DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/28/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 3, 4, 6, 11, 13, 14, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“generate an updated descriptor trail as a function of the advisor input;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate a updated descriptor trail as a result of the advisor input.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A system for updating a descriptor trail using artificial intelligence, the system comprising a processor connected to a memory, wherein the processor is designed and configured to: display, on a graphical user interface, a descriptor trail wherein the descriptor trail includes an element of diagnostic data pertaining to a user including a prognostic output and a correlated ameliorative output and an element of machine-learning data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive an element of user constitutional data comprising a discovery center experience score;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“prompt, at an advisor client device, an advisor input based on the discovery experience score;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“receive, from an advisor client device, the advisor input containing an element of advisory data wherein the advisory data is generated as a function of the discovery center experience score;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and display the updated descriptor trail on the graphical user interface.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A system for updating a descriptor trail using artificial intelligence, the system comprising a processor connected to a memory, wherein the processor is designed and configured to: display, on a graphical user interface, a descriptor trail wherein the descriptor trail includes an element of diagnostic data pertaining to a user including a prognostic output and a correlated ameliorative output and an element of machine-learning data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receive an element of user constitutional data comprising a discovery center experience score;” (well-understood, routine, conventional MPEP 2106.05(d))
“prompt, at an advisor client device, an advisor input based on the discovery experience score;” (well-understood, routine, conventional MPEP 2106.05(d))
“receive, from an advisor client device, the advisor input containing an element of advisory data wherein the advisory data is generated as a function of the discovery center experience score;” (well-understood, routine, conventional MPEP 2106.05(d))
“and display the updated descriptor trail on the graphical user interface.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 3:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“select a first ameliorative output from the plurality of ameliorative outputs as a function of the element of adherence data;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select a first ameliorative output from plurality of ameliorative outputs as a function of adherence data.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The system of claim 2, wherein the processor is further designed and configured to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and display the first ameliorative output on the graphical user interface.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The system of claim 2, wherein the processor is further designed and configured to:” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and display the first ameliorative output on the graphical user interface.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 4:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“generate a second ameliorative output as a function of the advisory data, wherein the advisory data comprises an element of advisory data modifying the first ameliorative output.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate a second ameliorative output as a function of the advisory data.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The system of claim 3, wherein the processor is further designed and configured to” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The system of claim 3, wherein the processor is further designed and configured to” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 6:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The system of claim 1, wherein generating the updated descriptor trail as a function of the advisor input comprises generating an updated discovery center experience score.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate an updated discovery center experience score.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 11:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“generating by the processor an updated descriptor trail as a function of the advisor input;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate a updated descriptor trail as a result of the advisor input.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A method of updating a descriptor trail using artificial intelligence the method comprising: displaying, by a processor connected to a memory on a graphical user interface, a descriptor trail wherein the descriptor trail includes an element of diagnostic data pertaining to a user including a prognostic output and a correlated ameliorative output and an element of machine-learning data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving, by the processor from a user client device, an element of user constitutional data comprising a discovery center experience score;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“prompting, at an advisor client device by the processor, an advisor input based on the discovery experience score;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“receiving, by the processor from an advisor client device, the advisor input containing an element of advisory data wherein the advisory data is generated as a function of the discovery center experience score;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“and displaying by the processor the updated descriptor trail on the graphical user interface.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A method of updating a descriptor trail using artificial intelligence the method comprising: displaying, by a processor connected to a memory on a graphical user interface, a descriptor trail wherein the descriptor trail includes an element of diagnostic data pertaining to a user including a prognostic output and a correlated ameliorative output and an element of machine-learning data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving, by the processor from a user client device, an element of user constitutional data comprising a discovery center experience score;” (well-understood, routine, conventional MPEP 2106.05(d))
“prompting, at an advisor client device by the processor, an advisor input based on the discovery experience score;” (well-understood, routine, conventional MPEP 2106.05(d))
“receiving, by the processor from an advisor client device, the advisor input containing an element of advisory data wherein the advisory data is generated as a function of the discovery center experience score;” (well-understood, routine, conventional MPEP 2106.05(d))
“and displaying by the processor the updated descriptor trail on the graphical user interface.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 13:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 12, further comprising: selecting, by the processor, a first ameliorative output from the plurality of ameliorative outputs as a function of the element of adherence data;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select a first ameliorative output from plurality of ameliorative outputs as a function of adherence data.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and displaying, by the processor, the first ameliorative output on the graphical user interface.” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and displaying, by the processor, the first ameliorative output on the graphical user interface.” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 14:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 13, further comprising generating, by the processor, a second ameliorative output as a function of the advisory data, wherein the advisory data comprises an element of advisory data modifying the first ameliorative output.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate a second ameliorative output as a function of the advisory data.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 16:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 11, wherein generating the updated descriptor trail as a function of the advisor input comprises generating an updated discovery center experience score.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could generate an updated discovery center experience score.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-7 and 11-17 are rejected under 35 U.S.C. 103 as being unpatentable over Uri Kartoun et al; US 20190180870 A1 (hereinafter “Kartoun”) in view of Joske et al; “Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects” (hereinafter “Joske”) in further view of Adrienne et al; “Building a Patient Experience Program” (hereinafter “Adrienne”).
Regarding claim 1, Kartoun teaches A system for updating a descriptor trail using artificial intelligence, the system comprising a processor connected to a memory, (Kartoun Fig 3 shows processor (306) connected to a memory (308))
wherein the processor is designed and configured to: display, on a graphical user interface, a descriptor trail (Kartoun Paragraph 0069; " The physician interface may comprise portions of a graphical user interface that set forth the relevant patient clinical data 133" Examiner notes that clinical data is a descriptor trail)
wherein the descriptor trail includes an element of diagnostic data pertaining to a user including a prognostic output and a correlated ameliorative output and an element of machine-learning data; (Kartoun Paragraph 0053; "The analyzer modules 112-119 may apply cognitive analysis to the correlated information based on machine learned and trained logic for identifying patterns or trends in the correlated information leading to an assessment of lifestyle behaviors, patterns, and habits of the patient." Kartoun Paragraph 0058; "the patient EMR data 131 to determine the impact of the patient's lifestyle choices, habits, and activities on the patient's health, medical conditions, and treatments." Kartoun Paragraph 0059; "patient EMR data 131 may comprise a collection of clinical data for a patient" Kartoun Paragraph 0080; "The cognitive analysis pipelines 136 are trained to cognitively correlate and analyze patient EMR data and lifestyle behavior information from the commercial transaction analysis system 100 to identify relevant portions of such data to the health of a particular patient, the medical conditions of that patient, and the treatments of that patient" Examiner notes descriptor trail includes an element of diagnostic data (collection of clinical data) pertaining to a user including a prognostic output (medical conditions) and a correlated ameliorative output (treatments) and an element of machine learning data (EMR data is machine learning data because the cognitive analysis pipelines are trained to analyze it))
receive an element of user constitutional data [comprising a discovery center experience score]; (Kartoun Paragraph 0068; "the patient may report to the physician that they have had multiple occurrence of dizziness in the past month, and this may be recorded in the patient EMR." Examiner notes that physician receives an element of user constitutional data (patient report))
prompt, at an advisor client device, an advisor input [based on the discovery experience score]; (Kartoun Paragraph 0023 and Fig 4; "Thus, the mechanisms of the illustrative embodiments may be integrated in, or operate with, a decision support system, which may be implemented as a cognitive system, which provides notifications and/or the recommendations based on lifestyle decisions, habits, and activities to appropriate medical personnel to assist them in making decisions regarding the treatment of the patient so as to improve the likelihood of a desired result in the patient's medical condition(s)." Examiner notes that the advisor client device (health services computing system) prompts (sends notification) for an advisor input (decisions regarding the treatment from appropriate medical personnel))
receive, from an advisor client device, the advisor input [containing an element of advisory data wherein the advisory data is generated as a function of the discovery center experience score;] (Kartoun Paragraph 0097; "For example, in one illustrative embodiment, in response to the physician 406 requesting to view a particular patient 402 EMR data, the request to review the EMR 407 may trigger the health services computing system 130 sending a request to the commercial transaction analysis system 100 to retrieve commercial transaction data for the patient from the data sources 140 and analyze it to provide lifestyle behavior information to be processed by the health services computing system 130" Examiner notes that advisor client device (health services computing system) receives the advisor input (request to review))
generate an updated descriptor trail as a function of the advisor input; (Kartoun Paragraph 0097; "This may be done automatically at periodic times, according to a schedule, or in response to a particular defined event. For example, in one illustrative embodiment, in response to the physician 406 requesting to view a particular patient 402 EMR data, the request to review the EMR 407 may trigger the health services computing system 130 sending a request to the commercial transaction analysis system 100 to retrieve commercial transaction data for the patient from the data sources 140 and analyze it to provide lifestyle behavior information to be processed by the health services computing system 130" Examiner notes that updated descriptor trail (latest/up to date EMR data) is generated/retrieved as a function/result of the advisor input (request for review))
and display the updated descriptor trail on the graphical user interface. (Kartoun Paragraph 0096; "The results of the evaluation take into account the most up-to-date and relevant lifestyle behavior information relevant to the particular patient's medical conditions, treatments, and/or reported symptoms and aids the physician or other medical personnel, or even the patient themselves" Kartoun Paragraph 0097; "the health services computing system 130, such as for use in presenting a physician interface to the physician 406." Examiner notes that updated descriptor trail is displayed/present on the graphical user interface (physician interface))
Kartoun does not teach the advisor input containing an element of advisory data
However, Adrienne does teach the advisor input containing an element of advisory data (Adrienne Section "Develop and implement your patient experience strategy" shows advisor input (strategy) containing an element of advisory data (processes or changes to implement))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Kartoun and Adrienne. Kartoun teaches a treatment recommendation decision support system. Adrienne teaches a program to utilize patient feedback in a medical setting. One of ordinary skill would have motivation to combine Kartoun and Adrienne to improve patient engagement, patient outcomes, and practice reputation and patient loyalty “A patient experience program can be utilized by practices of all sizes and is an effective way to:” (Adrienne Page 2 Paragraph 3).
Kartoun does not teach user constitutional data comprising a discovery center experience score;
an advisor input based on the discovery experience score;
wherein the advisory data is generated as a function of the discovery center experience score;
However, Joske does teach user constitutional data comprising a discovery center experience score; (Joske Page 9 Paragraph 2; "For an ensemble classifier containing s gene sets, this defines a classification score between 0 and s per patient. This score is thresholded by threshold T, which determines whether a patient is to benefit from the treatment of interest, where a patient with a score below the threshold is classified as not benefitting from treatment (“no benefit” class)." Examiner notes that user constitutional data comprises a discovery center experience score (classification score); Classification score is associated with discovery center experience (simulated treatment))
an advisor input based on the discovery experience score; (Joske Page 9 Paragraph 2; "For an ensemble classifier containing s gene sets, this defines a classification score between 0 and s per patient. This score is thresholded by threshold T, which determines whether a patient is to benefit from the treatment of interest, where a patient with a score below the threshold is classified as not benefitting from treatment (“no benefit” class)." Examiner notes that advisor input is prompted based on the discovery center experience score (classification score);)
wherein the advisory data is generated as a function of the discovery center experience score; (Joske Page 9 Paragraph 2; "For an ensemble classifier containing s gene sets, this defines a classification score between 0 and s per patient. This score is thresholded by threshold T, which determines whether a patient is to benefit from the treatment of interest, where a patient with a score below the threshold is classified as not benefitting from treatment (“no benefit” class)." Examiner notes that advisory data is generated/defined as a function/result of the discovery center experience score (classification score);)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Kartoun, Adrienne, and Joske. Kartoun teaches a treatment recommendation decision support system. Adrienne teaches a program to utilize patient feedback in a medical setting. Joske teaches simulating treatments to predict patient’s treatment benefit. One of ordinary skill would have motivation to combine Kartoun, Adrienne, and Joske to simulate the treatment to enable a more personalized approach to treatment “This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.” (Joske Page 1 Paragraph 1).
Regarding claim 2, Kartoun teaches The system of claim 1, wherein: the correlated ameliorative output comprises a plurality of ameliorative outputs; (Kartoun Paragraph 0058; "the patient EMR data 131 to determine the impact of the patient's lifestyle choices, habits, and activities on the patient's health, medical conditions, and treatments." Examiner notes that "treatments" suggests a plurality of ameliorative outputs)
the element of user constitutional data comprises an element of adherence data (Kartoun Paragraph 0068; "The patient's commercial transaction data shows that the patient has not filled their prescription for insulin in the last month and has been eating unhealthy with a high caloric intake.")
Regarding claim 3, Kartoun teaches The system of claim 2, wherein the processor is further designed and configured to: select a first ameliorative output from the plurality of ameliorative outputs as a function of the element of adherence data; (Kartoun Paragraph 0072; " The cognitive analysis pipelines 136 may determine a treatment for a medical condition of the patient based on the application of medical guidelines documents and the like from a corpus and may then evaluate the patient's ability to adhere to the treatment based on their lifestyle behavior patterns… determine an adjustment to bring the patient back into conformance with the prescribed treatment, e.g., increase activity by getting a gym membership and using it, reducing calorie intake by being on a low fat diet, etc. These recommendations may be generated through cognitive analysis by the cognitive analysis pipelines 136 and presented to the physician or other medical personnel via the physician interface generated by the physician interface engine 132." Examiner notes that a first ameliorative output (adjustment/recommendation) is selected/determined from the plurality of ameliorative outputs (these recommendations) as a function/result of the element of adherence data)
and display the first ameliorative output on the graphical user interface. (Kartoun Paragraph 0072; "These recommendations may be generated through cognitive analysis by the cognitive analysis pipelines 136 and presented to the physician or other medical personnel via the physician interface generated by the physician interface engine 132." Examiner notes that first ameliorative output (listed in recommendations) is displayed on the graphical user interface (physician interface))
Regarding claim 4, Kartoun teaches The system of claim 3, wherein the processor is further designed and configured to generate a second ameliorative output (Kartoun Paragraph 0026; "This information may then be presented to the medical professional when treating the patient, used by a computing system to automatically interact with the patient and/or medical professional, used by a cognitive system to generate treatment recommendations or recommendations for modifications in the patients' current treatment(s), etc." Examiner notes that processor (computing system) is further designed and configured to generate a second ameliorative output (treatment recommendations or recommendations for modifications in the current treatment))
Kartoun does not teach second ameliorative output as a function of the advisory data, wherein the advisory data comprises an element of advisory data modifying the first ameliorative output.
However, Adrienne does teach second ameliorative output as a function of the advisory data, wherein the advisory data comprises an element of advisory data modifying the first ameliorative output. (Adrienne Page 6 Paragraph 1; "Work with your patient experience program team to determine which processes or changes to implement first. To ensure success, start small and pick an option that is sustainable and scalable for your practice." Examiner notes that second ameliorative output is generated as a function/result of advisory data (processes or changes to implement) wherein the advisory data comprises and element of advisory data modifying (processes or changes to implement) the first ameliorative output (for your practice))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Kartoun and Adrienne. Kartoun teaches a treatment recommendation decision support system. Adrienne teaches a program to utilize patient feedback in a medical setting. One of ordinary skill would have motivation to combine Kartoun and Adrienne to improve patient engagement, patient outcomes, and practice reputation and patient loyalty “A patient experience program can be utilized by practices of all sizes and is an effective way to:” (Adrienne Page 2 Paragraph 3).
Regarding claim 5, Kartoun teaches The system of claim 1, wherein generating the updated descriptor trail as a function of the advisor input, wherein the advisor input comprises user physical data. (Kartoun Paragraph 0098; "The resulting physician interface 408 setting forth relevant patient lifestyle and EMR data may be used by the physician 406 to target the physician's interaction with the patient 402 via a question/response exchange 414, 416 so as to tailor the encounter with the patient 402 to the most up-to-date understanding of the patient's lifestyle as it contributes to the patient's overall health, specific medical conditions, and treatments." Examiner notes that advisor input comprises/accounts for physical data (patient's overall health and specific medical conditions))
Regarding claim 6, Kartoun does not teach The system of claim 1, wherein generating the updated descriptor trail as a function of the advisor input comprises generating an updated discovery center experience score.
However, Joske does teach The system of claim 1, wherein generating the updated descriptor trail as a function of the advisor input comprises generating an updated discovery center experience score. (Joske Page 9 Paragraph 2; "For an ensemble classifier containing s gene sets, this defines a classification score between 0 and s per patient. This score is thresholded by threshold T, which determines whether a patient is to benefit from the treatment of interest, where a patient with a score below the threshold is classified as not benefitting from treatment (“no benefit” class)." Examiner notes that wherein generating the updated descriptor trail comprises generating an updated discovery center experience score (perform again algorithm to obtain updated classification score with new data))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Kartoun, Adrienne, and Joske. Kartoun teaches a treatment recommendation decision support system. Adrienne teaches a program to utilize patient feedback in a medical setting. Joske teaches simulating treatments to predict patient’s treatment benefit. One of ordinary skill would have motivation to combine Kartoun, Adrienne, and Joske to simulate the treatment to enable a more personalized approach to treatment “This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.” (Joske Page 1 Paragraph 1).
Regarding claim 7, Kartoun does not teach The system of claim 1, wherein the discovery center experience score comprises a biological extraction measurement of a user.
However, Joske does teach The system of claim 1, wherein the discovery center experience score comprises a biological extraction measurement of a user. (Joske Page 8 Paragraph 1; "Data and processing. We pooled gene expression and survival data from three phase III trials:" Examiner notes that discovery center experience score (classification score) comprises/accounts for biological extraction measurement of a use (gene expression))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Kartoun, Adrienne, and Joske. Kartoun teaches a treatment recommendation decision support system. Adrienne teaches a program to utilize patient feedback in a medical setting. Joske teaches simulating treatments to predict patient’s treatment benefit. One of ordinary skill would have motivation to combine Kartoun, Adrienne, and Joske to simulate the treatment to enable a more personalized approach to treatment “This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.” (Joske Page 1 Paragraph 1).
Regarding claim 11, Kartoun teaches A method of updating a descriptor trail using artificial intelligence the method comprising: displaying, by a processor connected to a memory on a graphical user interface, a descriptor (Kartoun Fig 3 shows processor (306) connected to a memory (308) Kartoun Paragraph 0069; "The physician interface may comprise portions of a graphical user interface that set forth the relevant patient clinical data 133" Examiner notes that clinical data is a descriptor trail)
wherein the descriptor trail includes an element of diagnostic data pertaining to a user including a prognostic output and a correlated ameliorative output and an element of machine-learning data; (Kartoun Paragraph 0053; "The analyzer modules 112-119 may apply cognitive analysis to the correlated information based on machine learned and trained logic for identifying patterns or trends in the correlated information leading to an assessment of lifestyle behaviors, patterns, and habits of the patient." Kartoun Paragraph 0058; "the patient EMR data 131 to determine the impact of the patient's lifestyle choices, habits, and activities on the patient's health, medical conditions, and treatments." Kartoun Paragraph 0059; "patient EMR data 131 may comprise a collection of clinical data for a patient" Kartoun Paragraph 0080; "The cognitive analysis pipelines 136 are trained to cognitively correlate and analyze patient EMR data and lifestyle behavior information from the commercial transaction analysis system 100 to identify relevant portions of such data to the health of a particular patient, the medical conditions of that patient, and the treatments of that patient" Examiner notes descriptor trail includes an element of diagnostic data (collection of clinical data) pertaining to a user including a prognostic output (medical conditions) and a correlated ameliorative output (treatments) and an element of machine learning data (EMR data is machine learning data because the cognitive analysis pipelines are trained to analyze it))
receiving, by the processor from a user client device, an element of user constitutional data [comprising a discovery center experience score;] (Kartoun Paragraph 0068; "the patient may report to the physician that they have had multiple occurrence of dizziness in the past month, and this may be recorded in the patient EMR." Kartoun Paragraph 0081; “The network 202 includes multiple computing devices 204A-D, which may operate as server computing devices, and client computing devices 210-212, in communication with each other and with other devices” Examiner notes that physician receives an element of user constitutional data (patient report); report is received from the processor from a user client device (client computing devices))
prompting, at an advisor client device by the processor, an advisor input [based on the discovery experience score;] (Kartoun Paragraph 0023 and Fig 4; "Thus, the mechanisms of the illustrative embodiments may be integrated in, or operate with, a decision support system, which may be implemented as a cognitive system, which provides notifications and/or the recommendations based on lifestyle decisions, habits, and activities to appropriate medical personnel to assist them in making decisions regarding the treatment of the patient so as to improve the likelihood of a desired result in the patient's medical condition(s)." Examiner notes that the advisor client device (health services computing system) prompts (sends notification) for an advisor input (decisions regarding the treatment from appropriate medical personnel))
receiving, by the processor from an advisor client device, the advisor input [containing an element of advisory data wherein the advisory data is generated as a function of the discovery center experience score]; (Kartoun Paragraph 0097; "For example, in one illustrative embodiment, in response to the physician 406 requesting to view a particular patient 402 EMR data, the request to review the EMR 407 may trigger the health services computing system 130 sending a request to the commercial transaction analysis system 100 to retrieve commercial transaction data for the patient from the data sources 140 and analyze it to provide lifestyle behavior information to be processed by the health services computing system 130" Examiner notes that advisor client device (health services computing system) receives the advisor input (request to review))
generating by the processor an updated descriptor trail as a function of the advisor input; (Kartoun Paragraph 0097; "This may be done automatically at periodic times, according to a schedule, or in response to a particular defined event. For example, in one illustrative embodiment, in response to the physician 406 requesting to view a particular patient 402 EMR data, the request to review the EMR 407 may trigger the health services computing system 130 sending a request to the commercial transaction analysis system 100 to retrieve commercial transaction data for the patient from the data sources 140 and analyze it to provide lifestyle behavior information to be processed by the health services computing system 130" Examiner notes that updated descriptor trail (latest/up to date EMR data) is generated/retrieved as a function/result of the advisor input (request for review))
and displaying by the processor the updated descriptor trail on the graphical user interface. (Kartoun Paragraph 0096; "The results of the evaluation take into account the most up-to-date and relevant lifestyle behavior information relevant to the particular patient's medical conditions, treatments, and/or reported symptoms and aids the physician or other medical personnel, or even the patient themselves" Kartoun Paragraph 0097; "the health services computing system 130, such as for use in presenting a physician interface to the physician 406." Examiner notes that updated descriptor trail is displayed/present on the graphical user interface (physician interface))
Kartoun does not teach the advisor input containing an element of advisory data
However, Adrienne does teach the advisor input containing an element of advisory data (Adrienne Section "Develop and implement your patient experience strategy" shows advisor input (strategy) containing an element of advisory data (processes or changes to implement))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Kartoun and Adrienne. Kartoun teaches a treatment recommendation decision support system. Adrienne teaches a program to utilize patient feedback in a medical setting. One of ordinary skill would have motivation to combine Kartoun and Adrienne to improve patient engagement, patient outcomes, and practice reputation and patient loyalty “A patient experience program can be utilized by practices of all sizes and is an effective way to:” (Adrienne Page 2 Paragraph 3).
Kartoun does not teach user constitutional data comprising a discovery center experience score;
an advisor input based on the discovery experience score;
wherein the advisory data is generated as a function of the discovery center experience score;
However, Joske does teach user constitutional data comprising a discovery center experience score; (Joske Page 9 Paragraph 2; "For an ensemble classifier containing s gene sets, this defines a classification score between 0 and s per patient. This score is thresholded by threshold T, which determines whether a patient is to benefit from the treatment of interest, where a patient with a score below the threshold is classified as not benefitting from treatment (“no benefit” class)." Examiner notes that user constitutional data comprises a discovery center experience score (classification score); Classification score is associated with discovery center experience (simulated treatment))
an advisor input based on the discovery experience score; (Joske Page 9 Paragraph 2; "For an ensemble classifier containing s gene sets, this defines a classification score between 0 and s per patient. This score is thresholded by threshold T, which determines whether a patient is to benefit from the treatment of interest, where a patient with a score below the threshold is classified as not benefitting from treatment (“no benefit” class)." Examiner notes that advisor input is prompted based on the discovery center experience score (classification score);)
wherein the advisory data is generated as a function of the discovery center experience score; (Joske Page 9 Paragraph 2; "For an ensemble classifier containing s gene sets, this defines a classification score between 0 and s per patient. This score is thresholded by threshold T, which determines whether a patient is to benefit from the treatment of interest, where a patient with a score below the threshold is classified as not benefitting from treatment (“no benefit” class)." Examiner notes that advisory data is generated/defined as a function/result of the discovery center experience score (classification score);)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Kartoun, Adrienne, and Joske. Kartoun teaches a treatment recommendation decision support system. Adrienne teaches a program to utilize patient feedback in a medical setting. Joske teaches simulating treatments to predict patient’s treatment benefit. One of ordinary skill would have motivation to combine Kartoun, Adrienne, and Joske to simulate the treatment to enable a more personalized approach to treatment “This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.” (Joske Page 1 Paragraph 1).
Regarding claim 12, Kartoun teaches The method of claim 11, wherein: the correlated ameliorative output comprises a plurality of ameliorative outputs; (Kartoun Paragraph 0058; "the patient EMR data 131 to determine the impact of the patient's lifestyle choices, habits, and activities on the patient's health, medical conditions, and treatments." Examiner notes that "treatments" suggests a plurality of ameliorative outputs)
the element of user constitutional data comprises an element of adherence data (Kartoun Paragraph 0068; "The patient's commercial transaction data shows that the patient has not filled their prescription for insulin in the last month and has been eating unhealthy with a high caloric intake.")
Regarding claim 13, Kartoun teaches The method of claim 12, further comprising: selecting, by the processor, a first ameliorative output from the plurality of ameliorative outputs as a function of the element of adherence data; (Kartoun Paragraph 0072; " The cognitive analysis pipelines 136 may determine a treatment for a medical condition of the patient based on the application of medical guidelines documents and the like from a corpus and may then evaluate the patient's ability to adhere to the treatment based on their lifestyle behavior patterns… determine an adjustment to bring the patient back into conformance with the prescribed treatment, e.g., increase activity by getting a gym membership and using it, reducing calorie intake by being on a low fat diet, etc. These recommendations may be generated through cognitive analysis by the cognitive analysis pipelines 136 and presented to the physician or other medical personnel via the physician interface generated by the physician interface engine 132." Examiner notes that a first ameliorative output (adjustment/recommendation) is selected/determined from the plurality of ameliorative outputs (these recommendations) as a function/result of the element of adherence data)
and displaying, by the processor, the first ameliorative output on the graphical user interface. (Kartoun Paragraph 0072; "These recommendations may be generated through cognitive analysis by the cognitive analysis pipelines 136 and presented to the physician or other medical personnel via the physician interface generated by the physician interface engine 132." Examiner notes that first ameliorative output (listed in recommendations) is displayed on the graphical user interface (physician interface))
Regarding claim