DETAILED ACTION
1. Claims 1-14 have been presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
PRIORITY
3. Acknowledgment is made that this application is a 371 of PCT/IB2021/060108 filed 11/02/2021.
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d) to FINLAND 20206162 filed 11/17/2020.
Information Disclosure Statement
4. The information disclosure statements (IDS) submitted on 6/28/23, 6/4/25, 8/6/25, and 12/22/25 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the Examiner has considered the IDS’ as to the merits.
Claim Objection
5. Claim 14 is objected to as the claim does not end in a period.
Appropriate correction is required.
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.
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.
6. 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.
7. 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: in claims 8 and 14, the “an encoder module”, “a clustering module”, “clustering control module”, and a “decoder module.”
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.
8. Claim limitation in claims 8 and 14, the “an encoder module”, “a clustering module”, “clustering control module”, and a “decoder module” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. See [0068]-[0072]. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
9. Claims 1-14 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.
i) Claim 1 recites the limitation "the modules" in “backpropagate the reconstruction loss and the clustering loss through the modules of the network element to train the modules.” There is insufficient antecedent basis for this limitation in the claim. Specifically the claim recites a plurality of distinct modules and therefore it is unclear if this limitation refers back to some or all of those respective modules. As such the claim is rendered vague and indefinite. This analysis further applies to the analogous recitation in claims 8 and 14.
Appropriate correction is required.
All claims dependent upon a rejected base claim are rejected by virtue of their dependency.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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 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.
10. Claims 1-14 are rejected under 35 U.S.C. 102(a)(1) as being clearly anticipated by Kajó, Márton, Benedek Schultz, and Georg Carle. "Deep Clustering of Mobile Network Data with Sparse Autoencoders." NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2020.
Regarding Claim 1: The reference discloses A network element for network state modelling of a communication network, comprising an encoder module comprising at least one processor; and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the network element to: (Abstract : ...network management management use cases...", Section I, right column, second paragraph: 'The use case we consider here is the modeling of network behavior as transitions between network states. Here individual network elements exist in a shared state-space"),
obtain as an input network data that is representative of the current condition of the communications network, the network data comprising a plurality of values indicative of the performance of network elements and perform feature reduction providing at its output a set of activations; (Section II:"The clustering method proposed here is based on an autoencoder, a neural network topology used for data compression, feature extraction or denoising. Autoencoders are trained in an unsupervised manner, their target being the perfect reconstruction of the original observations without any additional labeled information to encode the observations into a lower dimensional space, and be able to reconstruct decode them from this simplified representation. an encoded representation of an observations as an encoded vector encoding also called activations. Fig.2);
a clustering module comprising at least one processor; and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the network element to:
perform batch normalisation and an amplitude limitation to the output of the encoder module; (Section II.C: Guidance Module: 'The anchoring module provides the sparseness loss, but does not directly interact with the encodings. Contrarily, the guidance module, which is located in the middle of the autoencoder and precedes the anchoring module, does directly change the activations. One of the tasks of this module is to limit the encodings to the range of [0, 1], by using a sigmoid nonlinearity on the activations. The sigmoid nonlinearity ensures that the encodings fall within the unit hypercube in RD, turning the linear projection onto the affine subspace e into a projection onto the simplex. Instead of an additional loss, we use a weight-shared batch-normalization layer before the sigmoid nonlinearity)
a clustering control module comprising at least one processor; and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the network element to:
obtain as an input data a sparsity constraint and the activations from the clustering module; (Section 'Anchoring Module, sparseness loss calculation: Sparsity in neural networks is usually enforced by masking of activations through multiplication by 0. In many cases, this operation means quite a radical transformation of the propagated vectors, and as such can easily degrade the established logic in the net. In SCA, we invoke sparseness through a softer, more forgiving enforcement, by introducing a sparseness loss on the encodings. To calculate the sparseness loss, we calculate an anchor point Q for every encoding Q, which is the orthogonal projection of Q onto the closest subhull of S. The anchor describes the position where the encoded point should be according to the current sparseness setting (s). The sparseness loss is then calculated as: Isparse = dist(Q, Q), where dist is the Euclidean or L2 distance The sorting and translation has to be done on the encodings which we wish to project. After the translation, we can apply the base change to A. In this modified basis the projection to the s-sparse subspace can be done by masking the last D – S bases. This is done similar to regular sparseness enforcement, by multiplying the activations with the mask µ(s) = (1 1, 1, 0 0), in which the first S elements are 1, and the rest 0 (Fig. 5c). This procedure yields the anchor point Q in the sorted, translated and rebased space.")
calculate a projection of the input data by utilising a mask controlled by the sparsity constraint; (Section II.C: Guidance Module: 'The anchoring module provides the sparseness loss, but does not directly interact with the encodings. Contrarily, the guidance module, which is located in the middle of the autoencoder and precedes the anchoring module, does directly change the activations. One of the tasks of this module is to limit the encodings to the range of [0, 1], by using a sigmoid nonlinearity on the activations. The sigmoid nonlinearity ensures that the encodings fall within the unit hypercube in RD, turning the linear projection onto the affine subspace e into a projection onto the simplex. Instead of an additional loss, we use a weight-shared batch-normalization layer before the sigmoid nonlinearity)
determine a clustering loss controlling the clustering module by calculating distance between the activations and the projection; (Section II:" The encoding and decoding is learned together at training, during which the autoencoder tries to minimize a reconstruction loss, measured in the data-space between the original and the reconstructed observations. Our additions to the autoencoder are two modules: The anchoring module, which calculates an additional clustering loss term on the encoded representation during training, so that the autoencoder is forced to learn a clustered representation; The guidance module, which constrains activations to a specific range, and helps in balancing cluster populations. The anchoring module only plays a role during training, by indirectly affecting the encoded activations through a calculated sparseness loss. Contrarily, the guidance module is present in the network both during training and inference, and directly modifies activations",
a decoder module comprising at least one processor; and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the network element to:
form from the output of the clustering module reconstructed network data and determine a reconstruction loss; the network element being configured to backpropagate the reconstruction loss and the clustering loss through the modules of the network element to train the modules by gradually reducing the value of the sparsity constraint. (Section II:" The encoding and decoding is learned together at training, during which the autoencoder tries to minimize a reconstruction loss, measured in the data-space between the original and the reconstructed observations. Our additions to the autoencoder are two modules: The anchoring module, which calculates an additional clustering loss term on the encoded representation during training, so that the autoencoder is forced to learn a clustered representation; The guidance module, which constrains activations to a specific range, and helps in balancing cluster populations.", Section II.C:" The idea behind batch-normalization is that the centering effect helps in the early training by easing discovery of the activation space, while later in the training the network is able to counteract possibly unwanted normalization by separately changing the learnable parameters for any feature [4]. In our case, to aid the even distribution of encodings among clusters all through the training, the learnable parameters are shared across features. This retains the centering effect of the batch normalization layer, but gives the network enough flexibility to adhere to the required sparseness.", Section II.D: Training Training of the SCA can be split into 3 phases. In the exploration phase the sparseness parameter S is held at the initial D - 1 value, which only limits the activations onto the simplex, leaving a large amount of freedom for the autoencoder to explore the encoding space and establish a mapping that can later be focused into a clustering. In the clustering phase, the S parameter is gradually decreased to encourage the grouping of encodings. The refinement phase then allows the network to smooth out imperfections that might have arisen during the clustering phase.")
Regarding Claim 2: The reference discloses The network element of claim 1, wherein the mask removes the smallest activations based on the sparsity constraint. (Section II(B) “Sparsity in neural networks is usually enforced by the masking of activations, through multiplication by 0. In many cases, this operation means quite a radical transformation of the propagated vectors, and as such can easily degrade the established logic in the net. In SCA, we invoke sparseness through a softer, more forgiving enforcement, by introducing a sparseness loss on the encodings.”)
Regarding Claim 3: The reference discloses The network element of claim 1 [[or 2]], further configured to reduce the value of the sparsity constraint to between the range of [0,1]. (Section II(C) “The anchoring module provides the sparseness loss, but does not directly interact with the encodings. Contrarily, the guidance module, which is located in the middle of the autoencoder and precedes the anchoring module, does directly change the activations. One of the tasks of this module is to limit the encodings to the range of [0; 1], by using a sigmoid nonlinearity on the activations.”)
Regarding Claim 4: The reference discloses The network element of claim 1, 2 or 3, further configured to calculate a base change by obtaining the output of the clustering module Q; calculating affine subspace B = {bi, b2, ... , bD} based on Q. translating B with t = -bi, to obtain B = {0, b2 - bl, ... , bD - bl}. obtaining base of the linear subspace B = { b2 - b 1, ... , bD - b 1} which spanned by B; orthogonalizing the base using a Gram-Schmidt orthogonalization to obtain orthogonalized A; adding a unit length vector to A to obtain an orthonormal base as a matrix whose column are the elements of A; and forming a matrix A whose column are the elements of A and storing A and t. (Figure 5 and left column of page 3)
Regarding Claim 5: The reference discloses The network element of claim 4, further configured to obtain as input the output activations of the clustering module Q; sort the input in a descending order; translate the sorted input by subtracting the value t; change the base of the translated input to an orthonormal base utilising a transpose of the matrix A; calculate the projection of the input data by multiplying the projection with the mask controlled by the given sparsity constraint; change the base back to non-orthonormal utilising the matrix A; perform detranslation by adding the value t; perform unsorting to obtain anchor points Q; calculate clustering loss by determining distance between the anchor points Q and the activations Q. (Figure 5 and left column of page 3)
Regarding Claim 6: The reference discloses The network element of any preceding claim 1, wherein the clustering module comprises a weight-shared batch normalization module followed by a sigmoid nonlinearity module configured to limit the values of the output of the batch normalization module to the range of [0,1]. (Section II(C) “The anchoring module provides the sparseness loss, but does not directly interact with the encodings. Contrarily, the guidance module, which is located in the middle of the autoencoder and precedes the anchoring module, does directly change the activations. One of the tasks of this module is to limit the encodings to the range of [0; 1], by using a sigmoid nonlinearity on the activations.”)
Regarding Claim 7: The reference discloses The network element of claim 5, wherein the mask is a vector comprising values between [0, 1] based on the sparsity constraint. (Section II(C) “The anchoring module provides the sparseness loss, but does not directly interact with the encodings. Contrarily, the guidance module, which is located in the middle of the autoencoder and precedes the anchoring module, does directly change the activations. One of the tasks of this module is to limit the encodings to the range of [0; 1], by using a sigmoid nonlinearity on the activations.”)
Regarding Claim 8: The reference discloses A method for a network element, comprising: obtaining by an encoder module as an input network data that is representative of the current condition of the communications network, the network data comprising a plurality of values indicative of the performance of network elements and perform feature reduction providing at its output a set of activations; performing in a clustering module batch normalisation and an amplitude limitation to the output of the encoder module to obtain normalised activations; obtaining by a clustering control module as an input a sparsity constraint, calculating a projection of the normalised activations by utilising a mask controlled by the sparsity constraint and determining a clustering loss controlling the clustering module by calculating distance between the normalised activations and the projection; forming by a decoder module from the normalised activations reconstructed network data and determine a reconstruction loss; and backpropagating, by the network element, the reconstruction loss and the clustering loss through the modules to train the modules by gradually reducing the value of the sparsity constraint. (See rejection for claim 1)
Regarding Claim 9: The reference discloses The method of claim 8, wherein the mask removes the smallest activations based on the sparsity constraint. (See rejection for claim 2)
Regarding Claim 10: The reference discloses The method of claim 8[[ or 9]], further comprising: reducing the value of the sparsity constraint to between the range of [0,1]. (See rejection for claim 3)
Regarding Claim 11: The reference discloses The method of claim 8, 9 or 10, further comprising: calculating a base change by obtaining the output of the clustering module Q; calculating affine subspace B = {bi, b2, ... , bD} based on Q. translating B with t = -bi, to obtain B = {0, b2 - bl, ... , bD - bl}. obtaining base of the linear subspace B = { b2 - b 1, ... , bD - b 1 } which spanned by B; orthogonalizing the base using a Gram-Schmidt orthogonalization to obtain orthogonalized A; adding a unit length vector to A to obtain an orthonormal base as a matrix whose column are the elements of A; and forming a matrix A whose column are the elements of A and storing A and t. (See rejection for claim 4)
Regarding Claim 12: The reference discloses The method of claim 11, further comprising: obtaining as input the output activations of the clustering module Q; sorting the input in a descending order; translating the sorted input by subtracting the value t; changing the base of the translated input to an orthonormal base utilising a transpose of the matrix A; calculating the projection of the input data by multiplying the projection with the mask controlled by the given sparsity constraint; changing the base back to non-orthonormal utilising the matrix A; performing detranslation by adding the value t; performing unsorting to obtain anchor points Q; calculating clustering loss by determining distance between the anchor points Q and the activations Q. (See rejection for claim 5)
Regarding Claim 13: The reference discloses The method of any preceding claim 8 to 12, further comprising: performing in the clustering module a weight-shared batch normalization and limiting the values of the output of the batch normalization to the range of [0,1]. (See rejection for claim 6)
Regarding Claim 14: The reference discloses A computer program comprising instructions for causing an apparatus to perform at least the following: obtaining by an encoder module as an input network data that is representative of the current condition of the communications network, the network data comprising a plurality of values indicative of the performance of network elements and perform feature reduction providing at its output a set of activations; performing in a clustering module batch normalisation and an amplitude limitation to the output of the encoder module to obtain normalised activations; obtaining by a clustering control module as an input a sparsity constraint, calculating a projection of the normalised activations by utilising a mask controlled by the sparsity constraint and determining a clustering loss controlling the clustering module by calculating distance between the normalised activations and the projection; forming by a decoder module from the normalised activations reconstructed network data and determine a reconstruction loss; and backpropagating the reconstruction loss and the clustering loss through the modules to train the modules by gradually reducing the value of the sparsity constraint. (See rejection for claim 1)
Conclusion
11. All Claims are rejected.
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) Min, Erxue, et al. "A survey of clustering with deep learning: From the perspective of network architecture." IEEE access 6 (2018): 39501-39514.
ii) Zhang, Mingqiang, et al. "Compressive sensing and autoencoder based compressed data aggregation for green IoT networks." 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019.
iii) Fei, Rong, et al. "A new deep sparse autoencoder for community detection in complex networks." EURASIP Journal on Wireless Communications and Networking 2020.1 (2020): 91
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2186
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