DETAILED ACTION
Status of Claims
This is a Final Office Action in response to the arguments and/or amendments filed on 22 September 2025.
Claim(s) 5, 6, 20, 27, 28, 30, and 32 is/are canceled. Claim(s) 1, 22, and 24 is/are amended.
Claim(s) 1-4, 7-19, 21-26, 29, and 31 is/are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
This application is claiming the benefit of prior-filed application No. 16/975927 under 35 U.S.C. 120, 121, 365(c), or 386(c). Copendency between the current application and the prior application is required. Since the applications were not copending, the benefit claim to the prior-filed application is improper. Applicant is required to delete the claim to the benefit of the prior-filed application, unless applicant can establish copendency between the applications.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 recites “the predetermined action comprising: the error; re-running the medical test related to the medical data”. The inclusion of “the error” in this limitation, as it is not a type of action and as the words are struck in the similarly amended claims, presents as an error in the amendments. The limitation appears intended to read: “the predetermined action comprising: re-running the medical test related to the medical data”.
Appropriate correction is required.
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.
Claim(s) 1-4, 7-19, 21-26, 29, and 31 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1-4, 7-11, 21, 22, 24, 26, 29, and 31:
Claim 1, which is representative of claims 22 and 24, recites in part: obtaining target medical data generated in a diagnostic laboratory instrument medical test; obtaining a model for validating the medical data, the model representing an association between the medical data and validation results, and the validation results indicating information about predetermined actions to be performed on the medical data; wherein the model is [based on] data comprising historical medical data for a plurality of medical tests using diagnostic laboratory instruments and associated labeling information, the medical data and labeling information comprising measurement values from the laboratory instruments; determining a target validation result for the target medical data by applying the target medical data to the model, the target validation result indicating information about a predetermined action to be performed on the target medical data, the predetermined action comprising: re-running the medical test related to the medical data.
These limitations describe a concept of receiving and analyzing medical data to determine a validation result. Like the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping, this concept describes a mental process that someone should follow to evaluate medical data for validation. As such, these limitation set forth a method of organizing human activity. Alternatively, the identified concept is analogous to the examples of “observation”, “evaluation” “judgement”, and “opinion” given in MPEP 2106.04(a)(2)(III) and can be performed in the human mind. As such, these limitations set forth a mental process. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 1 describes the method as computer-implemented. Claim 22 recites the additional element of an electronic device comprising: at least one processor; and at least one memory. Claim 24 recites the additional element of a non-transitory computer readable storage medium. These additional elements are recited at an extremely high level of generality, and may be interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application.
The claims further recite the additional element of applying a machine learning model and wherein the machine learning model is trained with training data This additional element is recited at an extremely high level of generality, and amounts to instructions to implement the abstract idea with a computing device or to use a computer as a tool to perform the abstract idea. As such, this additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional element of operating a system based the re-running of the medical test. This additional element does not require any particular machine, does not reflect any improvement to technology, does not transform an article, and does not meaningfully limit the implementation of the abstract idea. Instead, this additional element describes an expected response or output at a high level of generality, and thus is insignificant extra-solution activity. As such, this additional element does not integrate the abstract idea into a practical application.
There are no further additional elements. When considered as a combination, the additional elements only generally amount to instructions to implement the abstract idea with a computing device or to use a computer as a tool to perform the abstract idea in conjunction with insignificant extra-solution activity. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Therefore the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea. As previously noted, the claims recite additional elements which may be interpreted as using generic computing devices to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not amount to significantly more. As previously noted, the claims recite the additional element of operating a system based on the re-running of the medical test. However, when re-evaluated at Step 2B, this additional element continues to amount to only insignificant extra-solution activity. As such, this additional element does not amount to significantly more. There are no further additional elements. As previously noted, the additional elements in combination, amount to instructions to implement the abstract idea with a computing device or to use a computer as a tool to perform the abstract idea in conjunction with insignificant extra-solution activity. As such, the combination of additional elements does not amount to significantly more than the abstract idea.
Therefore, when considered individually and as an ordered combination, the additional elements of the independent claims do not amount to significantly more than the judicial exception. Thus the independent claims are not patent eligible.
Claims 2-4, 7-11, 21, 26, 29, and 31 further narrow the abstract idea, but the claims continue to recite an abstract idea. These claims recite no further additional elements. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above. Therefore these claims continue to be directed to an abstract idea. The previously identified additional elements, individually and as a combination, do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above. Thus as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Regarding claims 12-19, 23, and 25:
Claim 12, which is representative of claims 23 and 25, recites in part: obtaining data comprising historical medical data and associated labeling information, the labeling information indicating predetermined actions performed on the historical medical data; generating a first model for validating medical data such that the first model represents an association between medical data and validation results indicating information about the predetermined actions to be performed on the medical data; wherein the model is [based on] data comprising historical medical data for a plurality of medical tests using diagnostic laboratory instruments and associated labeling information, the medical data and labeling information comprising measurement values from the laboratory instruments.
These limitations describe a concept of creating a model for determining a validation result for medical data. Like the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping, this concept describes a mental process that someone should follow to create a model for validating medical data. As such, these limitation set forth a method of organizing human activity. Alternatively, the identified concept is analogous to the examples of “observation”, “evaluation” “judgement”, and “opinion” given in MPEP 2106.04(a)(2)(III) and can be performed in the human mind. As such, these limitations set forth a mental process. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 23 recites the additional element of an electronic device comprising: at least one processor; and at least one memory. Claim 25 recites the additional element of a non-transitory computer readable storage medium. These additional elements are recited at an extremely high level of generality, and may be interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application.
The claims further recite the additional element of a machine learning model and wherein the machine learning model is trained with training data This additional element is recited at an extremely high level of generality, and amounts to instructions to implement the abstract idea with a computing device or to use a computer as a tool to perform the abstract idea. As such, this additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional element of operating a system based on the predetermined actions. This additional element does not require any particular machine, does not reflect any improvement to technology, does not transform an article, and does not meaningfully limit the implementation of the abstract idea. Instead, this additional element describes an expected response or output at a high level of generality, and thus is insignificant extra-solution activity. As such, this additional element does not integrate the abstract idea into a practical application.
There are no further additional elements. When considered as a combination, the additional elements only generally amount to instructions to implement the abstract idea with a computing device or to use a computer as a tool to perform the abstract idea in conjunction with insignificant extra-solution activity. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Therefore the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea. As previously noted, the claims recite additional elements which may be interpreted as generic computing devices used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not amount to significantly more. As previously noted, the claims recite the additional element of operating a system based on the predetermined actions. However, when re-evaluated at Step 2B, this additional element continues to amount to only insignificant extra-solution activity. As such, this additional element does not amount to significantly more. There are no further additional elements. As previously noted, the additional element in combination, amount to instructions to implement the abstract idea with a computing device or to use a computer as a tool to perform the abstract idea in conjunction with insignificant extra-solution activity. As such, the combination of additional elements does not amount to significantly more than the abstract idea.
Therefore, when considered individually and as an ordered combination, the additional elements of the independent claims do not amount to significantly more than the judicial exception. Thus the independent claims are not patent eligible.
Claims 13-19 further narrow the abstract idea, but the claims continue to recite an abstract idea. These claims recite no further additional elements. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above. Therefore these claims continue to be directed to an abstract idea. The previously identified additional elements, individually and as a combination, do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above. Thus as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims 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 1-4, 7-19, 21-26, 29, and 31 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al. (EP3786966A1).
Regarding Claim 1, 22, and 24: Liu discloses a computer implemented method for the automated operation of a medical system using improved medical data validation, comprising:
obtaining target medical data generated in a diagnostic laboratory instrument medical test (See at least [0006] and [0021]).
obtaining a machine learning model for validating medical data, the machine learning model representing an association between the medical data and validation results, and the validation results indicating information about predetermined actions to be performed on the medical data (See at least [0006]), wherein the machine learning model is trained with training data comprising historical medical data for a plurality of medical tests using diagnostic laboratory instruments and associated labeling information (See at least [0042]), the medical data and labeling information comprising measurement values from the laboratory instruments (See at least [0036]).
determining a target validation result for the target medical data by applying the target medical data to the machine learning model, the target validation result indicating information about a predetermined action to be performed on the target medical data (See at least [0006]).
the predetermined action comprising: re-running the medical test related to the medical data (See at least [0039]).
operating the medical system based on the re-running of the medical test (See at least [0040]).
Regarding Claim 2: Liu discloses the above limitations. Additionally, Liu discloses wherein determining the target validation result comprises: obtaining a further machine learning model for validating the medical data, the further machine learning model representing a different association between the medical data and the validation results than the association represented by the machine learning model; applying the target medical data to the machine learning model and the further machine learning model, respectively, to obtain respective validation results; and determining the target validation result based on the respective validation results (See at least [0124]).
Regarding Claim 3: Liu discloses the above limitations. Additionally, Liu discloses determining a similarity between the target medical data and candidate medical data, the candidate medical data being selected from historical medical data that is used to generate the machine learning model, and/or historical medical data that has been applied to the machine learning model (See at least [0125]), in response to the similarity exceeding a predetermined similarity threshold, selecting the candidate medical data as reference medical data for the target medical data (See at least [0125]); and providing the reference medical data in association with the target medical data for presentation to a viewer of the target medical data (See at least [0125]).
Regarding Claim 4: Liu discloses the above limitations. Additionally, Liu discloses wherein determining the similarity comprises: selecting the candidate medical data based on at least one of the following: a determination that the target action is to be performed on the candidate medical data, and a determination that the candidate medical data have one or more test items that are the same as the target medical data (See at least [0126]).
Regarding Claim 7: Liu discloses the above limitations. Additionally, Liu discloses wherein the information about the target action comprises at least one of the following: an indication of the target action, and a confidence level of selecting the target action for the target medical data by the machine learning model (See at least [0129]).
Regarding Claim 8: Liu discloses the above limitations. Additionally, Liu discloses wherein the target medical data comprises medical data generated in an in-vitro diagnostic test (See at least [0130]).
Regarding Claim 9: Liu discloses the above limitations. Additionally, Liu discloses wherein the machine learning model is selected from a plurality of available machine learning models based on respective performance measures of the plurality of available machine learning models (See at least [0131]).
Regarding Claim 10: Liu discloses the above limitations. Additionally, Liu discloses wherein obtaining the target medical data comprises: obtaining the target medical data that is determined by a rule-based engine as to be further validated, the rule-based engine being configured to validate the target medical data | based on at least one predetermined rule (See at least [0132]).
Regarding Claim 11: Liu discloses the above limitations. Additionally, Liu discloses providing the target validation result in association with the target medical data to a laboratory information system (LIS), the target medical data comprising at least one data item presented in a medical test report, and the target validation result presented in the medical test report as a further data item (See at least [0133]).
Regarding Claim 12, 23, and 25: Liu discloses a method of operating a medical system using a machine learning model for validating medical data, comprising:
obtaining training data comprising historical medical data and associated labeling information, the labeling information indicating predetermined actions performed on the historical medical data (See at least [0007]);
generating a first machine learning model for validating medical data based on the training data such that the first machine learning model represents an association between the medical data and validation results indicating information about the predetermined actions to be performed on the medical data (See at least [0007]);
operating the medical system based on the predetermined actions (See at least [0049]);
wherein the machine learning model is trained with training data comprising historical medical data for a plurality of medical tests using diagnostic laboratory instruments and associated labeling information (See at least [0042]), the medical data and labeling information comprising measurement values from the laboratory instruments (See at least [0036]).
Regarding Claim 13: Liu discloses the above limitations. Additionally, Liu discloses wherein the predetermined actions comprise a first action of releasing the medical data to an entity requesting a medical test related to the medical data, and obtaining the training data comprises: obtaining a first set of available historical medical data that are marked as being associated with labeling information indicating the first action; selecting, from the first set of available historical medical data, historical medical data that has higher reliability in the labeling information than other historical medical data in the first set; and determining the selected historical medical data and the associated labeling information as the training data (see at least [0136]).
Regarding Claim 14: Liu discloses the above limitations. Additionally, Liu discloses wherein obtaining the training data comprises: selecting outlier historical medical data from a second set of available historical medical data; presenting the outlier historical medical data to a user; in response to receiving, from the user, a user input indicating one of the predetermined actions, marking the outlier historical medical data to be associated with labeling information indicating the indicated action; and determining the outlier historical medical data and the associated labeling information as the training data (See at least [0137]).
Regarding Claim 15: Liu discloses the above limitations. Additionally, Liu discloses wherein the predetermined actions comprise a first action of releasing the medical data to an entity requesting a medical test related to the medical data, and obtaining the training data comprises: selecting, from a third set of available historical medical data, first historical medical data and second historical medical data based on a predetermined ratio of an amount of the first medical data to an amount of the second medical data, the first historical medical data being associated with the labeling information that indicates the first action, and the second historical medical data being associated with the labeling information that indicates a different action in the | predetermined actions than the first action (See at least [0138]).
Regarding Claim 16: Liu discloses the above limitations. Additionally, Liu discloses in response to a predetermined trigger for model evolution, determining a second machine learning model for validating medical data by: updating the first machine learning model, or generating a new machine learning model based on the training data, the new machine learning model having a different model configuration than the first machine learning model (See at least [0139]).
Regarding Claim 17: Liu discloses the above limitations. Additionally, Liu discloses wherein determining the second machine learning model comprises: in response to determining that a further action is to be performed on medical data, adding, into the training data, further historical medical data and associated further labeling information indicating the further action; and generating the new machine learning model as the second machine learning model such that the second machine learning model represents an association between the medical data and further validation results indicating the predetermined actions and the further action to be performed on the medical data (See at least [0140]).
Regarding Claim 18: Liu discloses the above limitations. Additionally, Liu discloses wherein the first machine learning model is provided in use for validating medical data, the method further comprising: determining a first performance measure of the first machine learning model and a second performance measure of the second machine learning model; and in response to the second performance measure exceeding the first performance measure, providing the second machine learning model to replace the first machine learning model in use (See at least [0141]).
Regarding Claim 19: Liu discloses the above limitations. Additionally, Liu discloses wherein the historical medical data comprises a test result for at least one of a plurality of predetermined test items, and generating the first machine learning model comprises: processing the historical medical data by filling test results for other test items of the plurality of test items than the at least one test item, the filled test results being determined from test results of the other test items comprised in other historical medical data; and generating the first machine learning model based on the processed historical medical data (see at least [0142]).
Regarding Claim 21: Liu discloses the above limitations. Additionally, Liu discloses wherein the machine learning model is provided by the method comprising: obtaining training data comprising historical medical data and associated labeling information, the labeling information indicating predetermined actions performed on the historical medical data; and generating a first machine learning model for validating medical data based on the training data such that the first machine learning model represents an association between the medical data and validation results indicating information about the predetermined actions to be performed on the medical data (See at least [0135]).
Regarding Claim 26: Liu discloses the above limitations. Additionally, Liu discloses wherein the machine learning model comprises at least one of Bayesian models, random forest models, support vector machines, K-nearest neighbor (KNN) models, or a neural network (See at least [0061]).
Regarding Claim 29: Liu discloses the above limitations. Additionally, Liu discloses in which the validating comprises validating the medical data as having an error due to: the test sample, the performed medical diagnostic testing procedures, the reagent used in the medical test, and/or mismatching with the physical condition of the biological object of the test sample, and the validating further comprises performing an action needed to be performed to correct the error (See at least [0038]).
Regarding Claim 31: Liu discloses the above limitations. Additionally, Liu discloses in which the validating comprises the action of performing an automated re-running of the medical test (See at least [0039]).
Response to Arguments
Applicant’s Argument Regarding 101 Rejections of claims 1-27 and 29-32:
In order to establish the required technical improvement, it is required by the USPTO that specific details of the improvement be provided in the specification. The Examiner argues that the application does not provide the required details. Applicant responds that the claims have been amended to claim “a method for the automated operation of a medical system using improved medical data validation … comprising performing an automated action of re-running the medical test.” The present invention is described in the specification as providing an improvement upon the conventional process for operating a medical system.
Another option for establishing patentable subject matter is to integrate the abstract idea into a practical application. In the previous office action response, the independent claims were amended to add a final step of: ‘operating the medical system based on the predetermined actions.” However, the Examiner contends that the claims are insufficient to meet this requirement. … In response, Applicant submits that the system is technical improved.
Examiner’s Response: Applicant's arguments filed 22 September 2025 have been fully considered but they are not persuasive.
As Applicant alludes to, per MPEP 2106.05(a), “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.” The portions of the specification which Applicant identifies responsive to recognizing this requirement do not provide “a technical explanation as to how to implement the invention.”
As noted in the response to the prior argument, the disclosure does not provide “a technical explanation as to how to implement the invention.” As such, the claimed invention does not provide an eligibility rendering technical improvement.
Applicant’s Argument Regarding 102 Rejections of claims 1-27 and 29-32: The present application has priority to the Liu reference, and the rejection over Liu is therefore unavailable. Applicant anticipates having confirmation of the priority claim in the near future.
Examiner’s Response: Applicant's arguments filed 22 September 2025 have been fully considered but they are not persuasive. Copendency between the current application and the prior application is required. As of 3 October 2025, the current application does not appear to have been copending with 16/975927. As such, the benefit claim to the prior-filed application is improper and the Liu reference is prior art for the current application.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 of the prior office actions dated 22 May 2025 and 22 May 2024.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Bion A Shelden/Primary Examiner, Art Unit 3685 2025-10-03