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 argument filed 11/19/2025 have been fully considered but they are not persuasive in regards to 101 rejections. The amendments have overcome 103 rejections and thus, the 103 rejections have been withdrawn. A new search has been conducted and no new prior art teaches all the claim limitations of the invention.
Applicant’s Argument: On pages 18-19 of Applicant’s response, Applicant states that the technical improvement of the invention is training a machine learning model that results in a lower rate of false positives by enhancing response to diverse behavior in certain populations using an optimization and regularization process.
Examiner’s Response: Applicant’s argument is not persuasive. An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome (see MPEP 2106.05(a)). The amended claims do not provide sufficient details to describe any technological improvement. If the specifications explicitly set forth an improvement but in a conclusory manner (see MPEP 2106.04(d)(1): a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. The claim recites “wherein the machine learning model's response to diverse behavior in the one or more populations is improved by adjusting the score to increase diversity for low-diversity detected in a high-scoring population using an optimization process, and adjusting the score to increase diversity for low-diversity detected in a low-scoring population using a regularization process” and it is not clear how the process is directed to an improvement of a machine learning model. The claim limitation merely states that the model’s output (“response”) to diverse behavior is improved by adjusting the score. The score is defined as an output of the model in the claims. Therefore, modifying the output of a model does not directly correlate to the improvement of the model. There is insufficient support in the claims as a whole to describe the technical improvement of generating a more efficient and productive machine learning model.
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 1-23 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.
Independent claims 1, 22, and 23 recites “extracting, using the at least one processor, one or more first features from the generated data structure based on one or more current time-series data”. It is unclear what are the first features that are extracted from the generated data structure. The generated data structure is based on the times series data representing one or more actions. The time series data is interpreted to represent past data and it is distinct from the current time-series data. The generated data structure is not constructed using the information of the current time-series data. Thus, what constitutes as extracting one or more first features from the generated data structure based on one or more current time-series data. The claims do not define any correlation between the generated data structure using past time series data and the current time series data. The examiner interprets the claim as extracting one or more first features representing actions executed by an entity that is the same as the current action from the generated data structure based on the detection of the current action.
Claim 1, 22, and 23 recites the limitation "... wherein the feedback data includes received feedback data from the multiple investigator entities". There is insufficient antecedent basis for this limitation in the claim. “The multiple investigator entities” lacks antecedent basis.
Claim 1, 22, and 23 recites “wherein the machine learning model's response to diverse behavior in the one or more populations is improved by adjusting the score to increase diversity for low-diversity detected in a high-scoring population using an optimization process, and adjusting the score to increase diversity for low-diversity detected in a low-scoring population using a regularization process”. It is not clear how the claim as a whole lead to improving the machine learning model’s response to diverse behavior by adjusting the score to increase diversity. The score is the output of the machine learning model and it is not explicit on what entity performs the step of adjusting the score. The adjustment of the score using an optimization process is vague and it is not clear whether the model parameters are updated to adjust the score outputted by the model. The claims as a whole should be amended to clearly define the process of “adjusting the score to increase diversity for low-diversity detected in a high-scoring population”. In all previous steps leading up to the last claim limitation of claim 1, it is not well defined how the claim as a whole is directed to the machine learning model’s response to diverse behavior. The claims as recited are broad and the claim limitations does not define how the training of the model is directed to the technical improvement of increasing diversity for low-diversity detected in a high-scoring population. The claim limitation is being interpreted as the investigator entities review a high-scoring population to identify additional actions or behavior of importance and adjusting the scores of those additional actions to increase the diversity of a population.
Claims 2-21 are dependent claims of independent claim 1. Therefore, the dependent claims 2-21 are rejected on the same basis as claim 1.
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 1-23 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 computer implemented method for improving a machine learning model, the 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:
“processinga plurality of time-series data sources, the time-series data representing one or more actions executed by an entity in one or more populations and stored by at least one time-series data source in the plurality of time-series data sources” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. evaluation)
“generating, ” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“detecting, ” (a mental process that can be performed in the human mind, i.e. judgement)
“extracting, ” (a mental process that can be performed in the human mind, i.e. judgement)
“comparing, one or more populations, and determining, based on the comparing, one or more difference parameters being indicative of differences between selected one or more first and second features” (a mental process that can be performed in the human mind, i.e. evaluation)
“determining, according to a function of a classifier adjusted density estimation determined for the one or more actions” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. evaluation, see Specification par. 49)
“identifying, ” (a mental process that can be performed in the human mind, i.e. judgement)
“increasing a weight associated with the entity, responsive to two or more investigator entities from among the multiple investigator entities identifying two or more actions similar to the identified at least one action” (a mental process that can be performed in the human mind, i.e. judgement)
“wherein the machine learning model's response to diverse behavior in the one or more populations is improved by adjusting the score to increase diversity for low-diversity detected in a high-scoring population using an optimization process, and adjusting the score to increase diversity for low-diversity detected in a low-scoring population using a regularization process” (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:
"... receiving one or more current time-series data corresponding to the current action and associated with data structure corresponding to the entity” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“... using at least one processor ...” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“training, using the machine learning model, using the one or more difference parameters” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“determining, using the trained machine learning model” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“updating, using the at least one processor, the training of the machine learning model in response to receiving feedback data, wherein the feedback data includes received feedback data from the multiple investigator entities” (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:
"... receiving one or more current time-series data corresponding to the current action and associated with data structure corresponding to the entity” (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))
“... using at least one processor ...” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“training, using the machine learning model, using the one or more difference parameters” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“determining, using the trained machine learning model” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“updating, using the at least one processor, the training of the machine learning model in response to receiving feedback data, wherein the feedback data includes received feedback data from the multiple investigator entities” (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 itself 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 2:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the identified at least one action indicates a high likelihood of failure in a monitored system due to the occurrence of the identified at least one action” (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 3:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the training of the machine learning model is performed using the selected one or more first and second features” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the training includes selecting at least one over- and under-representation of a training exemplar or no change to representation” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the received feedback data includes feedback data responsive to the identified at least one action” (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:
“monitoring, ” (a mental process, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“receiving, using the at least one processor, the time-series data from the plurality of time- series data sources” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
“monitoring, using the at least one processor” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the one or more actions executed by the entity are summarized by the one or more representations and include at least one previously executed action” (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 8:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the time-series data is received during at least one of the following time periods: one or more periodic time intervals, one or more irregular time intervals, and any combination thereof” (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 9:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the time-series data represents one or more actions executed by the entity during a predetermined period of time” (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 10:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the at least one entity and the at least another entity include at least one of the following: related entities, unrelated entities, and any combination thereof” (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:
“wherein the one or more difference parameters of the one or more representations include at least one of the following: latent parameters determined for least comparable entities, parameters determined for most comparable entities, and any combination thereof” (a mental process, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the at least another identified action includes at least one of the following: an action identified in addition to the at least one identified action, an action identified for replacing the at least one identified action, no action, and any combination thereof” (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 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“assigning, ” (a mental process, 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))
“generating, using the at least one processor, an updated model and an updated score for one or more actions executed by the at least one entity based on the one or more weight parameters; wherein the one or more weight parameters are determined based on at least the received feedback data” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the received feedback data include one or more labels associated with at least one of the at least one entity and the one or more actions executed by the at least one entity” (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 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the one or more weight parameters are determined based on a number of times the feedback data is received for the at least one entity or another entity within a predetermined distance of the at least one entity with respect to a similarity measure” (a mental process, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the received feedback data includes feedback data associated with the at least another entity being similar to the at least one entity” (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 17:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the received feedback data includes an aggregate feedback data associated with the at least one entity and the at least another entity being similar to the at least one entity” (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 18:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the received feedback data includes a data associated with the one or more actions executed by at least one of the at least one entity and the at least another entity being similar to the at least one entity” (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 19:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the one or more actions include at least one of the following: the at least one identified action, an action identified for replacing the at least one identified action, no action, and any combination thereof” (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 20:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“generating a consistency score for the received feedback data, the consistency score being determined based on receiving a number of times a similar feedback data is received for at least one of: the at least one entity, the at least another entity being similar to the at least one entity and determined to be within a predetermined distance of the at least one entity, and the one or more actions executed by at least one of: the at least one entity and the at least another entity being similar to the at least one entity, and any combination thereof” (a mental process, i.e. judgement)
“determining, based on the generated consistency score, whether to use the received feedback data in the updating” (a mental process, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 21:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the monitored system is a system monitored for failure and action is taken to prevent or reduce the identified high likelihood of failure in the system” (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:
The claim recites a system that performs the method as described in claim 1. Therefore, claim 22 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 22 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 & 2B:
“at least one programmable processor” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
“a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 23:
The claim recites an article of manufacture that performs the method as described in claim 1. Therefore, claim 23 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 23 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 & 2B:
“A computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Conclusion
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