DETAILED ACTION
This Office Action is in response to the amendments filed on 11/07/2025.
Claims 1, 9, and 16 are currently amended.
Claims 1-7, 9- 14, and 16-20 are currently pending in this application and have been examined.
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
In reference to Applicant’s arguments on page(s) 9-15 regarding rejections made under 35 U.S.C. 101:
Claims 1-7, 9-14, and 16-20 are rejected under 35 U.S.C. § 101 as being directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea), without significantly more. This rejection is respectfully traversed.
Respectfully, in what manner is a student model comprising a second model type, different than the first model type, a mental activity? The student model is a machine learning model having various parameters and associated weights, as explained throughout Applicant's originally filed specification. The student model has a specific initialization scheme and a specific updating scheme, neither of which can be practically performed mentally. In particular, by the time a person had manually computed the point-data updates to the teacher model as required to update the student model, the time to functionally respond to a given selected activity would be long over.
Thus, even if it were theoretically possible to complete some of the claimed steps mentally, it is not practical in the claimed context.
Notwithstanding the above, the claims are further amended to require, at minimum, outputting, by the student model, a visual output for local interpretation explaining individual contributions of features and their respective weights to a decision recommendation for the set of features. Applicant submits that such steps cannot be completed mentally in a practical manner.
In any case, the Office Action then finds several of Applicant's "additional elements" to be merely recited at a high level of generality. Applicant cannot agree with these characterizations, as explained in detail below.
The Office Action states that "updating...the teacher model with each data point that was collected subsequent to the first data point, wherein the teacher model comprises a first model type comprising a variable number of parameters that is updated with a single data point at a time" is recited at a "high level of generality".
Why is this a "high level of generality"? This limitation is very specific and describes how to update the teacher model. A "high level of generality" might be to merely claim "updating the teacher model" or "updating the teacher model with each collected data point". Applicant provides more than this generic description-more specifically, a specific updating procedure for the teacher model is described in which updates occur per data point.
The Office Action states that initializing a student model "only after every designated class is encountered at least once, the student model further comprising a fixed number of parameters that is updated concurrently with a batch of new data points" is recited at a "high level of generality".
Why is this a "high level of generality"? This limitation is very specific and describes how to initialize the student model. A "high level of generality" might be to merely claim "initializing a student model" or "initializing a student model with a set of data points". Applicant provides more than this generic description-more specifically, a specific initialization procedure is described in which the student model is initialized only after every designated class is encountered at least once, the student model further comprising a fixed number of parameters that is updated concurrently with a batch of new data points. Thus, the teacher model and student model are fundamentally different, with the teacher model updated per-data-point and the student model updated per-data-batch (refer, e.g., to paragraph 16 of the originally filed specification explaining that the student model facilitates global and local interpretation but requires more data than the teacher model).
The Office Action states that "updating the student model with the teacher model following a collection of each subsequent data point after the set of data points such that weights of the student model are updated using a per-data point update of the teacher model, wherein the per-data point update comprises updating all weights of the student model with less than all of the weights of the teacher model" is recited at a "high level of generality".
Why is this a "high level of generality"? This limitation is very specific and describes how to update the batch-type student model via the per-data-point-type teacher model. A "high level of generality" might be to merely claim "updating the student model with the teacher model". Applicant provides more than this generic description-more specifically, it is described how to update the student model. Notably, it is not routine or well-understood to update student models according to per-data-point updates to some subset of the weights of a teacher model. Normally, batch-type student models are updated batch-to-batch. However, as described in at least paragraph 17 of Applicant's originally filed specification, the present approach enables a student model that can be interpreted globally and locally but can also achieve the requisite accuracy with less training data than would typically be required for such a model.
Limitations that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include: "i. improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258- 59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a))." MPEP 2106.05.A.
Here, the claims at issue recite an improvement to the functioning of a computer, and to a technological field, as discussed above with respect to Step 2A Prong Two.
Limitations that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include: "v. Adding a specific limitation other than what is well- understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d))." MPEP 2106.05.A.
Examiner’s response: Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the partial limitation of “the student model comprising a second model type, different than the first model type” does not recite an abstract idea of a mental process. Examiner disagrees. Under the broadest reasonable interpretation, simply identifying that the student model type is different from the parent model type is a mental observation and therefore an abstract idea.
Applicant argues that limitations reciting the updating or initializing of models are not recited at a high level of generality. Examiner disagrees. No specific details are provided as to how the updates are performed. Is old data overwritten? Is there any aggregation performed when the updating occurs? Are data points averaged to obtain the updated values? Without any underlying details given, the simple action of updating data in a model is performed at a high level of generality. The same argument holds for initializing a model; the act of assigning data to variables in the first pass is a generic concept.
Applicant argues that that specification provides more than the generic description of the updating/initializing process in [0017] of the submitted application. Examiner directs Applicant to MPEP 2106.05.(a) where it states, “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”.
Applicant argues that the claims, as amended, add specific limitations other than what is considered well understood, routine, or conventional. Examiner disagrees. The amended limitations do not add anything that could be considered not well understood, routine, or conventional by one skilled in the art. If Applicant believes otherwise that the specification includes information that would deem the amendments to not be well understood, routine, or conventional by one skilled in the art, Applicant is encouraged to incorporate those details in the claims, as was recommended above in MPEP 2106.05.(a).
In light of the amendments made on the claims, the rejections made under 35 U.S.C 101 are maintained and updated below.
In reference to Applicant’s arguments on page(s) 15-18 regarding rejections made under 35 U.S.C. 103:
Claims 1-5, 9-12, and 16-19 are rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Di Mauro et al., Activity Prediction of Business Process Instances with Inception CNN models, 11/12/2019 (hereinafter, "Di Mauro") in view of Fukuda et al., U.S. Patent Publication No. 2020/0034702 (hereinafter, "Fukuda"), Li et al., U.S. Patent Publication No. 2019/0287515 (hereinafter, "Li"), Tama et al., An Empirical Comparison of Classification Techniques for Next Event Prediction Using Business Process Event Logs, 4/12/2019 (hereinafter, "Tama"), and Yu et al., U.S. Patent Publication No. 2020/0394458 (hereinafter, "Yu").
Claims 6, 7, 13, 14, and 20 are rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Di Mauro, Fukuda, Li, Tama, and Yu, in further view of Kopitar et al., Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening, 1/3/2020 (hereinafter, "Kopitar").
To render a claim unpatentable under 35 U.S.C. § 103, at a minimum, each and every element of the claim must be disclosed in the cited art.
Applicant respectfully submits that Di Mauro, Fukuda, Li, Tama, Yu, and Kopitar, alone or in any combination, fail to teach or fairly suggest each and every element of independent claim 1.
As an initial matter, the Office Action agrees that Di Mauro fails to teach or suggest at least "updating...the teacher model with each data point that was collected subsequent to the first data point, wherein the teacher model comprising a first model type comprising a variable number of parameters that is updated with a single data point at a time". (Office Action, page 25). Fukuda is relied upon for these teachings.
Applicant has reviewed the cited portions of Fukuda, and the remaining disclosure, and does not agree that Fukuda teaches or even fairly suggests the above-emphasized limitations. In fact, the very portions recited as evidence by the Office Action state explicitly that Fukuda's teacher model is updated per batch, rather than per data point. Paragraphs 30, 31, and 40 are illustrative and reproduced below for convenience (see also FIG. 2 referring to the "batch" processing of the teacher model).
In short, a plain reading of paragraphs 30, 31, and 40 shows that Fukuda is implementing a batch style updating scheme for the teacher model.
Notwithstanding the above, the Office Action further agrees that Di Mauro and Fukuda fail to teach or suggest at least "updating, using the processor, the student model with the teacher model following a collection of each subsequent data point after the set of data points such that weights of the student model are updated using a per-data point update of the teacher model" and "wherein the per-data point update comprises updating all weights of the student model with less than all of the weights of the teacher model". (Office Action, page 28). Li is relied upon for these teachings.
Respectfully, the Office Action relies an interpretation of Li that is not reasonable to arrive at the conclusion offered. More specifically, the Office Action states that "It is noted that only the initial weights of the teacher model are used, this reads on 'less than all of the weights of the teacher model', referring to Li's discussion in paragraph 56.
In response, Applicant offers that setting "initial weights" for the posteriors of the student model 208 cannot be fairly interpreted, even under the broadest reasonable interpretation standard, as somehow reading on updating... the student model... using a per-data point update of the teacher model... wherein the per-data point update comprises updating all weights of the student model with less than all of the weights of the teacher model. This interpretation completely ignores the actual claimed context in which this limitation rests, namely, that we are dealing with a per-data point update of all weights of the student model (not simply the posteriors) according to a subset of per-data-point updated teacher weights (not simply during initialization).
Tama, Yu, and Kopitar do not cure these defects. Consequently, Di Mauro, Fukuda, Li, Tama, Yu, and Kopitar, alone or in any combination, fail to disclose each and every element of amended independent claim 1. As such, amended independent claim 1 patentably defines over the cited art. Therefore, Applicant respectfully submits that amended independent claim 1 is in condition for allowance. Independent claims 9 and 16 have been similarly amended and are allowable over the art of record for at least the same reasons.
Moreover, since the dependent claims depend, either directly or indirectly, from Applicant's independent claims 1, 9, and 16, Applicant respectfully submits that the dependent claims are in condition for allowance as well, notwithstanding their independent recitation of patentable features. Therefore, Applicant respectfully requests that these rejections be withdrawn.
Examiner’s response:
Applicant’s arguments have been fully considered and are found to be persuasive.
Applicant argues that the prior art references of Di Mauro and Fukuda do not teach the claim of “updating...the teacher model with each data point that was collected subsequent to the first data point, wherein the teacher model comprising a first model type comprising a variable number of parameters that is updated with a single data point at a time”. Examiner agrees. The reference of Di Mauro does not teach the recited limitation and Fukuda was brought in to remedy the deficiencies. The cited portion of Fukuda has been reviewed and it is clear to the Examiner that the teacher model updating of Fukuda is done in a per batch manner, instead of a per data point manner, as is required by the claims.
Applicant argues that the prior art reference of Li used to remedy the deficiencies of Di Mauro and Fukuda is incorrectly characterized and relies on an unreasonable interpretation of the art. Examiner agrees. The characterization of the Li reference does not reasonably make sense for someone skilled in the art. The use of initial weights for updating a student model cannot be fairly interpreted as updating a student model on a per-data point basis as is claimed in the instant application.
The references relied upon for the dependent claims do not remedy the deficiencies set forth in the prior art references used for the independent claims.
In light of the arguments presented, the rejections made under 35 U.S.C. 103 are withdrawn.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-7, 9-14, and 16-20 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 1 recites, in part, a computer-implemented method, therefore falling into the statutory category of process. Independent Claim 9 recites, in part, a system comprising a memory and a processor, therefore falling into the statutory category of machine. Independent Claim 16 recites, in part, a computer program product, therefore falling into the statutory category of manufacture.
Regarding Claim 1:
Step 2A: Prong 1 analysis:Claim 1 recites in part:
“selecting an activity among a set of activities associated with a process as a selected activity, wherein prediction of an outcome of the selected activity is of interest”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses selecting one item of a set of one or more items.
“designating one or more activities of the set of activities as classes, wherein a respective activity among the set of activities is designated as a class when that respective activity directly connects to the selected activity”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses designating data as class types.
“implementing the process one or more times, wherein implementing the process comprises encountering at least one activity among the set of activities, wherein encountering an activity comprises providing an input to the activity and receiving an output from the activity”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses performing a process one or more times.
“the student model comprising a second model type, different than the first model type”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses making sure that a second model type is different from a first model type.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“using a processor”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (processor) (See MPEP 2106.05(f)).
“collecting a log of inputs and outputs of each encountered activity among the set of activities as a data point each time the process is implemented”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“extracting features from each data point that is collected to generate a feature vector from each data point”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“initializing a teacher model with a first data point that was collected”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“updating first weights of the teacher model responsive to receiving each data point that was collected subsequent to the first data point, wherein the teacher model comprises a first model type comprising a variable number of parameters and the weights are updated with a single data point at a time”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“initializing a student model with a set of data points including the first data point”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“that is initialized only after every designated class is encountered at least once, the student model further comprising a fixed number of parameters having second weights that is-are updated concurrently with a batch of new data points comprising two or more data points at a time”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“updating the second weights of the student model with the teacher model following a collection of each subsequent data point after the set of data points such that weights of the student model are updated using a per-data point update of the teacher model, wherein the per-data point update comprises updating all weights of the student model with less than all of the weights of the teacher model”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“inputting a set of features to the student model to obtain a prediction of the outcome of the selected activity based on determining that the student model is ready for use, wherein the student model is ready for use when a prediction accuracy of the student model exceeds a threshold accuracy value”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (predicting an outcome) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“outputting a visual output for local interpretation explaining individual contributions of features and their respective weights to a decision recommendation for the set of features”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. post-solution activity of outputting/displaying data for use in the claimed process.
“by the student model”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “using a processor” and “by the student model” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
The additional element(s) of “collecting a log of inputs and outputs of each encountered activity among the set of activities as a data point each time the process is implemented”, “extracting features from each data point that is collected to generate a feature vector from each data point”, “initializing a teacher model with a first data point that was collected”, “updating first weights of the teacher model responsive to receiving each data point that was collected subsequent to the first data point, wherein the teacher model comprises a first model type comprising a variable number of parameters and the weights are updated with a single data point at a time”, “initializing a student model with a set of data points including the first data point”, “that is initialized only after every designated class is encountered at least once, the student model further comprising a fixed number of parameters having second weights that is-are updated concurrently with a batch of new data points comprising two or more data points at a time”, and “updating the second weights of the student model with the teacher model following a collection of each subsequent data point after the set of data points such that weights of the student model are updated using a per-data point update of the teacher model, wherein the per-data point update comprises updating all weights of the student model with less than all of the weights of the teacher model” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As discussed above, the additional element(s) of “inputting a set of features to the student model to obtain a prediction of the outcome of the selected activity based on determining that the student model is ready for use, wherein the student model is ready for use when a prediction accuracy of the student model exceeds a threshold accuracy value” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome (configuring a machine for a user) i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
The additional element(s) of “outputting a visual output for local interpretation explaining individual contributions of features and their respective weights to a decision recommendation for the set of features” is/are recited at a high level of generality and amount(s) to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying/outputting a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 1 analysis:
Claim 2 recites in part:
“wherein the designating the one or more activities as classes includes identifying all activities that interact directly with the selected activity”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses identifying all the activities related to a selected activity.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 3:
Step 2A: Prong 1 analysis:
Claim 3 recites in part:
“ensuring that the feature vector from each data point has a same length by padding the feature vector if needed”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses making sure that all the vectors are the same length, and adding arbitrary data if not.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 4:
Step 2A: Prong 1 analysis:
Claim 4 recites in part:
“wherein the initializing the student model includes determining an initial weight associated with each of the features”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses determining an initial weight value for data.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 5:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the updating the teacher model includes improving weight values associated with the teacher model based on each subsequent data point”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (predicting an outcome) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“and the updating the student model with the teacher model includes extracting the weight values that correspond with the features of the student model and improving the corresponding weight of each of the features of the student model with a corresponding one of the weight values from the teacher model”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein the updating the teacher model includes improving weight values associated with the teacher model based on each subsequent data point” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome (configuring a machine for a user) i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
The additional element(s) of “and the updating the student model with the teacher model includes extracting the weight values that correspond with the features of the student model and improving the corresponding weight of each of the features of the student model with a corresponding one of the weight values from the teacher model” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“presenting an indication of the features and the corresponding weights used in the student model as a global interpretation of the student model”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. post-solution activity of outputting/displaying data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “presenting an indication of the features and the corresponding weights used in the student model as a global interpretation of the student model” is recited at a high level of generality and amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying/outputting a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“presenting the set of features that are input to the student model to obtain the prediction and the corresponding weights as a local interpretation of the student model”. This additional elements is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. post-solution activity of outputting/displaying data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “presenting the set of features that are input to the student model to obtain the prediction and the corresponding weights as a local interpretation of the student model” is recited at a high level of generality and amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying/outputting a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Due to claim language similar to that of Claim 1, Claim 9 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of the limitation(s) covered below.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“a memory having computer readable instructions”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (memory) (See MPEP 2106.05(f)).
“one or more processors for executing the computer readable instructions”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (memory) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “a memory having computer readable instructions” and “one or more processors for executing the computer readable instructions” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 10:
Due to claim language similar to that of Claim 2, Claim 10 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 11:
Due to claim language similar to that of Claim 3, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 12:
Due to claim language similar to that of claims 4 and 5, Claim 12 is rejected for the same reasons as presented above in the rejection of claims 4 and 5.
Regarding Claim 13:
Due to claim language similar to that of Claim 6, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 14:
Due to claim language similar to that of Claim 7, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 7.
Regarding Claim 16:
Due to claim language similar to that of claims 1 and 9, Claim 16 is rejected for the same reasons as presented above in the rejection of claims 1 and 9.
Regarding Claim 17:
Due to claim language similar to that of claims 2 and 10, Claim 17 is rejected for the same reasons as presented above in the rejection of claims 2 and 10.
Regarding Claim 18:
Due to claim language similar to that of claims 3 and 11, Claim 18 is rejected for the same reasons as presented above in the rejection of claims 3 and 11.
Regarding Claim 19:
Due to claim language similar to that of claims 4, 5, and 12, Claim 19 is rejected for the same reasons as presented above in the rejection of claims 4, 5, and 12.
Regarding Claim 20:
Due to claim language similar to that of claims 6, 7, 13, and 14, Claim 20 is rejected for the same reasons as presented above in the rejection of claims 6, 7, 13, and 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200034702 A1 – A student neural network may be trained by a computer-implemented method
US 20200394458 A1 – Apparatuses, systems, and techniques to detect object in images including digital representations of those objects
US 20190287515 A1 – Methods, systems, and computer programs are presented for training, with adversarial constraints, a student model for speech recognition based on a teacher model\
Di Mauro, Nicola & Appice, Annalisa & Basile, Teresa. (2019). Activity Prediction of Business Process Instances with Inception CNN Models. 10.1007/978-3-030-35166-3_25. – The proposed neural network architecture leads to better results when compared to RNNs architectures both in terms of computational efficiency and prediction accuracy on different real-world datasets
Bayu Adhi Tama, Marco Comuzzi, An empirical comparison of classification techniques for next event prediction using business process event logs, Expert Systems with Applications, Volume 129, 2019, Pages 233-245, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2019.04.016. (https://www.sciencedirect.com/science/article/pii/S0957417419302465) – we focus on one particular predictive monitoring task that is solved using classification techniques, i.e. predicting the next event in a case.
Kopitar, L., Cilar, L., Kocbek, P., Stiglic, G. (2019). Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening. In: Marcos, M., et al. Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems. KR4HC TEAAM 2019 2019. Lecture Notes in Computer Science(), vol 11979. Springer, Cham. https://doi.org/10.1007/978-3-030-37446-4_9 – three machine learning based prediction models were compared: Gradient Boosting Machine (GBM), Random Forest (RF) and Generalized linear model with regularization (GLM)
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128