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 .
DETAILED ACTION
This office action in response to the claims filed on 01/26/2026. However, this is a second non-final since some of the claim limitations were not corrected identified under the 101 rejection.
Claims 1-9, 11-12, 14-20 are presented for examination.
Response to Argument
In reference to applicant’s argument regrading rejections under 35 U.S.C. § 112:
Applicant’s Argument:
The applicant’s argument for the 112 (a) and 112(b) rejections based on the remark filed on 01/26/2026.
Examiner’s Response:
The 112 (a) and 112(b) rejections are withdrawn in view of the remark filed on 01/26/2026.
In reference to applicant’s argument regrading rejections under 35 U.S.C. § 101:
Applicant’s Argument in the remark page 11 is not persuasive :
Applicant notes that it is desirable to apply machine learning to sets of "big data" or "mixed data" in which units of the sets incorporate many covariates ("high-dimensionality" data). This is desirable because comprehending such high-dimensionality data is beyond the capabilities of the human mind. However, in general, deriving actionable conclusions from high-dimensionality data also is beyond the capabilities of all or almost all extant computer systems (even including massively parallel multi-processor systems). It is difficult to obtain values such as "ATT" (Average Treatment effect on the Treated), because predicting ATT requires matching covariates across units. Even for machine learning algorithms, it is prohibitively difficult to match covariates in order to predict ATT on big data sets. The claimed embodiments provide the technical improvements that solve these technical issues.
…Applicant notes that "generating an artificial neural network configured as a self- organizing map by the self-organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network" constitutes an improvement of the technology in machine learning as the generated neural network is improved compared to conventional neural networks. Similarly, "training the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map" results in an improved classifying neural network as it considers a "combined total loss of the classification and of the self-organizing map," as recited in independent Claim 1.
Examiner’s Response:
Examiner respectfully disagrees to applicant’s argument since the claim limitation does not recite an improvement of the functioning of the computer in the technology field nor an improvement of the machine learning model, because classifying the set of the mixed data by “apply machine learning to sets of "big data" or "mixed data”, this is direct to the mental process, as the human mind can classify the treated and untreated portion based on the mixed data (the human mind can tell whether a portion of the data is treated or untreated), predict/ obtain values such as "ATT" (Average Treatment effect on the Treated) is an improvement of the mental process, as the doctor can predict the (Average Treatment effect on the Treated), as the doctor predict/obtain the effective value of the treatment. The “apply machine learning to sets of "big data" or "mixed data”, this is not an improvement of the machine learning model on or the improvement of the function of the computer in the technology field., it’s applying the generic computer component ( the machine learning model/neural network) on the big data or mixed data, which is analyzed under step 2A prong 2, as the high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)). Therefore, predicting the Average Treatment effect on the treated, that is the improvement of the abstract idea, which is not considered as the improvement of the machine learning model or the improvement of the functioning of the computer in the technology field. Therefore, the applicant’s argument is not persuasive, the rejection is still maintained.
Furthermore, "generating an artificial neural network configured as a self- organizing map by the self-organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network" this limitation is not recite the improvement of the machine learning model by itself nor the improvement of the functionally of the computer in the technology field. The claim using the generic computer component (artificial neural network) to generate the self-organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network, as mapping the output (latent representation) from first layer (input layer) to the next layer (intermediate layer ) of the neural network . Therefore, this limitation is analyzed under Step 2A: Prong 2 analysis and step 2B as the limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
Applicant’s Argument on page 12 in the remark is not persuasive:
Respectfully, as recently recognized by the PTAB in Ex parte Desjardins, Appeal 2024- 000567, Decision on Request for Rehearing, BPAI September 26, 2025, improvements to computerized machine learning are patent eligible under Section 101. The limitations of the independent claims are indicative of integration of the respective improvements into a practical application at least because the limitations are directed to improvements in the relevant technical field and because the limitations are patentable subject matter under the 2-part rubric of MPEP 2106.04(d)(1). The improvements are set forth in the specification (e.g., paragraphs 0010-0013): simultaneously learning a meaningful latent representation and a corresponding metric with respect to a treatment assignment and an observational covariate space; and automated selection for the size of latent dimensions with respect to an assignment by finding the minimum loss on a validation.
Furthermore, the claims include the components or steps of the invention that provide the improvement described in the specification, such as the generation of "an artificial neural network configured as a self-organizing map by the self-organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network", as achieved by the steps of "delivering a latent representation of the classified units of the mixed data set from an intermediate layer of the classifying neural network to a self-organizing map engine" and "generating an artificial neural network configured as a self- organizing map by the self-organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network."
Thus, under the 2-part rubric of MPEP 2106.04(d)(1), and the holding of Exparte HANNUN, even if reciting a judicial exception, the present claims are not directed to a judicial exception, since the claims as a whole integrate the recited judicial exception into a practical application of that exception.
Examiner’s Response:
Examiner respectful disagrees to applicant argument because the applicant’s argument regarding “the PTAB in Ex parte Desjardins, Appeal 2024- 000567, Decision on Request for Rehearing, BPAI September 26, 2025, improvements to computerized machine learning are patent eligible under Section 101.” is not related to the claim limitation, as the claim limitations in the EX parte Dejardins recite the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about, and the specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. However, the current claim limitations do not recite the improvement of the machine learning model by itself or the improvement of the technology in the field, the current claim recite the generic computer component is implementing on the mental process. Therefore, the applicant’s argument is not persuasive, the rejection is still maintained.
The applicant’s Argument in the remark page 13 is not persuasive:
Regarding the Examiner's assertion that "the claim limitation is not integrated into the practical application because the limitations are not directed to improvements in the relevant technical field, or use, by a particular machine," Applicant notes, as argued above and below, that "training the classifying neural network to optimize a combined total loss of the classification and of the self- organizing map" constitutes an improvement to machine learning.
Regarding the Examiner's assertion that "these paragraphs does not reflects to the improvement of the machine learning model or the improvement of the functioning of the computer in the technology field, these paragraph recites the improvement of the abstract idea," Applicant notes that, similar to the decision in Ex parte Desjardins, at least the "training [of] the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map" constitutes an improvement to machine learning.
Regarding the Examiner's assertion that "the current claim limitation does not recite the improvement of the machine learning model or the improvement of the functioning of the computer in the technology field, because the claim recites the calculating the classification loss but the claim does not specific recite the use of the calculated classification loss in the claim," Applicant notes that Claim 1 recites, in part, "training the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map" and thus, contrary to the Examiner's assertion, clearly recites the use of the calculated classification loss in the claim.
Examiner’s Response:
Examiner respectfully disagrees to applicant’s argument because the claim recite the training the classifying neural network to optimize a combined total loss of the classification and of the self- organizing map, as optimizing the total loss (combined classification loss and the self-organizing map) this is a general purpose of training of the machine learning model (neural network), however, the improvement of the result or optimize the loss of the classification result, which is not considered as the improvement of the machine learning technique, this is a generic machine learning (neural network).
Additionally, examiner respectfully remind the applicant’s argument that the current claim limitation and the Ex parte Desjardins are not similar, they are different, as the current claim recites: apply the neural network on the mental process, as apply machine learning to sets of "big data" or "mixed data”, this is direct to the mental process, as the human mind can classify the treated and untreated portion based on the mixed data (the human mind can tell whether a portion of the data is treated or untreated). “training the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map" this is a generic neural network (optimizing the total loss of the classification result). However, as the explanation above, the claim limitations in the EX parte Dejardins recite the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about, and the specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system.
Therefore, the application’s argument is not persuasive, the 101 rejection is still maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9, 11-12, 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a method (claims 1-9), computer program product (claims 11, 12, 14) and apparatus (claims 15-20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically, the abstract idea of “Mental Processes/Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”.
The claim 1 recites:
Step 2A: prong 1 analysis:
- classifying one or more units of the mixed data set as treated or untreated” this is a mental process, the human mind can classify the treated and untreated portion based on the mixed data (the human mind can tell whether a portion of the data is treated or untreated), (Observation/Evaluation),
-“calculating a loss of the classification L(BCE) by comparing predicted treatment probabilities against true treatment assignments, the calculating the classification loss L(BCE) further comprising”, this is a mental process, the human mind calculate the loss of the classification by compare the predicted probabilities and the treatment assignment, as the human mind can tell the different between the predicted result and the true treatment assignment as the professional assigns, (Observation/Evaluation).
-“calculating the classification loss L(BCE) as an average binary cross-entropy between the predicted probabilities and actual binary treatment assignments, where the classification loss penalizes both false positives and false negatives:” this is mathematical concept.
-“ and estimating average treatment effect on the treated units by comparing the outcome information for at least one of the treated units to outcome information for an untreated unit that is a nearest- neighbor of the at least one treated unit” this is a mental process, the human mind can estimate the average treatment based on the comparing the outcome information of the treated unit and the outcome information of the untreated unit, for example, the human can the estimate the average treatment effect based on the different outcome from before and after treatment (observation/Evaluation).
a) Step 2A: Prong 2 analysis:
- obtaining by a classifying neural network a mixed data set that comprises a priori information and outcomes information for a plurality of treated units and a plurality of untreated units”, “delivering a latent representation of the classified units of the mixed data set from an intermediate layer of the classifying neural network to a self-organizing map engine” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more (than the judicial exception and cannot integrate a judicial exception into a practical application.
“classifying one or more units of the mixed data set as treated or untreated, by running the classifying neural network on the a priori information for at least the one or more units;” “generating an artificial neural network configured as a self-organizing map by the self- organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network” “training the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map;” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“for each unit of the mixed data set, generating a latent representation of covariates of the corresponding unit:”, “passing the latent representation of covariates through a sigmoid activation function to obtain a predicted probability:”, “on the self-organizing map.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See 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 and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
- obtaining by a classifying neural network a mixed data set that comprises a priori information and outcomes information for a plurality of treated units and a plurality of untreated units”, “delivering a latent representation of the classified units of the mixed data set from an intermediate layer of the classifying neural network to a self-organizing map engine” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
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").
“classifying one or more units of the mixed data set as treated or untreated, by running the classifying neural network on the a priori information for at least the one or more units;”, “generating an artificial neural network configured as a self-organizing map by the self- organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network” “training the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map;” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“for each unit of the mixed data set, generating a latent representation of covariates of the corresponding unit:”, “passing the latent representation of covariates through a sigmoid activation function to obtain a predicted probability:”, “on the self-organizing map.” This/these limitation(s) is/are amount 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.
The claim 2 recites:
Step 2A: prong 1 analysis:
“A/B testing of the mixed data set based on comparison of outcome information for the treated units and for their nearest-neighbor counterfactuals”. As drafted and under its broadest reasonable interpretation, this limitation falls under the abstract idea of a mental process. A/B testing is a known statistical test. For example, this limitation encompasses evaluating the impact of a specific intervention on a population.
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 3 recites:
Step 2A: prong 1 analysis:
-“enrolling an insurance plan participant into a care management program in response to the A/B testing based on the nearest-neighbor counterfactuals” this is a mental process, the human can enroll in particular insurance plan based on the testing result, as they can know what kind of the insurance plan is benefit for their health condition, (Observation/Evaluation)
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 4 recites:
Step 2A: prong 1 analysis:
-“ supplying a wearable fitness tracker to an insurance plan participant in response to the A/B testing based on the nearest-neighbor counterfactuals” this is a mental process, the human mind can supplying/buying the fitness tracker device based on the test result and allowable by the health insurance company
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 5 recites:
Step 2A: prong 1 analysis:
-“ approving a vaccine for delivery to the general population in response to the A/B testing based on the nearest-neighbor counterfactuals.” this is a mental process, the human mind can provide the particular vaccine based on the testing result
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 6 recites:
Step 2A: prong 1 analysis:
-“ authorizing advertising spend on a new campaign in response to the A/B testing based on the nearest-neighbor counterfactuals.” this is a mental process, the human mind can provide the authorize amount of fund on the campaign for particular health issue based on the testing result, (Observation/Evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 7 recites:
Step 2A: prong 1 analysis:
-“ supplying a smart home control device to a utility consumer in response to the A/B testing based on the nearest-neighbor counterfactuals.” this is a mental process, the human mind can supply the smart home control device to the customer based on their health condition corresponding to the testing result, (Observation/Evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 8 recites:
Step 2A: prong 1 analysis:
-“ administering a pharmacological treatment to a clinical patient in response to the A/B testing based on the nearest-neighbor counterfactuals..” this is a mental process, the human mind can provide the particular treatment based on the testing result, (Observation/Evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 9 recites:
Step 2A: prong 1 analysis:
-“
PNG
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212
717
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Greyscale
”
This is a mathematic concept.
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 11 recites:
Step 2A: prong 1 analysis:
“classifying one or more units of the mixed data set as treated or untreated” this is a mental process, the human mind can classify the treated and untreated portion based on the mixed data (the human mind can tell whether a portion of the data is treated or untreated), (Observation/Evaluation),
“computing a loss associated with the self-organizing map, the computing the loss associated with the self-organizing map further comprising: for each iteration and each classified unit, comparing a map latent representation of covariates of a given unit to a set of weight vectors associated with nodes in the self-organizing map;’ this is a mathematical concept.
“ and computing the loss associated with the self-organizing map as a weighted sum of squared distances between the map latent representation and the set of weight vectors” this is a mathematical concept.
-“ estimating average treatment effect on the treated units among the one or more units by comparing outcome information for at least one treated unit of the one or more units to outcome information for an untreated unit of the one or more units that is a nearest-neighbor” this is a mental process, the human mind can estimate the average treatment based on the comparing the outcome information of the treated unit and the outcome information of the untreated unit, for example, the human can the estimate the average treatment effect based on the different outcome from before and after treatment (observation/Evaluation).
a) Step 2A: Prong 2 analysis:
- “obtaining, by a classifying neural network, a mixed data set that comprises a priori information and outcomes information for a plurality of treated units and a plurality of untreated units;”, “delivering a latent representation of the classified units of the mixed data set from an intermediate layer of the classifying neural network to a self-organizing map engine;” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more (than the judicial exception and cannot integrate a judicial exception into a practical application.
“classifying one or more units of the mixed data set as treated or untreated, by running the classifying neural network on the a priori information for at least the one or more units”,” generating an artificial neural network configured as a self-organizing map by the self- organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network; training the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“where weights of the weight vectors are determined by a Gaussian kernel centered at a best-matching node for the map latent representation, wherein the best-matching node has a corresponding weight vector closest to the map latent representation in Euclidean distance;”, “on the self- organizing map to the at least one treated unit” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See 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 and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
- “obtaining, by a classifying neural network, a mixed data set that comprises a priori information and outcomes information for a plurality of treated units and a plurality of untreated units;”, “delivering a latent representation of the classified units of the mixed data set from an intermediate layer of the classifying neural network to a self-organizing map engine;” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
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").
““classifying one or more units of the mixed data set as treated or untreated, by running the classifying neural network on the a priori information for at least the one or more units”, “generating an artificial neural network configured as a self-organizing map by the self- organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network; training the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“where weights of the weight vectors are determined by a Gaussian kernel centered at a best-matching node for the map latent representation, wherein the best-matching node has a corresponding weight vector closest to the map latent representation in Euclidean distance;”, “on the self- organizing map to the at least one treated unit”” This/these limitation(s) is/are amount 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.
The claim 12 is rejected for the same reason as the claim 2, since these claims are recite the same limitations.
The claim 14 recites:
Step 2A: prong 1 analysis:
-“
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265
730
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Greyscale
”
This is a mathematic concept.
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 15 recites:
Step 2A: prong 1 analysis:
“classifying one or more units of the mixed data set as treated or untreated” this is a mental process, the human mind can classify the treated and untreated portion based on the mixed data (the human mind can tell whether a portion of the data is treated or untreated), (Observation/Evaluation),
-“ and estimating average treatment effect on the treated units by comparing the outcome information for at least one of the treated units to outcome information for an untreated unit that is a nearest- neighbor of the at least one treated unit” this is a mental process, the human mind can estimate the average treatment based on the comparing the outcome information of the treated unit and the outcome information of the untreated unit, for example, the human can the estimate the average treatment effect based on the different outcome from before and after treatment( observation/Evaluation).
a) Step 2A: Prong 2 analysis:
- obtaining by a classifying neural network a mixed data set that comprises a priori information and outcomes information for a plurality of treated units and a plurality of untreated units”, “delivering a latent representation of the classified units of the mixed data set from an intermediate layer of the classifying neural network to a self-organizing map engine” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more (than the judicial exception and cannot integrate a judicial exception into a practical application.
“classifying one or more units of the mixed data set as treated or untreated, by running the classifying neural network on the a priori information for at least the one or more units” , “generating an artificial neural network configured as a self-organizing map by the self- organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network”, “training the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map;” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“on the self-organizing map to at least one treated unit.”” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See 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 and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
- obtaining by a classifying neural network a mixed data set that comprises a priori information and outcomes information for a plurality of treated units and a plurality of untreated units”, “delivering a latent representation of the classified units of the mixed data set from an intermediate layer of the classifying neural network to a self-organizing map engine” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
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").
“classifying one or more units of the mixed data set as treated or untreated, by running the classifying neural network on the a priori information for at least the one or more units” , “generating an artificial neural network configured as a self-organizing map by the self- organizing map engine based on the latent representation of the classified units of the mixed data set from the intermediate layer of the classifying neural network”, “training the classifying neural network to optimize a combined total loss of the classification and of the self-organizing map;” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“on the self-organizing map to at least one treated unit.” This/these limitation(s) is/are amount 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.
The claim 16 recites:
Step 2A: Prong 1 analysis:
“Calculating a loss of the classification L(BCE) by comparing predicted treatment probabilities against true treatment assignments, the calculating the classification loss L(BCE) further comprising:” this is a mental process, the human mind calculate the loss of the classification by compare the predicted probabilities and the treatment assignment, as the human mind can tell the different between the predicted result and the true treatment assignment as the professional assigns, (Observation/Evaluation).
“ and calculating the classification loss L(BCE) as an average binary cross-entropy between the predicted probabilities and actual binary treatment assignments,” this is a mathematical concept.
a) Step 2A: Prong 2 analysis:
- “where the classification loss penalizes both false positives and false negatives” this/these additional element(s) is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
-“ for each unit of the mixed data set, generating a latent representation of covariates of the corresponding unit; passing the latent representation through a sigmoid activation function to obtain a predicted probability” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See 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 and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
-“ where the classification loss penalizes both false positives and false negatives” this/these additional element(s) is/are recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
-“ for each unit of the mixed data set, generating a latent representation of covariates of the corresponding unit; passing the latent representation through a sigmoid activation function to obtain a predicted probability” This/these limitation(s) is/are amount 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.
The claim 17 recites:
a) Step 2A: Prong 1 analysis:
“computing a loss associated with the self-organizing map, the computing the loss associated with the self-organizing map further comprising: for each iteration and each classified unit, comparing a map latent representation of covariates of a given unit to a set of weight vectors associated with nodes in the self-organizing map;” this is a mathematical concept.
“and computing the loss associated with the self-organizing map as a weighted sum of squared distances between the map latent representation and the set of weight vectors” this is a mathematical concept.
a) Step 2A: Prong 2 analysis:
“where weights of the weight vectors are determined by a Gaussian kernel centered at a best-matching node for the map latent representation, wherein the best-matching node has a corresponding weight vector closest to the map latent representation in Euclidean distance;”, “on the self- organizing map to the at least one treated unit” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See 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 and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
“where weights of the weight vectors are determined by a Gaussian kernel centered at a best-matching node for the map latent representation, wherein the best-matching node has a corresponding weight vector closest to the map latent representation in Euclidean distance;”, “on the self- organizing map to the at least one treated unit”” This/these limitation(s) is/are amount 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.
The claim 18 is rejected for the same reason as the claim 2, since these claims recite the same limitations.
The claim 19 is rejected for the same reason as the claim 7, since these claims recite the same limitations.
The claim 20 is rejected for the same reason as the claim 8, since these claims recite the same limitations.
Conclusion
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/E.T./ Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/ Supervisory Patent Examiner, Art Unit 2128
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