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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/29/2024 has been considered and is in compliance with the provisions of 37 CFR 1.97.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 12 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 12 recites the limitation “a data processing apparatus or a material handling apparatus comprising data processing means” without any supporting structure for the data processing apparatus. The examiner has found this claim limitation to be indefinite as to which apparatus is comprising data processing means, and furthermore it is unclear as to if the claimed “a material handling apparatus” is the same “a material handling apparatus” disclosed in the independent claim 1. Therefore the claim is rejected under 35 U.S.C. 112(b).
Claim 13 recites the claim limitation “comprising data processing means for carrying out the method of claim 1” for the same apparatus. The examiner has found it unclear as to the applicant’s purpose for the redundancy and unnecessary repetition of the data processing means for carrying out the method of claim 1. Therefore the claim is rejected under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 7-8, 10, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Eckman et al. (USP 10,984,378; hereinafter Eckman).
Regarding Claim 7:
Eckman discloses a computer-implemented method for generating training data for a machine-learning model for operating a material handling apparatus, comprising: while the material handling apparatus is performing a handling process, receiving an alert signal indicating an imminent interruption of the handling process (Eckman, Column 9, Lines 30-58, Eckman discloses determining an error or malfunction in the operation process of the material handling apparatus);
receiving user input from a user indicating a reaction to the alert signal, and including an instruction to continue the handling process in case the alert signal is determined to be a false positive (Eckman, Column 9 Lines 30-58, Column 11 Lines 18-64, Eckman discloses sending error information to the user and the user indicates a potential remedy through the user input);
obtaining recorded image data of the handling process; and generating a training dataset comprising the user input and the image data (Eckman, Column 11, Lines 1-17, Eckman discloses collecting the processed images and training the modules based on the determined parameters and collected visual data).
Regarding Claim 8:
Eckman discloses the method of claim 7.
Eckman further discloses wherein the user input comprises at least one of the following: an instruction to one or more of continue the handling process and disregard the alert signal; an instruction to reset the material handling apparatus; an instruction to switch the material handling apparatus into a manual control mode allowing the user to manually control the material handling apparatus; and opening a safety barrier surrounding the material handling apparatus (Eckman, Column 9 Lines 30-58, Column 11 Lines 18-64, Eckman discloses sending error information to the user and the user indicates a potential remedy through the user input).
Regarding Claim 10:
Eckman discloses a machine-learning training dataset obtained by a method for generating training data for a machine-learning model according claim 7 (Eckman, Column 11, Lines 1-17, Eckman discloses collecting the processed images and training the modules based on the determined parameters and collected visual data).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Eckman in view of Kuzhinjedathu et al. (USP 11,845,191; hereinafter Kuzhinjedathu).
Regarding Claim 11:
Eckman discloses a computer-implemented method of training a machine- learning model for operating a material handling apparatus, comprising: transmitting one or more training datasets, to a cloud-based machine-learning environment, wherein the plurality of training datasets which are associated with a plurality of material handling apparatuses according to claim 10 (Eckman, Column 11, Lines 1-17, Eckman discloses collecting the processed images, to a 3D point cloud structure, and training the modules based on the determined parameters and collected visual data).
Eckman fails to explicitly disclose receiving a trained machine-learning model, wherein the machine-learning model is preferably is in a binary format.
However Kuzhinjedathu, in the same field of endeavor, discloses receiving a trained machine-learning model, wherein the machine-learning model is preferably is in a binary format (Kuzhinjedathu, Column 9 Line 52 – Column 10 Line 17, Kuzhinjedathu discloses the predictive model (i.e. machine-learning model) comprises a binary matrix).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as disclosed by Eckman to include a model with a binary format as disclosed by Kuzhinjedathu with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this combination in order to further distinguish between edges of the cuboidal items and other features in the images, see at least Column 10 Lines 5-9.
Regarding Claim 15:
Eckman discloses a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of claim 11 (Eckman, Column 49 lines 30-44, Eckman discloses a computer program executing the operations of the material handling apparatus).
Allowable Subject Matter
Claim 1 has been found to be allowable by the examiner. The following is a statement of reasons for the indication of allowable subject matter: Claim discloses claim limitations which the examiner has found to overcome the cited art above and the cited pertinent art below, with the particular limitations including “determining, using the machine-learning model, that the alert signal is a false positive, wherein the step of determining that the alert signal is a false positive comprises: generating sub-probabilities for the alert signal being a false positive per camera; and determining a combined probability for the alert signal being a false positive based on the sub-probabilities”.
Claim 9 is 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. Claim 9 recited a claim limitation which the examiner has found to not be explicitly disclosed by the cited art, with the limitation including “if the user input comprises manually controlling the material handling apparatus or if the user input comprises opening the safety barrier surrounding the material handling apparatus, the respective user input and image data is excluded from the training dataset”. While Eckman discloses the user controlling the material handling apparatus and recording the user input and image data collected by the sensors for training purposes, Eckman does not appear to disclose excluding the user input and image data from the training dataset.
Claims 12 and 13 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claims 2-6, 14, and 16-17 are found to be allowable for their dependency upon an allowed independent claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stoeffler et al. (USP 12,459,121) – discloses systems, devices, and methods for efficiently checking the integrity of the robotic system, including a system failure determination. However Stoeffler does not explicitly disclose “determining, using the machine-learning model, that the alert signal is a false positive, wherein the step of determining that the alert signal is a false positive comprises: generating sub-probabilities for the alert signal being a false positive per camera; and determining a combined probability for the alert signal being a false positive based on the sub-probabilities”.
Konolige et al. (USP 9,227,323) – discloses methods and systems for recognizing machine-model information of three dimensional objects. However Konolige fails to explicitly disclose “determining, using the machine-learning model, that the alert signal is a false positive, wherein the step of determining that the alert signal is a false positive comprises: generating sub-probabilities for the alert signal being a false positive per camera; and determining a combined probability for the alert signal being a false positive based on the sub-probabilities”.
Agarwal et al. (US 2023/0071384) – discloses a method for tracking a pose of objects for various positions and velocities. However Agarawal fails to explicitly disclose “determining, using the machine-learning model, that the alert signal is a false positive, wherein the step of determining that the alert signal is a false positive comprises: generating sub-probabilities for the alert signal being a false positive per camera; and determining a combined probability for the alert signal being a false positive based on the sub-probabilities”.
Majumdar et al. (US 2022/0203547) – discloses pick planning for a robotic picking applications to improve efficiency of picking operations and reduce robot down time. However Majumdar fails to explicitly disclose “determining, using the machine-learning model, that the alert signal is a false positive, wherein the step of determining that the alert signal is a false positive comprises: generating sub-probabilities for the alert signal being a false positive per camera; and determining a combined probability for the alert signal being a false positive based on the sub-probabilities”.
Okorn et al. (US 2024/0096074) – discloses apparatus, systems, and techniques for identifying objects using one or more neural networks based on collected image data. However Okorn fails to explicitly disclose “determining, using the machine-learning model, that the alert signal is a false positive, wherein the step of determining that the alert signal is a false positive comprises: generating sub-probabilities for the alert signal being a false positive per camera; and determining a combined probability for the alert signal being a false positive based on the sub-probabilities”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZACHARY JOSEPH WALLACE whose telephone number is (469)295-9087. The examiner can normally be reached 7:00 am - 5:00 pm, Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached at (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Z.J.W./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656