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 .
Status of Claims
This Office Action is responsive to the response filed May 12, 2025.
Claims 1-20 are amended and claims 21-27 are newly added.
Claims 1-27 are currently pending and have been fully examined.
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 1-27 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claim 1), a machine (claim 14), and an article of manufacture (claim 8) which is recited as a method, system, and non-transitory computer readable medium that performs the steps and/or functions of:
receiving a historical patient dataset comprising historical health parameters associated with a plurality of historical patients;
receiving a patient dataset associated with a patient, the patient dataset comprising a first set of time-independent patient health parameters and a first set of time-dependent patient health parameters;
generating a second set of time-independent patient health parameters based on the first set of time-dependent patient health parameters of the patient dataset;
based on the one or more historical health parameters and the patient dataset, generating a plurality of clusters, wherein each cluster, of the plurality of clusters, comprises a particular set of historical patients from the plurality of historical patients;
identifying a first cluster of the plurality of clusters, based on a similarity of the patient dataset to a first set of historical health parameters associated with a first set of historical patients corresponding to the first cluster;
generating a ranking of the first set of time-independent patient health parameters and the second set of time-independent patient health parameters based on an effect of a virus on a mortality of the first set of historical patients corresponding to the first cluster; and
based on the ranking, generating a treatment recommendation for the patient for treating at least one of the second set of time-independent patient health parameter to increase a likelihood of surviving a virus-related illness.
Step 2A: Prong 1
When taken individually and as a whole, the steps corresponds to concepts identified as abstract ideas by the courts, such as “certain methods of organizing human activity”, which are interactions between individuals that can include: fundamental economic principles or practices; commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
The claim is directed to a system to perform the process of analyzing received data to generate treatment recommendations for a patient, which is performed by the system performing the above underlined steps in the independent claim.
The steps of “generating a second set of time-independent patient health parameters…”, “generating a plurality of clusters…”, “identifying a first cluster.. based on a similarity of the patient dataset…”, “generating a ranking of the first set of time-independent patient health parameters…”, and “generating a treatment recommendation for the patient for treating at least one of the second set of time-independent health parameter to increase a likelihood of surviving a virus-related illness” is a certain method of organizing human activity because it is managing the behavior of an individual by providing rules or instructions a medical professional would follow regarding the care of the patient and the patient’s future behavior (i.e., following the health recommendation).
The steps of “generating a second set of time-independent patient health parameters based on the first set of time-dependent patient health parameters of the patient dataset”, “generating a plurality of clusters…”, “identifying a first cluster.. based on a similarity of the patient dataset…”, “generating a ranking of the first set of time-independent patient health parameters…” all relate to mathematical concepts because it is executing a mathematical algorithm and uses mathematical calculations and mathematical relationships to determine which treatment recommendations should be generated and displayed.
Because the mathematical concepts are used in generating the treatment recommendations that are considered certain methods of organizing human activity, they can also be considered part of the overall abstract idea or organizing human activity (see July 2024 Subject Matter Eligibility Examples, pg. 7-8).
Step 2A: Prong 2
The claims do not include additional elements that are sufficient to be considered a practical application because the additional elements amount to: insignificant extra-solution activity (MPEP 2106.05(g)), generally linking the application of the abstract idea to a particular field of use or technological environment (2106.05(h)), or mere instructions to apply it with a computer (MPEP 2106.05(f)), as discussed below.
Insignificant Extra-Solution Activity
The steps of “receiving a historical patient dataset comprising historical health parameters associated with a plurality of historical patients” and “receiving a patient dataset associated with a patient, the patient dataset comprising a first set of time-independent health parameters and a first set of time-dependent patient health parameters” are examples of mere data gathering, which is an insignificant extra-solution activity (MPEP 2106.5(g)).
The steps specifying the data to be a historical patient dataset comprising one or more time-independent and time-dependent patient health parameters and historical health parameters associated with a plurality of historical patients are examples of selecting by type or source the data to be manipulated, which is an extra-solution activity (MPEP 2106.05(g)).
Insignificant extra-solution activities are not sufficient to integrate the abstract idea into a practical application or cause the claim to amount to significantly more than the abstract idea (MPEP 2106.05(g))
Generally Linking Implementation a Particular Technological Environment or Field of Use
The steps describing the data as being health parameters and patient data are steps that are used to generally link the performance of the algorithm to the field of health care.
Mere Instructions to Apply the Abstract Idea Using a Computer
The steps reciting the use of computer components, such as the computer readable media executing instructions by one or more hardware processors in independent claims 8 and 14, serve as mere instructions to apply the abstract idea using a computer. Mere instructions to apply the abstract idea using a computer are not sufficient to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (MPEP 2106.05(f)). It is noted that independent claim 1 does not recite any such computer components.
Step 2B
The claims also do not include additional elements that are sufficient to be considered a significantly more than the abstract idea because the additional elements amount to: insignificant extra-solution activity (MPEP 2106.05(g)), mere instructions to apply it with a computer (MPEP 2106.05(f)), generally linking the application of the abstract idea to a particular field of use or technological environment (MPEP 2106.05(h)), or a well-understood, routine, and conventional limitation (MPEP 2106.05(d)), as discussed below.
The steps addressed above in Step 2A: Prong 2, when considered again under Step 2B are not considered to make the claims amount to significantly more than the abstract idea because those steps, when considered additionally with regards to Step 2B, are still considered to be either insignificant extra-solution activity, mere instructions to apply an abstract idea with a computer, or generally linking the application of the abstract idea to a particular field of use or technological environment, which are types of limitations that are not sufficient to make the claims amount to significantly more than the abstract idea (MPEP 2106.05.I.A).
The steps recited as either being part of the abstract idea or insignificant extra-solution activity are all examples of at least one of: storing and retrieving data from a memory (receiving data stored locally), sending and receiving data over a network (receiving data from an external device), electronic recordkeeping, or performing repetitive calculations. All of those functions have been identified as well-understood, routine, and conventional functions of a generic computer that are not significantly more than the abstract idea when claimed broadly or as an extra-solution activity (MPEP 2106.05(d).II).
The recited computer components (e.g., the one or more hardware processors, the memory, and the computer readable medium) are all generically recited components (see specification, par. [0055]-[0059]). Commercially available components, generic computer components, and specially-programmed computer components performing the functions of a generic computer are not considered to be amount to significantly more than the abstract idea (MPEP 2106.05(b)).
When considered as a whole, the components do not provide anything that is not present when the component parts are considered individually. Using the broadest reasonable interpretation, the system as a whole is a general purpose computer that receives data and executes an algorithm to generate a recommendation. This is a general purpose computer performing the abstract idea and insignificant extra-solution activities through these generically described devices performing well-understood, routine, and conventional functions of a generic computer (MPEP 2106.05(d).II).
Dependent Claim Analysis
Claims 2-7 are ultimately dependent from Claim(s) 1 and includes all the limitations of Claim(s) 1. Therefore, claim(s) 2-7 recite the same abstract idea of certain methods of organizing human activity of claim 1.
Claim 2 recites additional limitations that serve to select by type or source the data to be manipulated by reciting the types of health data parameters that are used. Selecting by type or source the data to be manipulated is an example of insignificant extra-solution activity that is not sufficient to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (MPEP 2106.05(g)). Claim 2 further specifies that the generated treatment recommendation for the patient is for treating the one or more treatable health parameters; this further narrows the abstract idea identified in the independent claim.
Claims 3-7 and 21-27 further describe the abstract idea by providing additional and/or narrowing limitations regarding the determinations (e.g., survival percentage, generating/determining the ranking by using a self-organizing map, converting a first set of time-dependent patient health parameters into the second set of time-independent health parameters, the detail of what the first and second set of time-dependent and time-independent parameters comprise, the time-dependent patient health parameters being obtained over a period of time, only using the first and not the second time-independent patient health parameter when generating a treatment recommendation, the virus being coronavirus, the treatable health parameters comprising comorbidities) are used to generate the treatment recommendation recited in the independent claim. Because the abstract idea cannot integrate the abstract idea into a practical application, these limitations are not sufficient to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (MPEP 2106.04.II.A.2).
Claim 4 specifies that the first cluster is determined using an unsupervised learning model, which is a new additional element beyond those introduced in the independent claim. However, there are no further details, and the unsupervised learning model is utilized in an “apply it” manner and thus does not constitute an improvement to technology, technical field, or to the underlying computer or computer components.
Claim 6 explicitly recites the use of a k-nearest neighbor learning algorithm in analyzing the self-organizing map and generating the ranking. However, k-nearest neighbor learning algorithm is a mathematical concept. Similarly, claim 21 specifies that a conversion algorithm is utilized when converting the first set of time-dependent patient health parameters into the second set of time-independent patient health parameters, and claims 22-23 recites further details on the conversion.
Claim 25 introduces a new additional element, a graphical user interface of a healthcare system, that is used to display the treatment recommendation. However, the graphical user interface simply displays the treatment recommendation and is not disclosed as doing so in an improved way; thus, this element does not provide an improvement to technology, technical field, or to the underlying computer or computer components.
Claims 9-13 are ultimately dependent from Claim(s) 8 and includes all the limitations of Claim(s) 8. Therefore, claim(s) 9-13 recite the same abstract idea of certain methods of organizing human activity of claim 8.
Claims 9-13 all recite limitations that are the same or substantially similar to the limitations of claims 2-6, respectively. Claims 9-13 are rejected for the same reasons as claims 2-6.
Claims 15-20 are ultimately dependent from Claim(s) 14 and includes all the limitations of Claim(s) 14. Therefore, claim(s) 15-20 recite the same abstract idea of certain methods of organizing human activity of claim 14.
Claims 15-20 all recite limitations that are the same or substantially similar to the limitations of claims 2-7, respectively. Claims 15-20 are rejected for the same reasons as claims 2-6.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1-2, 4-9, 11-15, 17-23, 25 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Lynn (US PG Pub 2009/0281838) in view of Moturu (US PG Pub. 2017/0000422) in view of Constantine (US PG Pub 20220215963).
Claim 1
Regarding claim 1, Lynn teaches:
A method comprising:
receiving a historical patient dataset comprising historical health parameters associated with a plurality of historical patients (System receives historical patient data, e.g. see Lynn [0096] and [0113], Fig. 3A, 3C, and the data processed by the system may be for multiple patients, e.g. see Lynn [0054].);
receiving a patient dataset associated with a patient, the patient dataset comprising a first set of time-independent patient health parameters and a first set of time-dependent patient health parameters (The system generates a large set of time-series of data of a patient including at least data relating to the physiologic state and/or care of a patient (i.e. the time-series data is “time-independent” parameters), e.g. see Lynn [0057], and various events comprising a single or few parameters that may be converted to a time-series (i.e. the single or few parameters are “time-dependent” parameters), e.g. see Lynn [0073].);
generating a second set of time-independent patient health parameters based on the first set of time-dependent patient health parameters of the patient dataset (The system converts a single or few parameters (i.e. the first set of time-dependent parameters) obtained from a plurality of sources into a time-series (i.e. a second set of time-independent parameters), e.g. see Lynn [0073].);
Although not taught by Lynn, Moturu discloses
based on the one or more historical health parameters and the patient dataset, generating a plurality of clusters, wherein each cluster, of the plurality of clusters, comprises a particular set of historical patients from the plurality of historical patient
Par. [0077], “In variations, the machine learning algorithm(s) can be characterized by a learning style including any one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering),”
Par. [0077], “Furthermore, the machine learning algorithm can implement any one or more of:… a clustering method (e.g., k-means clustering, expectation maximization, etc.),”
Par. [0076], “In a variation, the method 100 can include forming a subgroup of patients based on medical status (e.g., symptoms, cardiovascular diseases, treatments, medication regimens, etc.); generating a cardiovascular health predictive model from training data (e.g., log of use data, supplementary data, survey data, etc.) associated with patients from the subgroup; and extracting a cardiovascular health parameter from an output of the cardiovascular health predictive model.”
identifying a first particular cluster of the plurality of clusters, based on a similarity of the patient dataset to a first set of historical health parameters associated with a first set of historical patients corresponding to the first cluster
Par. [0076], “In a specific example, the method 100 can include assigning the patient to a cardiovascular subgroup of a set of cardiovascular subgroups, based on a survey dataset including a patient response to a the cardiovascular evaluation digital survey; and retrieving a subgroup predictive model corresponding to the cardiovascular subgroup, wherein generating the cardiovascular health metric is in response to retrieving the subgroup predictive model, and wherein the cardiovascular health predictive model is the subgroup predictive model.” (the system assigns a patient to a subgroup of the set of subgroups based on the patient data, for example obtained from a patient survey).
Although not explicitly taught by Lynn or Moturu, Constantine teaches
generating a ranking of the first set of time-independent patient health parameters and the second set of time-independent patient health parameters based on an effect of a virus on a mortality of each of the first set of historical patients corresponding to the first cluster
(System stores a dataset including patient biomarkers, clinical variables, and/or genetic polymorphisms and outcomes, wherein dataset is used to determine a multiple organ dysfunction (MOD) score that is predictive of multiple organ dysfunction syndrome (MODS) and ranks patient values, e.g. see Constantine [0031], [0034] and [0037], wherein MODS is used to assess risk of intensive care unit (ICU) mortality, and values are ranked in order of their contribution to a High aMOD group and the Low aMOD group, e.g. see Constantine [0026], and to identify meaningful outcome-based endpoints, in addition to mortality, and the validation of methods to expeditiously stratify for patients most likely to benefit from an intervention, e.g. see Constantine [0054].); and
based on the ranking, generating a treatment recommendation for the patient for treating at least one of the second set of time-independent patient health parameter to increase a likelihood of surviving a virus-related illness
(The rankings are used to classify patients into cohorts, wherein the cohorts dictates treatment for the patients, for testing the efficacy of the treatment (i.e. increasing the likelihood of survival), e.g. see Constantine [0040].).
Lynn searches electronic medical records of patients to identify patterns of evolving pathophysiologic cascades, and to seek the type of associated procedures and treatments to treat the cascade. Moturu evaluates the health of a patient by analyzing a dataset associated with a time period to cluster the patient with similar patients and provide a therapeutic intervention. Constantine clusters patients into cohorts using patient data obtained within a time window as a way to provide treatment to those patients. Thus, the references are considered to be analogous as they are all using similar techniques to solve similar problems in the same field of endeavor, namely, analyzing medical records of patients in comparison to other patients in order to identify and provide necessary treatment to address adverse health outcomes. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to cluster patients into groups based on similar and common health data, as taught by Moturu, and rank health parameters based on the impact or effect of a virus on mortality as a basis for treatment recommendations for the patient, as taught by Constantine, because doing so enhances the ability of Lynn to detect issues to improve the prognosis for the patient and to apply goal-directed therapy while clinical intervention is still beneficial [paragraph 72].
Claims 8 and 14 recite limitations that are substantially similar to those of claim 1; thus, the same rejection applies.
Further regarding claim 8, Lynn teaches one or more non-transitory computer-readable media comprising instructions that, when executed by one of more hardware processors, cause performance of operations (paragraph 97 – The data management system 300 includes a monitor 302, a processor 304… alternatively, processors 336, 348 and 360 or instructions for performing the processing steps… may be located on one or more additional processing components in communication with processor 304 that are part of the system 300; paragraph 98 - The data management system 300 may include one or more processor-based components, such as general purpose or application-specific computers. In addition to the processor-based components, the data management system 300 may include various memory and/or storage components including magnetic and optical mass storage devices and/or internal memory, such as RAM chips. The memory and/or storage components may be used for storing programs and routines for performing the techniques described herein that are executed by the processor 304 or by associated components of the data management system 300.).
Further regarding claim 14, Lynn teaches one or more hardware processors, one or more non-transitory computer-readable media and program instructions stored on the one or more non-transitory computer-readable media that when executed by the one or more hardware processors, cause the system to perform operations (paragraph 97 – The data management system 300 includes a monitor 302, a processor 304… alternatively, processors 336, 348 and 360 or instructions for performing the processing steps… may be located on one or more additional processing components in communication with processor 304 that are part of the system 300; paragraph 98 - The data management system 300 may include one or more processor-based components, such as general purpose or application-specific computers. In addition to the processor-based components, the data management system 300 may include various memory and/or storage components including magnetic and optical mass storage devices and/or internal memory, such as RAM chips. The memory and/or storage components may be used for storing programs and routines for performing the techniques described herein that are executed by the processor 304 or by associated components of the data management system 300.).
Claim 2
Regarding claim 2, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Although not taught by Lynn, Constantine teaches
identifying one or more treatable health parameters from among at least one the second set of time-independent patient health parameters
(The system utilizes various patient parameters (i.e. treatable health parameters) to calculate the MOD score, e.g. see Constantine [0034].); and
generating the treatment recommendation for the patient for treating the one or more treatable health parameters
(The MOD score determines a ranking for patient parameters, e.g. see Constantine [0031], wherein the ranking is further used to classify patients into cohorts, wherein the cohorts dictates treatment for the patients, e.g. see Constantine [0040].).
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to use time-independent health parameters to identify treatable parameters as the basis for generating treatment recommendations, as taught by Constantine, because doing so enhances the ability of Lynn to detect issues to improve the prognosis for the patient and to apply goal-directed therapy while clinical intervention is still beneficial [paragraph 72].
Claims 9 and 15 recite limitations that are substantially similar to those of claim 2; thus, the same rejection applies.
Claim 4
Regarding claim 4, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Although not taught by Lynn, Moturu further discloses
wherein the first cluster is determined using an unsupervised learning model.
Par. [0077], “In variations, the machine learning algorithm(s) can be characterized by a learning style including any one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering),”
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to cluster patients using an unsupervised learning model, as taught by Moturu, because doing so enhances the ability of Lynn to detect complex patterns in the sequential and timed trends of both physiologic parameters and laboratory data and to identify the timing of treatment in relation to said trends, as such analysis may benefit caregivers in determining which therapies have the highest success rate for a particular physiological condition cascade [paragraphs 55-57, 86].
Claims 11 and 17 recite limitations that are substantially similar to those of claim 4; thus, the same rejection applies.
Claim 5
Regarding claim 5, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Moturu further discloses
wherein generating a self-organizing map comprising the first set of time-independent patient health parameters and the second set of time-independent patient health parameters;
Par. [0077], “Furthermore, the machine learning algorithm can implement any one or more of: a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.)… an artificial neural network model (e.g., a Perceptron method, a back-propagation method, a Hopfield network method, a self-organizing map method” (e.g., generating a self-organizing map)
Constantine teaches determining the ranking of parameters based on their effect on mortality [the self-organizing map of patient health parameters]
Par. [0031], “at least a ranking algorithm is used to determine if the ranking of a patient’s values, when ranked in comparison to a sufficiently large, statistically-significant set of stored patient values, is greater than a threshold ranking capable of differentiating patients having a significant risk of developing MODS from patients who do not.”
Par. [0034], “By comparing newly-obtained values for a blunt trauma patient or patients to the existing, stored data set, and applying at least a ranking algorithm to those data, a patient can be classified”
Par. [0037], “In a ranking method, for all variables , the values are ranked in order of their contribution to a High aMODD2 - Ds group and the Low aMODD2 Ds group , ranging from the lowest rank value , corresponding to the least contribution to a High aMODD2 - Ds phenotype , to the highest rank value , corresponding to the greatest contribution to a High aMODD2 - Ds phenotype”
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to cluster patients using a self-organizing map, as taught by Moturu, and ranking health parameters, as taught by Constantine, because doing so enhances the ability of Lynn to detect complex patterns in the sequential and timed trends of both physiologic parameters and laboratory data and to identify the timing of treatment in relation to said trends, as such analysis may benefit caregivers in determining which therapies have the highest success rate for a particular physiological condition cascade and to apply goal-directed therapy while clinical intervention is still beneficial [paragraphs 55-57, 72, 86].
Claims 12 and 18 recite limitations that are substantially similar to those of claim 5; thus, the same rejection applies.
Claim 6
Regarding claim 6, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Moturu further discloses
Wherein determining the ranking based on the self-organizing map comprises analyzing the self-organizing map using a k- nearest neighbor learning algorithm to determine, for the first set of time-independent patient health parameters and the second set of time-independent patient health parameters, the effect of the virus on the mortality of the first set of historical patients corresponding to the first cluster; and [generating the ranking based on] the effect determined using the k-nearest neighbor learning algorithm
Par. [0077], “In variations, the machine learning algorithm(s) can be characterized by a learning style including any one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering),”
Par. [0077], “Furthermore, the machine learning algorithm can implement any one or more of: a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.)… an artificial neural network model (e.g., a Perceptron method, a back-propagation method, a Hopfield network method, a self-organizing map method”
Constantine teaches generating and determining the ranking of parameters based on the effect on mortality [determined using the k-nearest neighbor learning algorithm]
Par. [0031], “at least a ranking algorithm is used to determine if the ranking of a patient’s values, when ranked in comparison to a sufficiently large, statistically-significant set of stored patient values, is greater than a threshold ranking capable of differentiating patients having a significant risk of developing MODS from patients who do not.”
Par. [0034], “By comparing newly-obtained values for a blunt trauma patient or patients to the existing, stored data set, and applying at least a ranking algorithm to those data, a patient can be classified”
Par. [0037], “In a ranking method , for all variables , the values are ranked in order of their contribution to a High aMODD2 - Ds group and the Low aMODD2 Ds group , ranging from the lowest rank value , corresponding to the least contribution to a High aMODD2 - Ds phenotype , to the highest rank value , corresponding to the greatest contribution to a High aMODD2 - Ds phenotype”
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to use k-nearest neighbor learning algorithm to evaluate patient health data, as taught by Moturu, and ranking parameters based on mortality risk, as taught by Constantine, because doing so enhances the ability of Lynn to detect complex patterns in the sequential and timed trends of both physiologic parameters and laboratory data and to identify the timing of treatment in relation to said trends, as such analysis may benefit caregivers in determining which therapies have the highest success rate for a particular physiological condition cascade and to apply goal-directed therapy while clinical intervention is still beneficial [paragraphs 55-57, 72, 86].
Claims 13 and 19 recite limitations that are substantially similar to those of claim 6; thus, the same rejection applies.
Claim 7
Regarding claim 7, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Moturu further discloses
wherein determining the ranking based on the self-organizing map comprises analyzing the self-organizing map using a neural network to determine, for the first set of time-independent patient health parameters and the second set of time-independent patient health parameters, the effect of the virus on the mortality of the first set of historical patients corresponding to the first cluster; and [generating the ranking based on] the effect determined using neural network
Par. [0077], “Furthermore, the machine learning algorithm can implement any one or more of: a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.)… an artificial neural network model (e.g., a Perceptron method, a back-propagation method, a Hopfield network method, a self-organizing map method”
Constantine teaches determining the effect on mortality and generating a ranking based on the effect
Par. [0031], “at least a ranking algorithm is used to determine if the ranking of a patient’s values, when ranked in comparison to a sufficiently large, statistically-significant set of stored patient values, is greater than a threshold ranking capable of differentiating patients having a significant risk of developing MODS from patients who do not.”
Par. [0034], “By comparing newly-obtained values for a blunt trauma patient or patients to the existing, stored data set, and applying at least a ranking algorithm to those data, a patient can be classified”
Par. [0037], “In a ranking method , for all variables , the values are ranked in order of their contribution to a High aMODD2 - Ds group and the Low aMODD2 Ds group , ranging from the lowest rank value , corresponding to the least contribution to a High aMODD2 - Ds phenotype , to the highest rank value , corresponding to the greatest contribution to a High aMODD2 - Ds phenotype”
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to use neural network to analyze the self-organizing map, as taught by Moturu, and ranking parameters based on the effect on mortality risk, as taught by Constantine, because doing so enhances the ability of Lynn to detect complex patterns in the sequential and timed trends of both physiologic parameters and laboratory data and to identify the timing of treatment in relation to said trends, as such analysis may benefit caregivers in determining which therapies have the highest success rate for a particular physiological condition cascade and to apply goal-directed therapy while clinical intervention is still beneficial [paragraphs 55-57, 72, 86].
Claim 20 recites limitations that are substantially similar to those of claim 7; thus, the same rejection applies.
Claim 21
Regarding claim 21, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Lynn further teaches
wherein generating the second set of time-independent patient health parameters comprises: converting, via at least one conversion algorithm, the first set of time-dependent patient health parameters into the second set of time-independent patient health parameters
(The system converts a single or few parameters (i.e. the first set of time-dependent parameters) obtained from a plurality of sources into a time-series (i.e. a second set of time-independent parameters), e.g. see Lynn [0073].).
Claim 22
Regarding claim 22, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 21. Lynn further teaches
wherein the first set of time-dependent patient health parameters comprises a time domain and the second set of time-independent patient health parameters comprises a frequency domain (The non-time-series data (i.e. the time-dependent parameters) converted into time series data may include event data comprising a single parameter such as various indices, and wherein the time-series data (i.e. the time-independent parameters) includes various rates (i.e. frequencies), e.g. see Lynn [0073] and [0115].);
wherein converting the first set of time-dependent patient health parameters into the second set of time-independent patient health parameters comprises: converting the time domain of the first set of time-dependent patient health parameters into the frequency domain of the second set of time-independent patient health parameters (The system converts a single or few parameters (i.e. the first set of time-dependent parameters) obtained from a plurality of sources into a time-series (i.e. a second set of time-independent parameters), e.g. see Lynn [0073].).
Claim 23
Regarding claim 23, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 21. Lynn further teaches
wherein a first time-dependent patient health parameter, of the first set of time-dependent patient health parameters, comprises a plurality of data measurements obtained over a period of time (The patient data obtained that is subsequently converted into time series data may include a single or few parameters including values such as patterns of systolic pressure variation, e.g. see Lynn [0073].);
wherein a first time-independent patient health parameter, of the second set of time-independent patient health parameters, comprises a time-independent value representing the plurality of data measurements of the first time-dependent patient health parameter (The converted time-series data (i.e. the second set of time-independent parameters) may be generated from a single or few parameters (i.e. a plurality of data measurements of a time-dependent parameter), e.g. see Lynn [0073], wherein the converted time-series data may be objectified, e.g. see Lynn [0073]. That is, the objectified converted time-series data represents the plurality of parameters used to generate it.);
wherein converting the first set of time-dependent patient health parameters into the second set of time-independent patient health parameters comprises: converting the plurality of data measurements into the time-independent value (The single or few parameters (i.e. plurality of data measurements) is converted into time-series data (i.e. a second set of time-independent parameters), and further objectified (i.e. the time-independent value), e.g. see Lynn [0073].);
Claim 25
Regarding claim 25, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Although not explicitly taught by Lynn, Constantine further teaches
initiating a treatment for the patient based on the treatment recommendation at least by displaying the treatment recommendation on a graphical user interface of a healthcare system, wherein the treatment recommendation is accessible by a clinician via the graphical user interface
(The system includes a display that provides the outputs of the system, e.g. see Constantine [0046], wherein the outputs include the results of the classification of the patients, and wherein the results of the classification of the patients includes dictating how the patient is treated, for example with a drug, e.g. see Constantine [0040].).
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to initiate treatment on a patient based on the recommendation, as taught by Constantine, because doing so enhances the ability of Lynn to determine which therapies have the highest success rate for a particular physiological condition cascade and to apply goal-directed therapy while clinical intervention is still beneficial [paragraphs 55-57, 72, 86].
Claim 27
Regarding claim 27, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 2. Although not explicitly taught by Lynn, Constantine further teaches
wherein the one or more treatable health parameters comprises one or more comorbidities
(The patient groups include patients grouped by comorbidities, e.g. see Constantine [0108].)
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to consider comorbidities as a treatable health parameters, as taught by Constantine, because doing so enhances the ability of Lynn to detect complex patterns in the sequential and timed trends of both physiologic parameters and laboratory data and to identify the timing of treatment in relation to said trends, as such analysis may benefit caregivers in determining which therapies have the highest success rate for a particular physiological condition cascade and to apply goal-directed therapy while clinical intervention is still beneficial [paragraphs 55-57, 72, 86].
Claims 3, 10, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lynn in view of Moturu in view of Constantine, and further in view of Aggarwal (US PG Pub 2010/0125462).
Claim 3
Regarding claim 3, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Although not explicitly taught by Lynn, Aggarwal teaches
determining, for one or more treatable health parameters from among at least one of the second set of time-independent patient health parameters, a survival percentage based on treating the one or more treatable health parameters
(The system includes an analytics engine that receives input parameters, for example age, ethnicity, stage of the disease, etc., and outputs an overall survival that represents the likelihood of patient survival for a number of years after a treatment, e.g. see Aggarwal [0026]-[0029].); and
generating the treatment recommendation for the patient for treating the one or more treatable health parameters based on the survival percentage
(The system presents the user with a plurality of potential treatments (i.e. treatment recommendations) and their respective likelihoods of survival, e.g. see Aggarwal [0053]-[0055], Fig. 3.).
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Aggarwal is deemed to be analogous, as it performs a statistical and computational analysis of case histories of other patients to identify the statistical significance and probability of output parameters in evaluating various treatment protocols. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to use survival percentage as the basis for generating treatment recommendations for treating a treatable health parameter, as taught by Aggarwal, because doing so enhances the ability of Lynn to detect complex patterns in the sequential and timed trends of both physiologic parameters and laboratory data and to identify the timing of treatment in relation to said trends, as such analysis may benefit caregivers in determining which therapies have the highest success rate for a particular physiological condition cascade and to apply goal-directed therapy while clinical intervention is still beneficial [paragraphs 55-57, 72, 86].
Claims 10 and 16 recite limitations that are substantially similar to those of claim 3; thus, the same rejection applies.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Lynn in view of Moturu in view of Constantine, and further in view of Bulat (US PG Pub 2006/0293572).
Claim 24
Regarding claim 24, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Lynn further teaches
wherein the ranking comprises:
a first time-independent patient health parameter, of the second set of time-independent patient health parameters generated based on the first set of time-dependent patient health parameters
(The system converts non-time-series data (i.e. a first set of time-dependent parameters) into time-series data (i.e. the second set of time-independent parameters), wherein the time-series data includes at least one data element, e.g. see Lynn [0073].); and
a second time-independent patient health parameter, of the first set of time-independent patient health parameters
(The time-series data includes at least one data element, e.g. see Lynn [0073].);
Although not explicitly taught by Lynn, Bulat teaches
wherein the treatment recommendation comprises treating the first time-independent patient health parameter and refraining from treating the second time-independent patient health parameter
(The system evaluates the patient data to determine if the patient is suffering from a treatable or non-treatable condition, e.g. see Bulat [0162]-[0163], Fig. 5B, wherein the system determines a treatment for the patient, for example a prescription, when it is determined that the patient condition is treatable, e.g. see Bulat [0163], and refers to patient to another process, for example sending the patient home, when it is determined that a patient condition is not treatable (i.e. refrains from treating the second time-independent parameter), e.g. see Bulat [0162].);
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Bulat teaches ways to deliver treatment services for a patient to treat a health condition. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to provide treatment recommendations to treat certain health parameter while refraining from treating a certain health parameters, as taught by Bulat, because doing so enhances the ability of Lynn to detect complex patterns in the sequential and timed trends of both physiologic parameters and laboratory data and to identify the timing of treatment in relation to said trends, as such analysis may benefit caregivers in determining which therapies have the highest success rate for a particular physiological condition cascade and to apply goal-directed therapy while clinical intervention is still beneficial [paragraphs 55-57, 72, 86].
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Lynn in view of Moturu in view of Constantine and further in view of Zhao (“The Mechanism of Multiple Organ Dysfunction Syndrome in Patients with COVID-19”, Journal of Medical Virology 2022; 94: 1886-1892).
Claim 26
Regarding claim 26, Lynn in view of Moturu in view of Constantine discloses all the limitations of claim 1. Although not explicitly taught by Lynn, Zhao teaches
wherein the virus comprises a corona virus
(System determines a MOD score that is predictive of MODS and ranks patient values, e.g. see Constantine [0031] and [0034], wherein MODS is used to assess risk of intensive care unit (ICU) mortality, e.g. see Constantine [0026], wherein COVID-19 may cause MODS, e.g. see Zhao Introduction and Section 2 “Mechanisms of Infected with COVID-19 Leading to MODS”.).
As previously stated, Lynn, Moturu and Constantine are analogous references as they are directed towards using similar techniques to solve similar problems in the same field of endeavor. Therefore, it would have been obvious before the effective filing date of the invention to modify the teachings of Lynn to apply to corona virus, as taught by Zhao, because doing so enhances the ability of Lynn to detect complex patterns in the sequential and timed trends of both physiologic parameters and laboratory data and to identify the timing of treatment in relation to said trends, as such analysis may benefit caregivers in determining which therapies have the highest success rate for a particular physiological condition cascade and to apply goal-directed therapy while clinical intervention is still beneficial [paragraphs 55-57, 72, 86].
Response to Arguments
Applicant’s arguments with respect to the prior art rejection of the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant is directed to the updated prior art rejection.
Applicant's arguments pertaining to the 35 USC 101 rejection filed on May 12, 2025 have been fully considered but they are not persuasive.
Applicant argues that claim 1 does not recite an abstract idea under Step 2A Prong One because the claim is not directed to “certain methods of organizing human activity” and cannot be practicably be performed in the human mind. Applicant argues that the elements recited in claim 1 are not rules or instructions to be performed by a human, would not be performed by a human, and cannot practically be performed in the human mind.
This argument is not persuasive. Firstly, it is noted that “mental processes” is not any of the abstract idea grouping applied in the rejection, so the premise that any of the steps not being capable of being in the human mind are misplaced. Further, even if “mental processes” was applied, MPEP 2106.04(a)(2)(III) states the “[t]he courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).” The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed “conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally,” i.e., “as a person would do it by head and hand.”). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, “[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.” Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). Claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”). In particular, applicant’s attention is directed towards Benson, in which converting data was deemed to be abstract.
Applicant argues that generating the treatment recommendation as recited in claim 1 would not be encompassed within “certain methods of organizing human activity”. Applicant argues that “generating a plurality of clusters” is improperly grouped within the abstract idea of “certain methods of organizing human activity” because they are technical elements that would not be performed by a human. Applicant argues that claim 1 is analogous to Ex parte Hannun because the claims are directed to a specific implementation of technical elements.
This argument is not persuasive. It is noted that claim 1 is entirely devoid of a computer, processor or any computer hardware or software. There are no “technical elements” recited or implemented in claim 1. The various steps, acts and limitations set forth in claim 1 are not specified as to how they are performed, only that they are. Looking to independent claims 8 and 14, the hardware processors, media/memory and executable program instructions are simply used in an “apply it” manner to automate the abstract idea, generally linking the abstract idea to a technological environment. It is not evident and applicant does not identify any specific limitation in any of the presented claims that any of these steps could not be performed by a human, or even constitutes a “technical element”.
Applicant makes several arguments that the claims are improperly categorized as “certain methods of organizing human activity” because the various elements are technical, are not rules or instructions to be performed by a human, would not be performed by a human, and cannot practically be performed in the human mind, citing various PTAB decisions, USPTO Examples 37, 38, and the July 17 2024 Guidance Update on Patent Subject Matter Eligibility.
This argument is not persuasive. As noted above, applicant does not identify any specific limitation as being a “technical element”. Assuming that the applicant interprets every limitation as being a “technical element”, the argument that they would not be performed by a human is not persuasive because, as noted above, claim 1 does not specify any manner in which they are performed or executed, which cannot exclude or prohibit them being performed by a human, whether or not a computer is also involved. The July 2024 Guidance Update does not apply to claim 1, as the examples contained therein all relate to artificial intelligence, which is not recited in any of the independent claims. Artificial intelligence is not introduced until dependent claims 4, 11 and 17 (unsupervised learning model), 6, 13 and 19 (k-nearest neighbor learning algorithm), 7 and 20 (neural network). However, each of these are known artificial intelligence/machine learning model types, with no details or specificity beyond their use or application, which is deemed to be “apply it”. None of these claims constitute a new or improved artificial intelligence or machine learning model or technique, and thus would not constitute any improvement or solution to any problem presented by generically using or applying artificial intelligence or machine learning.
The instant claims are not similar, comparable or analogous to Example 37. Claim 1 of Example 37 is eligible because the additional elements recite a specific manner of automatically displaying icons to the user based on usage which provides a specific improvement over prior systems, resulting in an improved user interface for electronic devices. The additional elements of the instant claims do not provide any similar function or improvement, and there is no improvement to the user interface, as the only change is to what data (e.g., recommendation) is presented rather than any improvement to how it is presented. Claim 2 of Example 37 is eligible, but because of a lack of judicial exception. However, the instant claims do recite a judicial exception, specifically an abstract idea. Claim 3 of Example 37 is most applicable to the instant claims, as it recites an abstract idea and does not recite additional elements that integrate the abstract idea into a practical application or provide an inventive concept. Similar to claim 3, the additional elements in the instant claims are recited at a high level of generality that are no more than mere instructions to apply the exception using a generic computer component which does not impose any meaningful limits on practicing the abstract idea, integrate a judicial exception into a practical application or provide an inventive concept.
The instant claims are not similar, comparable or analogous to Example 38. Claim 1 of Example 38 is eligible, but because of a lack of judicial exception. However, the instant claims do recite a judicial exception, specifically an abstract idea.
Applicant argues claim 1 integrates the contended judicial exception into a practical application under Step 2A Prong 2 because the claim improves healthcare system technology for determining virus mortality/survival prognosis for a patient and for generating a treatment recommendation for a patient to increase a likelihood of surviving a virus-related illness. Specifically, applicant points to converting time-dependent parameters into time-independent parameters so they can be clustered together and included in rankings together. Applicant argues that claim 1 provides a technological solution to a technological problem, arguing that the conversion of time-dependent parameters to time-independent parameters and clustering the converted parameters with other time-independent parameters is a “particular solution” to the problem that time-dependent parameters “may not be input into the current model as such and must be converted” to time-independent parameters.
This argument is not persuasive. The limitations identified by the applicant are part of the abstract idea. An abstract idea cannot integrate itself into a practical application. Any integration of the abstract idea into a practical application is provided by the additional elements. As noted above, claim 1 does not recite any additional elements. The other independent claims, claims 8 and 14, only recite additional elements (hardware processor, memory/media, executable instructions) that are recited at a high level of generality such that they are no more than mere instructions to apply the exception using a generic computer component which does not impose any meaningful limits on practicing the abstract idea, integrate a judicial exception into a practical application or provide an inventive concept.
Applicant argues that claim 1 amounts to significantly more than the contended judicial exception itself, because it recites an inventive concept at least in view of converting time-dependent parameters into time-independent parameters, clustering them together with other time-independent parameters, generating a ranking of patient health parameters that include both time-independent parameters as well as time-dependent parameters that were converted to time-independent parameters, and generating a treatment recommendation for the patient based on the ranking.
This argument is not persuasive. Similar to the previous argument, the limitations identified by the applicant are part of the abstract idea. An abstract idea cannot integrate itself into a practical application. Any integration of the abstract idea into a practical application is provided by the additional elements. As noted above, claim 1 does not recite any additional elements. The other independent claims, claims 8 and 14, only recite additional elements (hardware processor, memory/media, executable instructions) that are recited at a high level of generality such that they are no more than mere instructions to apply the exception using a generic computer component which does not impose any meaningful limits on practicing the abstract idea, integrate a judicial exception into a practical application or provide an inventive concept. Furthermore, based on the specification, the details of “converting” the parameters imply mathematical techniques. For example, use of a Fourier transform to convert time-dependent data into a frequency domain (paragraph 52 of the specification), using transformation and fizzy logic algorithms (paragraph 54 of the specification), using an algorithm to convert non-stationary features into a waveform by converting measurements to values such that the dataset may be clustered, using a conversion algorithm, which may use fuzzy logic and other frequency algorithms (paragraph 48 of specification).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sircar (US PG Pub 20140195269) teaches ranking and weighting health parameters based on their effect on human physiology.
Hanlon et al. (US Patent 12,136,493) teaches providing recommendations describing which wellness factors are most in need of remediation and specific recommended actions for the subject.
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.
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/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681