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 .
Response to Arguments
Applicant's arguments filed 07/24/2026 for 35 U.S.C. 101 rejections have been fully considered but they are not persuasive.
Applicant’s Argument: On page 11-15 of Applicant’s response, Applicant states that the claims as a whole is not directed to an abstract idea because the claims integrate the exception into a practical application of retraining a machine learning model that is directed to an improvement in machine learning algorithms. The claims are directed to an improvement that is similar to the improvement reflected in Ex Parte Desjardins. The claimed technical solution is selectively incorporating attribute types from auxiliary data sources, evaluating the effect of those attribute types on a target performance metric, modifying quality metrics associated with the attribute types based upon correction feedback, and re-training the machine learning algorithm using an augmented training data set that is expected to improve predictive performance.
Examiner’s Response: Applicant’s argument is not persuasive. During examination, the examiner should analyze the "improvements" consideration by evaluating the specification and the claims to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement (see MPEP §2106.05(a)). The MPEP (§2106.05(a)(II)) also warns, “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.” Here, the alleged improvement in the form of “selectively incorporating attribute types from auxiliary data sources, evaluating the effect of those attribute types on a target performance metric, modifying quality metrics associated with the attribute types based upon correction feedback” is an improvement to the abstract idea of a mental process that can be performed in the human mind. The claim as a whole is directed to modifying and improving a training dataset that enhances the machine learning model when it is trained with the improved training dataset. Training a machine learning model is a generic computer process and are mere instructions for using a computer as a tool to perform a mental process. The use of a computer or other machinery in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. In Ex Parte Desjardins, the improvements were directed to how the machine learning model itself would function in operation. The claimed invention does not provide an improvement in the functioning of a computer, or an improvement to other technology or a technical field. The claims as a whole recite the process of augmenting a training dataset that is expected to improve predictive performance of the machine learning model. The amended claims do not provide sufficient details to describe any technological improvement in training a machine learning model or how the machine learning model itself operates.
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, 3-4, 6-7, 9, 11-14, and 16-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites “a method comprising” and is thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
“identifying ... a target performance metric of a machine learning algorithm that is trained to validate self-identification information describing an individual, wherein the self- identification information is provided by the individual, and wherein the target performance metric measures an accuracy of validated self-identification information output by the machine learning algorithm” (a mental process that can be performed in the human mind, i.e. judgement)
“selecting ... a first candidate attribute type and a second candidate attribute type of the plurality of different attribute types from the set of auxiliary data, wherein each of the first candidate attribute type and the second candidate attribute type comprises an attribute type that is present in the set of auxiliary data but not present in the training data set or in the self-identification information” (a mental process that can be performed in the human mind, i.e. judgement)
“identifying ... a first quality metric indicating a degree of accuracy of data values of the first candidate attribute type and a second quality metric indicating a degree of accuracy of data values of the second candidate attribute type” (a mental process that can be performed in the human mind, i.e. judgement)
“calculating ... based on the first quality metric and the second quality metric, a change in the target performance metric of the machine learning algorithm when the data values of the first candidate attribute type and the data values of the second candidate attribute type are included in the training data set …” (a mathematical calculation)
"” (a mental process that can be performed in the human mind, i.e. judgement)
"adding ... in response to the confirmation, the data values of the first candidate attribute type to the training data set to produce an augmented training data set that is enhanced without requiring the individual to provide the data values of the first candidate attribute type” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
"determining, ... based on the correction, that auxiliary data of the second candidate attribute type is among a number of candidate attributes types of the auxiliary data that is most frequently corrected by individuals” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
"adjusting, ..., the second quality metric to indicate a lesser degree of accuracy of the data values of the second candidate attribute type, so that future augmented training data sets avoid adding data values of the second candidate attribute type” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Claim 1 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
“by a processing system including at least one processor” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“by the processing system” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
"obtaining ... a set of auxiliary data describing the individual from a plurality of auxiliary data sources, wherein the plurality of auxiliary data sources is separate from a source of a training data set that is used to train the machine learning algorithm and separate from the individual, and wherein the set of auxiliary data comprises a plurality of attributes having a plurality of different attribute types” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“… wherein the calculating comprises: re-training, by the processing system, the machine learning algorithm with the data values associated with the first candidate attribute type included in the training data set” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
"presenting ... a first portion of the set of auxiliary data corresponding to the first candidate attribute type and a second portion of the set of auxiliary data corresponding to the second candidate attribute type to the individual for review” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
"receiving ... from the individual, a confirmation ” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“re-training, by the processing system, the machine learning algorithm using the augmented training data set to produce an improvement in the target performance metric” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is directed to the abstract idea.
Subject Matter Eligibility Analysis Step 2B:
“by a processing system including at least one processor” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“by the processing system” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
"obtaining ... a set of auxiliary data describing the individual from a plurality of auxiliary data sources, wherein the plurality of auxiliary data sources is separate from a source of a training data set that is used to train the machine learning algorithm and separate from the individual, and wherein the set of auxiliary data comprises a plurality of attributes having a plurality of different attribute types” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
“… wherein the calculating comprises: re-training, by the processing system, the machine learning algorithm with the data values associated with the first candidate attribute type included in the training data set” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
"presenting ... a first portion of the set of auxiliary data corresponding to the first candidate attribute type and a second portion of the set of auxiliary data corresponding to the second candidate attribute type to the individual for review” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of gathering and analyzing information using conventional techniques and displaying the result as identified by the court - see MPEP 2106.05(d))
"receiving ... from the individual, a confirmation ” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
“re-training, by the processing system, the machine learning algorithm using the augmented training data set to produce an improvement in the target performance metric” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not recite significantly more than the abstract idea as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the target performance metric is identified in a signal by a human analyst” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the plurality of auxiliary data sources contains data that is not contained in the training data set” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the first candidate attribute type is known to influence the improvement in the target performance metric more than a third candidate attribute type” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the first candidate attribute type is selected in response to a signal from a human analyst” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“a signal ... that instructs the processing system to select the first candidate attribute type” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein each of the degree of accuracy of the data values of the first candidate attribute type and the degree of accuracy of the data values of the second candidate attribute type is expressed as a confidence” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein at least one of: the first quality metric or the second quality metric is computed by the processing system” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the first quality metric is directly proportional to a number of the plurality of auxiliary data sources containing values that agree with the data values associated with the first candidate attribute type” (a mathematical relationship)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the first quality metric depends on how closely the data values associated with the first candidate attribute type match crowdsourced values for the first candidate attribute type” (a mental process that can be performed in the human mind, i.e. observation)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the first quality metric depends on how closely the data values associated with the first candidate attribute type match values obtained from a focused survey delivered to a group of known subject matter experts” (a mental process that can be performed in the human mind, i.e. observation)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the first candidate attribute type results in the change in the target performance metric being greater than other candidate attribute types which have been evaluated for inclusion in the training data set, including the second candidate attribute type” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 17:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“determining, ” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“(mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 18:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the tradeoff is satisfied when the change in the target performance metric is an improvement that at least meets a first threshold and when the first quality metric at least meets a second threshold” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 19:
The claim recites an article of manufacture that performs the method as described in claim 1. Therefore, claim 19 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 19 are analyzed below
Subject Matter Eligibility Analysis Step 2A Prong 1:
Please see Step 2A Prong 1 analysis of claim 1
Subject Matter Eligibility Analysis Step 2A Prong 2:
“A non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 20:
The claim recites a system that performs the method as described in claim 1. Therefore, claim 20 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 20 are analyzed below
Subject Matter Eligibility Analysis Step 2A Prong 1:
Please see Step 2A Prong 1 analysis of claim 1
Subject Matter Eligibility Analysis Step 2A Prong 2:
“a processing system including at least one processor” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a non-transitory computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 21:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the confidence is assigned based on a source of at least one of: the data values of the first portion of the set of auxiliary data” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 22:
Subject Matter Eligibility Analysis Step 2A Prong 1:
"adding ... to the augmented training data set, data values associated with a corrected version of the second portion of the set of auxiliary data that has been corrected in a manner consistent with the correction” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
"repeating, by the processing system, the re-training using the augmented training data set including the data values associated with the corrected version of the second portion of the set of auxiliary data” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
"... by the processing system ...” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 23:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the set of auxiliary data is used to validate the self-identification information” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 24:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein an acquisition of data related to the second candidate attribute type by a source of the plurality of auxiliary data sources is increased to increase the second quality metric” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 25:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the determining is based on a consultation with a human analyst” (a mental process that can be performed in the human mind, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Conclusion
THIS ACTION IS MADE FINAL. 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 GARY MAC whose telephone number is (703)756-1517. The examiner can normally be reached Monday - Friday 8:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached on (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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/GARY MAC/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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