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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP 24160703.5, filed on February 29, 2024
Status of Claims
This action is in reply to the claims filed on February 24, 2025. Claim(s) 1-15 are currently pending and have been examined.
Claim Objections
Claim 1 objected to because of the following informalities: “future medical sample events” in p. 1, ll. 16-17. This appears to be a typographical error. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “future medical sample event”.
Claim 13 objected to because of the following informalities: “the laboratory data” in p. 3, ll. 11, “future medical sample events” in p. 3, ll. 16-17. These appear to be typographical errors. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “laboratory data”, “future medical sample event”.
Claim 14 objected to because of the following informalities: “the method” in p. 3, ll. 24. This appears to be a typographical error. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “the computer-implemented method”.
Claim 15 objected to because of the following informalities: “the medical sample event data” in p. 4, ll. 1. This appears to be a typographical error. Appropriate correction is required. For examination purposes, the Examiner will interpret the claimed portion as “the historical medical sample event data”.
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) 1-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception an abstract idea without significantly more.
Claim 1: Step 2A Prong One
Claim 1 recite(s):
augmenting the medical sample event data with laboratory data;
creating a feature set from the augmented medical sample event data, the feature set including time bracketed data derived from the augmented medical sample event data;
predict a future medical sample event within a forecast time range
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), which is a subgrouping of Certain Methods of Organizing Human Activity. That is, other than reciting, “computer-implemented method” to perform these functions, nothing in the claim precludes the limitations from practically being performed by a person monitoring resource information. Further, claim 3 recites “a pre-laboratory sample tracking system” and “an information system”. For example, the claim encompasses a person following instructions to add laboratory data to the medical sample event data, a person following instructions to create a feature set from the augmented medical sample event data, and person following instructions to predict a future medical event within a forecast time range. These steps could be accomplished by a person following instructions to make determinations by using obtained information, and therefore encompass Certain Methods of Organizing Human Activity.
Claim 1: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining
elements amount to no more than general purpose computer components programmed to perform
the abstract idea, insignificant extra-solution activity, and generally linking the abstract idea to a
technical environment.
Claim 1, directly or indirectly, recites the following generic computer components
configured to implement the abstract idea: “computer-implemented method”. As set forth in
the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely including instructions to implement an
abstract idea on a computer” is an example of when an abstract idea has not been integrated into
a practical application.
Additionally, the claim recites “receiving medical sample event data describing events relating to one or more medical samples to be processed by the healthcare laboratory;”, “receiving, a prediction of future medical sample events for the forecast time range as an output” at a high degree of generality, amount no more than receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). As set forth in MPEP 2106.05(d)(II), computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “from the trained machine learning model”, “using the feature set as an input for a trained machine learning model, the trained machine learning model having been trained on historical feature sets to” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has
not been integrated into a practical application.
Claim 1: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer
configured to perform above identified functions amounts to no more than mere instructions to
apply the exception using generic computer components. Mere instructions to apply an exception
using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea
into a patent-eligible invention.”)
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (See MPEP 2106.05(g)). Therefore, whether considered
alone or in combination, the additional elements do not amount to significantly more than the
abstract idea.
Additionally, generally linking the abstract idea to a particular technological environment
does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity
Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Dependent claims 2-12 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claims 2-3 further describe the medical sample event data. Similarly, claims 4 and 6 further describe augmenting the medical sample event data. Similarly, claim 5 further describes the input. Similarly, claims 7 and 9 further describe the feature set. Similarly, claim 8 further describes the laboratory data. Similarly, claim 10 further describes the forecast time range. Finally, claims 11-12 describe determining a recommendation.
Dependent claims 2-12 recite additional subject matter which amounts to limitations consisted with the additional elements in independent claim 1 (such as claim 5, “the input for the trained machine learning model” at a high degree of generality, amounts to no more than generally linking the abstract idea to a particular technical environment). The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application. The claims are not patent eligible.
Claims 13 and 14 recite similar functions as claim 1, but in system and computer-implemented method form.
The additional element of “the system comprising one or more processors and memory, wherein the memory contains machine executable instructions which, when executed on the one or more processors, cause the one or more processors to:” in independent claim 13 are recited at a high-level of generality that amounts to generic computer components. The claim is not patent eligible.
The elements of “historical medical sample event”, “historical feature set”, “predict a volume of future medical sample events” in independent claim 14 are recited as similar to the elements of “medical sample event”, “feature set”, “a prediction of future medical sample events” in independent claim 1.
Dependent claim 15 incorporates the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claim 15 further describes the historical medical sample event data.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application. The claims are not patent eligible.
Therefore, whether considered alone or in combination, the additional elements do not
amount to significantly more than the abstract idea.
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.
Claims 1-7, 9-10, 13-15 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being unpatentable over De Bruin et al. (U.S. Patent Pre-Grant Publication No. 2011/0119212).
As per independent claim 1, De Bruin discloses a computer-implemented method for predicting future operating conditions of a healthcare laboratory, the healthcare laboratory containing one or more in-vitro diagnostic laboratory instruments configured to process medical samples, the computer-implemented method comprising:
receiving medical sample event data describing events relating to one or more medical samples to be processed by the healthcare laboratory (See Fig. 3 and [0079]: The data acquisition step, in which neurophysiologic parameters that may include, but are not limited to, clinical and laboratory data are collected (101, 154 and FIG. 3), and then sent to the central processing system, which the Examiner is interpreting clinical and laboratory data to encompass one or more medical samples to be processed by the healthcare laboratory);
augmenting the medical sample event data with laboratory data (See [0112]: The first level is `feature-level data fusion` in which the data and information from variety of sources of information and features, including a variety of clinical and laboratory assessments and tests are combined in an efficient way, which the Examiner is interpreting feature-level data fusion to encompass augmenting the medical sample event data with laboratory data);
creating a feature set from the augmented medical sample event data, the feature set including time bracketed data derived from the augmented medical sample event data (See [0112], [0128]: The medical diagnosis and treatment planning can be done by collecting all available clinical and laboratory data at each consecutive time step, then analyzing and reporting the results in each time, which the Examiner is interpreting clinical and laboratory data at each consecutive time step to encompass including time bracketed data derived from the augmented medical sample event data, and interpreting the combination of variety of sources of information and features to encompass creating a feature set from the augmented medical sample event data);
using the feature set as an input for a trained machine learning model, the trained machine learning model having been trained on historical feature sets to predict a future medical sample event within a forecast time range (See [0083], [0106]-[0108], [0112]-[0114]: The medical digital expert system disclosed in this invention includes a machine learning, machine inference and computational learning and information processing procedure including the following key components: user-interface, data management, data communication, data encoding, data security, diagnosis and treatment data bases, data preprocessing, feature extraction, feature selection, detection, estimation, prediction models and data fusion, which the Examiner is interpreting a machine learning to encompass a trained machine learning model as the parameters and structure of data fusion is determined using training data, and interpreting prediction models to encompass predict a future medical sample event within a forecast time range as the prediction can be an outcome ([0106])); and
receiving, from the trained machine learning model, a prediction of future medical sample events for the forecast time range as an output (See [0128], [0184]: A collection of preliminary or low-level estimation/detection/prediction models (or processors) are employed which each use the discriminative features to calculate preliminary estimation, decision and prediction results, which the Examiner is interpreting prediction results to encompass a prediction of future medical sample events for the forecast time range as an output as the "digital clinician" or the "medical digital expert system" is used to estimate indicators or critical parameters of a condition, or to detect the presence of a condition when the condition is changing over time ([0128]).)
Claims 13 and 14 recite similar functions as claim 1, but in system and computer-implemented method form.
The elements of “historical medical sample event”, “historical feature set”, “predict a volume of future medical sample events” in independent claim 14 are recited as similar to the elements of “medical sample event”, “feature set”, “a prediction of future medical sample events” in independent claim 1.
As per claim 2, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin further teaches wherein the medical sample event data is augmented with dynamic laboratory data and static laboratory data (See [0106]: The classifier/predictor/estimator explores statistical, dynamic, geometrical or structural and hierarchical information, or combinations thereof, inherent in the training data, and employs predetermined optimal criteria to construct the discrimination/decision/estimation schemes or models, which the Examiner is interpreting dynamic or structural and hierarchical information to encompass dynamic laboratory data and static laboratory data.)
As per claim 3, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin further teaches wherein the medical sample event data includes data from a pre-laboratory sample tracking system, and wherein the laboratory data used to augment the medical sample event data comprises data from one or more of:
in-vitro diagnostic laboratory instruments, an information system of the healthcare laboratory, or a database of laboratory data (See [0108]: The unlabeled data corresponds to clinical and laboratory data derived from patients, but for which the true diagnosis or reference diagnosis is not known for the medical expert system, or not clinically confirmed, which the Examiner is interpreting the unlabeled data corresponds to clinical and laboratory data derived from patients to encompass an information system of the healthcare laboratory.)
As per claim 4, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin further teaches wherein augmenting the medical sample event data includes identifying one or more specific medical samples referenced in data obtained from more than one data source and combining the data from each data source of the more than one data source which relates to each specific medical sample (See [0112]: The first level is `feature-level data fusion` in which the data and information from variety of sources of information and features, including a variety of clinical and laboratory assessments and tests are combined in an efficient way, which the Examiner is interpreting the feature-level data fusion to encompass the claimed portion as the parameters and structure of data fusion and processor fusion is determined using training data.)
As per claim 5, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin further teaches wherein augmenting the medical sample event data includes identifying one or more specific medical samples referenced in data obtained from more than one data source and combining the data from each data source of the more than one data source which relates to each specific medical sample (See [0111]: The system can then process the clinical, personal and laboratory information in a manner similar to that employed by an expert physician, which the Examiner is interpreting laboratory information to encompass current state data relating to the healthcare relating to the healthcare laboratory.)
As per claim 6, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin further teaches wherein augmenting the medical sample event data includes identifying incomplete data fields for one or more medical samples (See [0078]: Therefore the disclosed system is designed to flexibly operate with incomplete data, provided that required minimum data requirements have been met (e.g. age, sex and EEG data in psychiatric illnesses and disorders).)
As per claim 7, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin further teaches wherein both augmented medical sample event data and data relating to the healthcare laboratory are used to create the feature set (See [0112], [0128]: The medical diagnosis and treatment planning can be done by collecting all available clinical and laboratory data at each consecutive time step, then analyzing and reporting the results in each time, which the Examiner is interpreting clinical and laboratory data at each consecutive time step to encompass the claimed portion.)
As per claim 9, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin further teaches wherein the feature set includes a fitted partial Fourier sum for a given time bracket or for given coefficients of a fit (See [0027], [0047]: The first level features can be transformed variables (e.g. Fourier coefficients at specific frequencies), and other types of coefficients that need some processing to extract, which the Examiner is interpreting to encompass the feature set includes a fitted partial Fourier sum for given coefficients of a fit.)
As per claim 10, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin further teaches wherein the forecast time range is defined at least in part by user input (See [0083], [0106]-[0108], [0112]-[0114]: The medical digital expert system disclosed in this invention includes a machine learning, machine inference and computational learning and information processing procedure including the following key components: user-interface, data management, data communication, data encoding, data security, diagnosis and treatment data bases, data preprocessing, feature extraction, feature selection, detection, estimation, prediction models and data fusion, which the Examiner is interpreting user interface to encompass the forecast time range is defined at least in part by user input.)
As per claim 15, De Bruin discloses the computer-implemented method of claim 14 as described above. De Bruin further teaches wherein the historical medical sample event data describes one or more of: a medical sample being ordered, or a medical sample arriving at the healthcare laboratory (See [0256]-[0257]: The basic principles described (i.e. using EEG alone or in combination with a variety of clinical, laboratory, physical, historical, psychological, cognitive and biological assessment information and variables and analyzing these in the described manner) can be applied to any of a number of other types of diseases or conditions., which the Examiner is interpreting laboratory information to encompass a medical sample arriving at the healthcare laboratory.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 8, 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over De Bruin et al. (U.S. Patent Pre-Grant Publication No. 2011/0119212) in view of Amarasingham et al. (U.S. Patent Pre-Grant Publication No. 2015/0213206).
As per claim 8, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin may not explicitly teach wherein the laboratory data comprises any of: in-vitro diagnostic laboratory instrument availability data, in-vitro diagnostic laboratory instrument throughput data, staff data, or laboratory layout data.
Amarasingham teaches a computer-implemented system wherein the laboratory data comprises any of: in-vitro diagnostic laboratory instrument availability data, in-vitro diagnostic laboratory instrument throughput data, staff data, or laboratory layout data (See [0038]: The holistic hospital patient care and management system 10 further receives input and data from a number of additional sources, including RFID (Radio Frequency Identification) tags 21 that are worn, associated with, or affixed to patients, medical staff, hospital equipment, hospital instruments, medical devices, supplies, and medication.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the computer-implemented method of De Bruin to include the laboratory data comprises any of: in-vitro diagnostic laboratory instrument availability data, in-vitro diagnostic laboratory instrument throughput data, staff data, or laboratory layout data as taught by Amarasingham. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify De Bruin with Amarasingham with the motivation of ensure that appropriate interventions and resources are available and deployed according to patients' needs (See Background of Amarasingham in [0012]).
As per claim 11, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin may not explicitly teach further comprising generating a recommendation of a distribution of staff throughout areas of the healthcare laboratory.
Amarasingham teaches a computer-implemented method further comprising generating a recommendation of a distribution of staff throughout areas of the healthcare laboratory (See [0047]: The system may further generate recommendations based on the simulated outcome to avoid adverse events or unfavorable results, which the Examiner is interpreting to encompass the claimed portion as the user may select one or more constraints, such as staffing level, hours of operation, the number of new patients, the number of available patient beds, the availability of certain medical equipment, the amount of supplies, and simulation time period, varying values to create a simulated scenario for purposes of generating possible outcomes.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the computer-implemented method of De Bruin to include generating a recommendation of a distribution of staff throughout areas of the healthcare laboratory as taught by Amarasingham. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify De Bruin with Amarasingham with the motivation of ensure that appropriate interventions and resources are available and deployed according to patients' needs (See Background of Amarasingham in [0012]).
As per claim 12, De Bruin discloses the computer-implemented method of claim 1 as described above. De Bruin may not explicitly teach further comprising generating a recommendation of a volume of supplies to be made available.
Amarasingham teaches a computer-implemented method further comprising generating a recommendation of a volume of supplies to be made available (See [0047]: The system may further generate recommendations based on the simulated outcome to avoid adverse events or unfavorable results, which the Examiner is interpreting to encompass the claimed portion as the user may select one or more constraints, such as staffing level, hours of operation, the number of new patients, the number of available patient beds, the availability of certain medical equipment, the amount of supplies, and simulation time period, varying values to create a simulated scenario for purposes of generating possible outcomes.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the computer-implemented method of De Bruin to include generating a recommendation of a volume of supplies to be made available as taught by Amarasingham. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify De Bruin with Amarasingham with the motivation of ensure that appropriate interventions and resources are available and deployed according to patients' needs (See Background of Amarasingham in [0012]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Avinash et al. (U.S. Patent Pre-Grant Publication No. 2007/0118399), describes an informatics system permits data entities from a wide range of data sources to be accessed and evaluated.
Dirghangi et al. (U.S. Patent Pre-Grant Publication No. 2019/0320898), describes a method of automating the collection, association, and coordination of multiple medical data sources using a coordinating service application, computer, database, and/or server system to manage devices, examinations, and people involved in the medical examination and treatment process.
Kolling et al. (“Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development”), describes results that make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bennett S Erickson whose telephone number is (571)270-3690. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683