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 .
Claims Status
Claims 1-15 are currently pending and under examination herein.
Claims 1-15 are rejected.
Priority
The instant application is a National Stage of International Application No. PCT/EP2022/062760 filed 11 May 2022 and claims foreign priority to Europe Patent Application No. 21173588, filed 12 May 2021. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. As such, the effective filing date of claims 1-15 is 12 May 2021.
For any intervening prior art before the international filing date but after foreign priority date, applicant cannot rely upon the certified copy of the foreign priority application to overcome a 35 U.S.C. §102 or §103 rejection because a translation of said application has not been made of record in accordance with 37 CFR 1.55. When an English language translation of a non-English language foreign application is required, the translation must be that of the certified copy (of the foreign application as filed) submitted together with a statement that the translation of the certified copy is accurate. See MPEP §§ 215 and 216. Applicant may provide an accurate English translation of the certified copy of the foreign application to perfect priority.
Information Disclosure Statement
The Information Disclosure Statements filed on 08 Nov 2023 are compliant with the provisions of 37 CFR 1.97 and has/have been considered. A signed copy of list of references cited from the/each IDS is included with this Office Action.
The information disclosure statements filed 13 Jun 2026 and 29 Jun 2026 fail to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because Patent references JP2017527399, JP2019511057, CN 109475602, JP20110536815, CN11244789 and WO2019035639 have no English translation or a concise explanation of the relevance has not been provided.
See MPEP 609.01 (B)(3) cited below:
(3) For non-English documents that are cited, the following must be provided:
(a) A concise explanation of the relevance, as it is presently understood by the individual designated in 37 CFR 1.56(c) most knowledgeable about the content of the information, unless a complete translation is provided; and/or
(b) A written English language translation of a non-English language document, or portion thereof, if it is within the possession, custody or control of, or is readily available to any individual designated in 37 CFR 1.56(c).
It has been placed in the application file, but the information referred to therein has not
been considered as to the merits. Applicant is advised that the date of any re-submission of any
item of information contained in this information disclosure statement or the submission of any
missing element(s) will be the date of submission for purposes of determining compliance with
the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Interpretation
Any reference to applicant’s specification will be in reference to to the PgPub document US 2024/0242836 A1.
For examination purposes, a value will be considered as “missing” if the values are beyond reasonable thresholds as is relevant in the art and/or specification.
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 1 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. Claim 1 recites “wherein the at least one missing value in the medical data set is a value that is beyond a predetermined threshold.” A value cannot be missing and be beyond a threshold.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon without significantly more.
Step 2A, Prong 1 Considerations
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea/law of nature/natural phenomenon:
Claim 1 recites receiving at least one medical dataset, extracting features from the dataset, imputing at least one missing value in the dataset, and determining an output parameter.
Claim 2 recites normalizing features from the dataset.
Claim 3 recites determining a missing value and a value preceding the missing value from the medical dataset, and substituting the missing value with the preceding value.
Claim 6 recites determining if the probability value exceeds a threshold.
Claim 7 recites identifying a number of generated warnings and determining if the generated warnings exceed a second threshold.
Claim 9 recites receiving a medical dataset, extracting one or more features, receiving the machine learning model, determining a probability value, receiving sepsis data and adjusting the model based on an outcome.
Claim 11 recites imputing at least one missing value in the sepsis data and normalizing the one or more features from the sepsis data.
Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 1-15 recite performing some aspects of the analysis by “ the processing unit,” “sepsis determination device,” “computer program product” or “computer readable medium,” there are no additional limitations that indicate that this analysis engine requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-15 recite an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1 : YES).
Step 2A, Prong 2 Considerations
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a computer medium and/or use of a hardware processor. Specifically, the claims recite the following additional elements:
Claim 1 recites generating an alert.
Claim 6 recited generating a warning.
There are no limitations that indicate that the claimed system, method or storage medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The steps for processing medical data to determine onset of sepsis using computational means are well-understood, routine and conventional functions such as performing repetitive calculations and mere data extracting (see MPEP 2106.05(d)(2)). As such, claims 1-15 are directed to an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 2 : NO).
Step 2B (MPEP 2106.05.A i-vi)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite the following additional elements:
Claim 13 recites a sepsis determination device.
Claim 14 recites a computer program product comprising machine readable instructions.
Claim 15 recites a computer readable medium.
As discussed above, there are no additional limitations to indicate that the claimed system and method requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The limitation for use of a computer-implemented system, one or more hardware processes, or use of a non-transitory computer-readable storage system amount to mere instructions to apply the judicial exception in a generic way because the executable steps are so generically recited. MPEP 2106.05(d) discloses that a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry are not enough to qualify as “significantly more” when recited in a claim with a judicial exception. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B : No). As such, claims 1-15 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Quinn (US 2021/0052218 A1).
Claim 1 1st element recites a method of determining an onset of sepsis in a patient, the method (200) comprising: receiving, by a processing unit, at least one medical dataset associated with the patient, wherein the medical dataset comprises a plurality of features. Quinn discloses a method of determining systems and methods to collect and analyze vital sign information to predict a likelihood of the subject having an adverse health condition, that condition being sepsis (see ¶s 0005, 0006 and 0009).
Claim 1 2nd element recites extracting, by the processing unit (101), one or more features from the medical dataset, wherein the one or more features comprises parameters associated with the patient which are indicators of sepsis. Quinn discloses normalizing vital signs and demographic information and feeding it int other models as an input vector (see Fig. 14 and ¶s 0049 and 0090).
Claim 1 3rd element recites imputing, by the processing unit, at least one missing value in the medical dataset, wherein the missing value is associated with the features in the medical dataset. Quinn discloses imputing missing data into datasets using different methods (see ¶0090).
Claim 1 4th element recites determining, by the processing unit, an output parameter indicative of the onset of sepsis in the patient by using the one or more features and the at least one imputed missing value in the medical dataset as an input for one or more trained machine learning model. Quinn discloses a trained algorithm comprising a machine learning based classifier (see Fig. 14 and ¶s 0009, 0049 and 0090).
Claim 1 5th element recites and generating, by the processing unit, an alert (ALT) indicating the onset of sepsis in the patient if the output parameter fulfills a pre-defined criterion associated with sepsis. Quinn discloses transmitting alters if a certain predetermined criterion is met (see ¶0016 and 0064).
Claim 2 recites the method according to claim 1, further comprising normalizing the one or more features from the medical dataset, wherein normalizing comprises defining a uniform minimum and maximum threshold for each of the one or more features. Quinn discloses normalizing features from the medical dataset (see ¶0049 and Fig. 14).
Claim 3 recites the method according to claim 1, wherein imputing the at least one missing value in the medical dataset comprises: determining, by the processing unit, a missing value associated with the features in the medical dataset; determining, by the processing unit, a value preceding the missing value associated with the features; and substituting, by the processing unit, the missing value with the value preceding the missing value or the value succeeding the missing value associated with the features in the medical dataset. Quinn discloses accommodating datasets having missing values (see ¶0090).
Claim 4 recites the method according to claim 1, wherein the one or more features in the medical dataset associated with the patient comprises at least one of vital signs associated with the patient, analytes present in a blood sample of the patient, and derivative parameters associated with the vital signs and the analytes present in a blood sample of the patient. Quinn discloses a plurality of vital sign measurements (see ¶0008).
Claim 5 recites the method according to claim 1, wherein the output parameter indicative of the onset of sepsis in the patient is a probability score, wherein a probability value of 0 indicates no sepsis and a probability value of 1 indicates onset of sepsis. Quinn discloses binary output probability values of 0 and 1 (see ¶0078).
Claim 6 recites the method according to claim 1, wherein generating the alert indicating the onset of sepsis in the patient comprises: determining, by the processing unit, if the probability value associated with the patient exceeds a first pre-defined threshold; and generating, by the processing unit, a warning (ALT) if the probability value exceeds the first pre-defined threshold. Quinn discloses alerts being generated by predetermined criterion (see ¶s 0064 and 0065).
Claim 7 recites the method according to claim 6, further comprising: identifying, by the processing unit, a number of generated warnings (ALT) indicative of the onset of sepsis; determining, by the processing unit, if the number of generated warnings (ALT) exceeds a second pre-defined threshold; and generating, by the processing unit, an alarm (ALM) indicative of the onset of sepsis if the number of generated warnings exceeds the second pre-defined threshold. Quinn discloses alerts being generated by predetermined criterion (see ¶s 0064 and 0065).
Claim 8 recites the method according to claim 1 wherein the trained machine learning model is a recurrent neural network, in particular, comprising a long-short term memory block. Quinn discloses a machine learning model comprising a long-short term memory block (see ¶0070).
Claim 9 1st element recites a method of training a machine learning model for determining an onset of sepsis in a patient. Quinn discloses a method of training a machine learning model for determining sepsis (see ¶s 0193, 0206 and 0212).
Claim 9 2nd element recites the method comprising: receiving, by a processing unit, a medical dataset associated with a patient, wherein the medical dataset comprises a plurality of features associated with the patient; Quin discloses receiving medical data with a plurality of features associated with the patient (see ¶s 0194-0195).
Claim 9 3rd element recites extracting, by the processing unit, one or more features from the plurality of features in the medical dataset, wherein the one or more features comprises parameters associated with the patient which are indicators of onset of sepsis at a given point in time. Quinn discloses extraction of features comprising parameters associated with onset of sepsis (see ¶0211).
Claim 9 4th element recites receiving, by the processing unit, the machine learning model. Quinn discloses a machine learning model (see Fig. 12 and ¶207).
Claim 9 5th element determining, by the machine learning model, a probability value for the onset of sepsis in the patient based on the one or more features present in the medical dataset. Quinn discloses prediction values corresponding to a probability value for the onset of sepsis (see ¶0207 and 0005).
Claim 9 6th element recites receiving, by the processing unit, sepsis data related to the medical dataset, wherein the sepsis data indicates an onset of sepsis or indicates no presence of sepsis at a defined time period in the patient associated with the medical dataset. Quinn discloses receiving sepsis data related to the medical dataset at predefined time periods (see ¶s 0214 and 0217).
Claim 9 7th element recites and adjusting the machine learning model based on an outcome of a comparison between the probability value and the sepsis data. Quinn discloses adjusting the machine learning model based on outcomes of a comparison between the probability value and sepsis data (see ¶s 0223 and 0224).
Claim 10 recites the method according to claim 9, wherein the sepsis data comprises a medical dataset labelled to be indicative of onset of sepsis or no presence of sepsis for a defined time period in a patient. Quinn discloses sepsis data comprising medical sepsis data within a time period (see ¶s 0208 and 0211).
Claim 11 1st element recites the method according to claim 9, further comprising pre-processing the sepsis data, wherein pre-processing the sepsis data comprises: imputing, by the processing unit, at least one missing value in the sepsis data, wherein the missing value is associated with one or more features in the sepsis data. Quinn discloses imputing missing data into datasets using different methods (see ¶s 0049 and 0090).
Claim 11 2nd element recites normalizing, by the processing unit, the one or more features from the sepsis data, wherein normalizing comprises defining a uniform minimum and maximum threshold for each of the one or more features. Quin discloses normalizing sepsis data (see ¶0049).
Claim 12 recites the method according to claim 10, wherein the label associated with the sepsis data is modified such that the defined time period indicated by the label is advanced by a range of 2 to 10 hours, preferably 3 to 9 hours, more preferably 6 to 8 hours, and most preferably 5 to 7 hours. Quinn discloses the label associated with the sepsis data modified such that the defined time period is within 24 or 72 hours (medical data being the data derived from a lab culture draw and administration of antibiotics, see ¶0198).
Claim 13 recites a sepsis determination device for determining an onset of sepsis in a patient, the device comprising: one or more processing units; a medical database coupled to the one or more processing units, the medical database comprising a plurality of medical datasets associated with the patient and sepsis data; and a memory coupled to the one or more processing units, the memory comprising a sepsis determination module configured to perform the method steps as claimed in claim 1 using at least one trained machine learning model. Quinn discloses a sepsis determining device by one or more computer processors and a medical database couped to the processing units, as well as a memory (see ¶005, 006, 016 and 0031).
Claim 14 recites a computer program product comprising machine readable instructions, that when executed by one or more processing units, cause the one or more processing units to perform method steps according to claim 1. Quinn discloses a computer program including instructions executable by a digital processing device (see ¶0018).
Claim 15 recites a computer readable medium on which program code sections of a computer program are saved, the program code sections being loadable into and/or executable in a system to make the system execute the method steps according to claim 1 when the program code sections are executed in the system. Quinn discloses a computer readable medium comprising machine executable code that implements the above methods (see ¶0030).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Delahanty et al. 2019 Annals of Emergency Medicine 73(4) pgs. 334-344; Nemanti et al. 2019 Crit Care Med 46(4) pgs. 557-553; and Giannini et al. 2019 Crit Care Med 47(11) pgs. 1485-1492.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Maranda S Cantrell whose telephone number is (571)270-5651. The examiner can normally be reached Monday - Thursday 7:30-4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at 571-272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.S.C./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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