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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/09/2026 has been entered.
Notice to Applicant
This communication is in response to the amendment filed 03/09/2026. Claims 1, 10, 15 have been amended. Claims 1-7, 9-18, 20 are presented for examination.
Subject Matter Free of Prior Art
Claim(s) 1-7, 9-18, 20 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “jointly training an encoder network and a classifier network with a decoder network using partial training data and imputed training data by: encoding the partial training data using the encoder network to generate a training vector, decoding the training vector using the decoder network to generate a reconstruction of the partial training data and the imputed training data, and training the encoder network with the decoder network based on the reconstructed partial training data and the imputed training data according to a loss function encoded to identify the imputed data, wherein weights associated with the imputed training data are not back propagated during the training”; “encoding the normalized knowledge graph using the encoder network to generate a vector of latent features in a latent space, the vector representing a state of the patient; determining an assessment of the patient for a medical condition using a classifier network based on the vector.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 10, 15, claims 1, 10, 15 are hereby deemed to be allowable over prior art. Originally numbered dependent claims 2-7, 9, 11-14, 16-18, 20 incorporate the allowable features of originally numbered independent claims 1, 10, 15, through dependency, respectively.
However, the claims are still rejected under 101.
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 1-7, 9-18, 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to a method which is within the four statutory categories (i.e., method). Claim 10 is drawn to an apparatus which is within the four statutory categories (i.e., machine). Claim 15 is drawn to a non-transitory computer readable medium which is within the four statutory categories (i.e., manufacture).
Independent claim 1 (which is representative of independent claims 10, 15) recites… jointly training an encoder network and a classifier network with a decoder network using partial training data and imputed training data by: encoding the partial training data using the encoder network to generate a training vector, decoding the training vector using the decoder network to generate a reconstruction of the partial training data and the imputed training data, and training the encoder network with the decoder network based on the reconstructed partial training data and the imputed training data according to a loss function encoded to identify the imputed data, wherein weights associated with the imputed training data are not back propagated during the training; …; normalizing the knowledge graph using a data normalization function; encoding the normalized knowledge graph using the encoder network to generate a vector of latent features…, the vector representing a state of the patient; determining an assessment of the patient for a medical condition using a classifier network based on the vector; and outputting the assessment of the patient.
Under the broadest reasonable interpretation, the limitations noted above, as drafted, covers mathematical relationships, but for the recitation of generic computer components. When given its broadest reasonable interpretation in light of the disclosure, training an encoder network and a classifier network by encoding data, decoding vectors, and using loss functions and weights represents the creation of mathematical interrelationships between data. See Example 47, Claim 2. Furthermore, but for the generic computer component language, normalizing data and encoding data encompasses transforming data using mathematical functions. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships and mathematical calculations, but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Independent claim 1 (which is representative of independent claims 10, 15) further recites… receiving input medical data of a patient; computing a knowledge graph based on the input medical data; …; determining an assessment of the patient for a medical condition…based on the vector; and outputting the assessment of the patient.
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and output relevant data (i.e., patient’s medical condition) based on the analysis accordingly in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “receiving,” “computing,” “determining,” “outputting,” as indicated supra. That is, other than reciting generic computer components (discussed infra) (i.e., a “computer” (claim 1); “at least one processor” (claims 10, 15)), the claim amounts to managing personal behavior or relationships or interactions between people following rules or instructions. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
For purposes of the following analysis, the aforementioned types of identified abstract ideas are considered together as a single abstract idea. See MPEP § 2106.04(II)(B).
Claim 1 recites additional elements (i.e., computer to implement the method; an encoder network; a classifier network; a decoder network; a latent space; a classifier network). Claim 10 recites additional elements (i.e., An apparatus comprising: a memory storing computer instructions; and at least one processor; an encoder network; a classifier network; a decoder network; a latent space; a classifier network). Claim 15 recites additional elements (i.e., A non-transitory computer readable medium storing computer program instructions; a processor; an encoder network; a classifier network; a decoder network; a latent space; a classifier network). Looking to the specifications, a computer having a memory storing computer instructions, at least one processor, non-transitory computer readable medium storing computer program instructions is described at a high level of generality (¶ 0081-0091), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “an encoder network,” “a classifier network,” “a decoder network,” and “a latent space” is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using the machine learning networks and spaces amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computer having a memory storing computer instructions, at least one processor, non-transitory computer readable medium storing computer program instructions) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, “an encoder network,” “a classifier network,” “a decoder network,” and “a latent space” is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using the machine learning networks and spaces amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2-7, 9, 11-14, 16-18, 20 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-7, 9, 11-14, 16-18, 20 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the aforementioned abstract idea groupings and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Response to Arguments
Applicant's arguments filed 03/09/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 03/09/2026.
In the remarks, Applicant argues in substance that:
Regarding the 101 rejections,
“Training a plurality of encoder networks with a plurality of decoder networks by "encoding," "decoding," and "training," as recited in amended claim 1, is not a mere rule or instruction followed by a person. Instead, paragraph [0085] of the Specification describes such methods as being performed using a computer. Further, the specification does not describe any of the steps as being required to be performed by a person or people. Accordingly, the amended claims are not directed to managing personal behavior or relations or interactions between people. Amended claim 1 also does not recite a concept performed in the human mind and therefore is not directed to a mental process. Amended claim 1 additionally does not recite a mathematical relationship, mathematic formula or equation, or a mathematical calculation and therefore is not directed to a mathematical concept”; and
“the claims are integrated into the practical application of an improvement to the training and operation of a machine learning based system for determining an assessment of a patient for a medical condition… missing training medical data, missing in the partial training data, may be imputed by encoding the partial training data into vectors using an encoder network and decoding the vectors to reconstruct the training medical data to include the reconstructed partial training data and the imputed training data using a decoder network. (SMED; para. 11). Advantageously, the imputed training data, imputed by reconstruction using an encoder network and decoder network, enables the encoder network to be trained to generate improved latent representations and to have enhanced generalization and robustness, bias reduction, and increased accuracy, as compared with state of the art approaches. (SMED; para. 11). Further, in accordance with embodiments of the invention, as explained at paragraph [0047] of the Specification, weights associated with the imputed training data are not backpropagated during the training of the encoder network and the decoder network. (SMED; para. 12). Accordingly, weights of the encoder network and the decoder network are updated based on an error associated with the real partial training data and not the possibly inaccurate imputed training data, resulting in the encoder network trained to generate improved latent representations and to have enhanced generalization and robustness, bias reduction, and increased accuracy. (SMED; para. 12).”
It is respectfully submitted that Examiner has considered Applicant’s arguments and does not find them persuasive. Examiner has attempted to address all of the arguments presented by Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
In response to Applicant’s argument that (b) regarding the 101 rejections,
“Training a plurality of encoder networks with a plurality of decoder networks by "encoding," "decoding," and "training," as recited in amended claim 1, is not a mere rule or instruction followed by a person. Instead, paragraph [0085] of the Specification describes such methods as being performed using a computer. Further, the specification does not describe any of the steps as being required to be performed by a person or people. Accordingly, the amended claims are not directed to managing personal behavior or relations or interactions between people. Amended claim 1 also does not recite a concept performed in the human mind and therefore is not directed to a mental process. Amended claim 1 additionally does not recite a mathematical relationship, mathematic formula or equation, or a mathematical calculation and therefore is not directed to a mathematical concept”:
It is respectfully submitted that per broadest reasonable interpretation of the claim in light of the specification, the claims of the present invention to which Applicant refer as “Training a plurality of encoder networks with a plurality of decoder networks by "encoding," "decoding," and "training"” represents the creation of mathematical interrelationships between data, which covers the sub-grouping of mathematical relationships and mathematical calculations, but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas, and not the “Mental Processes” or “Certain Methods of Organizing Human Activity” groupings, as Applicant now argues.
Furthermore, “encoder networks” and “decoder networks” are interpreted as additional elements to be interpreted in Step 2A, Prong Two, which is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using the machine learning networks amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “such methods as being performed using a computer.” However, the “computer” to which Applicant seems to refer is not interpreted as part of the abstract idea, but an additional element which is described at a high level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Applicant argues “the specification does not describe any of the steps as being required to be performed by a person or people.” However, the specification does not need to “describe any of the steps as being required to be performed by a person or people,” as long as the claim recites an abstract idea, which it does, but for the recitation of generic computer components, as explained previously above. Furthermore, per MPEP § 2106.04(a)(2)(II), “It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within [the "certain methods of organizing human activity"] grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
Thus, the claims are directed to an abstract idea.
“the claims are integrated into the practical application of an improvement to the training and operation of a machine learning based system for determining an assessment of a patient for a medical condition… missing training medical data, missing in the partial training data, may be imputed by encoding the partial training data into vectors using an encoder network and decoding the vectors to reconstruct the training medical data to include the reconstructed partial training data and the imputed training data using a decoder network. (SMED; para. 11). Advantageously, the imputed training data, imputed by reconstruction using an encoder network and decoder network, enables the encoder network to be trained to generate improved latent representations and to have enhanced generalization and robustness, bias reduction, and increased accuracy, as compared with state of the art approaches. (SMED; para. 11). Further, in accordance with embodiments of the invention, as explained at paragraph [0047] of the Specification, weights associated with the imputed training data are not backpropagated during the training of the encoder network and the decoder network. (SMED; para. 12). Accordingly, weights of the encoder network and the decoder network are updated based on an error associated with the real partial training data and not the possibly inaccurate imputed training data, resulting in the encoder network trained to generate improved latent representations and to have enhanced generalization and robustness, bias reduction, and increased accuracy. (SMED; para. 12)”:
Applicant argues “an improvement to the training and operation of a machine learning based system for determining an assessment of a patient for a medical condition.” However, the claims of the present invention do not provide “an improvement to the training and operation of…machine learning,” but use machine learning based architecture to allegedly improve “determining an assessment of a patient for a medical condition,” which is the abstract idea. Even if the claims provide the alleged improvements, any alleged benefits of the invention are at best, an improvement to the abstract idea of rules or instructions followed to collect data, analyze the collected data, and output relevant data (i.e., patient’s medical condition) based on the analysis accordingly. However, an improved abstract idea is still an abstract idea and the claims do not provide a technical improvement.
Applicant argues “missing training medical data, missing in the partial training data, may be imputed by encoding the partial training data into vectors using an encoder network and decoding the vectors to reconstruct the training medical data to include the reconstructed partial training data and the imputed training data using a decoder network. (SMED; para. 11). Advantageously, the imputed training data, imputed by reconstruction using an encoder network and decoder network, enables the encoder network to be trained to generate improved latent representations and to have enhanced generalization and robustness, bias reduction, and increased accuracy, as compared with state of the art approaches. (SMED; para. 11). Further, in accordance with embodiments of the invention, as explained at paragraph [0047] of the Specification, weights associated with the imputed training data are not backpropagated during the training of the encoder network and the decoder network. (SMED; para. 12). Accordingly, weights of the encoder network and the decoder network are updated based on an error associated with the real partial training data and not the possibly inaccurate imputed training data, resulting in the encoder network trained to generate improved latent representations and to have enhanced generalization and robustness, bias reduction, and increased accuracy.” However, it is unclear to which Subject Matter Eligibility Declaration ("SMED") Applicant refers, as there is no associated document submitted in the Application Contents of the present invention. Furthermore, as stated previously in Office Action dated 01/09/2026, providing missing data or only using certain data (i.e., weights) does not address a technical problem to any specific devices, technology, or computers for that matter, and thus, the claims do not provide a technical solution. For example, the computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined. While the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement or any physical improvement to the computer. See MPEP § 2106.04(d)(1) and 2106.05(a). Examiner notes that the declaration cannot be used to supply information that was required to be present in the original disclosure upon filing.
Furthermore, the claims of the present invention to which Applicant refer as enabling the alleged improvement (i.e., “jointly training an encoder network and a classifier network with a decoder network using partial training data and imputed training data by: encoding the partial training data using the encoder network to generate a training vector, decoding the training vector using the decoder network to generate a reconstruction of the partial training data and the imputed training data, and training the encoder network with the decoder network based on the reconstructed partial training data and the imputed training data according to a loss function encoded to identify the imputed data, wherein weights associated with the imputed training data are not back propagated during the training”) is interpreted as the creation of mathematical interrelationships between data, which but for the recitation of generic computer components, covers mathematical relationships and mathematical calculations within the “Mathematical Concepts” grouping of abstract ideas is still the abstract idea. Also, “an encoder network,” “a classifier network,” and “a decoder network” are interpreted as additional elements to be interpreted in Step 2A, Prong Two, which is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using the machine learning networks amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claim as a whole does not amount to significantly more than the judicial exception.
Thus, Examiner maintains the 101 rejections of claims 1-6, 9-17, 20, which have been updated to address Applicant’s remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily Huynh whose telephone number is (571)272-8317. The examiner can normally be reached on M-Th 8-5 PM.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683
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