DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Application filed on 2/24/2024. Claims 1-12 are pending in the case. Claims 1 and 11-12 are independent claims.
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. § 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. § 112(f) 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) 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) 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) because the claim limitations use 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 limitations are: “a step of generating,” a step of calculating,” “a step of training,” and “a step of causing” in claim 11.
Because these claim limitations are being interpreted under 35 U.S.C. § 112(f) they are being interpreted to cover the corresponding structure described in the specification as performing the claimed functions, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. § 112(f) applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. § 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. § 112(f).
Claim Objections
Claim 4 is objected to because it recites “the data set” where “the data sets” was apparently intended. Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 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.
Claims 1-12 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Each independent claim recites first “generating M training data sets by classifying pieces of medical data” and then “training one or a plurality of machine learning models … by using the M training data sets.” However, classifying requires an already-trained model. If the data is pre-labled it is unclear what, if anything, the “classifying” step does. If the data is not pre-labeled it is unclear how the classifier can function before it is trained. For the purposes of prior art and subject matter eligibility analyses Examiner assumes that the data is pre-labeled and “classifying … into any one of M types of predetermined attributes” means each data field is a predetermined attribute dimension regardless of the current value contained in that field. Dependent claims inherit the same issue from parent claims and do not resolve it.
Claim Rejections - 35 U.S.C. § 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 1-12 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “training data set generation processing of generating M training data sets by classifying pieces of medical data of a plurality of patients into any one of M types of predetermined attributes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “similarity calculation processing of calculating, for each pair of the M types of attributes, a similarity between the attributes” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a processor” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “a memory connected to or built in the processor, the processor being configured to execute” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “training processing of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “training processing of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “prediction processing of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “prediction processing of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a processor” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “a memory connected to or built in the processor, the processor being configured to execute” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “training processing of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “training processing of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “prediction processing of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “prediction processing of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 2:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “the M types of attributes are M types of diseases or M types of medical departments” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “the similarity between the attributes is a similarity between the diseases or a similarity between the medical departments” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “the M types of attributes are M types of diseases or M types of medical departments” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “the similarity between the attributes is a similarity between the diseases or a similarity between the medical departments” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 3:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “the similarity between the attributes is calculated based on at least one of a distance between organs, a distance on a circulatory system, or a metastasis route of a cancer” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 4:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “extracting M data sets by classifying pieces of medical data of a plurality of patients into any one of M types of predetermined attributes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “the similarity between the attributes is calculated based on information included in the data set” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “the processor is configured to execute data set extraction processing” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the processor is configured to execute data set extraction processing” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “the processor is configured to execute data set extraction processing” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “the processor is configured to execute data set extraction processing” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 5:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “the information included in the data set includes at least one of a symptom, an examination result, an examination image, an age of a patient, an attending physician, a medical department, a disease, a treatment, a medication, a candidate for differential diagnosis, or the number of co-occurrences” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “the information included in the data set includes at least one of a symptom, an examination result, an examination image, an age of a patient, an attending physician, a medical department, a disease, a treatment, a medication, a candidate for differential diagnosis, or the number of co-occurrences” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 6:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “determining an order of the M types of attributes based on a similarity between a prediction target and the attribute” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “the one or the plurality of machine learning models are a single machine learning model” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “the processor is further configured to execute order determination processing” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “the processor is configured to, in the training processing, train the single machine learning model by using the M training data sets related to the M types of attributes in order according to the order of the M types of attributes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the processor is configured to, in the training processing, train the single machine learning model by using the M training data sets related to the M types of attributes in order according to the order of the M types of attributes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “the one or the plurality of machine learning models are a single machine learning model” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “the processor is further configured to execute order determination processing” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “the processor is configured to, in the training processing, train the single machine learning model by using the M training data sets related to the M types of attributes in order according to the order of the M types of attributes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “the processor is configured to, in the training processing, train the single machine learning model by using the M training data sets related to the M types of attributes in order according to the order of the M types of attributes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 7:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “in the order determination processing, set an attribute corresponding to the prediction target as an M-th attribute” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “determine an order of the other M−1 attributes in descending order of a similarity between the M-th attribute and the attribute” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “set N to a natural number between 1 and M−1” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “train the untrained single machine learning model by using the training data set related to an N-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “train the untrained single machine learning model by using the training data set related to an N-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “retrain the trained single machine learning model by using the training data set related to an (N+1)-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “retrain the trained single machine learning model by using the training data set related to an (N+1)-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “perform retraining in order to the M-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “perform retraining in order to the M-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “train the untrained single machine learning model by using the training data set related to an N-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “train the untrained single machine learning model by using the training data set related to an N-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “retrain the trained single machine learning model by using the training data set related to an (N+1)-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “retrain the trained single machine learning model by using the training data set related to an (N+1)-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “perform retraining in order to the M-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “perform retraining in order to the M-th attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 9:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “the one or the plurality of machine learning models include a plurality of machine learning models” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “the processor is configured to further execute common layer addition/combination processing of adding a common layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the processor is configured to further execute common layer addition/combination processing of adding a common layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “the processor is configured to further execute common layer addition/combination processing of adding a common layer” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “combining the common layer on the plurality of machine learning models based on a similarity between a prediction target and the attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “combining the common layer on the plurality of machine learning models based on a similarity between a prediction target and the attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “in the training processing, training of the common layer is performed by using training data sets related to a plurality of attributes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “in the training processing, training of the common layer is performed by using training data sets related to a plurality of attributes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “the one or the plurality of machine learning models include a plurality of machine learning models” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “the processor is configured to further execute common layer addition/combination processing of adding a common layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “the processor is configured to further execute common layer addition/combination processing of adding a common layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “the processor is configured to further execute common layer addition/combination processing of adding a common layer” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “combining the common layer on the plurality of machine learning models based on a similarity between a prediction target and the attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “combining the common layer on the plurality of machine learning models based on a similarity between a prediction target and the attribute” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “in the training processing, training of the common layer is performed by using training data sets related to a plurality of attributes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “in the training processing, training of the common layer is performed by using training data sets related to a plurality of attributes” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 10:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “the one or the plurality of machine learning models include a first machine learning model related to a first attribute and a second machine learning model related to a second attribute” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “the processor is configured to execute constraint generation processing of generating a constraint that a similarity between the attributes and a similarity between configurations of the first machine learning model and the second machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “the processor is configured to execute constraint generation processing of generating a constraint that a similarity between the attributes and a similarity between configurations of the first machine learning model and the second machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “in the training processing, training of the first machine learning model and the second machine learning model is performed in consideration of the constraint” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “in the training processing, training of the first machine learning model and the second machine learning model is performed in consideration of the constraint” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “the one or the plurality of machine learning models include a first machine learning model related to a first attribute and a second machine learning model related to a second attribute” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “the processor is configured to execute constraint generation processing of generating a constraint that a similarity between the attributes and a similarity between configurations of the first machine learning model and the second machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “the processor is configured to execute constraint generation processing of generating a constraint that a similarity between the attributes and a similarity between configurations of the first machine learning model and the second machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “in the training processing, training of the first machine learning model and the second machine learning model is performed in consideration of the constraint” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “in the training processing, training of the first machine learning model and the second machine learning model is performed in consideration of the constraint” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 11:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “a step of generating M training data sets by classifying pieces of medical data of a plurality of patients into any one of M types of predetermined attributes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “a step of calculating, for each pair of the M types of attributes, a similarity between the attributes” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “a step of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a step of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “a step of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a step of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 12:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a manufacture.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “a step of generating M training data sets by classifying pieces of medical data of a plurality of patients into any one of M types of predetermined attributes” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “a step of calculating, for each pair of the M types of attributes, a similarity between the attributes” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a non-transitory computer readable medium storing a program for predicting information related to a patient based on medical data of the patient, the program causing a computer to execute a process” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “a step of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a step of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a non-transitory computer readable medium storing a program for predicting information related to a patient based on medical data of the patient, the program causing a computer to execute a process” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “a step of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a step of causing the one or the plurality of machine learning models to predict the information related to the patient” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
Claim Rejections - 35 U.S.C. § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 4-7, and 11-12 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Lisowska et al. (US 2019/0371439 A1, hereinafter Lisowska).
As to independent claim 1, Lisowska discloses a prediction device that predicts information related to a patient based on medical data of the patient, the prediction device comprising:
a processor (“a central processing unit (CPU),” paragraph 0034 lines 3-4); and
a memory connected to or built in the processor, the processor being configured to execute (“a computer program product comprising computer-readable instructions that are executable,” paragraph 0028 lines 1-3):
training data set generation processing of generating M training data sets by classifying (“The attributes a are representative of dimensions of similarity (which may also be described as aspects of similarity),” paragraph 0062 lines 1-3) pieces of medical data of a plurality of patients (“At stage 60, the training circuitry 24 receives a plurality of medical data sets. Each medical data set comprises data representing at least one property of a patient or other subject of the data set in question. Each medical data set comprises at least one medical image and associated medical record data. Each data set may comprise metadata concerning the patient, anatomy and/or acquisition,” paragraph 0060 lines 1-7) into any one of M types of predetermined attributes (“At stage 61, the training circuitry 24 selects a number m of attributes a. The attributes a may be selected, for example, based on a predetermined list or based on user input,” paragraph 0061 lines 1-4);
similarity calculation processing of calculating, for each pair of the M types of attributes, a similarity between the attributes (“At stage 66, the training circuitry 24 performs a machine learning process in which the convolutional neural network learns how to determine similarity for each of the attributes,” paragraph 0072 lines 1-4);
training processing of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets (“At stage 66, the training circuitry 24 performs a machine learning process in which the convolutional neural network learns how to determine similarity for each of the attributes,” paragraph 0072 lines 1-4); and
prediction processing of causing the one or the plurality of machine learning models to predict the information related to the patient (“The embedding model further outputs a prediction 194 for each of the classification tasks,” paragraph 0165 lines 7-8).
As to dependent claim 2, Lisowska further discloses a device wherein the M types of attributes are M types of diseases or M types of medical departments, and the similarity between the attributes is a similarity between the diseases or a similarity between the medical departments (“the selected attributes a may include at least one of age; gender; presence, absence or nature of pathology; presence or absence of anatomical feature; ethnicity; coexisting condition; point in treatment pathway; time since onset; treatment applied; time since treatment; or any other suitable attribute,” paragraph 0063 lines 5-10, emphasis added).
As to dependent claim 4, Lisowska further discloses a device wherein:
the processor is configured to execute data set extraction processing of extracting M data sets by classifying pieces of medical data of a plurality of patients into any one of M types of predetermined attributes (“At stage 66, the training circuitry 24 performs a machine learning process in which the convolutional neural network learns how to determine similarity for each of the attributes,” paragraph 0072 lines 1-4); and
the similarity between the attributes is calculated based on information included in the data set (“At stage 66, the training circuitry 24 performs a machine learning process in which the convolutional neural network learns how to determine similarity for each of the attributes,” paragraph 0072 lines 1-4).
As to dependent claim 5, Lisowska further discloses a device wherein the information included in the data set includes at least one of a symptom, an examination result, an examination image, an age of a patient, an attending physician, a medical department, a disease, a treatment, a medication, a candidate for differential diagnosis, or the number of co-occurrences (“the selected attributes a may include at least one of age; gender; presence, absence or nature of pathology; presence or absence of anatomical feature; ethnicity; coexisting condition; point in treatment pathway; time since onset; treatment applied; time since treatment; or any other suitable attribute. The attributes may comprise at least one image attribute and at least one non-image attribute,” paragraph 0063 lines 5-11; “Each medical data set comprises at least one medical image and associated medical record data. Each data set may comprise metadata concerning the patient, anatomy and/or acquisition. The medical images may comprise CT images, MR images, X-ray images, fluoroscopy images, PET images, or any other suitable medical images,” paragraph 0060 lines 4-10).
As to dependent claim 6, Lisowska further discloses a device wherein the one or the plurality of machine learning models are a single machine learning model (“At stage 66, the machine learning process outputs a model 70,” paragraph 0079 lines 1-3), the processor is further configured to execute order determination processing of determining an order of the M types of attributes based on a similarity between a prediction target and the attribute (“The machine learning process further outputs a respective weighting vector wa for each of the attributes a. The weighting vector wa is a one-dimensional vector of size n. The weighting vector wa comprises a set of learned weights for the attribute a,” paragraph 0084 lines 1-5), and the processor is configured to, in the training processing, train the single machine learning model by using the M training data sets related to the M types of attributes in order according to the order of the M types of attributes (“If we train a classifier on multiple classification tasks, then it will implicitly contain information about similarity in each dimension (or task) for which it was trained,” paragraph 0077 lines 1-4).
As to dependent claim 7, Lisowska further discloses a device wherein the processor is configured to, in the order determination processing, set an attribute corresponding to the prediction target as an M-th attribute, and determine an order of the other M−1 attributes in descending order of a similarity between the M-th attribute and the attribute (“The machine learning process further outputs a respective weighting vector wa for each of the attributes a. The weighting vector wa is a one-dimensional vector of size n. The weighting vector wa comprises a set of learned weights for the attribute a,” paragraph 0084 lines 1-5).
As to independent claim 11, Lisowska discloses an operation method of a prediction device that predicts information related to a patient based on medical data of the patient, the method comprising:
a step of generating M training data sets by classifying (“The attributes a are representative of dimensions of similarity (which may also be described as aspects of similarity),” paragraph 0062 lines 1-3) pieces of medical data of a plurality of patients (“At stage 60, the training circuitry 24 receives a plurality of medical data sets. Each medical data set comprises data representing at least one property of a patient or other subject of the data set in question. Each medical data set comprises at least one medical image and associated medical record data. Each data set may comprise metadata concerning the patient, anatomy and/or acquisition,” paragraph 0060 lines 1-7) into any one of M types of predetermined attributes (“At stage 61, the training circuitry 24 selects a number m of attributes a. The attributes a may be selected, for example, based on a predetermined list or based on user input,” paragraph 0061 lines 1-4);
a step of calculating, for each pair of the M types of attributes, a similarity between the attributes (“At stage 66, the training circuitry 24 performs a machine learning process in which the convolutional neural network learns how to determine similarity for each of the attributes,” paragraph 0072 lines 1-4);
a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets (“At stage 66, the training circuitry 24 performs a machine learning process in which the convolutional neural network learns how to determine similarity for each of the attributes,” paragraph 0072 lines 1-4); and
a step of causing the one or the plurality of machine learning models to predict the information related to the patient (“The embedding model further outputs a prediction 194 for each of the classification tasks,” paragraph 0165 lines 7-8).
As to independent claim 12, Lisowska discloses a non-transitory computer readable medium storing a program (“a computer program product comprising computer-readable instructions that are executable,” paragraph 0028 lines 1-3) for predicting information related to a patient based on medical data of the patient, the program causing a computer to execute a process comprising:
a step of generating M training data sets by classifying (“The attributes a are representative of dimensions of similarity (which may also be described as aspects of similarity),” paragraph 0062 lines 1-3) pieces of medical data of a plurality of patients (“At stage 60, the training circuitry 24 receives a plurality of medical data sets. Each medical data set comprises data representing at least one property of a patient or other subject of the data set in question. Each medical data set comprises at least one medical image and associated medical record data. Each data set may comprise metadata concerning the patient, anatomy and/or acquisition,” paragraph 0060 lines 1-7) into any one of M types of predetermined attributes (“At stage 61, the training circuitry 24 selects a number m of attributes a. The attributes a may be selected, for example, based on a predetermined list or based on user input,” paragraph 0061 lines 1-4);
a step of calculating, for each pair of the M types of attributes, a similarity between the attributes (“At stage 66, the training circuitry 24 performs a machine learning process in which the convolutional neural network learns how to determine similarity for each of the attributes,” paragraph 0072 lines 1-4);
a step of training one or a plurality of machine learning models based on the similarity between the attributes by using the M training data sets (“At stage 66, the training circuitry 24 performs a machine learning process in which the convolutional neural network learns how to determine similarity for each of the attributes,” paragraph 0072 lines 1-4); and
a step of causing the one or the plurality of machine learning models to predict the information related to the patient (“The embedding model further outputs a prediction 194 for each of the classification tasks,” paragraph 0165 lines 7-8).
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
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 C.F.R. § 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 3 is rejected under 35 U.S.C. § 103 as being unpatentable over Lisowska in view of McNutt et al. (US 2011/0153547 A1, hereinafter McNutt).
As to dependent claim 3, the rejection of claim 1 is incorporated.
Lisowska does not appear to expressly teach a device wherein the similarity between the attributes is calculated based on at least one of a distance between organs, a distance on a circulatory system, or a metastasis route of a cancer.
McNutt teaches a device wherein the similarity between the attributes is calculated based on at least one of a distance between organs, a distance on a circulatory system, or a metastasis route of a cancer (“Since these distributions encode the distance between the organs at risk and the tumor volume, and since the spareability of an organ depends on its proximity to the irradiated tumor, these descriptors can provide a simple shape signature that helps find treatment plans utilized in similar patients,” paragraph 0006 lines 7-12).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the similarity of Lisowska to comprise the distance between organs of McNutt. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely calculating similarity based on a distance between organs (“Since these distributions encode the distance between the organs at risk and the tumor volume, and since the spareability of an organ depends on its proximity to the irradiated tumor, these descriptors can provide a simple shape signature that helps find treatment plans utilized in similar patients,” McNutt paragraph 0006 lines 7-12). Therefore, the rationale to support a conclusion that the claim would have been obvious is that combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Claim 8 is rejected under 35 U.S.C. § 103 as being unpatentable over Lisowska in view of Vallée et al. (US 2016/0378943 A1, hereinafter Vallée).
As to dependent claim 8, the rejection of claim 7 is incorporated.
Lisowska does not appear to expressly teach a device wherein:
set N to a natural number between 1 and M−1;
train the untrained single machine learning model by using the training data set related to an N-th attribute;
retrain the trained single machine learning model by using the training data set related to an (N+1)-th attribute; and
perform retraining in order to the M-th attribute.
Vallée teaches a device wherein:
set N to a natural number between 1 and M−1 (“Other types of validation can also be used, such as k-fold cross-validation, including 10-fold cross-validation, which comprises dividing a data set in k=10 different sets and using 9 out of 10 data sets for training and the remaining one for validation. This is then performed repeatedly by permuting data sets,” paragraph 0090 lines 6-11);
train the untrained single machine learning model by using the training data set related to an N-th attribute (“Other types of validation can also be used, such as k-fold cross-validation, including 10-fold cross-validation, which comprises dividing a data set in k=10 different sets and using 9 out of 10 data sets for training and the remaining one for validation. This is then performed repeatedly by permuting data sets,” paragraph 0090 lines 6-11);
retrain the trained single machine learning model by using the training data set related to an (N+1)-th attribute (“Other types of validation can also be used, such as k-fold cross-validation, including 10-fold cross-validation, which comprises dividing a data set in k=10 different sets and using 9 out of 10 data sets for training and the remaining one for validation. This is then performed repeatedly by permuting data sets,” paragraph 0090 lines 6-11); and
perform retraining in order to the M-th attribute (“Other types of validation can also be used, such as k-fold cross-validation, including 10-fold cross-validation, which comprises dividing a data set in k=10 different sets and using 9 out of 10 data sets for training and the remaining one for validation. This is then performed repeatedly by permuting data sets,” paragraph 0090 lines 6-11).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the training of Lisowska to comprise the k-fold cross-validation of Vallée. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely training via k-fold cross-validation (“Other types of validation can also be used, such as k-fold cross-validation, including 10-fold cross-validation, which comprises dividing a data set in k=10 different sets and using 9 out of 10 data sets for training and the remaining one for validation. This is then performed repeatedly by permuting data sets,” Vallée paragraph 0090 lines 6-11). Therefore, the rationale to support a conclusion that the claim would have been obvious is that combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Claims 9-10 are rejected under 35 U.S.C. § 103 as being unpatentable over Lisowska in view of Hotson et al. (US 2019/0065944 A1, hereinafter Hotson).
As to dependent claim 9, the rejection of claim 1 is incorporated.
Lisowska does not appear to expressly teach a device wherein the one or the plurality of machine learning models include a plurality of machine learning models, the processor is configured to further execute common layer addition/combination processing of adding a common layer and combining the common layer on the plurality of machine learning models based on a similarity between a prediction target and the attribute, and in the training processing, training of the common layer is performed by using training data sets related to a plurality of attributes.
Hotson teaches a device wherein the one or the plurality of machine learning models include a plurality of machine learning models, the processor is configured to further execute common layer addition/combination processing of adding a common layer and combining the common layer on the plurality of machine learning models based on a similarity between a prediction target and the attribute, and in the training processing, training of the common layer is performed by using training data sets related to a plurality of attributes (“FIGS. 2-4 are schematic block diagrams illustrating training of layers or subsets of a neural network for multiple tasks with one or more common layers 202 constrained by EWC. FIG. 2 illustrates a neural network 200 during training of common layers 202 and first task layers 204 for a first task. The common layers 202 may receive training data, such as an image and a label for the image corresponding to the first task,” paragraph 0028 lines 1-8; “the use of elastic weight consolidation (EWC) on a base CNN that is then connected to multiple other networks for accomplishing various tasks may be used to reduce computational load while limiting ‘forgetting’ of previously learned tasks,” paragraph 0015 lines 1-5).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning of Lisowska to comprise the common layer of Hotson. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely “reduce computational load while limiting ‘forgetting’ of previously learned tasks” (Hotson paragraph 15 lines 4-5). Therefore, the rationale to support a conclusion that the claim would have been obvious is that combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 10, the rejection of claim 1 is incorporated.
Lisowska does not appear to expressly teach a device wherein the one or the plurality of machine learning models include a first machine learning model related to a first attribute and a second machine learning model related to a second attribute, the processor is configured to execute constraint generation processing of generating a constraint that a similarity between the attributes and a similarity between configurations of the first machine learning model and the second machine learning model have a positive correlation, and in the training processing, training of the first machine learning model and the second machine learning model is performed in consideration of the constraint.
Hotson teaches a device wherein the one or the plurality of machine learning models include a first machine learning model related to a first attribute and a second machine learning model related to a second attribute, the processor is configured to execute constraint generation processing of generating a constraint that a similarity between the attributes and a similarity between configurations of the first machine learning model and the second machine learning model have a positive correlation, and in the training processing, training of the first machine learning model and the second machine learning model is performed in consideration of the constraint (“FIGS. 2-4 are schematic block diagrams illustrating training of layers or subsets of a neural network for multiple tasks with one or more common layers 202 constrained by EWC. FIG. 2 illustrates a neural network 200 during training of common layers 202 and first task layers 204 for a first task. The common layers 202 may receive training data, such as an image and a label for the image corresponding to the first task,” paragraph 0028 lines 1-8; “the use of elastic weight consolidation (EWC) on a base CNN that is then connected to multiple other networks for accomplishing various tasks may be used to reduce computational load while limiting ‘forgetting’ of previously learned tasks,” paragraph 0015 lines 1-5).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning of Lisowska to comprise the constraint of Hotson. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely “reduce computational load while limiting ‘forgetting’ of previously learned tasks” (Hotson paragraph 15 lines 4-5). Therefore, the rationale to support a conclusion that the claim would have been obvious is that combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
US 2016/0253467 A1 disclosing learning similarity in medical data to make predictions
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/Ryan Barrett/
Primary Examiner, Art Unit 2148
