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 § 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 1-3 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 1 recites the limitation "the function in the same or more software or hardware" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required. Applicant may choose to remove the added text and revert back to invoking 112(f). While the use of module does invoke 112(f) the specification does provide support for modules being implemented in software and/or hardware which provides structural support.
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 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claims 1-3 are to a system (i.e., a machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1 is:
Mental Process:
“collect medical waste information and equipment processing data” (observation/data gathering)
“train and process the medical waste information”
“train and process the equipment processing data”
“performing data preprocessing”
“calculating a difference between the output data… and actual values”
“provide feedback and monitoring based on the trained and processed datasets”
Certain Methods of Organizing Human Activity:
“intelligent data platform system for medical waste management and equipment monitoring” (preamble)
Medical waste information including “hospital information, department information, medical waste type information, department specific collection frequency information”
“provide feedback and monitoring”
Mathematical Concepts:
“performing forward propagation calculations through the convolutional layer, the pooling layer, and the fully connected layer”
Equation (1): k-1 Σ Oi(l) = Σj=0 Ii+j(l-1) · Kj(l) (convolutional kernel operation)
Equation (2): Ai(l) = f(Oi(l)) (activation function)
Equation (3): Pi(l) = maxj=0s-1 Ai·s+j(l) (max pooling operation)
Equation (4): y = W · x + b (fully connected layer linear transformation)
“calculating a difference between the output data of the model and actual values using a preset loss function”
“updating weights and biases of the convolutional neural network model using an optimization algorithm based on a gradient of the loss function”
“propagating and updating node features layer-by-layer through graph convolutional operations”
Equation (5): H(l+1) = σ(Â H(l) W(l)) (graph convolution propagation)
Equation (6): Â = D-½(A + I)D-½ (adjacency matrix normalization)
“calculating a difference between output data of the model and actual values using a preset loss function”
“updating weight parameters of the graph convolutional neural network model based on a gradient of the loss function”
The abstract idea steps listed above could be performed mentally, including with pen and paper. The steps describe, at a high level, of collecting, processing, evaluating data/judgement/comparison, organizing information, observing. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim is directed to medical waste management involving compliance, resource allocation and organizational workflows, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by [3]-[6] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim is directed to mathematical relationships, formulas, equations and calculations, which constitutes a process that, under its broadest reasonable interpretation, covers mathematical concepts. The claim recites standard neural network mathematical operations (convolution operations, matrix multiplications, gradient-based optimization) without any claimed deviation from conventional mathematics. This is further supported by [15], [79] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f). Further, in MPEP 2106.05(f) it is noted that "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology.
Claim 1 recites the following additional elements:
“which implements the function in the same or more software and hardware”
“data collection module”
“data processing module”
“data storage module”
“data feedback and monitoring module”
“human-machine interaction terminal configured to display data and receive user input commands”
“convolutional neural network module” and “graph convolutional neural network module”
The claim recites generic, functional modules (“data collection module,” “data processing module,” “data storage module,” “data feedback and monitoring module”) without specifying any particular technical architecture, hardware configuration, or novel computing mechanism.
The phrase “which implements the function in the same or more software and hardware” is a generic recitation that does not describe how the function is implemented or what specific technical improvement is achieved. This language merely indicates that software and/or hardware can perform the recited functions.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [110] of applicant’s specification as filed.
Examiner interprets the CNN and GCN modules described in terms of standard mathematical operations. Equations 1-6 represent conventional neural network mathematics without any claimed deviation or improvement. [66]-[67] of applicant’s specification shows these are standard CNN and GCN techniques applied to medical waste. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, the CNN and GCN modules are block-box functional units that could be implemented on any general purpose computer that are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f), they do not integrate the abstract idea into a practical application.
Neither the claims nor specification assert or teach faster processing, reduced memory usage, improved network efficiency, enhanced data security mechanisms, or any other technical metric that would indicate a computer functionality improvement.
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system with machine learning models. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Dependent claims further limit the abstract idea.
Accordingly, claims 1-3 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
No Prior Art
The closest prior art is as follows:
US20250186693A1: A method, computer program product, and computer system for an intelligent injection device for medical waste tracking.
CN111160412A: The invention relates to a method for classifying medical waste based on a neural network, which comprises the following steps acquiring an image to be identified; inputting the image into a pre-trained neural network model, and outputting the image type: determining a network structure of an initial neural network model and initializing network parameters of the initial neural network model; obtaining a sample set; selecting samples from the sample set, and performing the following training steps: inputting the selected sample image into an initial neural network model to obtain the prediction category of the sample; comparing the predicted category of the sample with the category in the annotation information; determining whether the initial neural network model reaches a preset standard-reaching condition or not according to the comparison result; and in response to determining that the initial neural network model reaches the standard reaching condition, taking the initial neural network as a trained neural network.
Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron: According to a research, the amount global annual waste is considered to reach up to 2.2 billion tons in the next three to four years, which could cost round about USD 375 billion for waste management [1].
Review on Deep learning based biomedical waste Detection and classification: Public health and the environment are in danger from the poor handling of biomedical waste produced by medical institutions and biomedical research institutes. The necessity for a system to detect and categorize biomedical waste products is brought on by the fact that the current human sorting procedure is not only ineffective but also risky for waste handlers and garbage collectors. In the existing system, the identified problem highlights the inefficiency and risks associated with manual sorting. In order to improve safety, effectiveness, and environmental sustainability in biomedical waste management practices, this study suggests a deep learning-based system that makes use of convolutional neural networks (CNNs) to reliably recognize and categorize items that are part of biomedical waste. The proposed approach might eventually achieve a 90% accuracy rate, which could result in cost savings and a decrease in the dangers related with manual sorting.
Response to Arguments
Applicant’s arguments, see the Amendment/Response, filed 16 October 2025, have been fully considered and are persuasive. The 112b and 103 rejections of claim 1-3 have been withdrawn.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sarah M Monfeldt whose telephone number is (571)270-1833. The examiner can normally be reached M-F.
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SARAH M. MONFELDT
Supervisory Patent Examiner
Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629