DETAILED ACTION
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 .
Style
In this action unitalicized bold is used for claim language, while italicized bold is used for emphasis.
Information Disclosure Statement
All information disclosure statements were submitted prior to the first action and are incompliance with the provisions of 37 C.F.R. § 1.97. Accordingly, they have been considered. The following inventor publication, which is material to patentability, does not appear to have been included in any IDS or otherwise disclosed to the Office: Prediction of Uric Acid Component in Nephrolithiasis Using Simple Clinical Information: A Machine Learning-Based model; Published 2 Nov 2021. If this is incorrect, Applicant may correct the record in the next communication.
Applicant Reply
“The claims may be amended by canceling particular claims, by presenting new claims, or by rewriting particular claims as indicated in 37 CFR 1.121(c). The requirements of 37 CFR 1.111(b) must be complied with by pointing out the specific distinctions believed to render the claims patentable over the references in presenting arguments in support of new claims and amendments. . . . The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. . . . An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” MPEP § 714.02. Generic statements or listing of numerous paragraphs do not “specifically point out the support for” claim amendments. “With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4, 83 USPQ2d 1373, 1376, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a ‘simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.’)” MPEP § 2163(II)(A).
Allowable Subject Matter under 35 U.S.C. §§ 102 and 103
Claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and if all other rejections applying to this claim are overcome.
Claim Rejections - 35 USC § 112
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.
Claim 3 is 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Generally: separately listed claim elements are construed as distinct components, all claim terms must be given weight, and there is presumed to be a difference in meaning and scope when different words or phrases are used in separate claims. Since different term or phrases are presumed to differ in scope and each term or phrase in the claims must find clear support in the description, a description of a single element in the Specification may fail to support multiple claim terms. “[C]laims must ‘conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description.’ 37 C.F.R. § 1.75(d)(1).” Phillips v. AWH Corp., 415 F.3d 1303, 1316 (Fed. Cir. 2005) (as cited in MPEP § 2111). Further, a lack of lack of detail in the Specification describing how a claimed result is achieved can support a finding that the Applicant was not in possession of the claimed invention at the time of filing, notwithstanding verbatim support. “It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made.” MPEP § 2161.01(I). “An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved[.] See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc). The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. ‘Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement.’” MPEP § 2163.03.
Claim 3 recites “concatenating the average model, the male-related re-scaler and the female-related re-scaler to obtain the prediction model for determining whether the subject has nephrolithiasis.” Generally, concatenation refers to placing some sequential set of numbers next to some other sequential set of numbers, in an end-to-end fashion. The plain meaning of this term is inconsistent with the description of the “average model” outputting to both the male related re-scaler and the female related re-scaler. See e.g. Spec. Fig. 4. Since the term “concatenate” seems to be used in a way that is inconsistent with the plain meaning, but the Specification does not provide an alternate meaning that would be consistent with the disclosure, the claimed “concatenating the average model, the male-related re-scaler and the female-related re-scaler” is not supported. Further, it is not only the arrangement of the claimed “re-scaler[s]” is inconsistent with the term “concatenate.” The Specification also fails to describe any particular architecture consistent with concatenating an entire model with the re-scalers. Generally, models have multidimensional layers, which could conceivably be concatenated with some other sequence of values. But such a configuration is not claimed, and does not appear to be consistent with the supporting description. Without any description of a particular architecture including all three claim elements being concatenated, the scope of claim language directed to concatenation of the three claim elements is not supported by the original disclosure.
All dependent claims are rejected as containing the limitations of the claims from which they depend.
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 are 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 pre-AIA the applicant regards as the invention.
Generally: separately listed claim elements are construed as distinct components, that all claim terms must be given weight, there is presumed to be a difference in meaning and scope when different words or phrases are used in separate claims, and repeated and consistent descriptions in the specification indicate the proper scope of a claimed term. “[C]laims must ‘conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description.’ 37 C.F.R. § 1.75(d)(1).” Phillips v. AWH Corp., 415 F.3d 1303, 1316 (Fed. Cir. 2005) (as cited in MPEP § 2111). Therefore, use of two different terms in the claims that both rely on the description of a single structure in the Specification may render at least one term indefinite because there is no way to determine which term should be construed in view of the description of the single structure.
Claim 1 recites: “averaging the preliminary models to obtain an average model; and obtaining, based on the average model, a prediction model for determining whether the subject has nephrolithiasis.” The limitation above recites both “the average model” and “a prediction model[.]” It is not clear whether the average model refers to the same model as the prediction model. Generally, separately recited claim elements are interpreted as distinct components. But the Specification explains that the “average model . . . is directly used as the prediction model” in at least one embodiment. Spec. ¶30. Since the specification describes an embodiment where both the prediction model and the average model refer to the same model, it is not clear whether the separately recite elements must refer to distinct components, when the claims are read in view of the Specification.
Claim 3 recites “concatenating the average model, the male-related re-scaler and the female-related re-scaler to obtain the prediction model for determining whether the subject has nephrolithiasis.” Generally, concatenation refers to placing some sequential set of numbers next to some other sequential set of numbers, in an end-to-end fashion. The plain meaning of this term is inconsistent with the description of the “average model” outputting to both the male related re-scaler and the female related re-scaler. See e.g. Spec. Fig. 4. Since the term “concatenate” seems to be used in a way that is inconsistent with the plain meaning, but the Specification does not provide an alternate meaning that would be consistent with the disclosure, the claimed “concatenating the average model, the male-related re-scaler and the female-related re-scaler” is indefinite. Further, it is unclear what would be meant by concatenating a model with the re-scalers in the first place. Generally, models have multidimensional layers, which could conceivably be concatenated with another series of values. But such a configuration is not claimed, and does not appear to be consistent with the supporting description. Without any description of a particular architecture including a model concatenated with the re-scalers, the scope of claim language directed to concatenation of entire models with the re-scalars is indefinite.
All dependent claims are rejected as containing the limitations of the claims from which they depend.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) and the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
All claims are found to be directed to one of the four statutory categories, unless otherwise indicated in this action.
Step 2A Prongs One and Two (Alice Step 1): According to Office guidance, claims that read on math do not recite an abstract idea at step 2A1, when the claims fail to refer to the math by name.1 The MPEP also equates “recit[ing] a judicial exception” with “state[ing]” or “describ[ing]” an abstract idea in the claims.2 Consistent with this guidance, an abstract idea may be first recited in a dependent claim even though the independent claims read on that abstract idea. Claim limitations which recite any of the abstract idea groupings set forth in the manual are found to be directed, as a whole, to an abstract idea unless otherwise indicated.3 The claims do not recite additional elements that integrate the abstract ideas into a practical application.4 To confer patent eligibility to an otherwise abstract idea, claims may recite a specific means or method of solving a specific problem in a technological field.5
Independent Claims
1. A method for establishing a model to determine whether a subject has nephrolithiasis, (Determining whether a subject has nephrolithiasis is a mental process. The claimed “establishing a model” to carry out this mental process is a mere instruction to implement the mental process using generic computer components.) comprising: grouping a plurality of training data sets that are respectively related to a plurality of patients into a number N of preliminary groups, where N is a positive integer; obtaining a number N of preliminary models based on the preliminary groups; averaging the preliminary models to obtain an average model; and obtaining, based on the average model, a prediction model for determining whether the subject has nephrolithiasis. (Grouping training data into groups is a mental process. Obtaining an average is also a mental process. Training models on separate data and averaging to create an “average model” also reads on an instruction to utilize a generic computer component, in the form of a generic ensemble model,6 for the mental process of making a determination, limited to the field of nephrolithiasis determination.)
Step 2B (Alice Step 2): The rejected claims do not recite additional elements that amount to significantly more than the judicial exception.
All additional limitations that do not integrate the claimed judicial exception into a practical application also fail to amount to significantly more, for the reasons given at step 2A2. All limitations found to be extra-solution activity at step 2A2 are found to be WURC, including limitations that read on mere data gathering, data storage, and data input/output/transfer. Should any claim limitations be rejected at step 2A1 as extra-solution activity, it should be understood that such limitations are also found to be WURC at this step. Generic data input/output, storage, repetitive processing operations, and generic display of information and have been found to be generic WURC operations that do not transform the abstract idea into patent eligible subject matter, at the Alice step two analysis.7 Other aspects of generic computing have also been found to be WURC.8 Further, the description itself may provide support for a finding that claim elements are WURC. The analysis under § 112(a) as to whether a claim element is “so well-known that it need not be described in detail in the patent specification” is the same as the analysis as to whether the claim element is widely prevalent or in common use.9 Similarly, generic descriptions in the Specification of claimed components and features has been found to support a conclusion that the claimed components were conventional.10 Improvements to the relevant technology may support a finding that the claims include a patent eligible inventive concept. But some mechanism that results in any asserted improvements must be recited in the claim, and the Specification must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing the improvement.11 This applies to the dependent claims below.
Dependent Claims:
2. The method as claimed in claim 1, wherein: each of the preliminary models is expressed as Mix=11+efi(x), i=1,2,…,N, where Mi represents the preliminary model, x represents an input of the preliminary model, and fi(x) is a nonlinear function that is obtained by using a multi-layer fully connected neural network; (This claim language recites math, implemented using without any clear inventive concept.) and the multi-layer fully connected neural network has a plurality of non-output layers, and each of the non-output layers consists of three concatenated components respectively for batch normalization, affine transformation and activation. (This also recites generic math, used to implement a generic neural network,12 with no inventive concept.)
3. The method as claimed in claim 1, further comprising: re-grouping the training data sets into a male-related group and a female-related group, the male-related group including those of the training data sets that are related to those of the patients who are male, the female-related group including those of the training data sets that are related to those of the patients who are female; (Regrouping data is a mental/mathematical process.) applying the average model to the training data sets included in the male-related group to obtain a plurality of male-related outputs, (This reads on utilizing a generic computer component, in the form of a generic model.) and then obtaining a male-related receiver operating characteristic (ROC) curve based on the male-related outputs; determining a male-related cut-off threshold based on the male-related ROC curve; determining a male-related re-scaler based on the male-related cut-off threshold; (This reads on utilization of math, with no inventive concept.13) applying the average model to the training data sets included in the female-related group to obtain a plurality of female-related outputs, and then obtaining a female-related ROC curve based on the female-related outputs; determining a female-related cut-off threshold based on the female-related ROC curve; determining a female-related re-scaler based on the female-related cut-off threshold; and (See explanation related to the male-related re-scaler.) concatenating the average model, the male-related re-scaler and the female-related re-scaler to obtain the prediction model for determining whether the subject has nephrolithiasis. (The claim recites “concatenating” the models, but the Specification shows the output of the average model being separately sent to the male or femal related re-scaler. See Spec. Fig. 4. Given the usage of “concatenate” in reference to generic communication between layers, and the lack of clarity of “concatenating” models, this language, as best understood refers to any connection between the models. The claimed combination of the models “to obtain a prediction model” reads on merely applying the math of the re-scalers to the outputs of the average model. More mathematical operations, without any clear inventive concept is not patentable.)
4. The method as claimed in claim 3, wherein the male-related re-scaler and the female-related re-scaler are connected to the average model in parallel. (As best understood, being “connected in parallel” refers to both models taking outputs from the average model. This reads on applying more math by the re-scalars to the outputs of the average model. More mathematical operations, without any clear inventive concept is not patentable.)
5. The method as claimed in claim 3, wherein: the male-related re-scaler is expressed as Smym=0.5Tmym+max0,(ym-Tm)0.51-Tm-0.5Tm, where Sm represents the male-related re-scaler, ym represents an output of the average model, Tm represents the male-related cut-off threshold, and maxa,b denotes a greater one of a value a and a value b, where the value a and the value b are arbitrary values; the female-related re-scaler is expressed as Sfyf=0.5Tfyf+max0,(yf-Tf)0.51-Tf-0.5Tf, where Sf represents the female-related re-scaler, yf represents an output of the average model and Tf represents the female-related cut-off threshold. (This reads on mathematical operations, without any clear inventive concept.)
6. The method as claimed in claim 3, wherein a comparison between a common threshold and an output of one of the male-related re-scaler and the female-related re-scaler is made for determining whether the subject has nephrolithiasis. (This reads on math, without any clear inventive concept.)
7. The method as claimed in claim 1, wherein each of the training data sets includes a gender indicator that indicates gender of the respective one of the patients, age of the respective one of the patients, and an estimated glomerular filtration rate (eGFR) that is related to the respective one of the patients. (This merely limits to a particular data environment associated with a field of use.)
8. The method as claimed in claim 7, wherein each of the training data sets further includes one of a number of red blood cells in a urine sample of the respective one of the patients, a blood creatinine concentration that is related to the respective one of the patients, a body mass index (BMI) that is related to the respective one of the patients, a value of urine pH that is related to the respective one of the patients, a gout indicator that indicates whether the respective one of the patients was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the respective one of the patients was ever diagnosed with DM, a bacteriuria indicator that indicates whether the respective one of the patients was ever diagnosed with bacteriuria, and any combination thereof. (This merely limits to a particular data environment associated with a field of use.)
9. The method as claimed in claim 1, wherein each of the training data sets includes a gender indicator that indicates gender of the respective one of the patients, age of the respective one of the patients, and a blood creatinine concentration that is related to the respective one of the patients. (This merely limits to a particular data environment associated with a field of use.)
10. The method as claimed in claim 9, wherein each of the training data sets further includes one of a number of red blood cells in a urine sample of the respective one of the patients, an estimated glomerular filtration rate (eGFR) that is related to the respective one of the patients, a body mass index (BMI) that is related to the respective one of the patients, a value of urine pH that is related to the respective one of the patients, a gout indicator that indicates whether the respective one of the patients was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the respective one of the patients was ever diagnosed with DM, a bacteriuria indicator that indicates whether the respective one of the patients was ever diagnosed with bacteriuria, and any combination thereof. (This merely limits to a particular data environment associated with a field of use.)
11. The method as claimed in claim 1, wherein each of the training data sets includes a gender indicator that indicates gender of the respective one of the patients, age of the respective one of the patients, and a number of red blood cells in a urine sample of the respective one of the patients. (This merely limits to a particular data environment associated with a field of use.)
12. The method as claimed in claim 11, wherein each of the training data sets further includes one of an estimated glomerular filtration rate (eGFR) that is related to the respective one of the patients, a blood creatinine concentration that is related to the respective one of the patients, a body mass index (BMI) that is related to the respective one of the patients, a value of urine pH that is related to the respective one of the patients, a gout indicator that indicates whether the respective one of the patients was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the respective one of the patients was ever diagnosed with DM, a bacteriuria indicator that indicates whether the respective one of the patients was ever diagnosed with bacteriuria, and any combination thereof. (This merely limits to a particular data environment associated with a field of use.)
13. The method as claimed in claim 1, further comprising feeding an input variable set into the prediction model so as to obtain an output indicating whether the subject has nephrolithiasis, the input variable set being related to the subject. (This merely limits to utilizing a generic model within a particular data environment associated with a field of use.)
14. The method as claimed in claim 13, wherein the input variable set includes a gender indicator that indicates gender of the subject, age of the subject, and an estimated glomerular filtration rate (eGFR) that is related to the subject. (This merely limits to a particular data environment associated with a field of use.)
15. The method as claimed in claim 14, wherein the input variable set further includes one of a number of red blood cells in a urine sample of the subject, a blood creatinine concentration that is related to the subject, a body mass index (BMI) that is related to the subject, a value of urine pH that is related to the subject, a gout indicator that indicates whether the subject was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the subject was ever diagnosed with DM, a bacteriuria indicator that indicates whether the subject was ever diagnosed with bacteriuria, and any combination thereof. (This merely limits to a particular data environment associated with a field of use.)
16. The method as claimed in claim 13, wherein the input variable set includes a gender indicator that indicates gender of the subject, age of the subject, and a blood creatinine concentration that is related to the subject. (This merely limits to a particular data environment associated with a field of use.)
17. The method as claimed in claim 16, wherein the input variable set further includes one of a number of red blood cells in a urine sample of the subject, an estimated glomerular filtration rate (eGFR) that is related to the subject, a body mass index (BMI) that is related to the subject, a value of urine pH that is related to the subject, a gout indicator that indicates whether the subject was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the subject was ever diagnosed with DM, a bacteriuria indicator that indicates whether the subject was ever diagnosed with bacteriuria, and any combination thereof. (This merely limits to a particular data environment associated with a field of use.)
18. The method as claimed in claim 13, wherein the input variable set includes a gender indicator that indicates gender of the subject, age of the subject, and a number of red blood cells in a urine sample of the subject. (This merely limits to a particular data environment associated with a field of use.)
19. The method as claimed in claim 18, wherein the input variable set further includes one of a blood creatinine concentration that is related to the subject, an estimated glomerular filtration rate (eGFR) that is related to the subject, a body mass index (BMI) that is related to the subject, a value of urine pH that is related to the subject, a gout indicator that indicates whether the subject was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the subject was ever diagnosed with DM, a bacteriuria indicator that indicates whether the subject was ever diagnosed with bacteriuria, and any combination thereof. (This merely limits to a particular data environment associated with a field of use.)
20. The method as claimed in claim 1, wherein the grouping a plurality of training data sets into a number N of preliminary groups is to group the training data sets into the number N of preliminary groups at random. (Grouping training data sets “at random” is a mental process. Further, this does not constitute an inventive concept.14)
All dependent claims are rejected as containing the material of the claims from which they depend.
Claim Rejections - 35 USC § 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 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, 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.
Claims 1 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Malmagro (EP 2743852; 2014) and Cunningham (Ensembles in Machine Learning, March 2022.)
1. A method for establishing a model to determine whether a subject has nephrolithiasis, (Malmagro teaches: “This invention, as stated above in the title, relates to the use of artificial neural networks for detecting the formation of kidney stones and identifying the chemical composition of such stones.” Malmagro ¶1.) comprising: grouping a plurality of training data sets that are respectively related to a plurality of patients into a number N of preliminary groups, where N is a positive integer; obtaining a number N of preliminary models based on the preliminary groups; averaging the preliminary models to obtain an average model; (The previously cited art does not expressly teach details of an ensemble model.
Cunningham teaches “The generic ensemble idea is presented in Figure 1. All ensembles are made up of a collection of base classifiers, also known as members or estimators. When presented with a query these will each make a prediction and these predictions will be combined to produce an ensemble prediction. Different ensemble strategies vary on how exactly the base classifiers are trained and how the combination of predictions is achieved. For the purpose of this tutorial we organise ensemble methods into four categories: . . . Bagging: Bootstrap aggregation (bagging) refers to ensembles that achieve diversity in the estimators by training on random bootstrap resamples of the data. The aggregation of the outputs of these estimators is achieved by averaging or majority voting. Under this category we also consider ensembles based on random subspaces rather than random subsets. Random forests are also included in this category.” Cunningham P. 3.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Cunningham because using ensemble models tends to improve accuracy. See Cunningham P. 5.) and obtaining, based on the average model, a prediction model for determining whether the subject has nephrolithiasis. (Obtaining a “prediction model” based on an “average model” is read to include merely using the averaging model for predictions. This is consistent with the description. The Specification explains that the “average model . . . is directly used as the prediction model” in at least one embodiment. Spec. ¶30. The language “for determining whether the subject has nephrolithiasis” is written as an intended use. Intended use language is explained in MPEP §§ 2103 and 2111.02. “Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure.” MPEP § 2111.04. Further, Milmagro teaches a model for detecting kidney stones. See Milmagro ¶1.)
20. The method as claimed in claim 1, wherein the grouping a plurality of training data sets into a number N of preliminary groups is to group the training data sets into the number N of preliminary groups at random. (See rejection of claim 1.)
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Malmagro, Cunningham Chen (Prediction of Uric Acid Component in Nephrolithiasis Using Simple Clinical Information: A Machine Learning-Based model, Nov 2021) and Doshi (Batch Norm Explained Visually – How it works, and why neural networks need it, 2021)
2. The method as claimed in claim 1, wherein: each of the preliminary models is expressed as as
M
i
x
=
1
1
+
e
f
i
(
x
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,
i
=
1,2
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,
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, where
M
i
represents the preliminary model,
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represents an input of the preliminary model, and
f
i
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is a nonlinear function that is obtained by using a multi-layer fully connected neural network; (The previously cited art does not explicitly teach the above mathematical expression for the models. Chen teaches “A model for predicting uric acid stones in nephrolithiasis was developed using machine learning methodologies. The mathematical representation of this model can be expressed as follows:
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200
400
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where represents the input vector of the eight decision variables, and the model output, which takes a value between 0 and 1. . . . The decision variables x1;ccc;X8 carry the values of “gender,” “age,” “eGFR,” “urine pH,” “BMI,” “DM,” “gout,” and “bacteriuria,” respectively.” Chen P. 6.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Chen because use of a sigmoid function (shown in the equation above) naturally normalizes outputs while being differentiable, which helps when performing gradient descent.) and the multi-layer fully connected neural network has a plurality of non-output layers, and each of the non-output layers consists of three concatenated components respectively for batch normalization, affine transformation and activation. (The previously cited art does not expressly teach the above claimed configuration.
Doshi teaches “Batch Norm is an essential part of the toolkit of the modern deep learning practitioner. Soon after it was introduced in [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Ioffe 2015], it was recognized as being transformational in creating deeper neural networks that could be trained faster.” Doshi PP. 1-2. See also Doshi P. 8 showing the batch normalization, affine transformation, and activation layers in the above claimed configuration.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Doshi because this reduces training time.)
Claims 3-4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Malmagro, Cunningham, Cha (US 2020/0005900) and Welk (Development of Youth Aerobic-Capacity Standards Using Receiver Operating Characteristic Curves, 2011).
3. The method as claimed in claim 1, further comprising: re-grouping the training data sets into a male-related group and a female-related group, the male-related group including those of the training data sets that are related to those of the patients who are male, the female-related group including those of the training data sets that are related to those of the patients who are female; (The previously cited art does not teach grouping datasets based on gender.
Cha teaches “At step 125, the system may generate a patient cohort comprising a subset of the preprocessed data records by, for example, filtering out any records that do not meet a cohorting criteria. Generally, cohorting criteria may relate to any information included in the preprocessed data records, such as various patient information. For example, cohorting criteria may relate to one or more of: demographic information (e.g., age, gender, race)[.]” Cha ¶60.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Cha because separating data based on gender can make model more accurate when the gender of the subject is known.) applying the average model to the training data sets included in the male-related group to obtain a plurality of male-related outputs, and then obtaining a male-related receiver operating characteristic (ROC) curve based on the male-related outputs; determining a male-related cut-off threshold based on the male-related ROC curve; (“In certain embodiments, a K-fold (e.g., 10-fold) cross-validation technique (Monte Carlo-style) may be utilized. In such cases, the training dataset may be randomly partitioned into equal-sized sub samples in order to determine optimal penalty parameters for selecting predictive features that maximize the area under the curve (“AUC”) of the receiver operating characteristics (“ROC”) curve on the randomly selected samples across folds.” Cha ¶101. “As shown in Equation 1, below, sensitivity (i.e., recall or true-positive rate) corresponds to the Y-axis of the ROC curve, where each point corresponds to a threshold at which a prediction is made. Sensitivity provides the percentage of patients who are correctly identified as having a condition for some predictive threshold. For example, sensitivity may indicate that 95% of the top 20% of scored patients are associated with an identified outcome. It will be appreciated that a higher sensitivity corresponds to a lower prediction threshold, which in turn reflects a preference to avoid false negatives over false positives.” Cha ¶154.) determining a male-related re-scaler based on the male-related cut-off threshold; (Cha teaches “It will be appreciated that, in many cases, a particular renal function decline outcome may only be applicable to a small subset of the patients included within the preprocessed data records. Accordingly, a cohorting criteria may be employed to precisely define a cohort comprising a population of interest and such a cohort may be employed to train a model.” Cha ¶61. Here, the “re-scalar” reads on a model trained using data for a particular cohort.) applying the average model to the training data sets included in the female-related group to obtain a plurality of female-related outputs, and then obtaining a female-related ROC curve based on the female-related outputs; determining a female-related cut-off threshold based on the female-related ROC curve; (“In certain embodiments, a K-fold (e.g., 10-fold) cross-validation technique (Monte Carlo-style) may be utilized. In such cases, the training dataset may be randomly partitioned into equal-sized sub samples in order to determine optimal penalty parameters for selecting predictive features that maximize the area under the curve (“AUC”) of the receiver operating characteristics (“ROC”) curve on the randomly selected samples across folds.” Cha ¶101. “As shown in Equation 1, below, sensitivity (i.e., recall or true-positive rate) corresponds to the Y-axis of the ROC curve, where each point corresponds to a threshold at which a prediction is made. Sensitivity provides the percentage of patients who are correctly identified as having a condition for some predictive threshold. For example, sensitivity may indicate that 95% of the top 20% of scored patients are associated with an identified outcome. It will be appreciated that a higher sensitivity corresponds to a lower prediction threshold, which in turn reflects a preference to avoid false negatives over false positives.” Cha ¶154.) determining a female-related re-scaler based on the female-related cut-off threshold; (Cha teaches “It will be appreciated that, in many cases, a particular renal function decline outcome may only be applicable to a small subset of the patients included within the preprocessed data records. Accordingly, a cohorting criteria may be employed to precisely define a cohort comprising a population of interest and such a cohort may be employed to train a model.” Cha ¶61. Here, the “re-scalar” reads on a model trained using data for a particular cohort.) and concatenating the average model, the male-related re-scaler and the female-related re-scaler to obtain the prediction model for determining whether the subject has nephrolithiasis. (Cha does not indicate whether the rescaling is carried out by a re-scalar that is “concatentated” with another model. Welk teaches “Separate ROC curves were created for boys and girls since there are established gender differences in aerobic capacity and metabolic syndrome. The ROC plots were generated within a customized SAS macro, and supplemental output yielded values reflecting the total AUC, the key diagnostic indicator in ROC curve analyses. Associated output files were examined to determine the relative changes in sensitivity and specificity for different z-score values. The point generally selected as the optimal threshold is the point that is closest to the upper-left part of the ROC plot. This point maximizes the sum of Se and Sp and can be considered to produce the best overall classification agreement.” Welk P. 113, col. 1. Based on the loose usage of “concatenate” in the Specification, that term is understood as referring to any connection.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Welk because application of the technique of setting different cutoff values associated with a high combination of sensitivity and specificity at the knee in the ROC curve separately for each to optimize model outputs for accuracy with respect to a given individual.
4. The method as claimed in claim 3, wherein the male-related re-scaler and the female-related re-scaler are connected to the average model in parallel. (See rejection of claim 3. As explained in the motivation to combine in the rejection of claim 3, one of ordinary skill in the art would understand the benefit of applying different cutoffs for each gender to the model outputs, as a way of making the model more accurate for a given individual.
6. The method as claimed in claim 3, wherein a comparison between a common threshold and an output of one of the male-related re-scaler and (See rejection of claim 3.) the female-related re-scaler is made for determining whether the subject has nephrolithiasis. (See rejection of claims 1 and 3.)
Claims 7-10 and 12-19 are rejected under 35 U.S.C. 103 as being unpatentable over Malmagro, Cunningham, and Chen.
7. The method as claimed in claim 1, wherein each of the training data sets includes a gender indicator that indicates gender of the respective one of the patients, age of the respective one of the patients, and an estimated glomerular filtration rate (eGFR) that is related to the respective one of the patients. (Malmagro teaches “Particularly, both for detection of kidney stones and identification of their chemical composition, the present invention provides artificial neural network analysis by using a certain and specific number of metabolic markers in urine which are as follows: Urine volume, pH, creatinine levels, uric acid, urea, sodium, potassium, chloride, citrate, calcium, oxalate, magnesium, phosphate and proteinuria.” Malmagro P. 2. Malmagro does not teach the use of gender, age, or eGFR.
Chen teaches: “This ML-based model provides a convenient and reliable method for diagnosing urolithiasis.” Chen Abstract. “We found eight variables to be related to the development of uric acid nephrolithiasis. In this study, we used these eight variables to build a diagnostic model based on machine learning methodologies.” Chen p. 4. “In summary, this ML-based model provides a simple, convenient and reliable method for the diagnosis of uric acid stones. With only eight easily-obtained clinical parameters, this ML-based model can distinguish pure uric acid stones from other stones without advanced equipment before urolithiasis treatment. This diagnostic approach may help optimize timely treatment strategies for urolithiasis.” Chen p. 5. “A model for predicting uric acid stones in nephrolithiasis was developed using machine learning methodologies. The mathematical representation of this model can be expressed as follows:
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where represents the input vector of the eight decision variables, and the model output, which takes a value between 0 and 1. . . . The decision variables x1;ccc;X8 carry the values of “gender,” “age,” “eGFR,” “urine pH,” “BMI,” “DM,” “gout,” and “bacteriuria,” respectively.” Chen P. 6.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Chen because the use of these additional parameters allows the model to find additional correlations, potentially improving accuracy.)
8. The method as claimed in claim 7, wherein each of the training data sets further includes one of a number of red blood cells in a urine sample of the respective one of the patients, a blood creatinine concentration that is related to the respective one of the patients, a body mass index (BMI) that is related to the respective one of the patients, a value of urine pH that is related to the respective one of the patients, a gout indicator that indicates whether the respective one of the patients was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the respective one of the patients was ever diagnosed with DM, a bacteriuria indicator that indicates whether the respective one of the patients was ever diagnosed with bacteriuria, and any combination thereof. (See rejection of claim 7.)
9. The method as claimed in claim 1, wherein each of the training data sets includes a gender indicator that indicates gender of the respective one of the patients, age of the respective one of the patients, and a blood creatinine concentration that is related to the respective one of the patients. (Note that the Provisional application does not input the creatinine concentration into the model during training or otherwise. This first appears in paragraph 15 of the Specification filed with the utility application. See rejection of claim 7. Chen teaches that the eGFR is calculated using Scr (creatinine), but does not teach that the Scr or creatinine concentration is included in the training data. See Chen p. 6.
10. The method as claimed in claim 9, wherein each of the training data sets further includes one of a number of red blood cells in a urine sample of the respective one of the patients, an estimated glomerular filtration rate (eGFR) that is related to the respective one of the patients, a body mass index (BMI) that is related to the respective one of the patients, a value of urine pH that is related to the respective one of the patients, a gout indicator that indicates whether the respective one of the patients was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the respective one of the patients was ever diagnosed with DM, a bacteriuria indicator that indicates whether the respective one of the patients was ever diagnosed with bacteriuria, and any combination thereof. (See rejection of claim 7.)
12. The method as claimed in claim 11, wherein each of the training data sets further includes one of an estimated glomerular filtration rate (eGFR) that is related to the respective one of the patients, a blood creatinine concentration that is related to the respective one of the patients, a body mass index (BMI) that is related to the respective one of the patients, a value of urine pH that is related to the respective one of the patients, a gout indicator that indicates whether the respective one of the patients was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the respective one of the patients was ever diagnosed with DM, a bacteriuria indicator that indicates whether the respective one of the patients was ever diagnosed with bacteriuria, and any combination thereof. (See rejection of claim 7.)
13. The method as claimed in claim 1, further comprising feeding an input variable set into the prediction model so as to obtain an output indicating whether the subject has nephrolithiasis, the input variable set being related to the subject. (See rejection of claim 7. Note that the model takes the 8 listed parameters as inputs.)
14. The method as claimed in claim 13, wherein the input variable set includes a gender indicator that indicates gender of the subject, age of the subject, and an estimated glomerular filtration rate (eGFR) that is related to the subject. (See rejection of claim 7.)
15. The method as claimed in claim 14, wherein the input variable set further includes one of a number of red blood cells in a urine sample of the subject, a blood creatinine concentration that is related to the subject, a body mass index (BMI) that is related to the subject, a value of urine pH that is related to the subject, a gout indicator that indicates whether the subject was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the subject was ever diagnosed with DM, a bacteriuria indicator that indicates whether the subject was ever diagnosed with bacteriuria, and any combination thereof. (See rejection of claim 8.)
16. The method as claimed in claim 13, wherein the input variable set includes a gender indicator that indicates gender of the subject, age of the subject, and a blood creatinine concentration that is related to the subject. (See rejection of claim 9.)
17. The method as claimed in claim 16, wherein the input variable set further includes one of a number of red blood cells in a urine sample of the subject, an estimated glomerular filtration rate (eGFR) that is related to the subject, a body mass index (BMI) that is related to the subject, a value of urine pH that is related to the subject, a gout indicator that indicates whether the subject was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the subject was ever diagnosed with DM, a bacteriuria indicator that indicates whether the subject was ever diagnosed with bacteriuria, and any combination thereof. (See rejection of claim 10.)
18. The method as claimed in claim 13, wherein the input variable set includes a gender indicator that indicates gender of the subject, age of the subject, and a number of red blood cells in a urine sample of the subject. (See rejection of claim 11.)
19. The method as claimed in claim 18, wherein the input variable set further includes one of a blood creatinine concentration that is related to the subject, an estimated glomerular filtration rate (eGFR) that is related to the subject, a body mass index (BMI) that is related to the subject, a value of urine pH that is related to the subject, a gout indicator that indicates whether the subject was ever diagnosed with gout, a diabetes mellitus (DM) indicator that indicates whether the subject was ever diagnosed with DM, a bacteriuria indicator that indicates whether the subject was ever diagnosed with bacteriuria, and any combination thereof. (See rejection of claim 12.)
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Malmagro, Cunningham, Chen, and Mayo (Blood in urine (hematuria), 2021)
11. The method as claimed in claim 1, wherein each of the training data sets includes a gender indicator that indicates gender of the respective one of the patients, age of the respective one of the patients, and a number of red blood cells in a urine sample of the respective one of the patients. (See rejection of claim 7.
The previously cited art does not teach use of the number of red blood cells in a urine sample. Mayo teaches “In hematuria, your kidneys — or other parts of your urinary tract — allow blood cells to leak into urine. Various problems can cause this leakage, including: A bladder or kidney stone. The minerals in concentrated urine sometimes form crystals on the walls of your kidneys or bladder. Over time, the crystals can become small, hard stones. The stones are generally painless, so you probably won't know you have them unless they cause a blockage or are being passed. Then there's usually no mistaking the symptoms — kidney stones, especially, can cause excruciating pain. Bladder or kidney stones can also cause both gross and microscopic bleeding.” Mayo P. 3.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teaching of Malmagro to include blood in urine as a parameter, because blood in urine is well known to be correlated with stones being lodged in kidneys.)
Conclusion
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PAUL M. KNIGHTPrimary ExaminerArt Unit 2148
/PAUL M KNIGHT/
Primary Examiner, Art Unit 2148
1 This distinction between claims which read on math and claims which recite an abstract idea is based on official USPTO Guidance. The 2019 Subject Matter Eligibility (SME) Examples instructs examiners that a claim reciting “training the neural network” where the background describes training as “using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network” “does not recite any mathematical relationships, formulas, or calculations.” See 2019 SME Example 39, PP. 8-9 (emphasis added). In this example, the plain meaning of “training the neural network” read in light of the disclosure reads on backpropagation using the gradient of a mathematical loss function. See MPEP § 2111.01. In contrast, the 2024 SME Examples instructs examiners that a claim reciting “training, by the computer, the ANN . . . wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” does recite an abstract idea because “[t]he plain meaning of [backpropagation algorithm and gradient descent algorithm] are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.” 2024 PEG Example 47, PP. 4-6. The Memorandum of August 4, 2025; Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, P. 3 also directs examiners that “training the neural network” recited in Example 39 merely “involve[s] . . . mathematical concepts” and contrasts claim 2 of example 47 as “referring to [specific] mathematical calculations by name[.]” (Emphasis added.)
2 “For instance, the claims in Diehr . . . clearly stated a mathematical equation . . . and the claims in Mayo . . . clearly stated laws of nature . . . such that the claims ‘set forth’ an identifiable judicial exception. Alternatively, the claims in Alice Corp. . . . described the concept of intermediated settlement without ever explicitly using the words ‘intermediated’ or ‘settlement.’” MPEP § 2106.04(II)(A).
3 “By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. . . . If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One.” MPEP § 2106.04(a). See also MPEP 2104(a)(2).
4 Step 2A prongs one and two are evaluated individually, consistent with the framework in the MPEP. Evaluation of relationships between abstract ideas and additional elements in one location promotes clarity of the record.
5 “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. . . . It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” MPEP 2106.04(d)(1). See also Koninklijke KPN N.V. v. Gemalto M2M GmbH, 942 F.3d 1143, 1150-1152 (Fed. Cir. 2019).
6 “In step S03, the preliminary models are averaged to obtain an average model. It is worth to note that such approach is commonly known as ensemble averaging. Since model averaging has been well known to one skilled in the relevant art, detailed explanation of the same is omitted herein for the sake of brevity.” Spec. ¶23. See also Cunningham P. 6 explaining Bagging as one of four categories of generic ensemble models.
7 See MPEP § 2106.05(d)(II) listing operations including “receiving or transmitting data,” “storing and retrieving data in memory,” and “performing repetitive calculations” as WURC. “The claims at issue do not require any nonconventional computer, network, or display components, or even a non-conventional and non-generic arrangement of known, conventional pieces, but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (emphasis added, internal quotes omitted).
8 “But ‘[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of 'well-understood, routine, [and] conventional activities previously known to the industry.’ Content Extraction, 776 F.3d at 1347-48 (quoting Alice, 134 S. Ct at 2359). Here, the server simply receives data, ‘extract[s] classification information . . . from the received data,’ and ‘stor[es] the digital images . . . taking into consideration the classification information.’ See ‘295 patent, col. 10 ll. 1-17 (Claim 17). . . . These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (‘Nearly every computer will include a 'communications controller' and a 'data storage unit' capable of performing the basic calculation, storage, and transmission functions required by the method claims.’); Content Extraction, 776 F.3d at 1345, 1348 (‘storing information’ into memory, and using a computer to ‘translate the shapes on a physical page into typeface characters,’ insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324-25 (generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’ fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a ‘database’ and ‘a communication medium’ ‘are all generic computer elements’); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (‘That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.’).” TLI Commc'ns LLC v. AV Auto., LLC, 823 F.3d 607, 614 (Fed. Cir. 2016), Emphasis Added.
9 “The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph)[.]” MPEP § 2106.05(d)(I).
10 “Similarly, claim elements or combinations of claim elements that are routine, conventional or well-understood cannot transform the claims. (Citing BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290-1291 (Fed. Cir. 2018)). When the patent's specification ‘describes the components and features listed in the claims generically,’ it ‘support[s] the conclusion that these components and features are conventional.’ Weisner v. Google LLC, 51 F.4th 1073, 1083-84 (Fed. Cir. 2022); see also Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1357-58 (Fed. Cir. 2024).” Broadband iTV, Inc. v. Amazon.com, Inc., 113 F.4th 1359 (Fed. Cir. 2024)
11 “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” MPEP § 2106.05(a).
12 “Referring to Figure 3, the output layer consists of two concatenated components respectively for batch normalization and affine transformation. Since batch normalization, affine transformation and activation have been well known to one skilled in the relevant art, detailed explanation of the same is omitted herein for the sake of brevity.” Spec. ¶22. See also Doshi PP. 1-2 (“Batch Norm is an essential part of the toolkit of the modern deep learning practitioner. Soon after it was introduced in [Batch Normalization: Accelerating Deep Network Training by
Reducing Internal Covariate Shift, Ioffe 2015], it was recognized as being transformational in creating deeper neural networks that could be trained faster.”) and P. 8 (showing the batch normalization, affine transformation, and activation layers.)
13 “Since plotting an ROC curve and determining a cut-off threshold based on a Youden’s index of the ROC curve have been well known to one skilled in the relevant art, detailed explanation of the same is omitted herein for the sake of brevity.” Spec. ¶25.
14 “In step S02, a number N of preliminary models are obtained by using N-fold cross-validation protocol based on the preliminary groups. Since the N-fold cross-validation protocol has been well known to one skilled in the relevant art, detailed explanation of the same is omitted herein for the sake of brevity and only a brief explanation is provided herein.” Spec. ¶20.
Prosecution Insights