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 .
Examiner's Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Response to Arguments
Applicant’s arguments, see REMARKS pages 15-21 filed 04/20th/2026, regarding the rejection of claims 1-3,6-7,9-12 and 19-29 under 35 U.S.C. §101 have been considered and are not persuasive.
Applicant Argument #1:
As an initial matter, the recent USPTO memorandum regarding Ex Parte Desjardins,
Appeal No. 2024-000567 (PTAB September 26, 2025), issued December 5, 2025 (hereinafter "the Desjardins memo"), provides relevant guidance. In particular, the Desjardins memo emphasizes that "Examiners and panels should not evaluate claims at such a high level of generality" that potentially meaningful technical limitations are dismissed without adequate explanation. Desjardins memo, p. 4. The Desjardins memo further instructs that "[w]hen evaluating a claim as a whole, examiners should not dismiss additional elements as mere 'generic computer components' without considering whether such elements confer a technological improvement to a technical problem, especially as to improvements to computer components or the computer system."
Desjardins memo, p. 4.
Examiner Response #1:
The examiner respectfully disagrees. Different application directed to different fields are analyzed differently. For example, Desjardins is directed to training machine learning models, while the instant application is directed to evaluating supervised learning models. The same distinctions applies to all other decisions the applicant argues in the response including but not limited to the Recentive Analytics decision.
Applicant Argument #2:
Here, the Office Action characterizes the claims at an impermissibly high level of
generality. For example, the Examiner has alleged that "detect one or more labelling errors based
on the clustering information" is a mental process like "an operator identifying images of cats that
were classified as monkeys." Office Action, p. 7. This characterization improperly abstracts away
the specific technical features recited in the amended claims. Claim 1, as amended, recites:
"detect one or more labelling errors based on the clustering information, wherein detecting
the one or more labelling errors comprises comparing, for each data point, the clustering
information to labelling information associated with the initial training data to identify
discrepancies between the clustering information and the labelling information, wherein the
comparison places a higher level of trust on the clustering information than on the labelling
information."
This is not a mental observation that could be performed by a human with pencil and paper.
Rather, the amended claims require systematic comparison of clustering information against
labelling information for each data point with a defined trust hierarchy. This is a concrete
computational process that leverages the insight that unsupervised clustering results are more
reliable than potentially erroneous human-generated labels. The Examiner's analogy to "an
operator identifying images of cats that were classified as monkeys" fails to account for the specific
technical mechanism recited in the claims. Office Action, p. 7.
Examiner Response #2:
The applicant respectfully disagrees. Under broadest reasonable interpretation, the element of detecting a labeling error by comparing clustering information is a mental process that could be performed by the human mind or with the aid of a pen and paper. Furthermore, the element of placing a higher level of trust on the clustering information than on the labeling information amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Applicant Argument #3:
Even assuming, without conceding, that the claims recite an abstract idea at Step 2A, Prong
One, the claims integrate any such exception into a practical application at Step 2A, Prong Two.
As noted in the previous section, the Desjardins memo instructs that "[w]hen evaluating a claim
as a whole, examiners should not dismiss additional elements as mere 'generic computer
components' without considering whether such elements confer a technological improvement to a technical problem, especially as to improvements to computer components or the computer
system." Desjardins memo, p. 4.
Examiner Response #3:
The applicant respectfully disagrees. While the specification [0071] recites “the model generation and data source evaluation platform 102 may have a higher level of trust in the clustering information (e.g., generated using unsupervised learning) than in the labeling information (which may be manually input). Accordingly, the model generation and data source evaluation platform 102 may identify discrepancies between the labeling information and the clustering information, which may indicate labeling errors”, and while [0083] recites “filtering mislabeled data prior to training the supervised learning model," the system "may result in a more accurate supervised learning model (e.g., because the supervised learning model may be trained using accurately labeled data rather than mislabeled data).”, and while [0118] recites “As a particular example, the composite model may include three supervised learning models, which may be correlated with reliability scores of .2, .1, and .9, respectively. In this example, if the first and second models output a prediction of 'Prediction A' and the third model outputs a prediction of 'Prediction B,' the composite model may generate a query response of 'Prediction B,' even though the quorum suggests that 'Prediction A' should be output.”, and while [0120] recites “Accordingly, as a summary of the application of the composite model, the model generation and data source evaluation platform 102 may identify predictions from each supervised learning model, and then may select the prediction corresponding to the highest average reliability score. If multiple predictions correspond to a common highest average reliability score, the model generation and data source evaluation platform 102 may select the prediction that was output by more models (e.g., a consensus).”, and while [0096] recite “the model generation and data source evaluation platform 102 may identify data drift in the stored datasets (e.g., stored data may become less accurate as time increases). In these instances, the model generation and data source evaluation platform 102 may quantify the data drift, and may tune the supervised learning model (and/or reassign grades to various data sources) based on the quantified data drift.”, there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of cluster the initial training data … , generate, based on the clustering, clustering information that indicates …. , detect one or more labelling errors based on the clustering information, remove the one or more labelling errors from the initial training data, compare the model-predicted outcome data to the one or more actual outcomes, score … each of the supervised learning models of the two or more supervised learning models, wherein each score reflects a reliability level of the corresponding supervised learning model, store a matrix relating the scores to their corresponding supervised learning models, weight results obtained from each supervised learning model of the two or more supervised learning models according to the weight values, generate …. a response to the query, or weighting each of the two or more supervised learning models based on the stored matrix rather than to an improvement on the functioning of a computer or to any other technology. See MPEP 2106.05(a). Thus, even when considering the elements alone or in combination, the claim as a whole does not integrate the recited exception into a practical application, nor does it amount to significantly more than the exception itself.
Applicant Argument #4:
Even if it were argued that the requirements of Step 2A were not satisfied, which Applicant
does not concede, further analysis is still required at Step 2B. As noted in the MPEP, "[e]valuating
additional elements to determine whether they amount to an inventive concept requires considering
them both individually and in combination to ensure that they amount to significantly more than
the judicial exception itself." MPEP 2106. 05.
This is further emphasized in BASCOM Global Internet Services v. AT&T Mobility, where
the Federal Circuit found that "[t]he inventive concept inquiry requires more than recognizing that
each claim element, by itself, was known in the art" and that "an inventive concept can be found
in the non-conventional and non-generic arrangement of known, conventional pieces." BASCOM
Global Internet Services v. AT&T Mobility, 827 F.3d 1341, 1350 (Fed. Cir. 2016)..
Examiner Response #4:
The applicant respectfully disagrees. BASCOM’s claim does not recite a mental process while the instant application recites multiple mental processes as highlighted under Step 2A Prong 1 Analysis below.
Claim Rejections - 35 USC § 101
101 Rejection
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3,6-7,9-12 and 19-29 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter
Step 1 Analysis:
Claims 1-3, 6-7, 9, 21-22, and 27-29 are directed to a computing platform, which is directed to a machine, one of the statutory categories. Claims 10-12 are directed to a method which is directed to a process, one of the statutory categories. Claims 19-20, and 23-26 are directed to a non-transitory computer-readable media which is directed to a product, one of the statutory categories.
Regarding Claim 1:
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1 Analysis:
Claim 1 recites in part process steps which, under the broadest reasonable interpretation, are a series of mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process or a mathematical concept but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. The claim recites in part:
cluster the initial training data into one or more clustering sets using an unsupervised learning method, wherein the unsupervised learning method comprises hierarchical clustering Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator classifying images of cats and dogs). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Furthermore, the recitation of using an unsupervised learning method, wherein the unsupervised learning method comprises hierarchical clustering is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
generate, based on the clustering, clustering information that indicates, for one or more data points associated with the initial training data, which of the one or more clustering sets that a corresponding data point of the one or more data points corresponds to Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator identifying images of cats and dogs). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
detect one or more labelling errors based on the clustering information Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator identifying images of cats that were classified as dogs). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Wherein detecting the one or more labelling errors comprises comparing, for each data point, the clustering information to labelling information associated with the initial training data to identify discrepancies between the clustering information and the labelling information, wherein the comparison places a higher level of trust on the clustering information than on the labelling information Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator identifying images labeled as cats that were clustered as dogs, identifying differences, and giving more weight to the way they were clustered in the process). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
increase an accuracy of two or more supervised learning models and a composite model by: removing the one or more labelling errors from the initial training data Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator removing images of cats that were incorrectly classified as dogs). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas
compare the model-predicted outcome data to the one or more actual outcomes corresponding to the one or more prediction parameters associated with the additional training data wherein comparing the model-predicted outcome data to the one or more actual outcomes corresponding to the one or more prediction parameters associated with the additional training data comprises identifying a Euclidian distance between the model predicted outcome data and the one or more actual outcomes Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator comparing the actual images of cats and dogs and comparing their observation to the predicted label for each image and identifying a Euclidian distance between different data points). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
score, based on results of the comparison of the model-predicted outcome data to the one or more actual outcomes corresponding to the one or more prediction parameters associated with the additional training data, each of the supervised learning models of the two or more trained supervised learning models, wherein each score reflects a reliability level of the corresponding supervised learning model Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator giving a grade to each model based on how accurate it was in identifying images as those of cats or dogs). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
store a matrix relating the scores to their corresponding supervised learning models Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator giving a grade to each model based on how accurate it was in identifying images as those of cats or dogs). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
train, using weight values corresponding to the scores, the composite model, wherein training the composite model causes the composite model to weight results obtained from each supervised learning model of the two or more trained supervised learning models according to the weight values Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator giving a higher weight to a more accurate model and a lower weight to a less accurate model). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Furthermore, the recitation of train, using weight values corresponding to the scores, the composite model, wherein training the composite model causes the composite model to is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
generate, using the composite model, a response to the query, wherein generating the response to the query comprises weighting each of the two or more supervised learning models based on the stored matrix by: selecting a prediction corresponding to a highest average reliability score among predictions generated by the two or more trained supervised learning models, and based on identifying that multiple predictions correspond to the highest average reliability score, selecting the prediction by identifying which of the multiple predictions was output by a greater number of the two or more supervised learning models Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator answering queries by weighing different responses and selecting images with high reliability scores from the most learning models). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Furthermore, the recitations of using the composite model are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
quantify data drift associated with the two or more trained supervised learning models Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator counting the number of data drifts associated with the models). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
avoid processing inaccuracy due to the data drift by: refining, based on the response to the query, the different response, and the quantified data drift the composite model, wherein refining the composite model comprises dynamically updating weight values assigned to the two or more trained supervised learning models based on the quantified data drift Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator manually updating model parameters based on the data drift detected). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Furthermore, the recitation of dynamically updating weight values assigned to the two or more supervised learning models is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2A Prong 2 Analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of:
A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
receive initial training data from two or more data sources is recited at a high-level of generality and amounts to extra-solution activity of gathering data (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
training the two or more supervised learning models using the initial training data to produce two or more trained supervised learning models is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Forming the composite model based on the two or more supervised learning models is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
wherein the composite model is more accurate than each of the two or more supervised learning models amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
receive additional training data and one or more prediction parameters associated with the additional training data, where the additional training data indicates one or more actual outcomes corresponding to the one or more prediction parameters associated with the additional training data is recited at a high-level of generality and amounts to extra-solution activity of gathering data (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
input the additional training data into the composite model to generate model- predicted outcome data is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
receive a query from an enterprise user device is recited at a high-level of generality and amounts to extra-solution activity of gathering data (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
wherein: at least one of the two or more trained supervised learning models generated a different response, different than the response to the query, wherein the different response is incorrect, and the response to the query was generated, despite generation of the different response, due to use of the composite model, wherein the response to the query is correct amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
generate one or more commands directing the enterprise user device to display the response to the query; send, to the enterprise user device, the response to the query and the one or more commands directing the enterprise user device to display the response to the query, wherein sending the one or more commands directing the enterprise user device to display the response to the query causes the enterprise user device to display the response to the query which 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)).
After considering all claim elements, both individually and in combination, it has been determined that the claim does not integrate the abstract idea into a practical application.
wherein the data drift indicates that stored data associated with at least one of the two or more trained supervised learning models has become less accurate over time amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B Analysis:
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
receive initial training data from two or more data sources is recited at a high-level of generality and amounts to extra-solution activity of gathering data (MPEP 2106.05(g): 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").
training the two or more supervised learning models using the initial training data to produce two or more trained supervised learning models is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Forming the composite model based on the two or more supervised learning models is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
wherein the composite model is more accurate than each of the two or more supervised learning models amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
receive additional training data and one or more prediction parameters associated with the additional training data, where the additional training data indicates one or more actual outcomes corresponding to the one or more prediction parameters associated with the additional training data is recited at a high-level of generality and amounts to extra-solution activity of gathering data (MPEP 2106.05(g): 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").
input the additional training data into the composite model to generate model- predicted outcome data is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
receive a query from an enterprise user device is recited at a high-level of generality and amounts to extra-solution activity of gathering data (MPEP 2106.05(g): i.e. pre-solution activity of gathering data for use in the claimed process.
wherein: at least one of the two or more trained supervised learning models generated a different response, different than the response to the query, wherein the different response is incorrect, and the response to the query was generated, despite generation of the different response, due to use of the composite model, wherein the response to the query is correct amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
generate one or more commands directing the enterprise user device to display the response to the query; send, to the enterprise user device, the response to the query and the one or more commands directing the enterprise user device to display the response to the query, wherein sending the one or more commands directing the enterprise user device to display the response to the query causes the enterprise user device to display the response to the query which 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)).
wherein the data drift indicates that stored data associated with at least one of the two or more trained supervised learning models has become less accurate over time amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101.
The additional limitations of the dependent claims contain no additional elements that provide a practical application or amount to significantly more than the abstract idea and are addressed briefly below
Dependent claim 2 recites:
Step 2A Prong 1:
identify, based on the model-predicted outcome data to the one or more actual outcomes corresponding to the one or more prediction parameters associated with the additional training data, an error percentage, for each supervised learning model of the two or more trained supervised learning models, indicating accuracy of each of two or more trained supervised learning models under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 3 recites:
Step 2A Prong 1:
scoring each of the trained supervised learning models of the two or more trained supervised learning models comprises scoring, based on the corresponding error percentages, each of the supervised learning models of the two or more trained supervised learning models under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 6 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
the query comprises a request for a prediction, and the response to the query comprises the requested prediction amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h).
Step 2B: 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 elements of:
the query comprises a request for a prediction, and the response to the query comprises the requested prediction amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Dependent claim 7 recites:
Step 2A Prong 1:
the scoring further comprises scoring each point of the model predicted outcome data under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 9 recites:
Step 2A Prong 1:
weighting the results obtained from each supervised learning model of the two or more trained supervised learning models when applying the composite model comprises multiplying, for each result, the corresponding Euclidian distance by the corresponding score under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment, opinion, or a mathematical concept that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 27 recites:
Step 2A Prong 1:
compare each reliability score to a reliability threshold; based on identifying that a reliability score of a supervised learning model exceeds the reliability threshold, assign a go determination to the supervised learning model indicating that the supervised learning model should be applied as part of the composite model; and based on identifying that the reliability score of the supervised learning model does not exceed the reliability threshold, assign a no-go determination to the supervised learning model indicating that the supervised learning model should not be applied as part of the composite model under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment, opinion, or a mathematical concept that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 28 recites:
Step 2A Prong 1:
clustering the initial training data comprises applying a plurality of unsupervised learning algorithms to generate a plurality of clustering results, detecting the one or more labeling errors comprises: generating polling information indicating labelling error results from each of the plurality of unsupervised learning algorithms; and comparing the polling information to a quorum threshold to identify whether a data point is mislabeled under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment, opinion, or a mathematical concept that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Furthermore, the recitation applying a plurality of unsupervised learning algorithms is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 29 recites:
Step 2A Prong 1:
identify, based on the clustering, proximity information indicating a proximity of each data point to a center of a corresponding clustering set; and generate, based on the proximity information, a confidence score for each data point, wherein a data point closer to the center of the corresponding clustering set is assigned a higher confidence score than a data point farther from the center under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment, opinion, or a mathematical concept that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 10:
Claim 10 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites similar steps to claim 1 (see above for analysis), with the additional element of a method.
Step 2A Prong 2, Step 2B:
The additional element of a method 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 (See MPEP 2106.05(f)). Implementing an abstract idea on generic computer components does not integrate the abstract idea into a practical application, nor does it add significantly more to the exception. Thus, the claim is not patent eligible.
Dependent claim 11 recites:
Claim 11 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method with similar steps to claim 2, and thus is not patent eligible for the same reasons (see above).
Dependent claim 12 recites:
Claim 12 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method with similar steps to claim 3, and thus is not patent eligible for the same reasons (see above).
Regarding Claim 19:
Claim 19 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites similar steps to claim 1 (see above for analysis), with the additional element of a non-transitory computer-readable media.
Step 2A Prong 2, Step 2B:
The additional element of a non-transitory computer-readable media 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 (See MPEP 2106.05(f)). Implementing an abstract idea on generic computer components does not integrate the abstract idea into a practical application, nor does it add significantly more to the exception. Thus, the claim is not patent eligible.
Dependent claim 20 recites:
Claim 20 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a non-transitory computer-readable media with similar steps to claim 2, and thus is not patent eligible for the same reasons (see above).
Dependent claim 21 recites:
Step 2A Prong 1:
generating the response to the query comprises selecting the prediction of the third supervised learning model based on an average reliability score associated with each of the predictions under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment, opinion, or a mathematical concept that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein the composite model comprises three supervised learning models, including a first supervised learning model, a second learning model, and a third supervised learning model amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h).
a first reliability score of the first supervised learning model is .2, wherein a prediction of the first supervised learning model corresponds to the different response amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h).
a second reliability score of the second supervised learning model is .1, wherein a prediction of the second supervised learning model corresponds to the different response amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h).
a third reliability score of the third supervised learning model is .9, wherein a prediction of the third supervised learning model corresponds to the response to the query amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h).
Step 2B: 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 elements of:
wherein the composite model comprises three supervised learning models, including a first supervised learning model, a second learning model, and a third supervised learning model amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
a first reliability score of the first supervised learning model is .2, wherein a prediction of the first supervised learning model corresponds to the different response amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
a second reliability score of the second supervised learning model is .1, wherein a prediction of the second supervised learning model corresponds to the different response amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
a third reliability score of the third supervised learning model is .9, wherein a prediction of the third supervised learning model corresponds to the response to the query amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Dependent claim 22 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
the query corresponds to geological exploration amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h).
Step 2B: 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 elements of:
the query corresponds to geological exploration amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Dependent claim 23 recites:
Step 2A Prong 1:
scoring each of the supervised learning models of the two or more trained supervised learning models comprises scoring, based on the corresponding error percentages, each of the supervised learning models of the two or more trained supervised learning models under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 24 recites:
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
the query comprises a request for a prediction, and the response to the query comprises the requested prediction amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h).
Step 2B: 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 elements of:
the query comprises a request for a prediction, and the response to the query comprises the requested prediction amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use MPEP 2106.05(h). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Furthermore, the courts have found limitations directed to linking data to a field of use, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II)).
Dependent claim 25 recites:
Step 2A Prong 1:
the scoring further comprises scoring each point of the model predicted outcome data under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 26 recites:
Claim 26 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a non-transitory computer-readable media with similar steps to claim 9, and thus is not patent eligible for the same reasons (see above).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SHEN (US20160078353A1)
“SHEN teaches a method for detecting anomalous correlations between public and private activities of a user”
HATONEN (US20040039968A1)
“HATONEN teaches a method for monitoring the behavior of at least one observable object, e.g. a network element, of a network, wherein at least one parameter of the observable object is repeatedly detected”
KIM (US 2021/0124981 Al)
“KIM teaches a method for determining one or more anomaly detection sub models for calculating an input data among generated anomaly detection sub models; and judging whether or not the anomaly is existed in the input data through using the one or more determined anomaly detection sub models”
OLINER (US 2018/0219889 Al)
“OLINER teaches a machine learning method for anomaly detection based on a determined error of a predictive model”
Kiefer (US 2021/0352609 Al)
“Kiefer teaches a method to use crowd sourced data from mobile devices to estimate site locations”
Zhang (Multilayer bootstrap networks)
“Zhang teaches Multilayer bootstrap networks”
Patel (US 2022/0101182 Al)
“Patel teaches a method for assessing the quality of a dataset, wherein the quality is assessed in view of an effect of the dataset on a performance of the machine-learning model”
Desmond (US 2021/0174196 Al)
“Desmond teaches a method for improving ground truth quality for modeling”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAMCY ALGHAZZY whose telephone number is (571)272-8824. The examiner can normally be reached on M-F 7:30am-5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, OMAR FERNANDEZ RIVAS can be reached on (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHAMCY ALGHAZZY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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