remaining limitations are no more than computer elements (i.e., a processor) to be used as a tool to perform this abstract idea.
The recited limitations cover a process that, under its broadest reasonable interpretation, covers subject matter viewed as a certain method of organizing human activity with the additional recitation of generic computer components. For example, but for the “at least one processor configured to” language, “acquire, identify, determine and determining” in the context of this claim encompasses the user acquiring a worker’s positioning information, a storage area image, analyzing the image to identify empty-container types and position relative to the worker’s position; choose the target empty container that is most convenient to determine the shortest route for the worker to the empty container.
The practice of acquiring, identifying, determining and determining data, as well as selecting the most convenient empty container based on location is a commercial or legal interaction long prevalent in our system of commerce. The claims recite the idea of performing various conceptual steps generically resulting in the selecting the most convenient empty container based on location. As determined earlier, none of these steps recites specific technological implementation details, but instead get to this result by receiving, selecting and determining data. Thus, the claims recite an abstract idea, specifically a certain method of organizing human activity.
STEP 2A, Prong 2
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
The claim recites one additional element: that a processor is used to perform steps.
The processor in the steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (acquiring, by a processor, worker positioning information). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea.
STEP 2B
The next issue is whether the claims provide an inventive concept because the additional elements recited in the claims provide significantly more than the recited judicial exception. Taking the claim elements separately, the function performed by the processor at each step of the process is purely conventional. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Considered as an ordered combination, the computer components of Applicants' claims add nothing that is not already present when the steps are considered separately. The claimed invention does not focus on an improvement in computers as tools, but rather certain independently abstract ideas that use computers as tools. {Elec. Power, 830 F.3d at 1354). (Step 2B: NO).
There is no indication that indication that the processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. Court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Independent claim 9 recites similar limitations as claim 1 and is therefore rejected under the same rationale.
The dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The claims provide minimal technical structure or components for further consideration either individually or as ordered combinations with the independent claims. As such, additional recited limitations in the dependent claims only refine the identified abstract idea further. Further refinement of an abstract idea does not convert an abstract idea into something concrete.
**For example, claims 5 and 6 recite -- performing feature extraction on the storage area image through the backbone identification network to obtain loading container attribute features; and performing feature classification on the loading container attribute features through the attribute classifier to obtain the empty container attribute set.
**Claim 7 recites determining the container classification label by a container detection model.
NOTE: The backbone identification network, the attribute classifier, the container detection model are used as tools to perform the identifying, extracting and classifying.
Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer Option 2.
See MPEP 2106.05(d)(II) The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350,1355,112 USPQ2d 1093,1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hoteis.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306,1334,115 USPQ2d 1681,1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363,115 USPQ2d at 1092-93.
The claims are ineligible.
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, 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, 2, 4, 7-10, 12, 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over HE et al. (CN114267032A), in view of Al et al. (CN 113978993 A).
Re-claim 1, HE et al. teach --A method for positioning an empty container, the method comprising:
--acquiring
(see e.g. HE -- preprocessing an image to be detected to obtain a processed image;
carrying out container positioning on the processed image through a trained container positioning model to obtain container positioning information, wherein the container positioning information comprises container position information, container type, container number position information and container number type;)
--identifying an empty container attribute set in the storage area image;
(see e.g. HE --according to the container positioning information, character positioning recognition is carried out on the processed image through a trained character recognition model to obtain a character recognition result; and detecting and checking the character recognition result, and outputting to obtain the container number.)
--determining, based on the storage area image, a container classification label corresponding to each loading container and container position information corresponding to each loading container; and
(see e.g. HE --wherein the container positioning model is used for identifying the positions of containers, container numbers and container types, and the character identification model is used for identifying the positions and the classifications of characters of container numbers.)
--determining a target empty container based on the empty container attribute set, the container classification label corresponding to each loading container, the container position information corresponding to each loading container
(see e.g. HE -the container positioning model can process the image containing a plurality of containers, and then quickly identify the container number of the container through the character identification model, thereby effectively improving the identification accuracy, aiming at the images of the plurality of container bodies, having wider application scenes compared with the identification method of the container number of the single container, reducing the operation links of a port, promoting the automation and the intellectualization of the port container management, and greatly improving the working efficiency.
HE does not teach the following limitations.
However, AI teach ----acquiring worker positioning information and--determining a target empty container based on [..] the container position information corresponding to each loading container and the worker positioning information.
(see e.g. AI -- Further, for each robot, based on the current position of the robot and the storage position of each empty material box, determining the number of empty material box in the preset range of the robot,
---Specifically, for each robot, according to the current position of the robot and the storage position of each empty material box, determining the first distance of the robot and each empty material box, according to the first distance and the number of cache bits above the shelf device of the warehousing work station, determining each target robot, to reduce the walking distance of each empty material box extracted by the target robot,
-- according to the order requirement of the warehousing order corresponding to the warehousing work station, determining one or more empty material boxes stored in the storage rack device of the warehousing work station as the storage box to be stored.
--, determining one or more empty material boxes stored in the storage rack device of the warehousing work station as the storage box to be stored.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify HE, and include the steps cited above, as taught by AI, in order to improve the efficiency of the order processing, and further, in order to reduce the walking path of each target robot (or worker) (see AI et al.).
Re-claim 2, HE teaches The method of claim 1, wherein the determining of the target empty container comprises:
a--determining each candidate empty container based on the empty container attribute set and the container classification label corresponding to each loading container;
(see e.g. HE---Aiming at the existing tasks of container positioning and box number identification, the method mainly comprises an algorithm based on template matching and an algorithm based on a neural network. The box number identification process is mainly divided into three steps: the first step is to position the area of the container number; the second step is to divide the single character in the serial number; and thirdly, identifying the characters one by one and finally forming the identified serial number character string. Such a technique can effectively identify the numbers in a single container in a relatively clear and relatively angularly correct photograph.
Although HE teaches for identifying the positions of containers (see e.g. HE--- carrying out container positioning on the processed image through a trained container positioning model to obtain container positioning information,
wherein the container positioning model is used for identifying the positions of containers, container numbers and container types, and the character identification model is used for identifying the positions and the classifications of characters of container numbers.
AI et al. explicitly teach--determining a candidate container position corresponding to each candidate empty container based on the container position information corresponding to each loading container; and -- determining the target empty container based on the candidate container position corresponding to each candidate empty container and the worker positioning information.
(see e.g. AI et al. ---Further, in order to reduce the walking path of each target robot, when determining the carrying task of the target robot, it also can consider the storage position of each empty material box.
--Specifically, for each robot, according to the current position of the robot and the storage position of each empty material box, determining the first distance of the robot and each empty material box, )
Further, according to the current position of each robot, determining one or more target robot, preferably selecting distance from the warehousing work station or empty box closer to the robot executing the warehousing work station taking and discharging task, so as to improve the efficiency of robot taking and placing goods, so as to improve the efficiency of the order processing.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify HE, and include the steps cited above, as taught by AI et al., in order to improve the efficiency of the order processing (see AI et al.).
Re-claims 4, 8, HE does not teach the limitations as claimed.
However, AI et al. teach The method of claim 2, wherein the determining of the target empty container based on the candidate container position corresponding to each candidate empty container and the worker positioning information comprises:
-- determining, based on the candidate container position corresponding to each candidate empty container and the worker positioning information, each movement trajectory between a worker and each candidate empty container and
(see e.g. AI et al. ---Specifically, for each robot, according to the current position of the robot and the storage position of each empty material box, determining the first distance of the robot and each empty material box, according to the first distance and the number of cache bits above the shelf device of the warehousing work station, determining each target robot, to reduce the walking distance of each empty material box extracted by the target robot,
Further, according to the current position of each target robot, the number of the empty buffer position and the storage position of each empty material box, determining each empty box needed to be carried by each target robot, so as to each target robot, according to the storage position of each empty material box needed to be carried by the target robot, generating the first conveying instruction of the target robot, so as to control the target robot to convey the corresponding empty box to the warehousing work station.
--determining the target empty container based on a moving distance of each movement trajectory.
(see e.g. AI et al. Further, according to the current position of each robot, determining one or more target robot, preferably selecting distance from the warehousing work station or empty box closer to the robot executing the warehousing work station taking and discharging task, so as to improve the efficiency of robot taking and placing goods, so as to improve the efficiency of the order processing.
---Further, in order to reduce the walking path of each target robot, when determining the carrying task of the target robot, it also can consider the storage position of each empty material box. namely can according to the storage position of each material box to be stored, the number of the material box to be stored, the storage position of each empty material box and the number of empty material box, determining the carrying task of each target robot.)
8. The method for positioning an empty container of claim 4, further comprising: after determining the target empty container based on the empty container attribute set, the container classification label corresponding to each loading container, the container position information corresponding to each loading container and the worker positioning information, sending the container position information of the target empty container and a movement trajectory corresponding to the target empty container to a worker task terminal.
(see e.g. AI et al. -- generating a carrying instruction of one or more target robot, to control one or more target robot to the storage bin
--according to the storage position of each empty material box needed to be carried by the target robot, generating the first conveying instruction of the target robot, so as to control the target robot to convey the corresponding empty box to the warehousing work station.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify HE, and include the steps cited above, as taught by AI et al., in order to reduce the walking path of each target robot (or worker) (see AI et al.).
Re-claim 7, HE teaches -- The method of claim 1, wherein the determining of the container classification label corresponding to each loading container and container position information corresponding to each loading container based on the storage area image comprises:
--determining the container classification label corresponding to each loading container and container position information corresponding to each loading container by a container detection model.
(see e.g. HE S200, locating the processed image by the trained container locating model to obtain the container locating information, wherein the container locating information comprises container position information, container type, container box number position information and container box number type.).
Claim 9 recites similar limitations as claim 1 and is therefore rejected under the same arts and rationale.
Claim 10 recites similar limitations as claim 2 and is therefore rejected under the same arts and rationale.
Claim 12 recites similar limitations as claim 4 and is therefore rejected under the same arts and rationale.
Claim 15 recites similar limitations as claim 7 and is therefore rejected under the same arts and rationale.
Claim 16 recites similar limitations as claim 8 and is therefore rejected under the same arts and rationale.
Claims 3, 5, 11, 13 are rejected under 35 U.S.C. 103 as being unpatentable over HE et al. (CN114267032A), in view of Al et al. (CN 113978993 A), in further view of Zass (US20210027051).
Re-claims 3, 5, HE, in view of AI et al., do not teach the limitation as claimed.
However, Zass teaches --The method of claim 2, wherein the determining of each candidate empty container comprises: if the container classification label of a current loading container matches an empty container attribute element in the empty container attribute set, determining that the current loading container is the candidate empty container.
(see e.g. [0191] In some embodiments, determining whether the identified fullness level is within a first group of at least one fullness level (Step 1130) may comprise determining whether the fullness level identified by Step 1120 is within a first group of at least one fullness level. In some examples, Step 1130 may compare the fullness level of the container and/or of the trash can identified by Step 1120 with a selected fullness threshold. Further, in response to a first result of the comparison of the identified fullness level of the container and/or the trash can with the selected fullness threshold, Step 1130 may determine that the identified fullness level is within the first group of at least one fullness level,
[0192] In some examples, the first group of at least one fullness level may be selected from a plurality of alternative groups of fullness levels based on the type of the container and/or of the trash can. In some examples, a parameter defining the first group of at least one fullness level may be calculated using the type of the container and/or of the trash can.)
5. The method of claim 1, wherein the identifying of the empty container attribute set in the storage area image comprises: --identifying the empty container attribute set in the storage area image by an attribute identification.
(see e.g. [0185] In some embodiments, analyzing the images to identify a fullness level of the container (Step 1120) may comprise analyzing the one or more images obtained by Step 810 to identify a fullness level of the container (such as a trash can and/or other type of containers). Some non-limiting examples of such fullness level may include a fullness percent (such as 20%, 80%, 100%, 125%, etc.), a fullness state (such as ‘empty’, ‘partially filled’, ‘almost empty’, ‘almost full’, ‘full’, ‘overfilled’, ‘unknown’, etc.), and so forth. For example, a machine learning model may be trained using training examples to identify fullness level of containers (for example of a trash cans and/or of other containers of other types), and the trained machine learning model may be used to analyze the one or more images obtained by Step 810 and identify the fullness level of the container and/or of the trash can. )
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify HE, in view of AI et al., and include the steps cited above, as taught by Zass, in order to identify a fullness level of the container (see e.g. [0023]).
Claim 11 recites similar limitations as claim 3 and is therefore rejected under the same arts and rationale.
Claim 13 recites similar limitations as claim 5 and is therefore rejected under the same arts and rationale.
Claims 6, 14 are rejected under 35 U.S.C. 103 as being unpatentable over HE et al. (CN114267032A), in view of Al et al. (CN 113978993 A), in view of Zass (US20210027051), in further view of IVENS et al. (US 20220084186 A1).
Re-claim 6, HE, in view of AI et al., in further view of Zass, do not teach the limitation as claimed.
However, IVENS et al. teach-- The method of claim 5, wherein the attribute identification model includes a backbone identification network and an attribute classifier, --wherein the identifying of the empty container attribute set in the storage area image by the attribute identification model comprises: -- performing feature extraction on the storage area image through the backbone identification network to obtain loading container attribute features; and
(see e.g. [0008] The shipping container profiling and inspection system uses high-definition images captured with video cameras, located at container operation facilities. By analyzing the acquired images, the container profile information is identified and extracted from container alphanumerical codes, signs, labels, seals and placards.
[0065] More specifically, the computational platform 535 comprises a shipping container code detection/recognition module 522. The proposed shipping container code detection/recognition method relies on a deep learning AI framework 522 that uses a neural network architecture which uses feature extraction, sequence modelling and transcription. The shipping container code recognition module 522 detects a text region and uses a customized deep convolutional recurrent neural network to predict the container character identification sequence.
[0070] In the first module, a deep learning model based on U-Net and ResNet can be used to accurately locate the vertical 11-digit shipping container code. The output of the model is a rectangle bounding box, which can capture the shipping container code. Then, the detected code area is cropped as input for the second module. In the second module, the cropped image is first rotated by 90 degrees anticlockwise. Thus, the code permutation changed from a vertical array to a horizontal array. Then, a convolutional recurrent neural network (CRNN) can be used to recognize the code from the rotated image. The CRNN scans the rotated image from left to right and treats every alphanumeric character as a symbol. When CRNN detects a symbol, it outputs the corresponding character or number. Finally, the recognition module gives the 11-digit shipping container code sequence.)
--performing feature classification on the loading container attribute features through the attribute classifier to obtain the empty container attribute set.
(see e.g. [0010] In possible implementations, detecting the container code and characteristics of the shipping container is performed using a framework for image classification comprising convolutional neural network (CNN) algorithms.
claim 52-the container code and characteristics being determined by machine learning algorithms previously trained on shipping container images captured in various lighting and environmental conditions, wherein detecting the container code and characteristics of the shipping container is performed using a framework for image classification comprising convolutional neural network (CNN) algorithms).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify HE, in view of AI et al., in further view of Zass, and include the steps cited above, as taught by IVENS et al., in order to identify and classify the identified shipping container shipping containers (see e.g. abstract, [0016]).
Claim 14 recites similar limitations as claim 6 and is therefore rejected under the same arts and rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A) SU et al. (US 20220172376 A1) --Target Tracking Method And Device, And Electronic Apparatus.
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/LUNA CHAMPAGNE/
Primary Examiner, Art Unit 3627
July 31, 2026
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