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 .
Response to Amendment
Applicant' s amendment and response filed 8/8/2025 has been entered and made record. This application contains 21 pending claims.
Claims 1, 19, 22, and 25 have been amended.
Claim 14 has been cancelled
Claim 26 has been added.
Response to Arguments
Applicant’s arguments filed 8/8/2025 regarding claims rejections under 35 U.S.C. 101 in claim 1-14, 17, 19, and 21-25 have been fully considered but they are not persuasive.
The applicant argues on page 10 of the remark filed on 8/8/2025 that “The Claims Are Not Directed to a Judicial Exception Under Step 2A Prong 1 The claims do not recite an abstract idea, but rather are directed to specific, computer-implemented methods, which are improvements in computer technology, for controlling diagnostic assessment stations ... These specific technical steps cannot practically be performed in the human mind and do not recite mathematical concepts. In particular, the automatic initiation of a follow-up medical examination or treatment for a patient, based on the detection of an asymptomatic medical state, is not something that can be performed in the human mind or by pen and paper; it requires a computing device to process patient data, generate control commands, and transmit actionable medical orders to physical medical devices.”
The Examiner respectfully disagrees applicant’s argument. In claim 1, the step of “processing the patient information, including quantifying the evolution over time of the at least one medical parameter and checking for a presence of an appointment, wherein quantifying the evolution over time of the at least one medical parameter comprises determining a comparison of the evolution of the at least one medical parameter with an evolution over time of a second medical parameter, wherein the second medical parameter is a further medical parameter of the patient related to the at least one medical parameter” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea. The steps of “automatically ascertaining, based on the quantified evolution over time of the at least one medical parameter, health information about the patient by applying the accessed first function to the patient information, the health information being configured to indicate one or more asymptomatic medical states of the patient”, “checking whether a trigger condition is fulfilled based on the ascertained health information about the patient”, “generating, based on the trigger condition, control commands adapted to control the diagnostic assessment station wherein the control commands are configured to prioritize the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information and based on the indication of the one or more asymptomatic medical states of the patient” are mental processes, therefore, they are considered to be an abstract idea. A human mind can determine the current limitation of the claim based on available data by checking whether a trigger condition is fulfilled based on the ascertained health information about the patient, generating control commands adapted to control the diagnostic assessment station based on the trigger condition, and make a judgment regarding them by prioritizing the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information, and therefore, the claims recite a mental step. According to MPEP 2106.04(a) III, “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions”.
The step of “based on the control commands indicating one or more asymptomatic medical states of the patient, automatically transmitting to an examination modality and/or a treatment device, a medical order comprising examination parameters or treatment instructions configured to initiate a corresponding medical examination or treatment for the patient” represents certain methods of organizing human activities-fundamental economic practices and principles”, therefore, it is considered to be an abstract idea.
In claim 22, the steps of “providing patient information of a plurality of comparison patients in each case, wherein each comparison patient is associated with previously known health information”; “determining one or more reference patients from a plurality of comparison patients based on similarity measures, wherein one similarity measure is based on a similarity between the patient information of the patient and the patient information of the comparison patients, wherein the similarity measures are determined by extracting or receiving a corresponding data descriptor in each case from the patient information of the comparison patients”; “ascertaining, based on the previously known health information of the reference patients, health information about the patient by applying the accessed first function to the patient information, the health information being configured to indicate one or more asymptomatic medical states of the patient”; “checking, whether a trigger condition is fulfilled based on the ascertained health information about the patient”; “generating, based on the trigger condition, control commands adapted to control the diagnostic assessment station, wherein the control commands are configured to prioritize the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information and based on the indication of the one or more asymptomatic medical states of the patient” are mental processes, therefore, they are considered to be an abstract idea.
The step of “wherein individual features of the data descriptors represent features extracted from image data establishing a pattern” is a mathematical concept, therefore, it is considered to be an abstract idea.
The step of “based on the control commands indicating one or more asymptomatic medical states of the patient, a medical order comprising examination parameters or treatment instructions configured to initiate a corresponding medical examination or treatment for the patient” represents certain methods of organizing human activities-fundamental economic practices and principles”, therefore, it is considered to be an abstract idea.
Thus, the claims recite an abstract idea.
The applicant argues on pages 11-12 of the remark filed that “Claims Integrate Any Abstract Idea Into a Practical Application Under Step 2A Prong 2 Even if the claims were found to recite an abstract idea, they integrate any such abstract idea into a practical application … As amended, the claims do not preempt the abstract idea of diagnosis or data analysis, but instead integrate any such idea into a practical application by requiring that the system automatically initiate a tangible, real-world medical intervention based on the determination of an asymptomatic medical state ...
The amended claims, as a whole, integrate the alleged abstract idea into a practical application by requiring a specific, computer-implemented medical action, including the automatic initiation of a follow-up examination or treatment ... The amended claims are not directed to a judicial exception, but to a specific, practical application of a diagnostic result.”
The Examiner respectfully disagrees applicant’s argument. Practical application can be demonstrated by limitations that are sufficient to integrate the judicial exception into a practical application. Additional elements of “receiving, at the computing device, patient information of the patient via an interface, wherein the patient information is selected independently of a medical condition of the patient to improve asymptomatic diagnosis by the diagnostic assessment station, the patient information including at least two different medical parameters assigned to the patient, wherein at least a piece of the patient information comprises a pointer to an evolution over time of at least one medical parameter of the patient”; “accessing a memory storing a first function to access the first function”; and “wherein the ascertained health information about the patient is output to the user via the diagnostic assessment station as a function of the presence of the appointment” are recited in generality and not sufficient to integrate the abstract idea into a practical application, therefore, only add insignificant extra-solution activities to the judicial exception. A computer-implemented specific medical action including the automatic initiation of a follow-up examination or treatment is routine in monitoring and providing preventive treatment to a patient. The alleged improvement of diagnostic assessment station technology by implementing the analysis with particular machines - namely the computing device and at least one diagnostic assessment station maintaining a data connection to the computing device relates to improvement to the abstract idea itself. Therefore, the current claims do not recite additional elements that are indicative of integration of an abstract idea into a practical application.
The applicant argues on page 11 of the remark filed that “Further, Applicant respectfully submits that the amended claims are directly analogous to the patent-eligible claims set forth in USPTO Subject Matter Eligibility Example 29, Claim 2, which is based on the Federal Circuit's Vanda decision. As provided in Example 29, a claim is patent-eligible because it does not merely recite a diagnostic step or a mental process but rather applies the result of the diagnostic determination to effect a specific, concrete medical treatment. The claim thus integrates any judicial exception into a practical application, as required by Step 2A, Prong 2 of the USPTO's eligibility framework. Like Example 29, the amended claims do not stop at analyzing patient information or ascertaining a health state, and instead, recite that, in response to the detection of one or more asymptomatic medical states, the system automatically transmits a follow-up medical order to an examination modality and/or treatment device, thereby initiating a specific medical examination or treatment for the patient. This automatic initiation step is functionally equivalent to the "administering" step in Example 29, Claim 2, in that both claims require a concrete, real-world medical action based on the diagnostic result.
The Examiner respectfully disagrees applicant’s argument. Claim 2 of Example 29 is ineligible, because it is directed to a judicial exception that could be termed either a law of nature or an abstract idea, and the recited additional elements do not amount to significantly more than the exception. The claim is directed to at least one exception, which may be termed a law of nature, an abstract idea, or both. Although the claim recites several nature-based product limitations (e.g., the plasma sample and JUL-1), the claim as a whole is focused on a process of detecting whether JUL-1 is present in a plasma sample, and is not focused on the products per se. The claim is recited at this high level of generality, thus, there is no meaningful limitation. it is well established that the mere physical or tangible nature of additional elements such as the obtaining and detecting steps does not automatically confer eligibility on a claim directed to an abstract idea. The claim limitations in Claim 2 of Examiner 29 are recited in generality and not sufficient to integrate the abstract idea into a practical application, therefore, only add insignificant extra-solution activities to the judicial exception. The Examiner also notes that "administering" step is not cited in Claim 2 of Example 29.
The applicant argues on page 12 of the remark filed that “The Claims Recite Significantly More Under Step 2B Even if the claims were found to be directed to an abstract idea, they recite significantly more through an inventive concept that improves technology and transforms the alleged abstract idea into a specific, practical, and technological solution to a real-world problem. The claims do not merely gather and analyze data, nor do they simply display results. Instead, the claims recite an inventive concept that includes the use of a computing device to automatically trigger a follow- up medical action (e.g., transmitting an imaging order or treatment instruction to a medical device) based on the detection of an asymptomatic state. This is not well-understood, routine, or conventional. The cited art does not disclose or suggest a system that, upon detecting an asymptomatic medical state, automatically initiates a specific medical examination or treatment for the patient. The claimed method therefore transforms the alleged abstract idea into a specific, practical, and technological solution to a real-world problem, including improving early detection and intervention for patients who may not yet show symptoms.”
The Examiner respectfully disagrees applicant’s argument. Significantly more can be demonstrated by additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application. However, the claims do not recite them. The additional elements of “receiving, at the computing device, patient information of the patient via an interface, wherein the patient information is selected independently of a medical condition of the patient to improve asymptomatic diagnosis by the diagnostic assessment station, the patient information including at least two different medical parameters assigned to the patient, wherein at least a piece of the patient information comprises a pointer to an evolution over time of at least one medical parameter of the patient”; “accessing a memory storing a first function to access the first function”; and “wherein the ascertained health information about the patient is output to the user via the diagnostic assessment station as a function of the presence of the appointment”; and the limitation of the use of a computing device to automatically trigger a follow-up medical action (e.g., transmitting an imaging order or treatment instruction to a medical device) based on the detection of an asymptomatic state are well-understood and conventional, and this is routine in controlling a diagnostic assessment stations in a medical information network, and providing medical findings for a patient by a user. Therefore, the claims do not contain additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application.
Therefore, claims 1-14, 17, 19, and 21-25 are not patent eligible.
Applicant’s arguments filed 8/8/2025 regarding claims rejections under 35 U.S.C. 103 in claims 1-13, 17, 19, and 21 have been fully considered and are persuasive. Applicant has amended independent claims 1 and 19, and incorporated certain features of allowable claim 14, respectively, and overcome the 103 rejections in claims 1-13, 17, 19, and 21. Therefore, the 103 claims rejections in claims 1-13, 17, 19, and 21 have been withdrawn.
For claim 22, the prior arts still teach the claim limitations as shown in the rejection below. Claim 23-25 and newly added claim 26 depend from claim 22, and therefore, claims 22-26 are not patent eligible.
The applicant argues on pages 13-14 of the remark filed on 8/8/2025 that “In rejecting claim 25, the Office relies on Rapaka, alleging that Rapaka teaches that features of data descriptors may be used. However, Rapaka only generally describes training the machine learning model in. For example, in paragraph [0063] cited by the Office, Rapaka merely describes that "[a]ny type of feature may be used," while providing only a specific discussion of morphological features. This general and high-level discussion does not disclose or suggest the claimed features, namely that the data descriptors represent features extracted from image data establishing a pattern. Oleynik, Rousseeuw, Gregson, Myint, and Nguyen fail to cure the deficiencies of Rapaka. Therefore, claim 22 is patentable over Rapaka, Oleynik, Rousseeuw, Gregson, Myint, and Nguyen, either alone or in combination.”
The Examiner respectfully disagrees applicant’s argument. Rapaka teaches that patient’s medical record includes information such as the text in the clinical reports, medical images, blood biomarker information, patient demographics such as age, race, gender, weight, BMI, and these are the data descriptors that include one or more features that have been extracted from the patient information, and patient history such as smoking, alcohol consumption, high blood pressure, drug use, current medicines being used, or others, (Rapaka, [0031]). Rapaka further teaches that the data is from a past examination of the patient such as previous image, demographics, and patient history, and the current medical information of the patient, and symptoms being currently experienced by the patient are also obtained, and other current measurements, such as CT imaging and blood biomarkers of the patient are also obtained (Rapaka, [0032]). Rapaka also teaches that feature is extracted from images, and the image is used to determine the values for the features (Rapaka, [0034]); and similar patients are identified based on any measure of similarity between any aspect such as condition, severity of condition, location, size, or other information which is a corresponding data descriptor from the patient information of the comparison patients, (Rapaka, [0039]). Thus, the similarity measures are determined by extracting or receiving a corresponding data descriptor in each case from the patient information of the comparison patients. Rapaka also teaches that past cases for other patients similar to the current patient are identified, and similarity are measured or compared; and thus, individual features of the data descriptors represent features extracted from image data establishing a pattern (Rapaka, [0080]).
Therefore, the combination of Rapaka and Oleynik teaches wherein individual features of the data descriptors further represent morphological features structural features.
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 1-13, 17, 19, and 21-26 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.
As to Claim 1, the claim recites “A computer-implemented method for controlling a diagnostic assessment station in a medical information network comprising a computing device and at least one diagnostic assessment station maintaining a data connection to the computing device and adapted to produce medical findings for a patient by a user, the method comprising:
receiving, at the computing device, patient information of the patient via an interface, wherein the patient information is selected independently of a medical condition of the patient to improve asymptomatic diagnosis by the diagnostic assessment station, the patient information including at least two different medical parameters assigned to the patient, wherein at least a piece of the patient information comprises a pointer to an evolution over time of at least one medical parameter of the patient;
processing the patient information, including quantifying the evolution over time of the at least one medical parameter and checking for a presence of an appointment, wherein quantifying the evolution over time of the at least one medical parameter comprises determining a comparison of the evolution of the at least one medical parameter with an evolution over time of a second medical parameter, wherein the second medical parameter is a further medical parameter of the patient related to the at least one medical parameter;
accessing a memory storing a first function to access the first function;
automatically ascertaining, by the computing device and based on the quantified evolution over time of the at least one medical parameter, health information about the patient by applying the accessed first function to the patient information, the health information being configured to indicate one or more asymptomatic medical states of the patient, wherein the ascertained health information about the patient is output to the user via the diagnostic assessment station as a function of the presence of the appointment;
checking, by the computing device, whether a trigger condition is fulfilled based on the ascertained health information about the patient;
generating, by the computing device and based on the trigger condition, control commands adapted to control the diagnostic assessment station wherein the control commands are configured to prioritize the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information and based on the indication of the one or more asymptomatic medical states of the patient; and
based on the control commands indicating one or more asymptomatic medical states of the patient, automatically transmitting, by the computing device and to an examination modality and/or a treatment device, a medical order comprising examination parameters or treatment instructions configured to initiate a corresponding medical examination or treatment for the patient.”
As to Claim 22, the claim recites “A computer-implemented method for controlling a diagnostic assessment station in a medical information network comprising a computing device and at least one diagnostic assessment station maintaining a data connection to the computing device and adapted to produce medical findings for a patient by a user, the method comprising:
receiving, at the computing device, patient information of the patient via an interface, wherein the patient information includes at least two different medical parameters assigned to the patient;
providing patient information of a plurality of comparison patients in each case, wherein each comparison patient is associated with previously known health information;
determining one or more reference patients from a plurality of comparison patients based on similarity measures, wherein one similarity measure is based on a similarity between the patient information of the patient and the patient information of the comparison patients, wherein the similarity measures are determined by extracting or receiving a corresponding data descriptor in each case from the patient information of the comparison patients, wherein individual features of the data descriptors represent features extracted from image data establishing a pattern;
accessing a memory storing a first function to access the first function;
ascertaining, by the computing device and based on the previously known health information of the reference patients, health information about the patient by applying the accessed first function to the patient information, the health information being configured to indicate one or more asymptomatic medical states of the patient;
checking, by the computing device, whether a trigger condition is fulfilled based on the ascertained health information about the patient;
generating, by the computing device and based on the trigger condition, control commands adapted to control the diagnostic assessment station, wherein the control commands are configured to prioritize the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information and based on the indication of the one or more asymptomatic medical states of the patient; and
based on the control commands indicating one or more asymptomatic medical states of the patient, automatically transmitting, by the computing device and to an examination modality and/or a treatment device, a medical order comprising examination parameters or treatment instructions configured to initiate a corresponding medical examination or treatment for the patient.”
Under the Step 1 of the eligibility analysis, we determine whether the claims are directed to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category (process for claims 1, 19 and 22).
Under the Step 2A, Prong One, we consider whether the claims recite a judicial exception (abstract idea). In the above claims, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, they recite limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in claims that cover certain methods of organizing human activities-fundamental economic practices and principles, mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes (concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions).
In claim 1, the step of “processing the patient information, including quantifying the evolution over time of the at least one medical parameter and checking for a presence of an appointment, wherein quantifying the evolution over time of the at least one medical parameter comprises determining a comparison of the evolution of the at least one medical parameter with an evolution over time of a second medical parameter, wherein the second medical parameter is a further medical parameter of the patient related to the at least one medical parameter” is a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
The steps of “automatically ascertaining, based on the quantified evolution over time of the at least one medical parameter, health information about the patient by applying the accessed first function to the patient information, the health information being configured to indicate one or more asymptomatic medical states of the patient”,
“checking, whether a trigger condition is fulfilled based on the ascertained health information about the patient”,
“generating, based on the trigger condition, control commands adapted to
control the diagnostic assessment station wherein the control commands are configured to prioritize the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information and based on the indication of the one or more asymptomatic medical states of the patient” are mental processes, therefore, they are considered to be an abstract idea.
The step of “based on the control commands indicating one or more asymptomatic medical states of the patient, automatically transmitting to an examination modality and/or a treatment device, a medical order comprising examination parameters or treatment instructions configured to initiate a corresponding medical examination or treatment for the patient” represents certain methods of organizing human activities-fundamental economic practices and principles”, therefore, it is considered to be an abstract idea.
In claim 22, The steps of “providing patient information of a plurality of comparison patients in each case, wherein each comparison patient is associated with previously known health information”;
“determining one or more reference patients from a plurality of comparison patients based on similarity measures, wherein one similarity measure is based on a similarity between the patient information of the patient and the patient information of the comparison patients, wherein the similarity measures are determined by extracting or receiving a corresponding data descriptor in each case from the patient information of the comparison patients”;
“ascertaining, based on the previously known health information of the reference patients, health information about the patient by applying the accessed first function to the patient information, the health information being configured to indicate one or more asymptomatic medical states of the patient”;
“checking whether a trigger condition is fulfilled based on the ascertained health information about the patient”;
“generating, based on the trigger condition, control commands adapted to control the diagnostic assessment station, wherein the control commands are configured to prioritize the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information and based on the indication of the one or more asymptomatic medical states of the patient” are mental processes, therefore, they are considered to be an abstract idea.
The step of “wherein individual features of the data descriptors represent features extracted from image data establishing a pattern” is a mathematical concept, therefore, it is considered to be an abstract idea.
The step of “based on the control commands indicating one or more asymptomatic medical states of the patient, automatically transmitting to an examination modality and/or a treatment device, a medical order comprising examination parameters or treatment instructions configured to initiate a corresponding medical examination or treatment for the patient” represents certain methods of organizing human activities-fundamental economic practices and principles”, therefore, it is considered to be an abstract idea.
Next, under the Step 2A, Prong Two, we consider whether the claims that recite a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claims recite additional elements that integrate the exception into a practical application of that exception.
Claim 1 comprises the following additional elements:
receiving, at the computing device, patient information of the patient via an interface, wherein the patient information is selected independently of a medical condition of the patient to improve asymptomatic diagnosis by the diagnostic assessment station, the patient information including at least two different medical parameters assigned to the patient, wherein at least a piece of the patient information comprises a pointer to an evolution over time of at least one medical parameter of the patient; accessing a memory storing a first function to access the first function; the computing device; and wherein the ascertained health information about the patient is output to the user via the diagnostic assessment station as a function of the presence of the appointment.
The additional element “receiving, at the computing device, patient information of the patient via an interface, wherein the patient information is selected independently of a medical condition of the patient to improve asymptomatic diagnosis by the diagnostic assessment station” represents necessary data gathering and does not integrate the abstract limitations into a practical application.
The additional element “the patient information including at least two different medical parameters assigned to the patient, wherein at least a piece of the patient information comprises a pointer to an evolution over time of at least one medical parameter of the patient”; “accessing a memory storing a first function to access the first function”; and “wherein the ascertained health information about the patient is output to the user via the diagnostic assessment station as a function of the presence of the appointment” are recited in generality and not sufficient to integrate the abstract idea into a practical application, therefore, only add insignificant extra-solution activities to the judicial exception. In addition, a generic computer or computing device and a generic memory are generally recited and therefore, not qualified as a particular machine.
Claim 22 comprises the following additional elements:
receiving, at the computing device, patient information of the patient via an interface, wherein the patient information includes at least two different medical parameters assigned to the patient; and accessing a memory storing a first function to access the first function.
The additional element “receiving, at the computing device, patient information of the patient via an interface, wherein the patient information includes at least two different medical parameters assigned to the patient” represents necessary data gathering and does not integrate the abstract limitations into a practical application. The additional element “accessing a memory storing a first function to access the first function” is recited in generality and not sufficient to integrate the abstract idea into a practical application, therefore, only add insignificant extra-solution activities to the judicial exception. In addition, a generic computer or computing device, and a generic memory are generally recited and therefore, not qualified as a particular machine.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
The above claims, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis).
For example, receiving, at the computing device, patient information of the patient via an interface, wherein the patient information is selected independently of a medical condition of the patient to improve asymptomatic diagnosis by the diagnostic assessment station is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
For example, outputting the ascertained health information about the patient to the user via the diagnostic assessment station as a function of the presence of the appointment is disclosed by “Rapaka US 20180315182”, FIG. 2, #32; [0003]; [0004]; [0029]; [0038], [0073], [0075]; and “Beeckel US 20080270184”, [0015], [0042], Claim 13.
The claims, therefore, are not patent eligible.
Independent claims 19 recites subject matter that is similar or analogous to that of claim 1, and therefore, the claim is also patent ineligible.
With regards to the dependent claims, Claims 2-13, 17, 21, and 23-26 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not eligible.
Claim Rejections - 35 USC § 103
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 22, and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Rapaka et al. (US 20180315182, hereinafter Rapaka), in view of Oleynik (US 20150161331, hereinafter Oleynik).
As to claim 22, Rapaka teaches receiving, at the computing device (FIG. 6, processor 13, [0021]), patient information of the patient via an interface ([0030] discloses data receive via an interface device, and the data is obtained from a computerized medical record), wherein the patient information includes at least two different medical parameters assigned to the patient ([0028] discloses past medical information and medical images of the patient are used in combination with the current medical information to get a comprehensive picture of the patient condition as well as how the condition has evolved (i.e., a pointer to an evolution over time of at least one medical parameter of the patient - emphasis added by Examiner); [0031] discloses hospital medical records along with any available patient outcome data are mined; and the medical records may be mined hospital medical records along with any available patient outcome data including blood pressure, heart rate, ECG signals, blood biomarker information (i.e., at least two different medical parameters assigned to the patient - emphasis added by Examiner), age, race, gender, weight, BMI, blood flow, electrophysiology, and biomechanics quantities),
providing patient information of a plurality of comparison patients in each case, wherein each comparison patient is associated with previously known health information ([0015] discloses “the system evaluates the existing sources of data (i.e., previously known health information – emphasis added by Examiner) to make a model prediction of the risk of presence of different pathological conditions. In addition, the model computes the likely probabilities of these different conditions and automatically flags high-risk patients who need priority evaluation from clinical providers data (i.e., providing patient information of a plurality of comparison patients in each case – emphasis added by Examiner).”);
determining one or more reference patients from a plurality of comparison patients based on similarity measures, wherein one similarity measure is based on a similarity between the patient information of the patient and the patient information of the comparison patients ([0072] discloses the results from multiple patients seeking emergency treatment are compared, and the patients are ranked in order of risk so that the higher risk patients are treated more rapidly than lower risk patients; [0080] discloses past cases for other patients similar to the current patient are identified, and similarity are measured (i.e., compared – emphasis added by Examiner); and the search may be limited by the predicted results, such as limiting to patients with a same condition; and based on the identified cases of other patients (i.e., identified cases would include a similarity between the first patient information and the second patient information), variation in treatment and outcome may be calculated – emphasis added by Examiner));
wherein the similarity measures are determined by extracting or receiving a corresponding data descriptor in each case from the patient information of the comparison patients ([0039] discloses similar patients are identified based on any measure of similarity between any aspect (e.g., condition, severity of condition, location, size, or other information (i.e., a corresponding data descriptor from the patient information of the comparison patients – emphasis added by Examiner); [0080] discloses past cases for other patients similar to the current patient are identified, and similarity are measured (i.e., compared – emphasis added by Examiner)); wherein individual features of the data descriptors represent features extracted from image data establishing a pattern ([0034] discloses feature is extracted from images, and the image is used to determine the values for the features; [0062] discloses “The same features or some subset of the features are extracted from the medical images of the patient in application (i.e., the same features that are extracted from the medical images would establish a pattern – emphasis added by Examiner) and used for predicting the results using the trained model.”));
accessing a memory storing a first function to access the first function ([0030] discloses the medical system obtains other data for the patients by accessing to a memory, and loading from memory; [0074] discloses performs continuous or regular monitoring and detects abnormal physiological parameters (i.e., a first function – emphasis added by Examiner) of the patient);
ascertaining, by the computing device (FIG. 6, processor 13, [0021]) and based on the previously known health information of the reference patients, health information about the patient by applying the accessed first function to the patient information ([0015] discloses “the system evaluates the existing sources of data to make a model prediction of the risk of presence of different pathological conditions of the patients (i.e., ascertaining previously known health information of the reference patients – emphasis added by Examiner). In addition, the model computes the likely probabilities of these different conditions and automatically flags high-risk patients (i.e., ascertaining health information about the patient by applying a first function hosted in the computing device – emphasis added by Examiner)”; [0032] discloses the data from a past examination of the patient and patient history, and symptoms being currently experienced and other current measurements are obtained, and obtaining the same type of data from different times may show progression (i.e., quantifying the evolution over time of medical parameter of the patient – emphasis added by Examiner)),
checking, by the computing device, whether a trigger condition is fulfilled based on the ascertained health information about the patient ([0015] discloses “the system evaluates the existing sources of data to make a model prediction of the risk of presence of different pathological conditions. In addition, the model computes the likely probabilities of these different conditions and automatically flags high-risk patients (i.e., a trigger condition is fulfilled)”),
generating, by the computing device and based on the trigger condition, control commands adapted to control the diagnostic assessment station, wherein the control commands are configured to prioritize the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information ([0015] discloses “the system evaluates the existing sources of data to make a model prediction of the risk of presence of different pathological conditions. In addition, the model computes the likely probabilities of these different conditions and automatically flags high-risk patients who need priority evaluation from clinical providers (i.e., based on the trigger condition, providing or generating control commands to control the diagnostic assessment station in order to prioritize the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information).”; [0070] The machine-learnt model is running directly on a medical scanner or a workstation connected to the patient information systems, and outputs from the model a clinical report with key findings presented in a structured format for easy search and retrieval, and other outputs may be used, such as an alert and notification, filling in one or more fields in a patient medical record (i.e., providing or generating control commands control the diagnostic assessment station – emphasis added by Examiner), or to a display); and
based on the control commands indicating one or more medical states of the patient, automatically transmitting, by the computing device and to an examination modality ([0015] discloses “the system evaluates the existing sources of data to make a model prediction of the risk of presence of different pathological conditions. In addition, the model computes the likely probabilities of these different conditions and automatically flags high-risk patients who need priority evaluation from clinical providers (i.e., based on the flagged signals or the control commands indicating one or more medical states of the patient, automatically indicating or transmitting to control the diagnostic assessment station in order to prioritize the patient in a worklist of the user hosted in the diagnostic assessment station as a function of the ascertained health information).”; and/or a treatment device, a medical order comprising examination parameters or treatment instructions configured to initiate a corresponding medical examination or treatment for the patient ([0071]; [0080]).
Rapaka does not explicitly teach the health information being configured to indicate one or more asymptomatic medical states of the patient; and based on the indication of the one or more asymptomatic medical states of the patient.
Oleynik teaches the health information being configured to indicate one or more asymptomatic medical states of the patient; and prioritize the patient in a worklist of the user hosted in the diagnostic assessment station based on the indication of the one or more asymptomatic medical states of the patient ([0063] discloses assessing the risk of a subject/patient in developing a disease or condition in the future using the patient’s medical history, and family medical history such as an asymptomatic subject with a family history of heart disease which is characterized as high blood pressure, a high total cholesterol level (over 370 mg/di (milligrams per deciliter)), a high LDL level (above 100 mg/di), and a high triglyceride level (above 100 mg/di), and overweight. The computer system can estimate the risk of the asymptomatic subject in developing a heart disease in the future; FIG. 26C; [0125]).
based on the control commands indicating one or more asymptomatic medical states of the patient, automatically transmitting to an examination modality and/or a treatment device, a medical order ([0063] discloses based on the assessment, the health care provider can provide recommendations for preventing the development of the disease or condition. For example, an asymptomatic subject with a family history of heart disease is characterized with factors, and these factors are inputted into the computer system as parameters for iterative comparison with the stored data of similar patients. Based on the risk assessment, the computer system can estimate the risk of the asymptomatic subject in developing a heart disease in the future (i.e., the estimations or control commands indicate one or more asymptomatic medical states or heart disease of the patient – emphasis added by Examiner), and the health care provider can recommend taking cholesterol lowering medication and changing lifestyle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Oleynik into Rapaka for the purpose of assessing the risk of a patient in developing a disease or condition in the future, and
estimating the patient's risk for developing a disease or condition in the future by analyzing asymptomatic subject with a family history of disease. This combination would improve in analyzing a mass amount of medical data from different sources across multiple geo-graphic regions to improve the treatment of patients and develop recommended treatment plans for patients.
As to claim 24, the combination of Rapaka and Oleynik teaches the claimed limitations as discussed in claim 22.
Rapaka teaches wherein the data descriptors include one or more features that have been extracted from the patient information or calculated therefrom ([0031] discloses patient’s medical record includes information such as the text in the clinical reports, medical images, blood biomarker information, patient demographics (e.g., age, race, gender, weight, BMI (i.e., the data descriptors include one or more features that have been extracted from the patient information - emphasis added by Examiner), patient history (e.g., smoking, alcohol consumption, high blood pressure, drug use, current medicines being used, or others); [0039] teaches the condition is one of coronary obstruction, aortic dissection, or a problem with the pulmonary arteries (i.e., the data descriptors include one or more features that have been extracted from the patient information - emphasis added by Examiner).
As to claim 25, the combination of Rapaka and Oleynik teaches the claimed limitations as discussed in claim 22.
Rapaka teaches wherein individual features of the data descriptors further represent morphological features ([0063] discloses “Any type of features may be used. Morphological features may be used.”), structural features ([0003], [0004]) and/or features relating to a texture and/or to a pattern.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable o