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 .
DETAILED ACTION
1. Claim 16 is withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected species, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 6/15/2026.
Information Disclosure Statement
2. The information disclosure statement(s) filed on 8/5/2024 is/are in compliance with the provisions of 37 CFR 1.97, and has/have been considered and a copy/copies is/are enclosed with this Office action.
Foreign Priority
3. Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C 119(a-d) based on JP 2020-093607, filed on 5/28/2020.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
4. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
‘acquisition unit’ in claim 17
‘training unit’ in claim 17
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections – 35 USC § 102
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.
5. Claims 8-10, 13-15, and 17-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hu (US PGPub 2018/0157972) [hereafter Hu].
6. As to claim 8, Hu discloses a neural network training method (as shown in Figures 5-6) that is a computer-implemented method for training a neural network (neural network shown in Figures 1-2) that includes a common network (common portion 110) that is common to a plurality of classes and a plurality of single-class networks (task portions 120-150) that are connected to the common network and each of which has a single-class output layer (task output layers 124-154) for individually classifying each of the plurality of classes, the neural network training method comprising: an acquisition step of acquiring training data (training data sample) that includes ground truth data corresponding to at least one of the plurality of classes; and a training step of training the neural network by using the training data, wherein, when training is performed by using training data (second training data sample) that includes missing ground truth data corresponding to at least one of the plurality of classes (annotated ground truth labels for first inference tasks but not second inference tasks), the training step trains a single-class network corresponding to a class of which ground truth data is not missing and does not train the single-class network corresponding to the class of which ground truth data is missing (Paragraphs 0008, 0017, 0020-0029, 0035, 0040-0048, 0055-0072, a general purpose computing device (shown in Figure 7) includes a main memory computer-accessible medium configured to store program instructions that allow a processor to perform tasks of training a neural network having a common portion that performs convolutional operations to generate intermediate results as low-level features on input data representative of a plurality of classes that are transferred to specific task portions that have single class output layers for outputting classification identifiers for the input intermediate results generated by the common portion, wherein the neural network is trained by feeding training data sets including ground truth labels corresponding to the plurality of classes and computing respective loss function results and backpropagating gradient values through the networks in order to tune the neurons/layers of the neural networks, wherein training includes providing a training data sample that is annotated with ground truth labels for first task portions of the single-class neural networks and adjusting the parameters of the task portions corresponding to the ground truth labels but missing ground truth labels for second task portions of the single-class neural networks and consequently refrains from updating the parameters of the single-class neural networks associated with the second task operations).
7. As to claim 9, Hu discloses the training step trains the common network, irrespective of whether the training data has missing ground truth data (Paragraphs 0058-0062, the common network is trained even when a task does not contain any ground truth labels associated with a data set by setting a weighting coefficient of that particular task to 0).
8. As to claim 10, Hu discloses the training step calculates a loss value for output of each of the single-class output layers by using a different loss function (Paragraphs 0008, 0018, 0054, 0058, 0061, 0064-0066, different losses are calculated for each of the single-class/task output layers based on the training data samples and the corresponding ground truth labels in order to correctly update the individual single-class/task layers).
9. As to claim 13, Hu discloses based on a class that corresponds to ground truth data that the training data has, the training step determines a layer of which parameters are to be updated, the parameters being used when training is performed by using the training data (Paragraphs 0008, 0018, 0055, 0059, 0064-0066, based on the ground truth labels associated with the input training data sample, layers of the single-class/task portions and parameters thereof are updated).
10. As to claim 14, Hu discloses the training step groups training data based on a class that corresponds to ground truth data that the training data has, and performs training by selecting the training data on a group basis to be used in one epoch (Paragraph 0056, the first training data samples includes ground truth labels that are used to simultaneously train all of the single-class/task portions during one epoch).
11. As to claim 15, Hu discloses the training step performs training by selecting a group of training data including ground truth data corresponding to all the classes at the end of one epoch (Paragraph 0056, the first training data samples includes ground truth labels that are used to simultaneously train all of the single-class/task portions during one epoch).
12. As to claim 17, Hu discloses an image processing apparatus (computing device as shown in Figure 7) training a neural network (as shown in Figures 1-2) including a a common network (common portion 110) that is common to a plurality of classes and a plurality of single-class networks (task portions 120-150) that are connected to the common network and each of which has a single-class output layer (task output layers 124-154) for individually classifying each of the plurality of classes, the image processing apparatus comprising: an acquisition unit (processor) that acquires training data (training data sample) that includes ground truth data corresponding to at least one of the plurality of classes; and a training unit (processor) that trains the neural network by using the training data, wherein, when training is performed by using training data (second training data sample) that includes missing ground truth data corresponding to at least one of the plurality of classes (annotated ground truth labels for first inference tasks but not second inference tasks), the training unit trains a single-class network corresponding to a class of which ground truth data is not missing and does not train the single-class network corresponding to the class of which ground truth data is missing (Paragraphs 0008, 0017, 0020-0029, 0035, 0040-0048, 0055-0072, a general purpose computing device (shown in Figure 7) includes a main memory computer-accessible medium configured to store program instructions that allow a processor to perform tasks of training a neural network having a common portion that performs convolutional operations to generate intermediate results as low-level features on input data representative of a plurality of classes that are transferred to specific task portions that have single class output layers for outputting classification identifiers for the input intermediate results generated by the common portion, wherein the neural network is trained by feeding training data sets including ground truth labels corresponding to the plurality of classes and computing respective loss function results and backpropagating gradient values through the networks in order to tune the neurons/layers of the neural networks, wherein training includes providing a training data sample that is annotated with ground truth labels for first task portions of the single-class neural networks and adjusting the parameters of the task portions corresponding to the ground truth labels but missing ground truth labels for second task portions of the single-class neural networks and consequently refrains from updating the parameters of the single-class neural networks associated with the second task operations).
13. As to claim 18, Hu discloses a computer-readable storage medium (main memory) non-transitorily storing a program for causing a computer to execute each step of the method according to claim 8 (Paragraphs 0067, 0069, 0072).
Claim Objections
14. Claims 11-12 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
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL S OSINSKI whose telephone number is (571) 270-3949. The examiner can normally be reached on Monday - Friday, 10:00am - 6:00pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oneal Mistry can be reached on (313) 446-4912. The fax phone number for the organization where this application or proceeding is assigned is (571)-273-8300.
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MO
/MICHAEL S OSINSKI/Primary Examiner, Art Unit 2674
8/18/2026
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