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 .
Status of the Claims
Claims 1 and 5-6 have been amended. Claims 1, 3-6, 10, 12-13, 15-16, and 18-21 are currently pending and have been considered by the Examiner.
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, 3-6, 10, 12-13, 15-16, and 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1, 3-4, 10, 12, and 19 each recites a non-transitory computer-readable recording medium (a product). Claims 5, 13, 15, and 20 each recites a method. Claims 6, 16, 18, and 21 each recites an apparatus comprising a processor. A product, a method, and an apparatus each falls within one of the four statutory categories of patent eligible subject matter.
CLAIM 1
Step 2A Prong 1: Generating a plurality of Betti series that is time series data based on Betti numbers obtained by applying persistent homology transform to a plurality of pseudo-attractors generated, respectively, from a plurality of pieces of time-series data obtained by sensing a physical target, the plurality of Betti series representing features of the plurality of pieces of time-series data is a mathematical calculation. In the instant specification, paragraphs [0041]-[0045] disclose Formulas (2) to (5) for generating a Betti series.
Thinning out the plurality of Betti series while decreasing a thinning interval is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. In the instant specification, paragraph [0046] and Fig. 8 disclose an example of thinning out a Betti series. A person can reasonably perform this example by hand on a piece of paper.
Generating a plurality of transformed Betti series representing global shape features of the physical target in which a region with a larger radius when generating the Betti numbers is weighted more than a region with a smaller radius to emphasize changes in the global shape features, from the plurality of Betti series thinned out by the thinning is a mathematical calculation. In the instant specification, paragraphs [0047]-[0049] disclose Formulas (6) to (7) for generating a transformed Betti series. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 and Step 2B: A non-transitory computer-readable recording medium having stored therein a machine learning program that causes a computer to execute a process amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Training a neural network to improve abnormality detection accuracy for unsupervised learning by inputting the generated plurality of transformed Betti series as a specialized training data structure, the trained neural network to output an estimation of labels for discrimination target data input thereto to indicate a change point in the discrimination target data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are generic computer functions as disclosed that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are generic computer functions as disclosed that are implemented to perform the abstract idea disclosed above. The claim is not patent eligible.
CLAIM 3 incorporates the rejections of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The generating the plurality of transformed Betti series includes acquiring Betti numbers at intervals monotonically decreasing as a radius increases from Betti numbers of each radius included in the plurality of Betti series, and generating the plurality of transformed Betti series using the acquired Betti numbers of the respective radii are mathematical calculations. In the instant specification, paragraphs [0047]-[0049] disclose Formulas (6) to (7) for generating a transformed Betti series.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application. The claim does not recite any additional elements which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
CLAIM 4 incorporates the rejections of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The generating the plurality of transformed Betti series includes calculating a plurality of weighted Betti numbers obtained by multiplying a Betti number of each radius included in the plurality of Betti series by a weight monotonically increasing with respect to the radius, and generating the plurality of transformed Betti series using the calculated plurality of weighted Betti number are mathematical calculations. In the instant specification, paragraphs [0047]-[0049] disclose Formulas (6) to (7) for generating a transformed Betti series.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application. The claim does not recite any additional elements which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 5 recites a method that implements the same features as the non-transitory computer-readable recording medium of claim 1, and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, a processing circuit amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 6 recites an apparatus comprising a processor that implements the same features as the non-transitory computer-readable recording medium of claim 1, and is therefore rejected for at least the same reasons therein.
In Step 2A Prong 2 and Step 2B, at least one processor and at least one memory including computer program code, where the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to perform operations amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
CLAIM 19 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: Inputting discrimination target data to the trained neural network and estimating labels for the discrimination target data to indicate a change point in the discrimination target data amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
CLAIM 10 incorporates the rejections of claim 19.
Step 2A Prong 1: The abstract ideas of claim 19 are incorporated.
Step 2A Prong 2: Outputting information relating to a graph of the estimated labels amounts to an insignificant extra-solution activity under MPEP 2106.05(g).
Step 2B: Outputting information relating to a graph of the estimated labels is analogous to presenting offers and gathering statistics, which is a well-understood, routine, conventional activity recognized by the courts under MPEP 2106.05(d)(II). The claim is not patent eligible.
CLAIM 12 incorporates the rejections of claim 19.
Step 2A Prong 1: The abstract ideas of claim 19 are incorporated.
Step 2A Prong 2: Displaying a graph of the estimated labels amounts to an insignificant extra-solution activity under MPEP 2106.05(g).
Step 2B: Displaying a graph of the estimated labels is analogous to presenting offers and gathering statistics, which is a well-understood, routine, conventional activity recognized by the courts under MPEP 2106.05(d)(II). The claim is not patent eligible.
Claim 20 recites a method that implements the same features as the non-transitory computer-readable recording medium of claim 19 and is therefore rejected for at least the same reasons.
Claims 13 and 15 each recites a method that implements the same features as the non-transitory computer-readable recording medium of claims 10 and 12, respectively, and are therefore rejected for at least the same reasons.
Claim 21 recites an apparatus comprising a processor that implements the same features as the non-transitory computer-readable recording medium of claim 19 and is therefore rejected for at least the same reasons.
Claims 16 and 18 each recites an apparatus comprising a processor that implements the same features as the non-transitory computer-readable recording medium of claims 10 and 12, respectively, and are therefore rejected for at least the same reasons.
Response to Arguments
The following are the Examiner’s responses to the Applicant’s arguments filed 02/11/2026.
Applicant’s Argument I. (page 8): The sheer volume and dimensionality of this data render manual thinning or weighting “impractical” for the human mind. Further, the calculation of Betti numbers through persistent homology is a multi-step topological transformation that is inherently a computer-driven process.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive. Examiner respectfully disagrees with Applicant’s argument that thinning out the plurality of Betti series while decreasing a thinning interval is impractical for the human mind. Paragraph [0046] and the corresponding Figure 8 in this application explain how to thin a series of m = 9 Betti numbers. The thinning algorithm is simple for a person to understand and perform. A person can reasonably transform a series [B(r1), … B(r9)] into a series [B(r4), B(r7), B(r9)] by hand by following the rules laid out in paragraph [0046]. A person would not need to consider anything about the volume and dimensionality of the time-series data obtained by sensing a physical target to perform this algorithm because they do not appear to affect thinning algorithm.
Calculating Betti numbers through persistent homology is not a mental process. It is a mathematical calculation as disclosed by paragraphs [0041]-[0044].
Applicant’s Argument II. (pages 8-9): The claims are eligible because they are integrated into a practical application through a technological improvement… The claims specifically generate a “plurality of transformed Betti series” that weights larger radii more heavily. This creates a specialized training data structure that allows a neural network to detect change points in unsupervised data that were previously undetectable. This provides a “technological solution to a technological problem,” satisfying eligibility under the logic of USPTO Example 47.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive. Creating a specialized training data structure according to the limitation in claim 1, lines 10-14 is a mathematical calculation as explained in the examiner’s response above, and it cannot provide an improvement in technology. MPEP 2106.05(a) states, “It is important to note, the judicial exception alone cannot provide the improvement.” MPEP 2106.05(a), II. states, “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
The entire limitation of claim 1, lines 15-19 amounts to mere instructions to apply the abstract ideas on a generic computer. The claim describes the types of training inputs and outputs of the neural network, and recites that the neural network is used to indicate a change point in the discrimination target data. However, the claim recites only the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished (see MPEP 2106.05(f), item 1). 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.”
Specifically, the unsupervised learning as recited amounts to traditional unsupervised learning without any details that might provide a technological improvement. The claim does not explain how the neural network’s structure, operations, etc. use the transformed Betti series to indicate a change point in the discrimination target data. Without these details, the limitation does not integrate a judicial exception into a practical application.
Examiner respectfully disagrees with Applicant’s argument that the claim 1 provides a technological solution to a technological problem, satisfying eligibility under the logic of USPTO Example 47. Both pending claim 1 and Example 47 claim 3 recite limitations of training a neural network to detect abnormalities or anomalies. Example 47 claim 3 is patent eligible because the claimed invention reflects an improvement in the technical field of network intrusion detection. Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets (see page 12 of the USPTO July 2024 Subject Matter Eligibility Examples). Pending claim 1 recites none of the same features which make claim 3 eligible.
Applicant’s Argument III. (page 9): The Examiner argues that the neural network and training are “generic computer components.” However, the Examiner has not established that generating a weighted, transformed Betti series as a training structure for an unsupervised neural network is “well-understood, routine, or conventional” in the art. Further, the combination of thinning-out (to manage data density) and radius-based weighting (to prioritize global features) is a specific, non-generic way to train a machine learning model for physical target monitoring. Thus, significantly more than an abstract idea is now claimed.
Examiner’s Response: Applicant’s arguments have been fully considered but they are not persuasive. The previous Office Action did not state the neural network and training are themselves generic computer components. The previous and current Office Actions state that training a neural network is an instruction to apply the abstract ideas on a generic computer under MPEP 2106.05(f) in Step 2B. The Examiner’s current response to the Applicant’s argument II explains this. Evaluating whether an additional element is a well-understood, routine, conventional activity (MPEP 2106.05(d)) is merely one of several considerations for evaluating whether additional elements amount to an inventive concept in Step 2B.
The combination of the thinning-out and radius-based weightings, used to create the specialized training data structure, cannot provide an improvement in technology because they constitute a judgement and evaluation mental process and a mathematical calculation. Training using the specialized training data structure amounts to mere instructions to apply the abstract ideas on a generic computer.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127