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 action is in reply to Applicant’s communication filed on 10/8/2025.
Claims 1-2, 4, 6, 8-9, 12, and 14-20 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL. Status of Claims
Claim Objections
Claims 1, 9 and 12 are objected to because of the following informalities:
Regarding Claim 1, this amended claim currently recites both “entity monitoring data” and “monitoring data” in lines 17-19. Examiner suggests amending to clarify whether these are the same data or different because as currently recited, the limitations could be interpreted, using broadest reasonable interpretation, to be different distinct types of data and/or sets of data.
Regarding Claim 9, Applicant amended claim 1 to now recite “a risk prevalence ratio” as is also recited in claim 9. Examiner suggests amending the claims in order to remain consistent and clear as to whether these are the same ratios, or different ratios.
Regarding Claim 12, this claim has been amended to now recite “the entity monitoring action is an entity monitoring action” in lines 2-3. Examiner notes the redundancy of this limitation and suggests amending the claim appropriately.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
Claims 1, 16 and 19 are directed to a method, system and a manufacture respectively and therefore all fall into one of the four statutory categories. (Step 1: Yes, the claims fall into one of the four statutory categories).
Step 2A analysis - Prong one:
The substantially similar independent method, system, and computer readable media claims, taking claim 1 as exemplary, recite the following limitations: inputting, by one or more processors, historical input data, associated with an entity of a disparity group associated with an entity cohort, to a risk prediction machine learning model to receive a first score for the entity, wherein the first score indicates a likelihood of a presence of one or more of a disease, a disorder, or an impairment within the entity; inputting, by the one or more processors, (i) the first score for the entity and (ii) a risk prevalence ratio associated with the disparity group to a disparity risk adjustment machine learning model to receive a disparity adjusted risk score for the entity, wherein the disparity adjusted risk score indicates a second score associated with one or more of the disease, the disorder, or the impairment; initiating, by the one or more processors and responsive to the second score satisfying a score threshold, an entity monitoring action for the entity, wherein the entity monitoring action comprises causing an entity monitoring computing device to collect entity monitoring data associated with the entity; and updating, by the one or more processors, the first score for the entity based on monitoring data generated by the entity monitoring computing device associated with the entity.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The un-bolded limitations above, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a system implemented by one or more processors (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the processor(s), this claim encompasses a person collecting a patients historical data, determining a first score for a patient and then a second adjusted score for a patient based on data associated with the patients respective disparity group, comparing the second score to a score threshold, collecting monitoring data if the second score satisfies a threshold, and updating the patients first score based on the new/monitoring data in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A – Prong 1: Yes, the claims are abstract).
Step 2A analysis - Prong two:
Claims 1, 16 and 19 recite additional elements beyond the abstract idea. Claims 1, 16 and 19 recite one or more processors, a risk prediction machine learning model, a disparity risk adjustment machine learning model, and an entity monitoring computing device. Claim 16 further recites a memory and instructions. Claim 19 further recites one or more non-transitory computer readable storage media and instructions. The claims are applying generic computer components to the recited abstract limitations. The recited instructions appear to be software.
This judicial exception is not integrated into a practical application. In particular, the claims recite one or more processors, a risk prediction model, a disparity risk adjustment model, an entity monitoring computing device, a memory, one or more non-transitory computer readable storage media and instructions which are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exceptions using a generic computer component. For example, Applicant’s specification explains that the processor(s) receives inputs, executes instructions stored in memory, analyzes inputs, and outputs results to a display (see Applicant’s specification paras 28, 35; FIGs. 2-3). Accordingly, this/these additional element(s), when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because it/they does/do not impose any meaningful limits on practicing the abstract idea. Therefore, Claims 1, 16 and 19 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional claimed elements are not integrated into a practical application).
Step 2B analysis:
For the next step of the analysis, it must be determined whether the limitations present in the claims represent a patent-eligible application of the abstract idea. A claim directed to a judicial exception must be analyzed to determine whether the elements of the claim, considered both individually and as an ordered combination are sufficient to ensure that the claim as a whole amounts to significantly more than the exception itself.
For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of well-understood, routine, and conventional activities previously known to the industry. Further, the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention. See MPEP 2106.05(d).
Applicant’s specification discloses the following:
Applicant describes embodiments of the disclosure at a very high level to include the use of a wide variety of computing devices, processors, networks, software, buses, memories, sensors, machine learning models, interfaces, etc. (see paras 25-32, 34-37, 39-45, 53, 65, 73). The invention, may use any computer via any transmission medium (a communication network or broadcast waves) capable of transmitting the program.
Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
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. The collective functions appear to be implemented using conventional computer systemization.
The claims 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 one or more processors, a risk prediction model, a disparity risk adjustment model, an entity monitoring computing device, a memory, one or more non-transitory computer readable storage media and instructions to perform all of the steps discussed above amount to no more than mere instructions to apply the exceptions using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims do not provide an inventive concept significantly more than the abstract idea. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2B: No, the claims do not provide significantly more).
Dependent Claims 2-15, 17-18 and 20 further define the abstract idea that is presented in independent Claims 1, 16 and 19 respectively, and are further grouped as certain methods of organizing human activity and are abstract for the same reasons and basis as presented above. Further, Claims 2, 6, 17 and 20 recite additional elements beyond the abstract idea. Claims 2, 17 and 20 recite an evaluation risk adjustment machine learning model. Claim 6 recites a multi-dimensional location ranking machine learning model. This/these additional element(s) is/are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. For example, as noted above, Applicant’s specification indicates the use of known machine learning models. Accordingly, this/these additional element(s), when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not recite additional elements that integrate the judicial exception into a practical application when considered both individually and as an ordered combination. Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 1-2, 4, 9-10, 16-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Brutsche et al. (US 20220036156), in view of Abou Shousha et al. (WO 2020023959), in view of Soto et al. (US 20130132323), further in view of Jain et al. (US 11456080).
Regarding Claim 1, Brutsche discloses the following limitations:
…an entity of a disparity group associated with an entity cohort,… receive a first score for the entity, (Examiner interprets “a disparity group associated with an entity cohort” to be any group of persons within a cohort or population; e.g., see Applicant’s specification paras 96, 119) (Brutsche discloses systems and methods for generating intermediate entity risk scores (receive a first score for the entity) using nested machine learning models. The entity may include a candidate, a proctor, etc. (an entity of a disparity group associated with an entity cohort). – abstract; paras 16)
inputting, by the one or more processors, (i) the first score for the entity…to a disparity risk adjustment machine learning model to receive a disparity adjusted risk score for the entity, wherein the disparity adjusted risk score indicates a second score… (Brutsche discloses producing one or more aggregate risk scores (receive a disparity adjusted risk score for the entity) based on the intermediate entity risk score (inputting (i) the first score for the entity) using nested machine learning models (to a disparity risk adjustment machine learning model). – abstract)
initiating, by the one or more processors and responsive to the second score satisfying a score threshold, an entity monitoring action for the entity, (Brutsche discloses comparing the one or more aggregate risk scores (the second score) to one or more thresholds (a score threshold) to determine when one or more predefined actions should be taken (initiating an entity monitoring action for the entity). The resource management processor (by the one or more processors) may execute computer-readable instructions for performing enhanced monitoring of the candidate in response to determining that the aggregate risk score exceeds the predetermined threshold (responsive to the second score satisfying a score threshold). – abstract; para 11)
wherein the entity monitoring action comprises causing an entity monitoring computing device to collect entity monitoring data associated with the entity; (Brutsche discloses that the resource management processor may execute computer-readable instructions for performing enhanced monitoring of the candidate (collect entity monitoring data associated with the entity) in response to determining that the aggregate risk score exceeds the predetermined threshold. Such predefined actions may be causing audio and/or video recording (e.g., executed by respective audio and/or video recording devices) to be activated (causing an entity monitoring computing device to collect entity monitoring data). – abstract; paras 11, 137)
Brutsche does not disclose the following limitations met by Abou Shousha:
A computer-implemented method, comprising: inputting, by one or more processors, historical input data, associated with an entity…, to a risk prediction machine learning model to receive a first score for the entity, ; (Abou Shousha teaches that the input data (e.g., provided to the prediction model (inputting to a risk prediction machine learning model) and on which the predictions may be based) may consist of past medical history (historical input data). The generated prediction may include a set of scores (receive a first score for the entity). – paras 19, 51, 78, 83)
wherein the first score indicates a likelihood of a presence of one or more of a disease, a disorder, or an impairment within the entity; …a second score associated with one or more of the disease, the disorder, or the impairment; (Abou Shousha teaches results derived from an AI model indicating the presence of a subclinical case of an eye disease (a likelihood of a presence of a disease). A first prediction (the first score) and a second prediction (a second score) may be obtained via the prediction model. The generated prediction may include a set of scores. Each score may represent a likelihood of a medical condition-related to a corneal or anterior segment condition. More specifically, the AI model 160 has been trained to predict corneal or anterior segment conditions, such as keratoconus (a disease), dry eye (a disorder), Fuchs’ dystrophy (a disease), graft rejection episode or failure (an impairment), cataract (a disease…an impairment), and glaucoma (a disease). – abstract; paras 19, 78, 83-84)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate using historical data as input and generating prediction scores indicating a likelihood of a medical condition as taught by Abou Shousha in order to avoid invasive and time consuming techniques (see Abou Shousha para 5).
Brutsche and Abou Shousha do not disclose the following limitations met by Soto:
(ii) a risk prevalence ratio associated with the disparity (Soto teaches that the “Odds Ratio” (a risk prevalence ratio) for “One-Year Mortality” is calculated as the actual mortality divided by the “Minimal” mortality for the test population. – paras 130-131)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate an odd ratio to assess the health risk or status of individual patients in a population as taught by Soto in order to make an expert description or proactive recommendation in the form of a risk assessment (see Soto para 30).
Brutsche, Abou Shousha, and Soto do not disclose the following limitations met by Jain:
and updating, by the one or more processors, the first score for the entity based on monitoring data generated by the entity monitoring computing device associated with the entity. (Jain teaches an infection prediction score (the first score for the entity), determined using a machine learning model, that indicates a likelihood that the first user has the disease. Monitoring data is obtained from individuals (based on monitoring data generated by the entity monitoring computing device associated with the entity) on an ongoing or continual basis using digital platforms, as well as obtain aggregated data regarding communities and the effects of a disease. The system can use these inputs to repeatedly generate updated predictions (updating the first score for the entity) and also to update the predictive models. – col 2, lines 9-14; col 8, lines 7-28; col 11, lines 10-16; col 15, lines 13-16)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate updating infection prediction scores by obtaining monitoring data as taught by Jain in order to prevent, contain, or otherwise manage the spread of disease (See Jain col 1, lines 21-24).
Regarding Claim 2, Brutsche, Abou Shousha, Soto and Jain disclose all the limitations above and further disclose the following limitations:
The computer-implemented method of claim 1, wherein the risk score threshold is a high risk score threshold, (Brutsche discloses that the risk score is considered a high risk score if the risk score exceeds the threshold (a high risk score threshold). – para 137)
the entity monitoring action is an entity evaluation action, (Brutsche discloses monitoring the entity more closely (an entity evaluation action) when the risk score exceeds the threshold. For example, activating audio or video during an exam event when the risk score is a high risk score. – para 137)
and the computer-implemented method further comprises: generating, using an evaluation risk adjustment machine learning model, an evaluation adjusted risk score for the entity based on the disparity adjusted risk score and an evaluation data object for the entity, (Brutsche discloses that the aggregate risk score (the disparity adjusted risk score) may be periodically or continuously updated (an evaluation adjusted risk score for the entity) in real-time while the exam is being delivered. After a predefined action is taken based on the threshold being exceeded, the process returns back to receiving data (an evaluation data object for the entity) and generating updated risk scores. – paras 137, 190-194, 202; FIG. 6C)
wherein the evaluation data object is generated based on the entity evaluation action; (Brutsche discloses generating updated risk scores based on received data (the evaluation data object) after taking a predefined action (based on the entity evaluation action). – paras 190-194, 202; FIG. 6)
and initiating, by the one or more processors, a subsequent prediction-based action for the entity based on a comparison between the evaluation adjusted risk score and the score threshold. (Brutsche discloses repeating the process of generating an updated risk score, comparing the updated risk score to the threshold (based on a comparison between the evaluation adjusted risk score and the score threshold), if it exceeds, then taking a predetermined action (a subsequent prediction-based action for the entity). – paras 190-194, 202; FIG. 6C)
Regarding Claim 4, Brutsche, Abou Shousha, Soto and Jain disclose all the limitations above and further disclose the following limitations:
The computer-implemented method of claim 2, wherein the computer-implemented method further comprises: generating, by the one or more processors and using the risk prediction machine learning model, correspondence associated with the entity evaluation action; and providing, by the one or more processors, data indicative of the correspondence associated with the entity evaluation action. (Brutsche discloses that if the threshold is exceeded one or more predefined actions may be taken. For example, requiring enhanced identity verification of the candidate at the end of exam delivery, triggering a human investigation of the candidate and/or test center, triggering advanced monitoring of the candidate while the exam delivery event is in progress, triggering active proctor review of a real-time video and/or audio feed of the candidate during the exam delivery event, etc. (generating correspondence associated with the entity evaluation action). The candidate and or the proctor may be notified of the action (providing data indicative of the correspondence associated with the entity evaluation action). – paras 194-201)
Regarding Claim 9, Brutsche, Abou Shousha, Soto and Jain disclose all the limitations above and further disclose the following limitations:
The computer-implemented method of claim 1, wherein generating the disparity adjusted risk score further comprises: performing, by the one or more processors, a disparity risk adjustment, wherein the disparity risk adjustment comprises: generating, using the risk prediction machine learning model, a risk prevalence ratio, wherein the risk prevalence ratio is a ratio of a documented risk prevalence associated with the disparity group to an estimated risk prevalence associated with the disparity group, (Soto teaches that the “Odds Ratio” (a risk prevalence ratio) for “One-Year Mortality” is calculated as the actual mortality (documented risk prevalence ) divided by the “Minimal” mortality (an estimated risk prevalence) for the test population (associated with the disparity group). – paras 130-131)
wherein the estimated risk prevalence is determined based on aggregating the first score for one or more respective entities in the disparity group associated with the entity cohort; (Soto teaches that the “Odds Ratio” for “One-Year Mortality” is calculated as the actual mortality divided by the “Minimal” mortality (the estimated risk prevalence) for the test population (aggregating the first score for one or more respective entities). Where the gathered data includes data collected from a test study group of persons and the responses are scored (aggregating the first score for one or more respective entities). – paras 109-110, 130-131)
and generating the disparity adjusted risk score based on applying at least the risk prevalence ratio and an entity-defined disparity weighting parameter to the first score associated with the entity. (Soto teaches assessing the health risk or status of individual patients based on input data by utilizing statistical models each using one or more parameters (weighting parameter to the first score), for example, regression coefficients are used for calculating point estimates for an outcome of interest. the “Odds Ratio” for “One-Year Mortality” is shown in Table 1. Table 1 may be used (applying at least the risk prevalence ratio) to relate a person's health status, demographic, or clinical information, with an outcome likelihood in the domain of mortality or ACS hospitalization. – paras 28, 87, 204)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate calculating an odd ratio to assess the health risk or status of individual patients in a population as taught by Soto in order to make an expert description or proactive recommendation in the form of a risk assessment (see Soto para 30).
Regarding Claim 10, Brutsche, Abou Shousha, Soto and Jain disclose all the limitations above and further disclose the following limitations:
The computer-implemented method of claim 9, wherein the estimated risk prevalence associated with the disparity group is updated based on the disparity adjusted risk score associated with the entity. (Soto teaches the ability to rapidly disseminate assessments in a variety of formats and to be updatable as new information becomes available. Thus, the data-gathering step may proceed over a period of time, such that the survey responses may continually update throughout this period. – paras 30, 110)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate calculating an odd ratio based on continually updated input data to assess the health risk or status of individual patients in a population as taught by Soto in order to make an expert description or proactive recommendation in the form of a risk assessment (see Soto para 30).
Regarding Claim 16, Brutsche discloses the following limitations:
A system comprising: one or more processors; and at least one memory storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to: (Brutsche discloses systems and methods for generating intermediate entity risk scores using nested machine learning models (one or more processors). The computer system may comprise one or more storage subsystems, comprising hardware and software components used for storing data and program instructions (storing processor-executable instructions), such as system memory (at least one memory) and computer-readable storage media. – abstract; paras 16, 54; FIG. 2)
…an entity of a disparity group associated with an entity cohort,… receive a first score for the entity (Examiner interprets “a disparity group associated with an entity cohort” to be any group of persons within a cohort or population; e.g., see Applicant’s specification paras 96, 119) (Brutsche discloses systems and methods for generating intermediate entity risk scores (receive a first score for the entity) using nested machine learning models. The entity may include a candidate, a proctor, etc. (an entity of a disparity group associated with an entity cohort). – abstract; paras 16)
input, (i) the first score for the entity…to a disparity risk adjustment machine learning model to receive a disparity adjusted risk score for the entity, wherein the disparity adjusted risk score indicates a second score… (Brutsche discloses producing one or more aggregate risk scores (receive a disparity adjusted risk score for the entity) based on the intermediate entity risk score (inputting (i) the first score for the entity) using nested machine learning models (to a disparity risk adjustment machine learning model). – abstract)
initiate, responsive to the second score satisfying a score threshold, an entity monitoring action for the entity, (Brutsche discloses comparing the one or more aggregate risk scores (the second score) to one or more thresholds (a score threshold) to determine when one or more predefined actions should be taken (initiating an entity monitoring action for the entity). The resource management processor (by the one or more processors) may execute computer-readable instructions for performing enhanced monitoring of the candidate in response to determining that the aggregate risk score exceeds the predetermined threshold (responsive to the second score satisfying a score threshold). – abstract; para 11)
wherein the entity monitoring action comprises causing an entity monitoring computing device to collect entity monitoring data associated with the entity; (Brutsche discloses that the resource management processor may execute computer-readable instructions for performing enhanced monitoring of the candidate (collect entity monitoring data associated with the entity) in response to determining that the aggregate risk score exceeds the predetermined threshold. Such predefined actions may be causing audio and/or video recording (e.g., executed by respective audio and/or video recording devices) to be activated (causing an entity monitoring computing device to collect entity monitoring data). – abstract; paras 11, 137)
Brutsche does not disclose the following limitations met by Abou Shousha:
input, historical input data, associated with an entity…, to a risk prediction machine learning model to receive a first score for the entity, (Abou Shousha teaches that the input data (e.g., provided to the prediction model (inputting to a risk prediction machine learning model) and on which the predictions may be based) may consist of past medical history (historical input data). The generated prediction may include a set of scores (receive a first score for the entity). – paras 19, 51, 78, 83)
wherein the first score indicates a likelihood of a presence of one or more of a disease, a disorder, or an impairment within the entity; …a second score associated with one or more of the disease, the disorder, or the impairment; (Abou Shousha teaches results derived from an AI model indicating the presence of a subclinical case of an eye disease (a likelihood of a presence of a disease). A first prediction (the first score) and a second prediction (a second score) may be obtained via the prediction model. The generated prediction may include a set of scores. Each score may represent a likelihood of a medical condition-related to a corneal or anterior segment condition. More specifically, the AI model 160 has been trained to predict corneal or anterior segment conditions, such as keratoconus (a disease), dry eye (a disorder), Fuchs’ dystrophy (a disease), graft rejection episode or failure (an impairment), cataract (a disease…an impairment), and glaucoma (a disease). – abstract; paras 19, 78, 83-84)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate using historical data as input and generating prediction scores indicating a likelihood of a medical condition as taught by Abou Shousha in order to avoid invasive and time consuming techniques (see Abou Shousha para 5).
Brutsche and Abou Shousha do not disclose the following limitations met by Soto:
(ii) a risk prevalence ratio associated with the disparity group (Soto teaches that the “Odds Ratio” (a risk prevalence ratio) for “One-Year Mortality” is calculated as the actual mortality divided by the “Minimal” mortality for the test population. – paras 130-131)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate an odd ratio to assess the health risk or status of individual patients in a population as taught by Soto in order to make an expert description or proactive recommendation in the form of a risk assessment (see Soto para 30).
Brutsche, Abou Shousha, and Soto do not disclose the following limitations met by Jain:
and update the first score for the entity based on monitoring data generated by the entity monitoring computing device associated with the entity. (Jain teaches an infection prediction score (the first score for the entity), determined using a machine learning model, that indicates a likelihood that the first user has the disease. Monitoring data is obtained from individuals (based on monitoring data generated by the entity monitoring computing device associated with the entity) on an ongoing or continual basis using digital platforms, as well as obtain aggregated data regarding communities and the effects of a disease. The system can use these inputs to repeatedly generate updated predictions (updating the first score for the entity) and also to update the predictive models. – col 2, lines 9-14; col 8, lines 7-28; col 11, lines 10-16; col 15, lines 13-16)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate updating infection prediction scores by obtaining monitoring data as taught by Jain in order to prevent, contain, or otherwise manage the spread of disease (See Jain col 1, lines 21-24).
Regarding Claim 17, Brutsche, Abou Shousha, Soto and Jain disclose all the limitations above and further disclose the following limitations:
The system of claim 16, wherein the score threshold is a high risk score threshold, (Brutsche discloses that the risk score is considered a high risk score if the risk score exceeds the threshold (a high risk score threshold). – para 137)
the entity monitoring action is an entity evaluation action, and the one or more processors are further configured to: generate, using an evaluation risk adjustment machine learning model, an evaluation adjusted risk score for the entity based on the disparity adjusted risk score and an evaluation data object for the entity, (Brutsche discloses that the aggregate risk score (the disparity adjusted risk score) may be periodically or continuously updated (an evaluation adjusted risk score for the entity) in real-time while the exam is being delivered. After a predefined action is taken based on the threshold being exceeded, the process returns back to receiving data (an evaluation data object for the entity) and generating updated risk scores. – paras 137, 190-194, 202; FIG. 6)
wherein the evaluation data object is generated based on the entity evaluation action; (Brutsche discloses generating updated risk scores based on received data (the evaluation data object) after taking a predefined action (based on the entity evaluation action). – paras 190-194, 202; FIG. 6)
and initiate a subsequent prediction-based action for the entity based on a comparison between the evaluation adjusted risk score and the score threshold. (Brutsche discloses repeating the process of generating an updated risk score, comparing the updated risk score to the threshold (based on a comparison between the evaluation adjusted risk score and the risk score threshold), if it exceeds, then taking a predetermined action (a subsequent prediction-based action for the entity). – paras 190-194, 202; FIG. 6)
Regarding Claim 19, Brutsche discloses the following limitations:
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: (Brutsche discloses systems and methods for generating intermediate entity risk scores using nested machine learning models (by one or more processors). – abstract)
an entity of a disparity group associated with an entity cohort,…receive a first score for the entity, (Examiner interprets “a disparity group associated with an entity cohort” to be any group of persons within a cohort or population; e.g., see Applicant’s specification paras 96, 119) (Brutsche discloses systems and methods for generating intermediate entity risk scores (receive a first score for the entity) using nested machine learning models. The entity may include a candidate, a proctor, etc. (an entity of a disparity group associated with an entity cohort). – abstract; paras 16)
input, (i) the first score for the entity…to a disparity risk adjustment machine learning model to receive a disparity adjusted risk score for the entity, wherein the disparity adjusted risk score indicates a second score… (Brutsche discloses producing one or more aggregate risk scores (receive a disparity adjusted risk score for the entity) based on the intermediate entity risk score (input (i) the first score for the entity) using nested machine learning models (to a disparity risk adjustment machine learning model). – abstract)
initiate, responsive to the second score satisfying a score threshold, an entity monitoring action for the entity, (Brutsche discloses comparing the one or more aggregate risk scores (the second score) to one or more thresholds (a score threshold) to determine when one or more predefined actions should be taken (initiating an entity monitoring action for the entity). The resource management processor (by the one or more processors) may execute computer-readable instructions for performing enhanced monitoring of the candidate in response to determining that the aggregate risk score exceeds the predetermined threshold (responsive to the second score satisfying a score threshold). – abstract; para 11)
wherein the entity monitoring action comprises causing an entity monitoring computing device to collect entity monitoring data associated with the entity; (Brutsche discloses that the resource management processor may execute computer-readable instructions for performing enhanced monitoring of the candidate (collect entity monitoring data associated with the entity) in response to determining that the aggregate risk score exceeds the predetermined threshold. Such predefined actions may be causing audio and/or video recording (e.g., executed by respective audio and/or video recording devices) to be activated (causing an entity monitoring computing device to collect entity monitoring data). – abstract; paras 11, 137)
Brutsche does not disclose the following limitations met by Abou Shousha:
input, historical input data, associated with an entity…to a risk prediction machine learning model to receive a first score for the entity, (Abou Shousha teaches that the input data (e.g., provided to the prediction model (inputting to a risk prediction machine learning model) and on which the predictions may be based) may consist of past medical history (historical input data). The generated prediction may include a set of scores (receive a first score for the entity). – paras 19, 51, 78, 83)
wherein the first score indicates a likelihood of a presence of one or more of a disease, a disorder, or an impairment within the entity; … a second score associated with one or more of the disease, the disorder, or the impairment; (Abou Shousha teaches results derived from an AI model indicating the presence of a subclinical case of an eye disease (a likelihood of a presence of a disease). A first prediction (the first score) and a second prediction (a second score) may be obtained via the prediction model. The generated prediction may include a set of scores. Each score may represent a likelihood of a medical condition-related to a corneal or anterior segment condition. More specifically, the AI model 160 has been trained to predict corneal or anterior segment conditions, such as keratoconus (a disease), dry eye (a disorder), Fuchs’ dystrophy (a disease), graft rejection episode or failure (an impairment), cataract (a disease…an impairment), and glaucoma (a disease). – abstract; paras 19, 78, 83-84)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate using historical data as input and generating prediction scores indicating a likelihood of a medical condition as taught by Abou Shousha in order to avoid invasive and time consuming techniques (see Abou Shousha para 5).
Brutsche and Abou Shousha do not disclose the following limitations met by Soto:
and (ii) a risk prevalence ratio associated with the disparity group (Soto teaches that the “Odds Ratio” (a risk prevalence ratio) for “One-Year Mortality” is calculated as the actual mortality divided by the “Minimal” mortality for the test population. – paras 130-131)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate an odd ratio to assess the health risk or status of individual patients in a population as taught by Soto in order to make an expert description or proactive recommendation in the form of a risk assessment (see Soto para 30).
Brutsche, Abou Shousha, and Soto do not disclose the following limitations met by Jain:
and update the first score for the entity based on monitoring data generated by the entity monitoring computing device associated with the entity. (Jain teaches an infection prediction score (the first score for the entity), determined using a machine learning model, that indicates a likelihood that the first user has the disease. Monitoring data is obtained from individuals (based on monitoring data generated by the entity monitoring computing device associated with the entity) on an ongoing or continual basis using digital platforms, as well as obtain aggregated data regarding communities and the effects of a disease. The system can use these inputs to repeatedly generate updated predictions (updating the first score for the entity) and also to update the predictive models. – col 2, lines 9-14; col 8, lines 7-28; col 11, lines 10-16; col 15, lines 13-16)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate updating infection prediction scores by obtaining monitoring data as taught by Jain in order to prevent, contain, or otherwise manage the spread of disease (See Jain col 1, lines 21-24).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Brutsche et al. (US 20220036156), in view of Abou Shousha et al. (WO 2020023959), in view of Soto et al. (US 20130132323), in view of Jain et al. (US 11456080), further in view of Lewis (US 20180166174).
Regarding Claim 3, Brutsche, Abou Shousha, Soto and Jain disclose all the limitations above, however do not disclose the following limitations met by Lewis:
The computer-implemented method of claim 2, wherein the entity evaluation action is associated with a cognitive evaluation for the entity, and wherein the cognitive evaluation is associated with at least one of a text-based entity evaluation, a speech-based entity evaluation, a gait-based entity evaluation, or a biomarker-based evaluation. (Lewis teaches a follow-up method for assessing the health and chronic disease state of a subject. The subject undergoes tests for physiological, pathophysiological, and pathological biomarkers (a biomarker-based evaluation). – para 122)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate follow-up biomarker testing as taught by Lewis in order to improve patient outcomes (see Lewis abstract).
Claims 5-7, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Brutsche et al. (US 20220036156), in view of Abou Shousha et al. (WO 2020023959), in view of Soto et al. (US 20130132323), in view of Jain et al. (US 11456080), further in view of Arora et al. (US 20240331872).
Regarding Claim 5, Brutsche, Abou Shousha, Soto and Jain disclose all the limitations above, however do not disclose the following limitations met by Arora:
The computer-implemented method of claim 2, wherein the subsequent prediction-based action is an image-based entity evaluation action, and the computer-implemented method further comprises: generating, by the one or more processors, a phenotypic profile for the entity based on the evaluation data object and an image-based evaluation data object for the entity, wherein the image-based evaluation data object is generated based on the image-based entity evaluation action; (Arora teaches the use of machine learning analysis on medical imaging procedure data. The medical imaging procedure data may include order data (such as data indicating a request for a radiological image read) (image-based entity evaluation action) produced to facilitate a medical imaging evaluation (based on the evaluation data object and an image-based evaluation data object for the entity). Detection of heart failure is determined by receiving one or more target chest X-rays (prediction-based action is an image-based entity evaluation action) for a patient (an image-based evaluation data object for the entity). The system may analyze one or more target chest X-ray image to identify and enhance one or more visual parameters of one or more Region of Interests (RoI’s). The system may perform an anatomical segmentation on the one or more ROI's to detect one or more medical abnormalities from a set of medical abnormalities using the trained artificial intelligence model (generating, by the one or more processors, a phenotypic profile for the entity). – abstract; para 22; FIG. 4)
and generating, by the one or more processors, a prediction-based action sequence for the entity based on the phenotypic profile. (Arora teaches that after detecting the patient’s risk of heart failure (based on the phenotypic profile), following up with confirmatory testing and a treatment may be initiated for the patients with confirmed diagnosis of heart failure on the basis of the one or more confirmatory test (generating a prediction-based action sequence for the entity). – paras 22, 89; FIG. 3)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate analyzing patient x-rays to detect risk of heart failure and determine follow up actions accordingly as taught by Arora in order to prevent the worsening of heart failure risk in the patients (see Arora para 89).
Regarding Claim 6, Brutsche, Abou Shousha, Soto, Jain and Arora disclose all the limitations above and further disclose the following limitations:
The computer-implemented method of claim 5, wherein generating the phenotypic profile further comprises: generating, by the one or more processors and using a multi-dimensional location ranking machine learning model, an entity rank associated with the entity based on the evaluation data object and the image-based evaluation data object; (Arora teaches that the term “machine learning” is used to refer to the various classes of artificial intelligence algorithms and algorithm-driven approaches that are capable of performing machine driven (e.g., computer-aided) identification of trained structures, with the term “deep learning” referring to a multiple-level operation of such machine learning algorithms using multiple levels of representation and abstraction (using a multi-dimensional location ranking machine learning model). After analyzing one or more chest X-ray images of a user (based on the evaluation data object and the image-based evaluation data object), a confidence score may be calculated (generating an entity rank associated with the entity). – paras 23, 71, 90-97; FIG. 4)
and mapping, by the one or more processors, the entity rank in a multi-dimensional disease space associated with a plurality of phenotypic profiles. (Arora teaches using a historical knowledge database for a set of patients diagnosed with medical abnormalities such as cardiomegaly, pleural effusion, presence of Kerley B lines, and presence of pulmonary edema and suffering a risk of heart failure (associated with a plurality of phenotypic profiles) in order to refine the set of training algorithms used for calculating the user’s confidence score (mapping the entity rank in a multi-dimensional disease space). – paras 42, 70-71)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified using nested machine learning models to generate risk scores as disclosed by Brutsche to incorporate calculating confidence scores for a user based on a historical knowledge dataset for a set of patients using a multiple-level operation of machine learning algorithms as taught by Arora in order to prevent the worsening of heart failure risk in the patients (see Arora para 89).
Regarding Claim 7, Brutsche, Abou Shousha, Soto, Jain and Arora disclose all the limitations above and further disclose the following limitations:
The computer-implemented method of claim 5, wherein the phenotypic profile associated with the entity comprises at least one of a disease severity or a disease subtype and the phenotypic profile describes whether a current condition of the entity is a normal condition, reversible condition, or irreversible condition. (Arora teaches that the user’s risk of heart failure may be categorized into at least one category (the phenotypic profile associated with the entity comprises a disease severity) based on their confidence score. The categories include ‘normal’ (a current condition of the entity is a normal condition) with no risk predicted for the heart failure, a risk of cardiac failure category for a user with the confidence score beyond a threshold value, and an abnormal category without risk of cardiac failure based on the confidence score. – paras 48, 78; Table 2)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores as disclosed by Brutsche to incorporate categorizing the user’s into categories as taught by Arora in order to avoid a delay or negligence in detection of serious life-threatening medical conditions (see Arora para 5).
Regarding Claim 18, Brutsche, Abou Shousha, Soto, Jain and Arora disclose all the limitations above and further disclose the following limitations:
The system of claim 17, wherein the subsequent prediction-based action is an image-based entity evaluation action and the one or more processors are further configured to: generate a phenotypic profile for the entity based on the evaluation data object and an image-based evaluation data object for the entity, wherein the image-based evaluation data object is generated based on the image-based entity evaluation action; (Arora teaches the use of machine learning analysis on medical imaging procedure data. The medical imaging procedure data may include order data (such as data indicating a request for a radiological image read) (image-based entity evaluation action) produced to facilitate a medical imaging evaluation (based on the evaluation data object and an image-based evaluation data object for the entity). Detection of heart failure id determined by receiving one or more target chest X-rays (prediction-based action is an image-based entity evaluation action) for a patient (an image-based evaluation data object for the entity). The system may analyze one or more target chest X-ray image to identify and enhance one or more visual parameters of one or more Region of Interests (RoI’s). The system may perform an anatomical segmentation on the one or more ROI's to detect one or more medical abnormalities from a set of medical abnormalities using the trained artificial intelligence model (generate, by the one or more processors, a phenotypic profile for the entity). – abstract; para 22; FIG. 4)
and generate a prediction-based action sequence for the entity based on the phenotypic profile. (Arora teaches that after detecting the patient’s risk of heart failure (based on the phenotypic profile), following up with confirmatory testing and a treatment may be initiated for the patients with confirmed diagnosis of heart failure on the basis of the one or more confirmatory test (generate a prediction-based action sequence for the entity). – paras 22, 89; FIG. 3)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate analyzing patient x-rays to detect risk of heart failure and determine follow up actions accordingly as taught by Arora in order to prevent the worsening of heart failure risk in the patients (see Arora para 89).
Regarding Claim 20, Brutsche, Abou Shousha, Soto, Jain and Arora disclose all the limitations above and further disclose the following limitations:
The one or more non-transitory computer-readable storage media of claim 19, wherein the score threshold is a high risk score threshold, (Brutsche discloses that the risk score is considered a high risk score if the risk score exceeds the threshold (a high risk score threshold). – para 137)
the entity monitoring action is an entity evaluation action, (Brutsche discloses monitoring the entity more closely (an entity evaluation action) when the risk score exceeds the threshold. For example, activating audio or video during an exam event when the risk score is a high risk score. – para 137)
and the one or more processors are further configured to: generate, using an evaluation risk adjustment machine learning model, an evaluation adjusted risk score for the entity based on the disparity adjusted risk score and an evaluation data object for the entity, (Brutsche discloses that the aggregate risk score (the disparity adjusted risk score) may be periodically or continuously updated (an evaluation adjusted risk score for the entity) in real-time while the exam is being delivered. After a predefined action is taken based on the threshold being exceeded, the process returns back to receiving data (an evaluation data object for the entity) and generating updated risk scores. – paras 137, 190-194, 202; FIG. 6)
wherein the evaluation data object is generated based on the entity evaluation action; (Brutsche discloses generating updated risk scores based on received data (the evaluation data object) after taking a predefined action (based on the entity evaluation action). – paras 190-194, 202; FIG. 6)
initiate a subsequent prediction-based action for the entity based on a comparison between the evaluation adjusted risk score and the risk score threshold, wherein the subsequent prediction-based action is an image-based entity evaluation action; (Brutsche discloses repeating the process of generating an updated risk score, comparing the updated risk score to the threshold (based on a comparison between the evaluation adjusted risk score and the risk score threshold), if it exceeds, then taking a predetermined action (a subsequent prediction-based action for the entity). – paras 190-194, 202; FIG. 6)
generate a phenotypic profile for the entity based on the evaluation data object and an image-based evaluation data object for the entity, wherein the image-based evaluation data object is generated based on the image-based entity evaluation action; (Arora teaches the use of machine learning analysis on medical imaging procedure data. The medical imaging procedure data may include order data (such as data indicating a request for a radiological image read) (image-based entity evaluation action) produced to facilitate a medical imaging evaluation (based on the evaluation data object and an image-based evaluation data object for the entity). Detection of heart failure is determined by receiving one or more target chest X-rays (prediction-based action is an image-based entity evaluation action) for a patient (an image-based evaluation data object for the entity). The system may analyze one or more target chest X-ray image to identify and enhance one or more visual parameters of one or more Region of Interests (RoI’s). The system may perform an anatomical segmentation on the one or more ROI's to detect one or more medical abnormalities from a set of medical abnormalities using the trained artificial intelligence model (generate a phenotypic profile for the entity). – abstract; paras 22)
and generate a prediction-based action sequence for the entity based on the phenotypic profile. (Arora teaches that after detecting the patient’s risk of heart failure (based on the phenotypic profile), following up with confirmatory testing and a treatment may be initiated for the patients with confirmed diagnosis of heart failure on the basis of the one or more confirmatory test (generating a prediction-based action sequence for the entity). – paras 22, 89; FIG. 3)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate analyzing patient x-rays to detect risk of heart failure and determine follow up actions accordingly as taught by Arora in order to prevent the worsening of heart failure risk in the patients (see Arora para 89).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Brutsche et al. (US 20220036156), in view of Abou Shousha et al. (WO 2020023959), in view of Soto et al. (US 20130132323), in view of Jain et al. (US 11456080), in view of Arora et al. (US 20240331872), further in view of Storey et al. (US 20200394737).
Regarding Claim 8, Brutsche, Abou Shousha, Soto, Jain and Arora disclose all the limitations above, however do not disclose the following limitations met by Storey:
The computer-implemented method of claim 5, wherein the computer-implemented method further comprises: generating, by the one or more processors and using the risk prediction machine learning model, a specialist referral associated with a respective medical specialist based on the prediction-based action sequence; and providing, by the one or more processors, data indicative of the specialist referral to the entity. (Storey teaches that if the user is at risk for any of such conditions or disorders, the severity of the risk of these disorders is assessed and a database of professional specialists is consulted (generating a specialist referral). Based on the severity of the risk for these disorders (based on the prediction-based action sequence), one or more professional specialists are selected (providing) based on their availability and/or fields of specialty. The user can then select one or more of these professional specialists (providing data indicative of the specialist referral to the entity). – abstract: paras 8, 20)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate selecting one or more professional specialists based on user risk as taught by Storey in order to provide a quick, accessible method and means for determining risks for conditions based on an assessment of a patient's self-identified symptoms and, based on that assessment, access to an available professional specialist who is equipped and trained to deal with the patient's possible condition (see Storey para 7).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Brutsche et al. (US 20220036156), in view of Abou Shousha et al. (WO 2020023959), in view of Soto et al. (US 20130132323), in view of Jain et al. (US 11456080), further in view of Will et al. (US 20200243167).
Regarding Claim 11, Brutsche, Abou Shousha, Soto and Jain disclose all the limitations above, however they do not disclose the following limitations met by Will:
The computer-implemented method of claim 1, wherein the disparity group is associated with a geographic region and the disparity group is associated with one or more contextual attributes. (Will teaches historical patient medical records of a plurality of patients may be analyzed to determine values of different patient attributes of each patient over time (e.g., a patient's body mass index (BMI), whether or not the patient is taking certain medications, whether or not the patient has undergone certain procedures, and the like at different time intervals) (the disparity group is associated with one or more contextual attributes). Historical patient data may also include personal data about the patient, such as geographic location (the disparity group is associated with a geographic region), age (one or more contextual attributes, gender (one or more contextual attributes), profession, and the like. – para 17)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate grouping patients based on certain criteria as taught by Will in order to avoid a patient being overlooked or otherwise omitted from consideration (see Will para 3).
Claims 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Brutsche et al. (US 20220036156), in view of Abou Shousha et al. (WO 2020023959), in view of Soto et al. (US 20130132323), in view of Jain et al. (US 11456080), further in view of Ryan at al. (US 20170177801).
Regarding Claim 12, Brutsche, Abou Shousha, Soto and Jain discloses all the limitations above, however does not disclose the following limitations met by Ryan:
The computer-implemented method of claim 1, wherein the score threshold is a medium risk score threshold and the entity monitoring action is an entity monitoring action. (Ryan teaches stratifying individuals into high, medium (is a medium risk score threshold) and low risk levels in order to output recommended health interventions such as tests, examinations, etc. (an entity monitoring action). – paras 50, 54; FIG. 1 item 70B)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have further modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate a medium risk category and recommending further testing as taught by Ryan in order that limited resources can be directed to those likely to benefit the most from a particular health intervention (see Ryan para 7).
Regarding Claim 13, Brutsche, Abou Shousha, Soto, Jain and Ryan disclose all the limitations above and further disclose the following limitations:
The computer-implemented method of claim 12, wherein the computer-implemented method further comprises: initiating, by the one or more processors based on the entity monitoring action, association of an entity monitoring computing device to the entity. (Brutsche discloses that the users (to the entity) may use one or more client devices (association of an entity monitoring computing device) to interact with the server. – paras 36, 46; FIG. 1A)
Regarding Claim 14, Brutsche, Abou Shousha, Soto, Jain and Ryan disclose all the limitations above and further disclose the following limitations:
The computer-implemented method of claim 13, wherein the computer-implemented method further comprises: receiving, by the one or more processors, entity monitoring data associated with the entity, wherein the entity monitoring data is generated by the entity monitoring computing device; (Brutsche discloses that the test may be conducted and proctored remotely online (receiving entity monitoring data associated with the entity) at the personal client device of the candidate (the entity monitoring data is generated by the entity monitoring computing device). – paras 46, 107)
and updating, by the one or more processors, the first risk score associated with the entity based on the entity monitoring data. (Brutsche discloses that the aggregate risk score may be periodically or continuously updated (updating) in real-time while the exam is being delivered. The aggregated risk score is determined based on the entity risk score/intermediate risk score (the individual risk score associated with the entity). After a predefined action is taken based on the threshold being exceeded, the process returns back to receiving data (based on the entity monitoring data) and generating updated risk scores. – paras 137, 190-194, 202; FIGs. 5, 6C)
Regarding Claim 15, Brutsche, Abou Shousha, Soto, Jain and Ryan disclose all the limitations above and further disclose the following limitations:
The computer-implemented method of claim 1, wherein the risk score threshold is a low risk score threshold and the entity monitoring action is an entity evaluation cessation action. (Ryan teaches stratifying individuals into high, medium and low risk levels (a low risk score threshold) in order to output recommended health interventions (if any) (this indicates that a recommendation may entail doing nothing) (an entity evaluation cessation action). – paras 50, 54; FIG. 1 item 70B)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified determining risk scores and initiating predefined actions as disclosed by Brutsche to incorporate a medium risk category and recommending further testing as taught by Ryan in order that limited resources can be directed to those likely to benefit the most from a particular health intervention (see Ryan para 7).
Response to Arguments
Regarding the drawing objections, the Applicant has amended the specification and submitted new drawings to overcome all of the previous drawing objections.
Regarding rejections under 35 USC § 101 to Claims 1-20, Applicant’s arguments have been fully considered, and are not persuasive. The rejection has been updated in light of latest amendments. Applicant argues:
(a) Each of the above steps leverage a specific machine learning framework, which is not practically performed within the human mind. Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101 at least because the claimed invention is not directed to a judicial exception under prong one of Step 2A. (p. 23).
Regarding (a), Examiner respectfully disagrees. Examiner has not asserted that the claims recite a mental process but rather that they recite the abstract idea of certain methods of organizing human activity. Therefore, this argument is moot.
(b) Claim 1 is directed to an improvement in computer technology that is directly tied to machine learning, specifically, techniques for improved inference operations through selectively employing an entity monitoring computing device to gather real-time data…claim 1 recites a method for selectively employing additional computer resources to boost the scores of a machine learning model. This allows for improved model performance at reduced processing expenditures. Thus, claim 1 recites features that provide specific improvements to the functioning of a computer or improvements to any other technology or technical field, and in particular, improving the accuracy and efficiency of machine learning models. For at least these reasons, independent claim 1 recites a combination of additional elements that improves a computer or technical field such that the claim as a whole integrates the alleged abstract idea into a practical application. Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101 because independent claim 1 integrates an alleged abstract idea into a practical application under Prong Two of Step 2A. (p. 24-26).
Regarding (b), Examiner respectfully disagrees. MPEP 2106.04(d)(1) states "the word 'improvements' in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B." See also MPEP2106.05(a)(I). Here there is no improvement to the computer nor is there an improvement to another technology. The technological environment of Applicant’s claim is a general-purpose computer (see Applicant’s Spec. Paras 25-32, 34-37, 39-45, 53, 65, 73). Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the computer that results from the implementation of Applicant’s claim. There is no indication that the computer is made to improve accuracy and efficiency. In fact, the computer may be caused to operate less efficiently through the implementation of Applicant’s claimed invention; we do not know. Because there is no improvement to the functioning of the computer, a practical application is not present.
(c) Applicant respectfully submits that the added elements cannot be considered to be well-understood, routine, or known within the industry at least because they do not appear to be taught by the prior art of record. Accordingly, Applicant respectfully submits that the Office Action improperly rejects claim 1 (and the claims depending therefrom) as being directed to patent ineligible subject matter and requests withdrawal of the rejection.
Regarding (c), Examiner respectfully disagrees. MPEP 2106.05(d) states: “Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry (emphasis added). In that regard, MPEP 2106.05(d)(I) indicates that in determining whether the additional elements represent are well-understood, routine, conventional activities, the Examiner should consider whether the additional elements (1) provide an improvement to the technological environment to which the claim is confined, (2) whether the additional elements are mere instructions to apply the judicial exception, or (3) whether the additional elements represent insignificant extra-solution activity. The additional elements of the claims do not provide significantly more based on this inquiry. The technological environment to which the claims are confined (a general-purpose computer performing generic computer functions [see Spec. Paras 25-32, 34-37, 39-45, 53, 65, 73]) is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component and has been found by the courts to be insufficient to provide a practical application (see MPEP 2106.05(d)(II); Alice Corp.). None of the additional elements of the claim were found to represent extra-solution activity and thus no well-understood, routine, conventional analysis is required.
(d) For at least the same reasons as set forth above, Applicant submits that the independent claims 16 and 19 recite patent eligible subject matter under 35 U.S.C. § 101 and requests withdrawal of the rejection to claims 16 and 19 (and the claims that depend therefrom) as well as allowance in due course.
Regarding (d), Examiner respectfully disagrees. Based on response to arguments above, claim 1 is unpatentable and therefore similar independent claims 16 and 19 as well as all claims depending therefrom, are unpatentable according to the same rationale.
Regarding rejections under 35 USC § 102/103 to Claims 1-20, Applicant’s arguments have been fully considered and are persuasive regarding the newly added limitations. Applicant argues (p. 19-20) that the previously cited prior art does not disclose the following limitations: inputting, by one or more processors, historical input data, associated with an entity of a disparity group associated with an entity cohort, to a risk prediction machine learning model to receive a first score for the entity, wherein the first score indicates a likelihood of a presence of one or more of a disease, a disorder, or an impairment within the entity. Examiner notes that while Brutsche discloses and is relied upon above to disclose “an entity of a disparity group associated with an entity cohort” and “receive a first score for the entity”, Brutsche does not disclose the rest of this argued limitation. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection necessitated by Applicant’s amendments is made in view of Abou Shousha et al. (WO 2020023959), as per the rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.E.V./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681