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 05/26/2026 have been fully considered but they are not persuasive in regards to 112 and 101 rejections.
Applicant’s Argument: On pages 12-14 of Applicant’s response, Applicant states that claims 1-23 are rejected under 35 U.S.C. §112(b). Applicant provides support for §112(a) and explains the subject matter in the claims are supported by the specification. The specification (par. 43) provides support that features may be extracted from the generated data structure based on the current time-series data. Applicant further provides support from the specification to clarify the improvement to the machine learning model’s output based on optimization for low-diversity in a high-scoring population and regularization for low-diversity in a low-scoring population.
Examiner’s Response: Applicant’s argument is not persuasive. The Office Action (sent 02/27/2026; pg. 4) rejects claims 1-23 under 35 U.S.C. §112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter. Claims 1-23 are not rejected because there is lack of support of the claimed subject matter in the written description of the invention. In claim 1, the amendments link the current time-series data corresponding to the current action with the data structure. Thus, the claim limitation “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” is no longer indefinite. The 112(b) rejection in regard to this claim element has been withdrawn.
The claim limitation “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” is merely a conclusory statement. Claim 1 as a whole does not particularly point out the optimization and regularization process. Examiner suggests to include additional details from the specification to further define the optimization and regularization process.
Applicant’s Argument: On pages 14-19 of Applicant’s response, Applicant states that the claims are directed to improvements in the recited computer implemented process itself and argues that the claims are not directed to an abstract idea based on Enfish. The claims are directed to improvements in the function of computers and the improvements focus on the investigation on a diverse and wide variety of outlier behaviors. Applicant states the claims are similar to McRO and the claims improves prior art computer implemented platforms by changing the model-training and alert-scoring process. Applicant also states that the claims are similar to Example 39 and the training of the model improves the detection of diverse rare behavior.
Examiner’s Response: Applicant’s argument is not persuasive. The claim as a whole is not directed to improvements in the computer technology because the claims does not recite a technical improvement in computer logical structure and/or processes. The claims recite a model that is trained and retrained using time-series entity profiles, current-event features, feature comparisons, feedback from multiple investigator entities, and diversity-based score adjustments. The claims do not recite any specific improvements to the model training process. The claims merely apply a specific set of input data to a model training process and the input data is directed to behavior types of entities. The claims also do not recite any improvement to specific computing structures.
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 “focusing the investigation on a diverse and wide variety of outlier behaviors as adjusted based on feedback to present more relevant entities for investigation ... by incorporating sufficient samples of new outlier behavior into supervised training” is an improvement to the abstract idea of a mental process that can be performed in the human mind. Identifying new outlier behavior is an abstract idea of a mental process and training a machine learning model using the diverse behavior is merely using computer as a tool to perform the abstract idea.
In McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. Applicant argues that the pending claims improves prior art computer-implemented platforms by changing the model training and alert-scoring process. 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 pending claims are not analogous to USPTO Subject Matter Eligibility Guidance Example 39. In Example 39, the claim does not recite a mental process because the claim as a whole is not practically performed in the human mind. In Example 39, the claim is eligible because it does not recite a judicial exception. The claimed invention does recite an abstract idea based on subject matter eligibility analysis step 2A prong 1. Thus, the claimed invention requires further analysis for subject matter eligibility.
Applicant’s Argument: On pages 19-22 of Applicant’s response, Applicant states that the claims are integrated into a practical application. Similar to DDR Holdings, the pending claims address the technical problems in rare-event detection systems. The claimed technological improvement includes the claimed training architecture using entity profiles, current time-series data, and feature extraction to improve how the machine learning model detects diverse rare behavior. Applicant states the claimed invention is similar to Ex parte Desjardins and the claims are directed to “an improvement to how the machine learning model itself operates.”
Examiner’s Response: Applicant’s argument is not persuasive. The claims do not provide sufficient details to describe the technological improvement. The claim as a whole does not recite a particular way to achieve a desired outcome. The claimed invention does not describe any specific process that improves the machine learning model in detecting diverse rare behavior. Applicant states that the technological improvement is based on applying the optimization and regularization process to adjust the score. The amended claims do no provide sufficient details to describe the technological improvement of the optimization and regularization process. Examiner suggest to include additional details from the Specification to define how the model training process is improved by the optimization and regularization process. The amended claims as recited does not describe any improvement to the functioning of a machine learning model. The claims merely recite a generic machine learning model training process that uses entity profiles, current time-series data, and feedback data. A machine learning machine that processes a particular dataset is not directed to an improvement of how the machine learning model operates.
Applicant’s Argument: On pages 22-25 of Applicant’s response, Applicant states that the claims recite significantly more than a generic computer implementation. Applicant uses BASCOM to describe the recited ordered combination of claim elements provide significantly more than any alleged abstract idea. Federal Circuit (BASCOM) held that an inventive concept can be found in the non-conventional and non-generic arrangement of known elements, when the ordered combination of recited elements is non-conventional and yields a technical improvement.
Examiner’s Response: Applicant’s argument is not persuasive. The claimed combination of computer-implemented elements of training a machine learning model using feedback data, updated weights, and score adjustments needs to be a non-conventional arrangement of known elements. Applicant fails to distinctly point out how the combination of claim elements is different from the conventional method of training and optimization process of a machine learning model. A feedback loop, updated model weights, and score adjustments are well-understood, routine, conventional activity of optimization process in the field of artificial intelligence. The amended claims do not recite a combination of elements that are non-conventional. Examiner suggest to include additional details about the optimization process and score adjustment in hopes to overcome 101 rejections.
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.
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”. It is not clear how the claim as a whole is directed to the claimed technological improvement of the machine learning model. For the combination of the steps recited in claim 1, none of the recited processes is tied to any of the claimed improvement of the model as disclosed by the last claim element of claim 1. Specifically, the recited steps of claim 1 are not tied to the claimed improvement of using an optimization process to adjust the score to increase diversity in any manner. It is unclear how the last claim limitation of claim 1 has any relation with the claimed retraining and increasing a weight step.
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:
“processing” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. evaluation)
“generating, feature representations of processed time-series data identifying one or more actions executed by the entity” (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, data” (a mental process that can be performed in the human mind, i.e. judgement)
“comparing, the one or more first features with one or more second features associated with at least another entity in the one or more populations to determine one or more difference parameters 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, ” (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)
“” (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 plurality of investigator entities identifying two or more actions determined to have a degree of similarity to the identified at least one action” (a mental process that can be performed in the human mind, i.e. judgement)
“wherein the updated 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 in association with the data structure identifying 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 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))
"receiving feedback data from a plurality of investigator entities ” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“retraining, using the at least one processor, the machine learning model to generate an updated machine learning model, in response to receiving the feedback data” (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 in association with the data structure identifying 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 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))
"receiving feedback data from a plurality of investigator entities ” (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))
“retraining, using the at least one processor, the machine learning model to generate an updated machine learning model, in response to receiving the feedback data” (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 incorporated into one or more data structures including one or more feature vectors” (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
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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
