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 .
Remarks
In response to communications sent April 28, 2023, claim(s) 14-26 are pending in this application; of these claims 1, 24, 25, and 26 are in independent form.
Response to Amendment
The preliminary amendments filed April 28, 2023 are acknowledged and have been entered into the record.
Drawings
The drawing(s) filed on April 28, 2023 are accepted by the Examiner.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: The concept of “autoantibodies” and “tumor markers” are recited in claim 19 of the instant application and has support in claim 4 of the priority document. However, the concepts of “autoantibodies” and “tumor markers” are not recited in the specification.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: April 28, 2023 and September 6, 2024.
Claim Objections
Claims 14-26 objected to because of the following informalities: Claims 14 and 24-26 recite the word “at” in duplicate. Claims 15-23 are objected to because they depend from the base claims that are objected to. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“the device configured to… provide…” in claim 25.
“the device configured to… ascertain…” in claim 25.
“the device configured to… determine…” in claim 25.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 25 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
1. Claim limitation “device configured to provide at least one predicted value for at least one medical laboratory variable for use in a medical laboratory value analysis” in claim 25 has been evaluated under the three-prong test set forth in MPEP § 2181, subsection I, but the result is inconclusive. Thus, it is unclear whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it is unclear whether the preamble is reciting a means-plus-function limitation or whether the preamble is merely stating the intended use of the claimed invention (see MPEP § 2181.I.A). The boundaries of this claim limitation are ambiguous; therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
In response to this rejection, applicant must clarify whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Mere assertion regarding applicant’s intent to invoke or not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is insufficient. Applicant may:
(a) Amend the claim to clearly invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by reciting “means” or a generic placeholder for means, or by reciting “step.” The “means,” generic placeholder, or “step” must be modified by functional language, and must not be modified by sufficient structure, material, or acts for performing the claimed function;
(b) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, should apply because the claim limitation recites a function to be performed and does not recite sufficient structure, material, or acts to perform that function;
(c) Amend the claim to clearly avoid invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by deleting the function or by reciting sufficient structure, material or acts to perform the recited function; or
(d) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply because the limitation does not recite a function or does recite a function along with sufficient structure, material or acts to perform that function.
2. Claim limitations “device configured to… provide…”, “device configured to… ascertain…”, and ““device configured to… determine…”” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For example, the specification only mentions the term “device” once and Figure 1 does not provide clarification of the structure and corresponding algorithms. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 14-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process (and claim 23 recites a mathematical calculation as well). This judicial exception is not integrated into a practical application because the additional limitations are limitations to the abstract idea itself, do not meet the specificity of treatment (claim 17), are mathematics (claim 23), or are mere instructions to apply it using a general purpose computer . The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an abstract idea on a general-purpose computer are not additional elements that are significantly more than the judicial exception (Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 216, 110 USPQ2d 1976, 1980 (2014) (citing Ass'n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 589, 106 USPQ2d 1972, 1979 (2013)).
See commentary below for further clarification of the grounds for rejection:
14. A computer-implemented method for providing at least one predicted value for at least one medical laboratory variable for use in a medical laboratory value analysis, comprising the following steps:
providing at least one laboratory value progression which specifies a progression of historical laboratory values of the at least one laboratory variable at at least two historical points in time (mental process of specifying data according to a particular format of a progression);
ascertaining at least one laboratory variable feature for each of the at least one laboratory variable from the corresponding laboratory value progression (mental process capable of being performed in the human mind with simple aids such as paper and pencil);
determining the at least one predicted value at a predetermined prediction time based on a trained, data-based prediction model and based on the at least one laboratory variable feature for each of the at least one laboratory value progression (mental process capable of being performed in the human mind with simple aids such as paper and pencil; note that no particular computerized model is claimed).
15. The method as recited in claim 14, wherein the prediction model is trained to provide, based on the at least one laboratory variable feature, at least one predicted laboratory value at the predetermined prediction time as the at least one predicted value (limitation to the mental process capable of being performed in the human mind with simple aids such as paper and pencil; note that the nature of the model is not specified to be an particular computerized algorithm).
16. The method as recited in claim 15, wherein a progression of predicted values is ascertained at a plurality of prediction times to determine a point in time at which the predicted value exceeds a predetermined limit value, wherein the point in time determines a point in time for a medical intervention (limitation to the mental process capable of being performed in the human mind with simple aids such as paper and pencil).
17. The method as recited in claim 15, where in the medical intervention includes administering medication (this intervention is not specific nor integrated with the mental process; therefore, it does not meet the standard for eligibility from the case Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117, 1135-36, 126 USPQ2d 1266, 1281 (Fed. Cir. 2018); “administering medication”, at a broad level of generality, is well-understood, routine, and conventional according to the case Mayo Collaborative Servs. v. Prometheus Labs., 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012)).
18. The method as recited in claim 14, wherein the prediction model is trained to provide, based on the at least one laboratory variable feature, at least one predicted quantile value at the predetermined prediction time as the at least one predicted value, wherein the quantile value specifies an upper or lower limit value of a reference range for the at least one laboratory variable at the prediction time, wherein a result of a comparison of a current laboratory value of the at least one laboratory variable with the corresponding quantile value at the current point in time is indicated as the prediction time (limitation to the mental process capable of being performed in the human mind with simple aids such as paper and pencil; note that the nature of the model is not specified to be an particular computerized algorithm).
19. The method as recited in claim 14, wherein the at least one laboratory variable includes at least one variable for hematology, or clinical chemistry, or endocrinology, or blood gas analysis, or autoantibodies, or tumor markers, or urine diagnostics (limitation to the mental process capable of being performed in the human mind with simple aids such as paper and pencil).
20. The method as recited in claim 14, wherein the laboratory variable features for each of the laboratory variables include one or more of the following features (limitation to the mental process capable of being performed in the human mind with simple aids such as paper and pencil, for the variables being contemplated):
a minimum value of the historical laboratory values,
different quantile values including a first and a third quartile and a median of the historical laboratory values,
a mean value of the historical laboratory values,
a maximum value of the historical laboratory values,
a standard deviation of the historical laboratory values,
a length of time by which a last-captured historical laboratory value is behind a current point in time,
a length of time by which a penultimate historical laboratory value is behind the current point in time,
a length of time by which an oldest laboratory value is behind the current point in time,
a mean value of time intervals between capturing times of the historical laboratory values,
a most recent historical laboratory value,
a second most recent historical laboratory value,
a value of a last gradient between the second most recent and the most recent historical laboratory value,
a length of time until a first outlier of the historical laboratory values,
a point in time for a most recent outlier of the historical laboratory values,
a number of historical laboratory values classified as outliers,
a maximum rise between two successively captured historical laboratory values,
a minimum drop between two successive historical laboratory values,
an estimated linear offset of the historical laboratory values,
an estimated linear increase in the historical laboratory values,
an estimated linear prediction of the historical laboratory values,
a number of historical laboratory values.
21. The method as recited in claim 14, wherein the prediction model is additionally configured to take into account, in addition to the at least one laboratory variable feature: (i) patient data, including as age, and/or gender, and/or BMI, and and/or other biometric data, including height and/or weight, and/or (ii) a diagnosis, and/or a finding, and/or a treatment, and/or (iii) a medication and/or a medication administration regime (limitation to the mental process capable of being performed in the human mind with simple aids such as paper and pencil).
22. The method as recited in claim 14, wherein at least one of the at least one laboratory variable feature is dependent on the predetermined prediction time (limitation to the mental process capable of being performed in the human mind with simple aids such as paper and pencil).
23. The method as recited in claim 14, wherein the prediction model includes a deep neural network, or a convolutional neural network, or a recurrent neural network, or a support vector machine, or a random-forest model, or a hidden Markov chain model, or a generalized linear model (mathematical calculation).
24. A method for training a prediction model, comprising the following steps:
providing at least one laboratory value progression of at least one laboratory variable, wherein each of the at least one laboratory value progression specifies a progression of historical laboratory values of the at least one laboratory variable at at least three historical points in time (mental process of specifying data according to a particular format of a progression);
ascertaining at least one laboratory variable feature for each of the at least one laboratory variable from the corresponding at least one laboratory value progression before a label time (mental process capable of being performed in the human mind with simple aids such as paper and pencil);
compiling training data sets by forming each training data set from the at least one laboratory variable feature for the at least one laboratory variable and the laboratory value of the at least one laboratory variable at the label time as the label (mental process of organizing information);
training the data-based prediction model on the basis of the training data sets (an additional element, beyond the abstract idea; the the ).
25. A device configured to provide at least one predicted value for at least one medical laboratory variable for use in a medical laboratory value analysis, the device configured to:
provide at least one laboratory value progression which specifies a progression of historical laboratory values of the at least one laboratory variable at at least two historical points in time (mental process of specifying data according to a particular format of a progression; performable by “applying it” on a general-puprose computer);
ascertain at least one laboratory variable feature for each of the at least one laboratory variable from the corresponding laboratory value progression (mental process capable of being performed in the human mind or “applying it” on a general-purpose computer);
determine the at least one predicted value at a predetermined prediction time based on a trained, data-based prediction model and based on the at least one laboratory variable feature for each of the at least one laboratory value progression (mental process capable of being performed in the human mind with simple aids such as paper and pencil; note that no particular computerized model is claimed).
26. A non-transitory machine-readable storage medium on which are stored commands for providing at least one predicted value for at least one medical laboratory variable for use in a medical laboratory value analysis, the commands, when executed by a computer, causing the computer to perform the following steps:
providing at least one laboratory value progression which specifies a progression of historical laboratory values of the at least one laboratory variable at at least two historical points in time (mental process of specifying data according to a particular format of a progression);
ascertaining at least one laboratory variable feature for each of the at least one laboratory variable from the corresponding laboratory value progression (mental process capable of being performed in the human mind with simple aids such as paper and pencil);
determining the at least one predicted value at a predetermined prediction time based on a trained, data-based prediction model and based on the at least one laboratory variable feature for each of the at least one laboratory value progression (mental process capable of being performed in the human mind with simple aids such as paper and pencil; note that no particular computerized model is claimed).
Claim Rejections - 35 USC § 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 14-26 is/are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by US 10,490,309 B1 (“McNair”).
As to claim 14, McNair teaches a computer-implemented method for providing at least one predicted value (McNair col 15 lines 26-51: extrapolating using time series forecasting) for at least one medical laboratory variable for use in a medical laboratory value analysis (McNair Col 17 lines 38-56: a physiological or clinical variable to forecast), comprising the following steps:
providing at least one laboratory value progression which specifies a progression of historical laboratory values of the at least one laboratory variable at at least two historical points in time (McNair col 15 lines 26-51: a timeseries progression);
ascertaining at least one laboratory variable feature for each of the at least one laboratory variable from the corresponding laboratory value progression (McNair Col 15 lines 26-51: modeling based on the last values of the time series to extrapolate into the future; the last value is a feature of the laboratory values according to Applicant’s dependent claim);
determining the at least one predicted value at a predetermined prediction time based on a trained, data-based prediction model and based on the at least one laboratory variable feature for each of the at least one laboratory value progression (McNair column 13 line 63 to column 14 line 16: determining a prediction based on an optimization model; the Examiner assumes that an optimization model is a type of trained model, since the model parameters are what is trained; the forecast value is based on the success factors of the time series data).
As to claim 15, McNair teaches the method as recited in claim 14, wherein the prediction model is trained to provide, based on the at least one laboratory variable feature, at least one predicted laboratory value at the predetermined prediction time as the at least one predicted value (McNair col 15 lines 26-51: extrapolating using time series forecasting).
As to claim 16, McNair teaches the method as recited in claim 15, wherein a progression of predicted values is ascertained at a plurality of prediction times to determine a point in time at which the predicted value exceeds a predetermined limit value, wherein the point in time determines a point in time for a medical intervention (McNair col 2 lines 32-40: predicted physiological parameter at a future particular time; Col 19 lines 39-65: physiological parameters are compared to critical ranges, which provides evidence of exceeding a predetermined limit value).
As to claim 17, McNair teaches the method as recited in claim 15, where in the medical intervention includes administering medication (McNair Col 19 line 66 to Col 20 line 10: alerting a caregiver instead of discharging; it is at-once envisaged that caregivers administer medications as one of a set of possible caregiver actions).
As to claim 18, McNair teaches the method as recited in claim 14, wherein the prediction model is trained to provide, based on the at least one laboratory variable feature, at least one predicted quantile value at the predetermined prediction time as the at least one predicted value, wherein the quantile value specifies an upper or lower limit value of a reference range for the at least one laboratory variable at the prediction time, wherein a result of a comparison of a current laboratory value of the at least one laboratory variable with the corresponding quantile value at the current point in time is indicated as the prediction time (McNair col 2 lines 32-40: predicted physiological parameter at a future particular time; Col 19 lines 39-65: physiological parameters are compared to critical ranges, which provides evidence of exceeding a predetermined limit value; Col 17 lines 1-29 provides evidence that percentiles are used, such as percentile limits 12%-22%).
As to claim 19, McNair teaches the method as recited in claim 14, wherein the at least one laboratory variable includes at least one variable for hematology, or clinical chemistry, or endocrinology, or blood gas analysis, or autoantibodies, or tumor markers, or urine diagnostics (McNair Col 4, line 61 to Col 5 line 6: laboratory test inclusion in an index or score to predict deterioration).
As to claim 20, McNair teaches the method as recited in claim 14, wherein the laboratory variable features for each of the laboratory variables include one or more of the following features:
a minimum value of the historical laboratory values,
different quantile values including a first and a third quartile and a median of the historical laboratory values,
a mean value of the historical laboratory values,
a maximum value of the historical laboratory values,
a standard deviation of the historical laboratory values,
a length of time by which a last-captured historical laboratory value is behind a current point in time,
a length of time by which a penultimate historical laboratory value is behind the current point in time,
a length of time by which an oldest laboratory value is behind the current point in time,
a mean value of time intervals between capturing times of the historical laboratory values,
a most recent historical laboratory value,
a second most recent historical laboratory value (McNair Col 15 lines 26-51: modeling based on the last values of the time series to extrapolate into the future; McNair Col 4, line 61 to Col 5 line 6 provides evidence that these may be laboratory tests),
a value of a last gradient between the second most recent and the most recent historical laboratory value,
a length of time until a first outlier of the historical laboratory values,
a point in time for a most recent outlier of the historical laboratory values,
a number of historical laboratory values classified as outliers,
a maximum rise between two successively captured historical laboratory values,
a minimum drop between two successive historical laboratory values,
an estimated linear offset of the historical laboratory values,
an estimated linear increase in the historical laboratory values,
an estimated linear prediction of the historical laboratory values,
a number of historical laboratory values (these elements are claimed in the alterative and do not all need to be mapped).
As to claim 21, McNair teaches the method as recited in claim 14, wherein the prediction model is additionally configured to take into account, in addition to the at least one laboratory variable feature: (i) patient data, including as age, and/or gender, and/or BMI, and and/or (the broadest reasonable interpretation of “and/or” is “or”) other biometric data, including height and/or weight, and/or (the broadest reasonable interpretation of “and/or” is “or”) (ii) a diagnosis, and/or a finding, and/or a treatment (McNair Para Col 10 line 63- Col 11 line 6: electronic health records), and/or (the broadest reasonable interpretation of “and/or” is “or”) (iii) a medication and/or a medication administration regime (these elements are claimed in the alternative and do not all need to be mapped).
As to claim 22, McNair teaches the method as recited in claim 14, wherein at least one of the at least one laboratory variable feature is dependent on the predetermined prediction time (McNair column 13 line 63 to column 14 line 16: determining a prediction based on an optimization model that is based on time series predictions).
As to claim 23, McNair teaches the method as recited in claim 14, wherein the prediction model includes a deep neural network, or a convolutional neural network, or a recurrent neural network, or a support vector machine, or a random-forest model, or a hidden Markov chain model, or a generalized linear model (McNair Col 15 lines 26-51: a neural network model).
As to claim 24, McNair teaches a method for training a prediction model (McNair col 15 lines 26-51: extrapolating using time series forecasting; McNair Col 17 lines 38-56: a physiological or clinical variable to forecast), comprising the following steps:
providing at least one laboratory value progression of at least one laboratory variable, wherein each of the at least one laboratory value progression specifies a progression of historical laboratory values of the at least one laboratory variable at at least three historical points in time (McNair col 15 lines 26-51: a timeseries progression);
ascertaining at least one laboratory variable feature for each of the at least one laboratory variable from the corresponding at least one laboratory value progression before a label time (McNair Col 15 lines 26-51: modeling based on the last values of the time series to extrapolate into the future; the last value is a feature of the laboratory values according to Applicant’s dependent claim);
compiling training data sets by forming each training data set from the at least one laboratory variable feature for the at least one laboratory variable and the laboratory value of the at least one laboratory variable at the label time as the label (McNair Col 18 lines 27-40: compiling “sufficient” measurements of time series data for training);
training the data-based prediction model on the basis of the training data sets (McNair column 13 line 63 to column 14 line 16: determining a prediction based on an optimization model; the Examiner assumes that an optimization model is a type of trained model, since the model parameters are what is trained; the forecast value is based on the success factors of the time series data).
As to claim 25, McNair teaches a device configured to provide at least one predicted value (McNair col 15 lines 26-51: extrapolating using time series forecasting) for at least one medical laboratory variable for use in a medical laboratory value analysis (McNair Col 17 lines 38-56: a physiological or clinical variable to forecast), the device configured to:
provide at least one laboratory value progression which specifies a progression of historical laboratory values of the at least one laboratory variable at at least two historical points in time (McNair col 15 lines 26-51: a timeseries progression);
ascertain at least one laboratory variable feature for each of the at least one laboratory variable from the corresponding laboratory value progression (McNair Col 15 lines 26-51: modeling based on the last values of the time series to extrapolate into the future; the last value is a feature of the laboratory values according to Applicant’s dependent claim);
determine the at least one predicted value at a predetermined prediction time based on a trained, data-based prediction model and based on the at least one laboratory variable feature for each of the at least one laboratory value progression (McNair column 13 line 63 to column 14 line 16: determining a prediction based on an optimization model; the Examiner assumes that an optimization model is a type of trained model, since the model parameters are what is trained; the forecast value is based on the success factors of the time series data).
As to claim 26, McNair teaches a non-transitory machine-readable storage medium on which are stored commands for providing at least one predicted value (McNair col 15 lines 26-51: extrapolating using time series forecasting) for at least one medical laboratory variable for use in a medical laboratory value analysis (McNair Col 17 lines 38-56: a physiological or clinical variable to forecast), the commands, when executed by a computer, causing the computer to perform the following steps:
providing at least one laboratory value progression which specifies a progression of historical laboratory values of the at least one laboratory variable at at least two historical points in time (McNair col 15 lines 26-51: a timeseries progression);
ascertaining at least one laboratory variable feature for each of the at least one laboratory variable from the corresponding laboratory value progression (McNair Col 15 lines 26-51: modeling based on the last values of the time series to extrapolate into the future; the last value is a feature of the laboratory values according to Applicant’s dependent claim) ;
determining the at least one predicted value at a predetermined prediction time based on a trained, data-based prediction model and based on the at least one laboratory variable feature for each of the at least one laboratory value progression (McNair column 13 line 63 to column 14 line 16: determining a prediction based on an optimization model; the Examiner assumes that an optimization model is a type of trained model, since the model parameters are what is trained; the forecast value is based on the success factors of the time series data).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10092685 B2 pertinence: extrapolating for parameters of a patient
Poole, Sarah, Lee Frederick Schroeder, and Nigam Shah. "An unsupervised learning method to identify reference intervals from a clinical database." Journal of biomedical informatics 59 (2016): 276-284.
Rappoport, Nadav, et al. "Comparing ethnicity-specific reference intervals for clinical laboratory tests from EHR data." The journal of applied laboratory medicine 3.3 (2018): 366-377.
US-20030018633-A1 pertinence: reference interval estimator
US-20080294350-A1 pertinence: reference intervals
US-20140236491-A1 pertinence: indirect determination of reference intervals; reference intervals for analytes
US-20150025808-A1 pertinence: individualized reference ranges
US-20190214147-A1 pertinence: personalized reference interval using profiles
US-10825102-B2 pertinence: personalized reference interval using profiles
US-20210121125-A1 pertinence: reference range model
WO-2017068146-A1 pertinence: indirect determination of reference intervals
WO-2022081103-A1 pertinence: arithmetic approach
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 August 31, 2026
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