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 .
Claims 1-17 are pending.
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.
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: “preprocessing unit for…”, “network traffic determination unit for…”, “label generation unit for…”, “data and model storage unit for…”, and “artificial intelligence model learning unit for…” in claims 1-9.
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 § 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.
Claim limitation “an artificial intelligence model learning unit for performing relearning…and updating an existing artificial intelligence model…” (emphasis added) 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. Specifically, the specification fails to disclose algorithms or steps for performing AI model “relearning”. “Relearning” is not a single, universally understood step that those of ordinary skill would readily agree upon: there is no disclosure of a specific “learning” technique applied; nor is any mathematical formula or algorithm disclosed. 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.
Claims 15 and 16 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.
The term “when necessary” in claim 15 is a relative term which renders the claim indefinite. The term “when necessary” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification fails to disclose what makes an update “necessary”. As such there are no objective criteria or thresholds that one of ordinary skill would apply to objective determine if a condition has been met. Claim 16 is rejected by virtue of its 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)(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.
Claims 1, 9, 10, 14, 15 and 17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sivashanmugam US 20220036202.
For claim 1, Sivashanmugam discloses:
An apparatus for labeling network traffic data, the apparatus comprising:
a preprocessing unit for performing preprocessing on network traffic data (par. 0024, 0025: A prediction function (PF) scans/receives traffic seen at network edge);
a network traffic determination unit for determining whether the network traffic data is a known type of network traffic (par. 0024, 0025: The prediction function (PF), utilizing light weight AI models, (PF) analyzes and classifies traffic seen at network edge);
a label generation unit for classifying the network traffic data and determining a label for the network traffic data, when the network traffic data is the known type of network traffic (par. 0034: AI based traffic classifier governs UE usage of network resource (i.e., network slice priority) by ensuring UE observed traffic type matches slice use case);
a data and model storage unit for storing the network traffic data and requesting relearning for an artificial intelligence model based on a preconfigured condition, when the network traffic data is not the known type of network traffic (par. 0023: Edge unit (CWS/DU) sends those packets and related metadata for traffic it cannot classify to HNG/DU (cloud unit). “HNG/DU will also archive (i.e., store) these packet traces from CWS/DU and the classified traffic class. This information is used to periodically train the light weight model automatically or manually and updated light weight model.”); and
an artificial intelligence model learning unit for performing relearning for the artificial intelligence model and updating an existing artificial intelligence model with the relearned artificial intelligence model (par. 0027: Cloud LF analyzes packets and metadata received from PF in order to retrain model and therefore push the updated model to LF).
For claims 9 and 14, Sivashanmugam discloses:
The apparatus of claim 1, wherein the preconfigured condition includes at least one of feedback on a result by the artificial intelligence model, a learning model usage period, and a relearning request event by an external system including a user (par. 0028: Learning Function AutoML periodically determines the appropriate model and training model parameters and pushes them to VRU (of the edge node) automatically).
For claims 10 and 17, Sivashanmugam discloses:
At least one non-transitory computer-readable medium storing at least one instruction, wherein the at least one instruction executable by at least one processor controls an apparatus to:
perform preprocessing on network traffic data (par. 0024, 0025: A prediction function (PF) scans/receives traffic seen at network edge);
determine whether the network traffic data is a known type of network traffic (par. 0024, 0025: The prediction function (PF), utilizing light weight AI models, (PF) analyzes and classifies traffic seen at network edge);
classify the network traffic data and determine a label for the network traffic data, when the network traffic data is the known type of network traffic (par. 0034: AI based traffic classifier governs UE usage of network resource (i.e., network slice priority) by ensuring UE observed traffic type matches slice use case);
store the network traffic data and perform relearning for an artificial intelligence model based on a preconfigured condition, when the network traffic data is not the known type of network traffic (par. 0023: Edge unit (CWS/DU) sends those packets and related metadata for traffic it cannot classify to HNG/DU (cloud unit). “HNG/DU will also archive (i.e., store) these packet traces from CWS/DU and the classified traffic class. This information is used to periodically train the light weight model automatically or manually and updated light weight model.”); and
update an existing artificial intelligence model with the relearned artificial intelligence model (par. 0027: Cloud LF analyzes packets and metadata received from PF in order to retrain model and therefore push the updated model to LF).
For claim 15, Sivashanmugam discloses:
The method of claim 10, wherein the existing artificial intelligence model is updated with the relearned artificial intelligence model when necessary or when a subscription condition is satisfied (par. 0028: Learning Function AutoML periodically determines the appropriate model and training model parameters and pushes them to VRU (of the edge node) automatically).
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 2, 3, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Sivashanmugam US 20220036202, in view of Vasudevan US 20210204152.
For claims 2 and 11, Sivashanmugam discloses:
The apparatus of claim 1, but fails to disclose “wherein the preprocessing unit reassembles the network traffic data into a predetermined unit and then converts the network traffic data into an input sequence.”
However, in a related field, Vasudevan discloses a system that caches/captures all irregular traffic, the captured data including all data detected as anomalous as well as associated meta-data such as DNS, SNI and IP address. The saved data can be later used to update training state of machine learning models in system (par. 0081: Anomalous traffic is stored in cache that is periodically sent to system configured to process collected data for analysis and labelling or for use as training data for machine learning models; par. 0074: Traffic classification pipeline 200 includes ML-based anomaly detector and ML-based traffic classifier; par. 0078: Both ML-based traffic classifier and ML-based anomaly detector examine connection-level information such as IP address, SNI, FQDN as well as other values or features derived from data streams in order to determine patterns and characteristics of flows).
It would have been obvious to one of ordinary skill before effective filing data of instant application to have introduced Vasudevan’s teachings alongside Sivashanmugam. The motivation to combine would have been to train classification system to detect and label previously unknown traffic flows (Vasudevan, par. 0082).
For claims 3 and 12, Sivashanmugam-Vasudevan discloses:
The apparatus of claim 2, wherein the network traffic determination unit determines whether the network traffic data is the known type of network traffic based on whether the converted input sequence is similar to an input sequence pattern used as learning data when training an artificial intelligence model for traffic classification (Vasudevan, par. 0069: “The anomaly detector 174 can be configured to filter out or label irregular traffic, e.g., traffic that does fit expected patterns. For example, regular traffic can be traffic patterns that have a distribution that matches or is within a threshold level of similarity to a distribution observed in training data for the machine learning traffic classifier model.”).
It would have been obvious to one of ordinary skill before effective filing data of instant application to have introduced Vasudevan’s teachings alongside Sivashanmugam. The motivation to combine would have been to train a classification system to detect and label traffic flows that are of a previously known but differ from exactly pattern stored/recognized in existing training model (Vasudevan, par. 0072).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Sivashanmugam US 20220036202, in view of Monjas Llorente US 20250007791.
For claim 16, Sivashanmugam discloses:
The method of claim 15, but fails to disclose “wherein the subscription condition includes at least one of a subscription period and a subscription cycle.”
However, in a related field, Monjas Llorente discloses a method wherein a NWDAF explicitly checks conditions provided in a notification subscription to determine whether retraining of ML model is needed (par. 0152).
It would have been obvious to one of ordinary skill before effective filing data of instant application to have introduced Monjas Llorente’s teachings alongside Sivashanmugam. The motivation to combine would have been to inform a consumer/user of ML model training as required by conditions of their subscription (Monjas Llorente, par. 0152).
Allowable Subject Matter
Claims 4-8 and 13 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLAYTON R WILLIAMS whose telephone number is (571)270-3801. The examiner can normally be reached M-F 10:00am - 6:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas Taylor can be reached at 571-272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLAYTON R WILLIAMS/Primary Examiner, Art Unit 2443
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