DETAILED ACTION
This action is in response to the application field on 08/09/2022. Claims 1-20 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/09/2022. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a data preprocessing component” as claimed in claim 1 stating “a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset”.
“a meta-training component“ as claimed in claim 1 stating “a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning”.
“a personalization component” as claimed in claim 1 stating “a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples”.
“a data preprocessing component” as claimed in claim 8 stating “a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset”.
“a meta-training component“ as claimed in claim 8 stating “a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning”.
“a personalization component” as claimed in claim 8 stating “a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples”.
“a data preprocessing component” as claimed in claim 15 stating “a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset”.
“a meta-training component“ as claimed in claim 15 stating “a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning”.
“a personalization component” as claimed in claim 15 stating “a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, “a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset; a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning” and “a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution sample” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In this instance, the corresponding structure refers to computer implemented means-plus function. The written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification does not provide sufficient details of any structure that is used to implement data processing component, the meta-training component and the personalization component. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claims 2-7 inherit the rejection of claim 1 above.
Regarding claim 8, “a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset; a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning” and “a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution sample” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In this instance, the corresponding structure refers to computer implemented means-plus function. The written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification does not provide sufficient details of any structure that is used to implement data processing component, the meta-training component and the personalization component. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claims 9-14 inherit the rejection of claim 8 above.
Regarding claim 15, “a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset; a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning” and “a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution sample” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In this instance, the corresponding structure refers to computer implemented means-plus function. The written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification does not provide sufficient details of any structure that is used to implement data processing component, the meta-training component and the personalization component. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claims 16-20 inherit the rejection of claim 15 above.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, “a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset; a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning” and “a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution sample” as described above, does not provide adequate structure to perform the claimed function. (See 112(b) rejection above). Therefore, the specification does not appear to provide sufficient detail such that one of ordinary skill can reasonably conclude that the inventor had possession of the claimed invention. Claims 2-7 inherit the rejection of claim 1 above.
Regarding claim 8, “a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset; a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning” and “a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution sample” as described above, does not provide adequate structure to perform the claimed function. (See 112(b) rejection above). Therefore, the specification does not appear to provide sufficient detail such that one of ordinary skill can reasonably conclude that the inventor had possession of the claimed invention. Claims 8-14 inherit the rejection of claim 1 above.
Regarding claim 15, “a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset; a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning” and “a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution sample” as described above, does not provide adequate structure to perform the claimed function. (See 112(b) rejection above). Therefore, the specification does not appear to provide sufficient detail such that one of ordinary skill can reasonable conclude that the inventor had possession of the claimed invention. Claims 16-20 inherit the rejection of claim 1 above.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to ab abstract idea without significantly more,
Regarding claim 1:
Subject Matter of Eligibility Analysis Step 1:
Claim 1 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
detecting out-of-distribution (OOD) events during model personalization by employing: a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset (this limitation is a mental process as it encompasses a human mentally taking different parts of patient data to create a dataset).
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples (this limitation is a mental process as it encompasses a human mentally estimating out-of-distribution samples based on a score and threshold).
Therefore, claim 1 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
learning a meta-training model that simultaneously classifies dialysis in-distribution events (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP2106.05(h))).
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
a storage component to store the meta-training model for distribution to local machines for further fine-tuning, personalization, and deployment (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 1 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
learning a meta-training model that simultaneously classifies dialysis in-distribution events recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h).
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a storage component to store the meta-training model for distribution to local machines for further fine-tuning, personalization, and deployment is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 1 is subject-matter ineligible.
Regarding claim 2:
Subject Matter of Eligibility Analysis Step 1:
Claim 2 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 2 recites
the data preprocessing component further removes noisy information and fills some missing values by using mean values of corresponding features in the historical medical records (this limitation is a mental process as it encompasses a human mentally removing noisy data and replacing missing values).
Therefore, claim 2 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 2 does not further recite any additional elements. Therefore, claim 2 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are not additional elements, claim 2 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 2 is subject matter ineligible.
Regarding claim 3:
Subject Matter of Eligibility Analysis Step 1:
Claim 3 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 3 is dependent on claim 2, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 2 is applied here. Therefore claim 3 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of
the training tasks of the first class pool include a support set and a query set, the support set generated by only selecting training classes representing in-distribution data (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 3 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 3 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the training tasks of the first class pool include a support set and a query set, the support set generated by only selecting training classes representing in-distribution data recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 3 is subject-matter ineligible.
Regarding claim 4:
Subject Matter of Eligibility Analysis Step 1:
Claim 4 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 4 is dependent on claim 3, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 3 is applied here. Therefore claim 4 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional elements of
the distribution statistics dictionary of the second class pool includes a mean and a variance of every class in the second class pool (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 4 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 4 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the distribution statistics dictionary of the second class pool includes a mean and a variance of every class in the second class pool recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 4 is subject-matter ineligible.
Regarding claim 5:
Subject Matter of Eligibility Analysis Step 1:
Claim 5 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 5 is dependent on claim 4, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 4 is applied here. Therefore claim 5 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
a sampler for sampling several classes as the in-distribution data in the support set (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply on a generic computer component (see MPEP 2106.05(f))).
a sampler for sampling data in the in-distribution classes to constitute the query set (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply on a generic computer component (see MPEP 2106.05(f))).
a sampler for randomly sampling several other classes as out-of-distribution data to constitute the query set (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply on a generic computer component (see MPEP 2106.05(f))).
Therefore, claim 5 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because
a sampler for sampling several classes as the in-distribution data in the support set uses a generic computer component to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a sampler for sampling data in the in-distribution classes to constitute the query set uses a generic computer component to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a sampler for randomly sampling several other classes as out-of-distribution data to constitute the query set uses a generic computer component to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 5 is subject-matter ineligible.
Regarding claim 6:
Subject Matter of Eligibility Analysis Step 1:
Claim 6 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 6 is dependent on claim 1, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 1 is applied here. Therefore claim 6 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 6 further recites additional elements of
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 6 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 6 is subject-matter ineligible.
Regarding claim 7:
Subject Matter of Eligibility Analysis Step 1:
Claim 7 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 7 is dependent on claim 6, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 6 is applied here. Therefore claim 7 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 7 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 7 is subject-matter ineligible.
Regarding claim 8:
Subject Matter of Eligibility Analysis Step 1:
Claim 8 recites a non-transitory computer-readable storage medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 8 recites
detecting out-of-distribution (OOD) events during model personalization by employing: a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset (this limitation is a mental process as it encompasses a human mentally taking different parts of patient data to create a dataset).
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples (this limitation is a mental process as it encompasses a human mentally estimating out-of-distribution samples based on a score and threshold).
Therefore, claim 8 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 8 further recites additional elements of
A non-transitory computer-readable storage medium comprising a computer-readable program for making prognostic prediction scores during a pre-dialysis period on an incidence of events in future dialysis, wherein the computer-readable program when executed on a computer causes the computer to perform the steps (this element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(b))).
learning a meta-training model that simultaneously classifies dialysis in-distribution events (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP2106.05(h))).
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
a storage component to store the meta-training model for distribution to local machines for further fine-tuning, personalization, and deployment (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 8 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A non-transitory computer-readable storage medium comprising a computer-readable program for making prognostic prediction scores during a pre-dialysis period on an incidence of events in future dialysis, wherein the computer-readable program when executed on a computer causes the computer to perform the steps is a generic component used to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(b)).
learning a meta-training model that simultaneously classifies dialysis in-distribution events recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h).
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
a storage component to store the meta-training model for distribution to local machines for further fine-tuning, personalization, and deployment is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h).
Therefore, claim 8 is subject-matter ineligible.
Regarding claim 9:
Subject Matter of Eligibility Analysis Step 1:
Claim 9 recites a non-transitory computer-readable storage medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 9 recites
the data preprocessing component further removes noisy information and fills some missing values by using mean values of corresponding features in the historical medical records (this limitation is a mental process as it encompasses a human mentally removing noisy data and replacing missing values).
Therefore, claim 9 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 9 does not further recite any additional elements. Therefore, claim 9 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are not additional elements, claim 9 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 9 is subject matter ineligible.
Regarding claim 10:
Subject Matter of Eligibility Analysis Step 1:
Claim 10 recites a non-transitory computer-readable storage medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 10 is dependent on claim 9, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 9 is applied here. Therefore claim 10 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 10 further recites additional elements of
the training tasks of the first class pool include a support set and a query set, the support set generated by only selecting training classes representing in-distribution data (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 10 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 10 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the training tasks of the first class pool include a support set and a query set, the support set generated by only selecting training classes representing in-distribution data recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 10 is subject-matter ineligible.
Regarding claim 11:
Subject Matter of Eligibility Analysis Step 1:
Claim 11 recites a non-transitory computer-readable storage medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 11 is dependent on claim 10, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 10 is applied here. Therefore claim 11 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 11 further recites additional elements of
the distribution statistics dictionary of the second class pool includes a mean and a variance of every class in the second class pool (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 11 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 11 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the distribution statistics dictionary of the second class pool includes a mean and a variance of every class in the second class pool recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 11 is subject-matter ineligible.
Regarding claim 12:
Subject Matter of Eligibility Analysis Step 1:
Claim 12 recites a non-transitory computer-readable storage medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 12 is dependent on claim 11, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 11 is applied here. Therefore claim 12 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 12 further recites additional elements of
a sampler for sampling several classes as the in-distribution data in the support set (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply on a generic computer component (see MPEP 2106.05(f))).
a sampler for sampling data in the in-distribution classes to constitute the query set (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply on a generic computer component (see MPEP 2106.05(f))).
a sampler for randomly sampling several other classes as out-of-distribution data to constitute the query set (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply on a generic computer component (see MPEP 2106.05(f))).
Therefore, claim 12 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 12 do not provide significantly more than the abstract idea itself, taken alone and in combination because
a sampler for sampling several classes as the in-distribution data in the support set uses a generic computer component to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a sampler for sampling data in the in-distribution classes to constitute the query set uses a generic computer component to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a sampler for randomly sampling several other classes as out-of-distribution data to constitute the query set uses a generic computer component to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 12 is subject-matter ineligible.
Regarding claim 13:
Subject Matter of Eligibility Analysis Step 1:
Claim 13 recites a non-transitory computer-readable storage medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 13 is dependent on claim 8, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 8 is applied here. Therefore claim 13 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 13 further recites additional elements of
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 13 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 13 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 13 is subject-matter ineligible.
Regarding claim 14:
Subject Matter of Eligibility Analysis Step 1:
Claim 14 recites a non-transitory computer-readable storage medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 14 is dependent on claim 13, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 13 is applied here. Therefore claim 14 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 14 further recites additional elements of
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 14 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 14 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 14 is subject-matter ineligible.
Regarding claim 15:
Subject Matter of Eligibility Analysis Step 1:
Claim 15 recites a system, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 15 recites
detecting out-of-distribution (OOD) events during model personalization by employing: a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset (this limitation is a mental process as it encompasses a human mentally taking different parts of patient data to create a dataset).
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples (this limitation is a mental process as it encompasses a human mentally estimating out-of-distribution samples based on a score and threshold).
Therefore, claim 15 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 15 further recites additional elements of
A system for making prognostic prediction scores during a pre-dialysis period on an incidence of events in future dialysis (this element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(b))).
learning a meta-training model that simultaneously classifies dialysis in-distribution events (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP2106.05(h))).
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
a storage component to store the meta-training model for distribution to local machines for further fine-tuning, personalization, and deployment (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 15 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 15 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A system for making prognostic prediction scores during a pre-dialysis period on an incidence of events in future dialysis is a generic component used to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(b)).
learning a meta-training model that simultaneously classifies dialysis in-distribution events recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h).
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
a storage component to store the meta-training model for distribution to local machines for further fine-tuning, personalization, and deployment is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h).
Therefore, claim 15 is subject-matter ineligible.
Regarding claim 16:
Subject Matter of Eligibility Analysis Step 1:
Claim 16 recites system, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 16 recites
the data preprocessing component further removes noisy information and fills some missing values by using mean values of corresponding features in the historical medical records (this limitation is a mental process as it encompasses a human mentally removing noisy data and replacing missing values).
Therefore, claim 16 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 16 does not further recite any additional elements. Therefore, claim 16 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are not additional elements, claim 16 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 16 is subject matter ineligible.
Regarding claim 17:
Subject Matter of Eligibility Analysis Step 1:
Claim 17 recites a system, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 17 is dependent on claim 16, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 16 is applied here. Therefore claim 17 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 17 further recites additional elements of
the training tasks of the first class pool include a support set and a query set, the support set generated by only selecting training classes representing in-distribution data (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 17 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 17 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the training tasks of the first class pool include a support set and a query set, the support set generated by only selecting training classes representing in-distribution data recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 17 is subject-matter ineligible.
Regarding claim 18:
Subject Matter of Eligibility Analysis Step 1:
Claim 18 recites a system, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 18 is dependent on claim 17, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 17 is applied here. Therefore claim 18 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 18 further recites additional elements of
the distribution statistics dictionary of the second class pool includes a mean and a variance of every class in the second class pool (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 18 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 18 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the distribution statistics dictionary of the second class pool includes a mean and a variance of every class in the second class pool recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 18 is subject-matter ineligible.
Regarding claim 19:
Subject Matter of Eligibility Analysis Step 1:
Claim 19 recites a system, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 19 is dependent on claim 18, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 18 is applied here. Therefore claim 19 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 12 further recites additional elements of
a sampler for sampling several classes as the in-distribution data in the support set (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply on a generic computer component (see MPEP 2106.05(f))).
a sampler for sampling data in the in-distribution classes to constitute the query set (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply on a generic computer component (see MPEP 2106.05(f))).
a sampler for randomly sampling several other classes as out-of-distribution data to constitute the query set (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply on a generic computer component (see MPEP 2106.05(f))).
Therefore, claim 19 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 19 do not provide significantly more than the abstract idea itself, taken alone and in combination because
a sampler for sampling several classes as the in-distribution data in the support set uses a generic computer component to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a sampler for sampling data in the in-distribution classes to constitute the query set uses a generic computer component to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a sampler for randomly sampling several other classes as out-of-distribution data to constitute the query set uses a generic computer component to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 19 is subject-matter ineligible.
Regarding claim 20:
Subject Matter of Eligibility Analysis Step 1:
Claim 20 recites a system, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 20 is dependent on claim 15, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 15 is applied here. Therefore claim 20 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 20 further recites additional elements of
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 20 is not integrated into a practical application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong et al. (OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification) (hereafter referred to as Jeong) in view of Makino et al. (Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning ) (hereafter referred to as Makino), Snell et al. (Prototypical Networks for Few-shot Learning) (hereafter referred to as Snell), Choi et al. (RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism) (hereafter referred to as Choi), and Liu et al. (Energy-based Out-of-distribution Detection) (hereafter referred to as Liu).
Regarding claim 1, Jeong teaches
learning a meta-training model that simultaneously classifies dialysis in-distribution events (Jeong, Abstract, “a few-shot learning method for detecting out-of-distribution (OOD) samples from classes that are unseen during training while classifying samples from seen classes using only a few labeled example”).
detecting out-of-distribution (OOD) events during model personalization by employing: a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset (Jeong, Abstract, “a few-shot learning method for detecting out-of-distribution (OOD) samples from classes that are unseen during training while classifying samples from seen classes using only a few labeled example” and “We first discuss the task formulation considered in general meta-learning algorithms. We deal with two types of meta-sets, one is for meta-training and the other is for meta testing, which are denoted by Dmeta−train and Dmeta−test, respectively. Each of Dmeta−train and Dmeta−test contains multiple datasets, each of which is divided into a training set Dtrain and a test set Dtest as in the typical classification task” (Jeong, Section 2.1)).
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (Jeong, Section 2.2, “MAML is a popular optimization-based meta-learning approach that learns the initial model parameters, which result in fast learning on new tasks through gradient-based optimization”).
a storage component to store the meta-training model for distribution to local machines for further fine-tuning, personalization, and deployment (Jeong, Section 1, “The objective of MAML is to find good initial parameters of a model (e.g., DNN), such that updating the initial parameters via one or a few gradient steps can result in a model that provides a good performance for a new task”).
Jeong does not teach, but Makino does teach
detecting out-of-distribution (OOD) events during model personalization by employing: a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset (Makino, Results Section, “From 858,660 EMR, we extracted 451,584 cases with relevant clinical data. According to our criteria, 64,059 patients could be defined as T2DM. From these patients, we extracted the clinical features using three different approaches: structural data, text data and longitudinal data from EMR (electronic medical records) … AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis”).
Jeong and Makino are considered analogous to the claimed invention because they deal with health data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong’s OOD-MAML to use the different approaches of clinical data from Makino. One of the ordinary skill in the art would have known to apply Makino’s technique of using different parts of a medical record. Therefore, applying Makino’s technique would have yielded the predictable results of teaching the model to rapidly learn from limited data. (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Jeong and Makino do not teach, but Snell does teach
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (Snell, Section 2.2,
PNG
media_image1.png
432
1060
media_image1.png
Greyscale
Examiner notes that the prototypical network maps to the prototype network. Snell also teaches “Training episodes are formed by randomly selecting a subset of classes from
the training set, then choosing a subset of examples within each class to act as the support set and a subset of the remainder to serve as query points”
PNG
media_image2.png
369
540
media_image2.png
Greyscale
).
Jeong, Makino, and Snell, are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong and Makino to use the prototypical network and algorithm 1 from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, and Snell do not teach, but Choi does teach
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (Choi, Abstract, “We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention”).
Jeong, Makino, Snell, and Choi are considered analogous to the claimed invention because they deal with electronic health care data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, and Snell to include the RETAIN model from Choi. Choi teaches “We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention” (Choi, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, Snell, and Choi do not teach, but Liu does teach
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples (Liu, Figure 1 caption, “Energy-based out-of-distribution detection framework. The energy can be used as a scoring function for any pre-trained neural network (without re-training), or used as a trainable cost function to fine-tune the classification model. During inference time, for a given input x, the energy score E(x;f) is calculated for a neural network f(x). The OOD detector will classify the input as OOD if the negative energy score is smaller than the threshold value”).
PNG
media_image3.png
367
982
media_image3.png
Greyscale
Jeong, Makino, Snell, Choi, and Liu are considered analogous to the claimed invention because they deal with out-of-distribution data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, and Choi to use the Energy-based out-of-distribution detection framework from Liu. Liu teaches that “energy scores better distinguish in- and out-of-distribution samples than the traditional approach using the softmax scores. Unlike softmax confidence scores, energy scores are theoretically aligned with the probability density of the inputs and are less susceptible to the overconfidence issue” (Liu, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 2 and 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Makino, Snell, Choi, Liu, and Che et al. (Interpretable Deep Models for ICU Outcome Prediction) (hereafter referred to as Che).
Regarding claim 2, Jeong, Makino, Snell, Choi, and Liu teach the method of claim 1, Che teaches
the data preprocessing component further removes noisy information and fills some missing values by using mean values of corresponding features in the historical medical records (Che, Section 4.1, “We apply simple imputation to fill in missing values, where we take the majority value for binary variables”).
Jeong, Makino, Snell, Choi, Liu, and Che are considered analogous to the claimed invention because they deal with health data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, and Liu to apply Che’s technique. One of the ordinary skill in the art would have known to apply Che’s technique of filling in missing values in a dataset. Therefore, applying Che’s technique would have yielded the predictable results of preserving sample size and avoid data loss. (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Regarding claim 3, Jeong, Makino, Snell, Choi, and Liu teach the method of claim 2, Snell further teaches
the training tasks of the first class pool include a support set and a query set, the support set generated by only selecting training classes representing in-distribution data (Snell, Section 2.2,
PNG
media_image2.png
369
540
media_image2.png
Greyscale
).
Jeong, Makino, and Snell, are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong and Makino to use algorithm 1 from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 4 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Makino, Snell, Choi, Liu, Che, and Bateni et al. (Improved Few-Shot Visual Classification) (hereafter referred to as Bateni).
Regarding claim 4, Jeong, Makino, Snell, Choi, Liu, Che, and Shukla teach the method of claim 3, Jeong, Makino, Snell, Choi, Liu, Che, and Shukla do not teach, but Bateni does teach
the distribution statistics dictionary of the second class pool includes a mean and a variance of every class in the second class pool (Bateni, Figure 4,
PNG
media_image4.png
788
1025
media_image4.png
Greyscale
Examiner notes that the class means and class covariance estimates are mapped to the mean and variance, respectively).
Jeong, Makino, Snell, Choi, Liu, Che, and Bateni are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Liu, and Che to use the means as well as the variance from Bateni. One of the ordinary skill in the art would have known to apply the known technique of using the means and variance for classification. Therefore, applying Bateni’s technique would yield the predictable result of allowing the model to distinguish between different classes based on data behavior (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results).
Regarding claim 5, Jeong, Makino, Snell, Choi, Liu, Che, and Bateni teach the method of claim 4, Jeong further teaches
a sampler for randomly sampling several other classes as out-of-distribution data to constitute the query set (Jeong, Abstract, “We propose a few-shot learning method for detecting out-of-distribution (OOD) samples from classes that are unseen during training while classifying samples from seen classes using only a few labeled examples. For detecting unseen classes while generalizing to new samples of known classes, we synthesize fake samples, i.e., OOD samples, but that resemble in-distribution samples, and use them along with real samples … For testing, OOD-MAML converts a K-shot N-way classification task into N sub-tasks of K-shot OOD detection with respect to each class. The joint analysis of N sub-tasks facilitates simultaneous classification and OOD detection”).
Jeong does not teach, but Snell does teach
a sampler for sampling several classes as the in-distribution data in the support set (Snell, Section 2.2,
PNG
media_image2.png
369
540
media_image2.png
Greyscale
Examiner notes that V <- RANDOMSAMPLE line takes N_c classes to be the in-distribution set for the support examples).
a sampler for sampling data in the in-distribution classes to constitute the query set (Snell, Section 2.2,
PNG
media_image2.png
369
540
media_image2.png
Greyscale
Examiner notes that V <- RANDOMSAMPLE line takes N_c classes to be the in-distribution set for the query examples).
Jeong, Makino, Snell, Choi, Liu, Che, and Bateni are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong to use the prototypical network and algorithm 1 from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Makino, Snell, Choi, Liu, and Shukla et al. (Interpolation-Prediction Networks for Irregularly Samples Time Series) (hereafter referred to as Shukla).
Regarding claim 6, Jeong, Makino, Snell, Choi, and Liu teach the method of claim 1, Snell further teaches
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors (Snell, Section 2.2, “Prototypical networks compute an M-dimensional representation ck ∈ RM, or prototype, of each class through an embedding function fφ : RD → RM with learnable parameters φ”).
Jeong, Makino, Snell, Choi, and Liu are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Choi, Bateni, and Liu to use the prototypical network from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, Snell, Choi, and Liu do not teach, but Shukla does teach
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors (Shukla, Abstract, “In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network”).
Jeong, Makino, Snell, Choi, Liu, and Shukla are considered analogous to the claimed invention because they deal with time-series data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Bateni , Liu , and Shukla to use the interpolation network from Shukla. Shukla teaches that “This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models” (Shukla, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Makino, Snell, Choi, Liu, Che and Shukla.
Regarding claim 7, Jeong, Makino, Snell, Choi, Liu, Che, and Shukla teach the method of claim 6, Shukla further teaches
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features (Shukla, Section 3.2, “The interpolation network interpolates the multivariate, sparse, and irregularly sampled input time series against a set of reference time points r = [r1,...,rT]. We assume that all of the time series are defined within a common time interval (for example, the first 24 or 48 hours after admission for MIMIC-III dataset). The T reference time points rt are chosen to be evenly spaced within that interval. In this work, we propose a two-layer interpolation network with each layer performing a different type of interpolation”).
Jeong, Makino, Snell, Choi, Liu, Che, and Shukla are considered analogous to the claimed invention because they deal with time-series data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Snell, Makino, Choi, Bateni , Liu, and Shukla to use the interpolation network from Shukla. Shukla teaches that “This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models” (Shukla, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Shukla does not teach, but Che does teach
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features (Che, Section 4.1, “We conduct experiments on a Pediatric ICU dataset collected at the Children’s Hospital Los Angeles. This dataset consists of health records from 398 patients with acute lung injury in the Pediatric Intensive Care Unit at Children’s Hospital Los Angeles. It contains a set of 27 static features such as demographic information and admission diagnoses, and another set of 21 temporal features (recorded daily) such as monitoring features and discretized scores made by experts, for the initial 4 days of mechanical ventilation”).
Jeong, Makino, Snell, Choi, Liu, Che, and Shukla are considered analogous to the claimed invention because they deal with health data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Bateni , Liu , and Shukla to apply Che’s technique. One of the ordinary skill in the art would have known to apply Che’s technique of using static and low frequency temporal features. Therefore, applying Che’s technique would have yielded the predictable results of minimizing power consumption, data volume, and network traffic. (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Claim(s) 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Makino, Snell, Choi, Liu, and Bunel et al. (US 12288393 B2) (hereafter referred to as Bunel)
Regarding claim 8, Jeong teaches
learning a meta-training model that simultaneously classifies dialysis in-distribution events (Jeong, Abstract, “a few-shot learning method for detecting out-of-distribution (OOD) samples from classes that are unseen during training while classifying samples from seen classes using only a few labeled example”).
detecting out-of-distribution (OOD) events during a model personalization by employing: a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset (Jeong, Abstract, “a few-shot learning method for detecting out-of-distribution (OOD) samples from classes that are unseen during training while classifying samples from seen classes using only a few labeled example” and “We first discuss the task formulation considered in general meta-learning algorithms. We deal with two types of meta-sets, one is for meta-training and the other is for meta testing, which are denoted by Dmeta−train and Dmeta−test, respectively. Each of Dmeta−train and Dmeta−test contains multiple datasets, each of which is divided into a training set Dtrain and a test set Dtest as in the typical classification task” (Jeong, Section 2.1)).
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (Jeong, Section 2.2, “MAML is a popular optimization-based meta-learning approach that learns the initial model parameters, which result in fast learning on new tasks through gradient-based optimization”).
a storage component to store the meta-training model for distribution to local machines for further fine-tuning, personalization, and deployment (Jeong, Section 1, “The objective of MAML is to find good initial parameters of a model (e.g., DNN), such that updating the initial parameters via one or a few gradient steps can result in a model that provides a good performance for a new task”).
Jeong does not teach, but Makino does teach
detecting out-of-distribution (OOD) events during model personalization by employing: a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset (Makino, Results Section, “From 858,660 EMR, we extracted 451,584 cases with relevant clinical data. According to our criteria, 64,059 patients could be defined as T2DM. From these patients, we extracted the clinical features using three different approaches: structural data, text data and longitudinal data from EMR (electronic medical records) … AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis”).
Jeong and Makino are considered analogous to the claimed invention because they deal with health data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong’s OOD-MAML to use the different approaches of clinical data from Makino. One of the ordinary skill in the art would have known to apply Makino’s technique of using different parts of a medical record. Therefore, applying Makino’s technique would have yielded the predictable results of teaching the model to rapidly learn from limited data. (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Jeong and Makino do not teach, but Snell does teach
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (Snell, Section 2.2,
PNG
media_image1.png
432
1060
media_image1.png
Greyscale
Examiner notes that the prototypical network maps to the prototype network. Snell also teaches “Training episodes are formed by randomly selecting a subset of classes from
the training set, then choosing a subset of examples within each class to act as the support set and a subset of the remainder to serve as query points”
PNG
media_image2.png
369
540
media_image2.png
Greyscale
).
Jeong, Makino, and Snell, are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong and Makino to use the prototypical network and algorithm 1 from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, and Snell do not teach, but Choi does teach
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (Choi, Abstract, “We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention”).
Jeong, Makino, Snell, and Choi are considered analogous to the claimed invention because they deal with electronic health care data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, and Snell to include the RETAIN model from Choi. Choi teaches “We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention” (Choi, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, Snell, and Choi do not teach, but Liu does teach
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples (Liu, Figure 1 caption, “Energy-based out-of-distribution detection framework. The energy can be used as a scoring function for any pre-trained neural network (without re-training), or used as a trainable cost function to fine-tune the classification model. During inference time, for a given input x, the energy score E(x;f) is calculated for a neural network f(x). The OOD detector will classify the input as OOD if the negative energy score is smaller than the threshold value”).
PNG
media_image3.png
367
982
media_image3.png
Greyscale
Jeong, Makino, Snell, Choi, and Liu are considered analogous to the claimed invention because they deal with out-of-distribution data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, and Choi to use the Energy-based out-of-distribution detection framework from Liu. Liu teaches that “energy scores better distinguish in- and out-of-distribution samples than the traditional approach using the softmax scores. Unlike softmax confidence scores, energy scores are theoretically aligned with the probability density of the inputs and are less susceptible to the overconfidence issue” (Liu, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, Snell, Choi, and Liu do not teach, but Bunel does teach
A non-transitory computer-readable storage medium comprising a computer-readable program (Bunel, paragraph 0091, “Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them”).
Jeong, Makino, Snell, Choi, Liu, and Bunel are considered analogous to the claimed invention because they deal with out-of-distribution data. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Jeong, Makino, Snell, Choi, Bateni, and Liu to include a non-transitory computer-readable medium. One of the ordinary skill in the art would have known to apply Bunel’s technique of a non-transitory computer readable medium to perform the instructions of Jeong, Makino, Snell, Choi, Bateni, and Liu. Therefore, applying Bunel’s technique would have yielded predictable results of running instructions on a non-transitory computer-readable medium (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Regarding claim 15, Jeong teaches
learning a meta-training model that simultaneously classifies dialysis in-distribution events (Jeong, Abstract, “a few-shot learning method for detecting out-of-distribution (OOD) samples from classes that are unseen during training while classifying samples from seen classes using only a few labeled example”).
detecting out-of-distribution (OOD) events during a model personalization by employing: a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset (Jeong, Abstract, “a few-shot learning method for detecting out-of-distribution (OOD) samples from classes that are unseen during training while classifying samples from seen classes using only a few labeled example” and “We first discuss the task formulation considered in general meta-learning algorithms. We deal with two types of meta-sets, one is for meta-training and the other is for meta testing, which are denoted by Dmeta−train and Dmeta−test, respectively. Each of Dmeta−train and Dmeta−test contains multiple datasets, each of which is divided into a training set Dtrain and a test set Dtest as in the typical classification task” (Jeong, Section 2.1)).
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (Jeong, Section 2.2, “MAML is a popular optimization-based meta-learning approach that learns the initial model parameters, which result in fast learning on new tasks through gradient-based optimization”).
a storage component to store the meta-training model for distribution to local machines for further fine-tuning, personalization, and deployment (Jeong, Section 1, “The objective of MAML is to find good initial parameters of a model (e.g., DNN), such that updating the initial parameters via one or a few gradient steps can result in a model that provides a good performance for a new task”).
Jeong does not teach, but Makino does teach
detecting out-of-distribution (OOD) events during model personalization by employing: a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset (Makino, Results Section, “From 858,660 EMR, we extracted 451,584 cases with relevant clinical data. According to our criteria, 64,059 patients could be defined as T2DM. From these patients, we extracted the clinical features using three different approaches: structural data, text data and longitudinal data from EMR (electronic medical records) … AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis”).
Jeong and Makino are considered analogous to the claimed invention because they deal with health data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong’s OOD-MAML to use the different approaches of clinical data from Makino. One of the ordinary skill in the art would have known to apply Makino’s technique of using different parts of a medical record. Therefore, applying Makino’s technique would have yielded the predictable results of teaching the model to rapidly learn from limited data. (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Jeong and Makino do not teach, but Snell does teach
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (Snell, Section 2.2,
PNG
media_image1.png
432
1060
media_image1.png
Greyscale
Examiner notes that the prototypical network maps to the prototype network. Snell also teaches “Training episodes are formed by randomly selecting a subset of classes from
the training set, then choosing a subset of examples within each class to act as the support set and a subset of the remainder to serve as query points”
PNG
media_image2.png
369
540
media_image2.png
Greyscale
).
Jeong, Makino, and Snell, are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong and Makino to use the prototypical network and algorithm 1 from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, and Snell do not teach, but Choi does teach
a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool for generating training tasks and a second class pool for generating a distribution statistics dictionary for transfer learning (Choi, Abstract, “We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention”).
Jeong, Makino, Snell, and Choi are considered analogous to the claimed invention because they deal with electronic health care data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, and Snell to include the RETAIN model from Choi. Choi teaches “We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention” (Choi, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, Snell, and Choi do not teach, but Liu does teach
a personalization component including a local data collection component, and a class and OOD detector component, the class and OOD detector component using an energy score and a pre-defined threshold for estimating out-of-distribution samples (Liu, Figure 1 caption, “Energy-based out-of-distribution detection framework. The energy can be used as a scoring function for any pre-trained neural network (without re-training), or used as a trainable cost function to fine-tune the classification model. During inference time, for a given input x, the energy score E(x;f) is calculated for a neural network f(x). The OOD detector will classify the input as OOD if the negative energy score is smaller than the threshold value”).
PNG
media_image3.png
367
982
media_image3.png
Greyscale
Jeong, Makino, Snell, Choi, and Liu are considered analogous to the claimed invention because they deal with out-of-distribution data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, and Choi to use the Energy-based out-of-distribution detection framework from Liu. Liu teaches that “energy scores better distinguish in- and out-of-distribution samples than the traditional approach using the softmax scores. Unlike softmax confidence scores, energy scores are theoretically aligned with the probability density of the inputs and are less susceptible to the overconfidence issue” (Liu, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, Snell, Choi, and Liu do not teach, but Bunel does teach
A system for making prognostic prediction scores during a pre-dialysis period on an incidence of events in future dialysis (Bunel, paragraph 0092, “The term “data processing apparatus” refers to data processing hardware and encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers”).
Jeong, Makino, Snell, Choi, Liu, and Bunel are considered analogous to the claimed invention because they deal with out-of-distribution data. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Jeong, Snell, Choi, Bateni, and Liu to include the data processing apparatus from Bunel. One of the ordinary skill in the art would have known to apply Bunel’s technique of using an apparatus to run the instructions of Jeong, Snell, Choi, Bateni, and Liu. Therefore, applying Bunel’s technique would have yielded predictable results of running instructions on an apparatus (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Claim(s) 9, 10, 16, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Makino, Snell, Choi, Liu, Bunel, and Che.
Regarding claim 9, Jeong, Makino, Snell, Choi, Liu, and Bunel teach the non-transitory computer-readable storage medium of claim 8, Che teaches
the data preprocessing component further removes noisy information and fills some missing values by using mean values of corresponding features in the historical medical records (Che, Section 4.1, “We apply simple imputation to fill in missing values, where we take the majority value for binary variables”).
Jeong, Makino, Snell, Choi, Liu, Bunel, and Che are considered analogous to the claimed invention because they deal with health data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Liu, and Bunel to apply Che’s technique. One of the ordinary skill in the art would have known to apply Che’s technique of filling in missing values in a dataset. Therefore, applying Che’s technique would have yielded the predictable results of preserving sample size and avoid data loss. (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Regarding claim 10, Jeong, Makino, Snell, Choi, Liu, Bunel, and Che teach the non-transitory computer-readable storage medium of claim 8, Snell further teaches
the training tasks of the first class pool include a support set and a query set, the support set generated by only selecting training classes representing in-distribution data (Snell, Section 2.2,
PNG
media_image2.png
369
540
media_image2.png
Greyscale
).
Jeong, Makino, Snell, Choi, Liu, Bundel, and Che are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong to use the prototypical network and algorithm 1 from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Regarding claim 16, Jeong, Makino, Snell, Choi, Liu, Bunel, and Che teach the system of claim 15, Che teaches
the data preprocessing component further removes noisy information and fills some missing values by using mean values of corresponding features in the historical medical records (Che, Section 4.1, “We apply simple imputation to fill in missing values, where we take the majority value for binary variables”).
Jeong, Makino, Snell, Choi, Liu, Bunel, and Che are considered analogous to the claimed invention because they deal with health data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Liu, and Bunel to apply Che’s technique. One of the ordinary skill in the art would have known to apply Che’s technique of filling in missing values in a dataset. Therefore, applying Che’s technique would have yielded the predictable results of preserving sample size and avoid data loss. (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Regarding claim 17 Jeong, Makino, Snell, Choi, Liu, Bunel, and Che teach the system of claim 16, Snell further teaches
the training tasks of the first class pool include a support set and a query set, the support set generated by only selecting training classes representing in-distribution data (Snell, Section 2.2,
PNG
media_image2.png
369
540
media_image2.png
Greyscale
).
Jeong, Makino, Snell, Choi, Liu, Bunel, and Che are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong to use the prototypical network and algorithm 1 from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 11, 12, 18, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Makino, Snell, Choi, Liu, Bunel, Che, and Bateni.
Regarding claim 11, Jeong, Makino, Snell, Choi, Liu, Bunel, and Che teach the non-transitory computer-readable storage medium of claim 10, Bateni teaches
the distribution statistics dictionary of the second class pool includes a mean and a variance of every class in the second class pool (Bateni, Figure 4,
PNG
media_image4.png
788
1025
media_image4.png
Greyscale
Examiner notes that the class means and class covariance estimates are mapped to the mean and variance, respectively).
Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Bateni are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Liu, and Che to use the means as well as the variance from Bateni. One of the ordinary skill in the art would have known to apply the known technique of using the means and variance for classification. Therefore, applying Bateni’s technique would yield the predictable result of allowing the model to distinguish between different classes based on data behavior (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results).
Regarding claim 12, Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Bateni teach the non-transitory computer-readable storage medium of claim 11, Jeong further teaches
a sampler for randomly sampling several other classes as out-of-distribution data to constitute the query set (Jeong, Abstract, “We propose a few-shot learning method for detecting out-of-distribution (OOD) samples from classes that are unseen during training while classifying samples from seen classes using only a few labeled examples. For detecting unseen classes while generalizing to new samples of known classes, we synthesize fake samples, i.e., OOD samples, but that resemble in-distribution samples, and use them along with real samples … For testing, OOD-MAML converts a K-shot N-way classification task into N sub-tasks of K-shot OOD detection with respect to each class. The joint analysis of N sub-tasks facilitates simultaneous classification and OOD detection”).
Jeong does not teach, but Snell does teach
a sampler for sampling several classes as the in-distribution data in the support set (Snell, Section 2.2,
PNG
media_image2.png
369
540
media_image2.png
Greyscale
Examiner notes that V <- RANDOMSAMPLE line takes N_c classes to be the in-distribution set for the support examples).
a sampler for sampling data in the in-distribution classes to constitute the query set (Snell, Section 2.2,
PNG
media_image2.png
369
540
media_image2.png
Greyscale
Examiner notes that V <- RANDOMSAMPLE line takes N_c classes to be the in-distribution set for the query examples).
Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Bateni are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong to use the prototypical network and algorithm 1 from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Regarding claim 18, Jeong, Makino, Snell, Choi, Liu, Bunel, and Che teach the system of claim 17, Bateni further teaches
the distribution statistics dictionary of the second class pool includes a mean and a variance of every class in the second class pool (Bateni, Figure 4,
PNG
media_image4.png
788
1025
media_image4.png
Greyscale
Examiner notes that the class means and class covariance estimates are mapped to the mean and variance, respectively).
Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Bateni are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Liu, and Che to use the means as well as the variance from Bateni. One of the ordinary skill in the art would have known to apply the known technique of using the means and variance for classification. Therefore, applying Bateni’s technique would yield the predictable result of allowing the model to distinguish between different classes based on data behavior (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results).
Regarding claim 19, Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Bateni teach the system of claim 18, Jeong further teaches
a sampler for randomly sampling several other classes as out-of-distribution data to constitute the query set (Jeong, Abstract, “We propose a few-shot learning method for detecting out-of-distribution (OOD) samples from classes that are unseen during training while classifying samples from seen classes using only a few labeled examples. For detecting unseen classes while generalizing to new samples of known classes, we synthesize fake samples, i.e., OOD samples, but that resemble in-distribution samples, and use them along with real samples … For testing, OOD-MAML converts a K-shot N-way classification task into N sub-tasks of K-shot OOD detection with respect to each class. The joint analysis of N sub-tasks facilitates simultaneous classification and OOD detection”).
Jeong does not teach, but Snell does teach
a sampler for sampling several classes as the in-distribution data in the support set (Snell, Section 2.2,
PNG
media_image2.png
369
540
media_image2.png
Greyscale
Examiner notes that V <- RANDOMSAMPLE line takes N_c classes to be the in-distribution set for the support examples).
a sampler for sampling data in the in-distribution classes to constitute the query set (Snell, Section 2.2,
PNG
media_image2.png
369
540
media_image2.png
Greyscale
Examiner notes that V <- RANDOMSAMPLE line takes N_c classes to be the in-distribution set for the query examples).
Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Bateni are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong to use the prototypical network and algorithm 1 from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Makino, Snell, Choi, Liu, Bunel, and Shukla.
Regarding claim 13, Jeong, Snell, Choi, Bateni, Liu and Bunel teach the non-transitory computer-readable medium of claim 8, Snell further teaches
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors (Snell, Section 2.2, “Prototypical networks compute an M-dimensional representation ck ∈ RM, or prototype, of each class through an embedding function fφ : RD → RM with learnable parameters φ”).
Jeong, Makino, Snell, Choi, Liu, and Bunel are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Choi, Liu and Bunel to use the prototypical network from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, Snell, Choi, Liu, and Bunel do not teach, but Shukla does teach
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors (Shukla, Abstract, “In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network”).
Jeong, Makino, Snell, Choi, Liu, Bunel, and Shukla are considered analogous to the claimed invention because they deal with time-series data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Snell, Makino, Choi, Liu, Bunel, and Shukla to use the interpolation network from Shukla. Shukla teaches that “This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models” (Shukla, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong in view of Makino, Snell, Choi, Liu, Bunel, Che, and Shukla.
Regarding claim 14, Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Shukla teach the non-transitory computer-readable storage medium of claim 13, Shukla further teaches
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features (Shukla, Section 3.2, “The interpolation network interpolates the multivariate, sparse, and irregularly sampled input time series against a set of reference time points r = [r1,...,rT]. We assume that all of the time series are defined within a common time interval (for example, the first 24 or 48 hours after admission for MIMIC-III dataset). The T reference time points rt are chosen to be evenly spaced within that interval. In this work, we propose a two-layer interpolation network with each layer performing a different type of interpolation”).
Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Shukla are considered analogous to the claimed invention because they deal with time-series data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Liu, Bunel, and Che to use the interpolation network from Shukla. Shukla teaches that “This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models” (Shukla, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Shukla does not teach, but Che does teach
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features (Che, Section 4.1, “We conduct experiments on a Pediatric ICU dataset collected at the Children’s Hospital Los Angeles. This dataset consists of health records from 398 patients with acute lung injury in the Pediatric Intensive Care Unit at Children’s Hospital Los Angeles. It contains a set of 27 static features such as demographic information and admission diagnoses, and another set of 21 temporal features (recorded daily) such as monitoring features and discretized scores made by experts, for the initial 4 days of mechanical ventilation”).
Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Shukla are considered analogous to the claimed invention because they deal with health data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Liu, Bunel, and Shukla to apply Che’s technique. One of the ordinary skill in the art would have known to apply Che’s technique of using static and low frequency temporal features. Therefore, applying Che’s technique would have yielded the predictable results of minimizing power consumption, data volume, and network traffic. (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Regarding claim 20, , Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Shukla teach system of claim 15, Snell further teaches
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors (Snell, Section 2.2, “Prototypical networks compute an M-dimensional representation ck ∈ RM, or prototype, of each class through an embedding function fφ : RD → RM with learnable parameters φ”).
Jeong, Makino, Snell, Choi, Liu, and Bunel are considered analogous to the claimed invention because they deal with few-shot learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Choi, Liu and Bunel to use the prototypical network from Snell. Snell teaches “Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning” (Snell, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Jeong, Makino, Snell, Choi, Liu, and Bunel do not teach, but Shukla does teach
the prototype network of the meta-training component is a dual-channel combination network that encodes input data into feature vectors (Shukla, Abstract, “In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network”).
Jeong, Makino, Snell, Choi, Liu, Bunel, and Shukla are considered analogous to the claimed invention because they deal with time-series data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Snell, Makino, Choi, Liu, Bunel, and Shukla to use the interpolation network from Shukla. Shukla teaches that “This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models” (Shukla, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features (Shukla, Section 3.2, “The interpolation network interpolates the multivariate, sparse, and irregularly sampled input time series against a set of reference time points r = [r1,...,rT]. We assume that all of the time series are defined within a common time interval (for example, the first 24 or 48 hours after admission for MIMIC-III dataset). The T reference time points rt are chosen to be evenly spaced within that interval. In this work, we propose a two-layer interpolation network with each layer performing a different type of interpolation”).
Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Shukla are considered analogous to the claimed invention because they deal with time-series data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Liu, Bunel, and Che to use the interpolation network from Shukla. Shukla teaches that “This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models” (Shukla, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Shukla does not teach, but Che does teach
the prototype network includes a static channel for processing static and low frequency temporal features and a temporal channel for processing high frequency temporal features (Che, Section 4.1, “We conduct experiments on a Pediatric ICU dataset collected at the Children’s Hospital Los Angeles. This dataset consists of health records from 398 patients with acute lung injury in the Pediatric Intensive Care Unit at Children’s Hospital Los Angeles. It contains a set of 27 static features such as demographic information and admission diagnoses, and another set of 21 temporal features (recorded daily) such as monitoring features and discretized scores made by experts, for the initial 4 days of mechanical ventilation”).
Jeong, Makino, Snell, Choi, Liu, Bunel, Che, and Shukla are considered analogous to the claimed invention because they deal with health data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Jeong, Makino, Snell, Choi, Liu, Bunel, and Shukla to apply Che’s technique. One of the ordinary skill in the art would have known to apply Che’s technique of using static and low frequency temporal features. Therefore, applying Che’s technique would have yielded the predictable results of minimizing power consumption, data volume, and network traffic. (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Finn et al. (Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks) discloses an algorithm for meta-learning that is model-agnostic using small number of training samples.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN VO whose telephone number is (571)272-9622. The examiner can normally be reached Monday - Friday from 7-3 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.V./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
Prosecution Insights