DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
In the response filed on 03 September 2025, the following changes have been made: amendments to claims 1 and 10. Claim 7 has been canceled. Claim 13 has been added.
Claims 1-2, 4-6, 8-10, and 12-13 are currently pending and have been examined.
Priority
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
An English translation as well as a statement that the translation is accurate is required. See 37 CFR 1.55 (g)(3)(iii) 37 CFR 1.55 (g)(4).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
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 1-2, 4-10, and 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a machine (claims 1-2, 4-9, and 12) and process (claim 10).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
a processor;
and a memory storing instructions, that when executed by a processor, cause the processor to perform operations
estimating a future living situation of a target patient for each treatment method to be applied to the target patient on the basis of attributes of the target patient,
outputting information based on the living situation via an output unit,
acquiring a first distribution representing attributes of each of a plurality of groups stratified from a plurality of patients on the basis of diagnostic information of the plurality of patients to which the same treatment method as that for the target patient has been applied,
acquiring a second distribution representing the attributes of the target patient,
calculating a degree of similarity between the first distribution and the second distribution,
and estimating a prognosis of the target patient on the based on the degree of similarity,
wherein the first distribution is a first two-dimensional statistical chart with the attribute as a dimension and the second distribution is a second two-dimensional statistical chart with the attribute as a dimension, and
wherein the processor calculates the degree of similarity between a graphic shape of the first two-dimensional statistical chart and a graphic shape of the second two-dimensional statistical chart,
displays a screen on a terminal device used by the target patient or a family for
the target patient or the family to input a first living situation, the first living situation being a future living situation of the target patient desired by the target patient or the family,
in response to the first living situation being input on the screen, determines
whether or not a first information gap has been generated between the first living situation and a second living situation, the second living situation being the estimated future living situation of the target patient, and
outputs different information to the output unit between a case in which the first
information gap has been generated or the first information gap has not been generated.
Claim 10 recites similar limitations as claim 1.
These steps directed to estimating a future living situation & prognosis of a target patient, as drafted, under the broadest reasonable interpretation, includes performance of the limitations in the mind but for recitation of generic computer components. That is, nothing in the claim element precludes the italicized portions from practically being performed in the mind through performing the same determinations (e.g., evaluation, judgment, opinion, etc.) or predictive analytics on data that medical workers already perform to forecast patient situations/outcomes. This could be analogized to collecting information, analyzing it, and displaying certain results of the collection and analysis. If a claim limitation, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 1 and 10, do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as by
a processor; a memory storing instructions, that when executed by a processor, cause the processor to perform operations; displays a screen on a terminal device used by the target patient or a family for the target patient or the family to input a first living situation and, using a computer amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of outputting information based on the living situation via an output unit; acquiring a first distribution representing attributes of each of a plurality of groups stratified from a plurality of patients on the basis of diagnostic information of the plurality of patients to which the same treatment method as that for the target patient has been applied; acquiring a second distribution representing the attributes of the target patient; and, outputs different information to the output unit between a case in which the first information gap has been generated or the first information gap has not been generated amounts to mere data gathering and output since it does not add meaningful limitations to the acquiring and outputting performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity, and add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as by a processor; a memory storing instructions, that when executed by a processor, cause the processor to perform operations; displays a screen on a terminal device used by the target patient or a family for the target patient or the family to input a first living situation and, using a computer, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f).
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of outputting information based on the living situation via an output unit; acquiring a first distribution representing attributes of each of a plurality of groups stratified from a plurality of patients on the basis of diagnostic information of the plurality of patients to which the same treatment method as that for the target patient has been applied; acquiring a second distribution representing the attributes of the target patient; and, outputs different information to the output unit between a case in which the first information gap has been generated or the first information gap has not been generated, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2, 4-6, 8-9, and 12 reciting particular aspects of estimating a future living situation & prognosis of a target patient such as
[Claim 2] wherein the processor estimates a prognosis of the target patient for each treatment method, and
estimates the future living situation of the target patient for each treatment method on the basis of each prognosis;
[Claim 4] wherein the processor stratifies the plurality of patients into the plurality of groups on the basis of both a first index value regarding a temporal or economic cost of each of the plurality of patients and a second index value regarding treatment effects of each treatment method applied to each of the plurality of patients;
[Claim 5] wherein the processor determines at least one of the first index value and the second index value regarding the target patient as the prognosis of the target patient on the basis of a comparison between the first distribution and the second distribution;
[Claim 6] wherein the processor calculates the degree of similarity between the second distribution and the first distribution of each of the plurality of groups, and estimates the first index value of the first distribution having a highest degree of similarity to the second distribution as the first index value regarding the target patient, or estimates the second index value of the first distribution having the highest degree of similarity to the second distribution as the second index value regarding the target patient;
[Claim 8] wherein the processor
further determines whether or not there is a solution to the first information gap when the first information gap has been generated, and
outputs the solution to the first information gap via the output unit when there is a solution to the first information gap;
[Claim 9] wherein the attributes of the target patient include a first factor that is able to be controlled by the target patient and a second factor that is unable to be controlled by the target patient, and
the processor further estimates a future living situation of the target patient on the basis of the attributes of the target patient whose first factor has been adjusted when the first information gap has been generated,
determines whether or not a second information gap has been generated between the first living situation and a third living situation, the third living situation being a future living situation of the target patient estimated when the first factor has been adjusted,
determines that there is a solution to the first information gap when the second information gap has not been generated, and
outputs there being an adjustment of the first factor in the solution to the first information gap via the output unit;
[Claim 12] wherein the processor calculates a distance between colors or shades of the first two-dimensional statistical chart and colors or shades of the second two-dimensional statistical chart, as the degree of similarity
these italicized portions covers performance of the limitations in the mind but for recitation of generic computer components since they merely describe types of data and determinations that can be performed by humans).
Step 2A Prong 2
Dependent claims 2-6, 8-9, and 12 recites additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claims 2-6, 8-9, and 12 (the processor) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)); and, add insignificant extra-solution activity to the abstract idea such as claim 8 (outputs the solution to the first information gap via the output unit when there is a solution to the first information gap); and, claim 9 (outputs there being an adjustment of the first factor in the solution to the first information gap via the output unit) amounts to mere output since it does not add meaningful limitations to the outputting performed, see MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 2-6, 8-9, and 12 recites additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f). Also, see [0025] which provides examples of terminal devices, [0038] & [0051] which provides examples of memory devices, and [0038] disclosing examples of processors. Dependent claims 8-9 amounts to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 8-10, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Pecora et al. (US20210082573A1) in view of Villazón-Terrazas et al. (US20180098737A1), and further in view of Zhang at al. (US20170124268A1).
Regarding claim 1, Pecora discloses an information processing device, comprising a processor ([0257] “A computer as referred to herein refers to any device with one or more processors capable of executing logic or coded instructions”)
and a memory storing instructions, that when executed by a processor, cause the processor to perform operations ([0250] “flash memory or other solid state memory technology […] instructions and which can be accessed by a computer or processor.” [0258] “CPU 3012 can then execute the stored process”)
estimating a future living situation of a target patient for each treatment method to be applied to the target patient on the basis of attributes of the target patient ([0086] “The treatment plan information may include information regarding treatment plans and/or treatment strategies specific to all of the treatment relevant variables or attributes of the patient.” [0110] “In some embodiments, the COTA module 220 tracks outcomes, such as overall survival (OS) […] expected prognosis-related outcome specific to the patient”)
outputting information based on the living situation via an output unit ([0040] “where it is determined that the one or more outcomes for the patient of interest are trending away from the standard, to send an alert to a health care provider or health payer of the patient of interest including information regarding the one or more outcomes that are trending away from the standard.”)
acquiring a first distribution representing attributes of each of a plurality of groups stratified from a plurality of patients on the basis of diagnostic information of the plurality of patients to which the same treatment method as that for the target patient has been applied
([0114] “In one embodiment, the COTA module 220 performs sorting/grouping 310, which sorts, groups, and/or identifies patients satisfying one or more parameters. […] The parameters may be a simple indicator (e.g., positive, negative, not accessed), a numerically based parameter (e.g., tumor size), a standards based parameter (e.g., tumor grade), etc. In some embodiments, the parameters may be received by the COTA module 220 as a user selected input. […] In some embodiments, the COTA module 220 may provide individual/and or collective information regarding patients in the group of patients having the same treatment relevant parameters as the identified patient without providing any personally identifiable information regarding patients in the group.”)
acquiring a second distribution representing the attributes of the target patient ([0007] “accessing or receiving a second set of data including updated and/or additional personal health information associated with the patient of interest at a second time”)
calculating a degree of similarity between the first distribution and the second distribution ([0213] “based on the outcomes of many patients whose similarity is determined based on the provisional nodal address or the refined nodal address assigned to the patient.” [0241] “The content 2015 may include the patient data associated with the alert 2010, a comparison, or any other relevant content. In one embodiment, the comparison may be, e.g., […] between one physician's patients and the whole patient population, [….]. The comparison may be based on a trending analysis to show where treatment is trending and if it is going off course (i.e., results are not as good as the standard).”)
and estimating a prognosis of the target patient on the based on the degree of similarity ([0114] “In some embodiments, a user selected input may identify a patient (e.g. a patient of interest) and the sorting, grouping, or identification identifies a group of patients having the same prognosis or outcome relevant parameters as the identified patient (e.g., a prognosis or outcome based group of patients). In some embodiments, the COTA module 220 may provide individual/and or collective information regarding patients in the group of patients having the same prognosis or outcome parameters as the identified patient without providing any personally identifiable information regarding patients in the group.”)
Pecora does not explicitly disclose however Villazón-Terrazas teaches wherein the first distribution is a first two-dimensional statistical chart with the attribute as a dimension ([0030] “inputting patient data including historical clinical data for a population of patients; and inputting open data, and using these inputs to create a patient clinical object, PCO representing each patient in the form of a graph” [0118] “The full patient graph is composed as follows: a vertex is created for each patient. The patient vertex contains attributes, e.g., ID numbers. Symptoms, medications, treatments, and diseases are key entities in the domain of discourse and are also modeled as vertices in the graph.”)
and the second distribution is a second two-dimensional statistical chart with the attribute as a dimension ([0119] “Effectively, a complete patient egocentric network (also known as PCO) presents a patient profile as a subgraph within the full graph: it is part of the graph representing the domain of discourse. So patient vertices have links to other types of vertices in the graph, such as instances of doctors who treat the patient, and instances of hospitals where the patient is treated.” [0117] “FIG. 5 illustrates fragments of a patient's ego-net or PCO: a subgraph including the subject patient and all those vertices directly related to the patient (or direct neighboring vertices). Key-value pairs such as “Gender: Female” and “Age: 58” are attributes of the patient vertex (labeled as 22242).”)
and wherein the processor calculates the degree of similarity between a graphic shape of the first two-dimensional statistical chart and a graphic shape of the second two-dimensional statistical chart ([0151] “Similarity of vertices of the same category can be computed using for instance string similarity algorithms as σc2(a, b)=jaro_winkler_distance(a, b).”)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Pecora’s techniques for clinical outcome tracking and analysis with Villazón-Terrazas’ techniques for context-based patient similarity. The motivation for the combination of Pecora and Villazón-Terrazas is to modify the system of Pecora to incorporate statistical charts and calculating degree of similarity as in Villazón-Terrazas to improve efficiency in prescribing treatments (See Villazón-Terrazas, Background).
Pecora in view of Villazón-Terrazas does not explicitly disclose however Zhang teaches displays a screen on a terminal device used by the target patient or a family for the target patient or the family to input a first living situation, the first living situation being a future living situation of the target patient desired by the target patient or the family ([0036] “The patient personalization system 12 enables the patient to input the patient values, lifestyle regimes, and preferences 66 related to diagnosis and treatment of the patient from a patient's perspective” [0037] “Examples of patient personalization systems 12 include, but are not limited to, a software application that could be accessed and/or displayed on a personal computer, web-based applications, tablets, mobile devices, cellular phones, and the like.”)
in response to the first living situation being input on the screen, determines whether or not a first information gap has been generated between the first living situation and a second living situation, the second living situation being the estimated future living situation of the target patient, ([0036] “the patient to input the patient values, lifestyle regimes, and preferences 66 related to diagnosis and treatment of the patient from a patient's perspective […]the patient personalization system 12 displays the quantitative evaluation and comparison of the choices of treatment and pathways including a comparison of alternative choices on the same measure.”)
and outputs different information to the output unit between a case in which the first information gap has been generated or the first information gap has not been generated ([0037] “display the evaluation and/or comparison of choices” [0054] “Using patient reported outcome data, the system can visually portray differences between the actual trends of recovery and side effects, and the expected trends based on available evidence.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Pecora’s techniques for clinical outcome tracking and analysis and Villazón-Terrazas’ techniques for context-based patient similarity with Zhang’s techniques for a patient-centric decision support tool. The motivation for the combination of Pecora, Villazón-Terrazas, and Zhang is to modify the system of Pecora and Villazón-Terrazas to incorporate displaying a living situation, determining a first information gap, and outputting different information as in Zhang in order to make it possible to select a treatment most suitable for a patient (See Zhang, Background).
Regarding claim 2, Pecora discloses wherein the processor estimates a prognosis of the target patient for each treatment method ([0122] “analyze the results of the treatment for the localized disease which renders the patient apparently disease free, such as surgery or surgery plus adjuvant therapy.”)
and estimates the future living situation of the target patient for each treatment method on the basis of each prognosis ([0007] “assigning a refined nodal address to the patient of interest based on the current assigned attributes for the set of treatment relevant variables […] determining the prognosis-related expected outcome with respect to occurrence of the defined end point event for the patient based on the refined nodal address assigned to the patient of interest.”)
Regarding claim 8, Pecora in view of Villazón-Terrazas does not explicitly disclose however Zhang teaches wherein the processor further determines whether or not there is a solution to the first information gap when the first information gap has been generated ([0030] “To assist the patient, the patient's preferences and rationale in making earlier decisions are stored to minimize the patient's confusion and ease decision making for later decisions to achieve global optimal solutions throughout the entire disease management time horizon.”)
and outputs the solution to the first information gap via the output unit when there is a solution to the first information gap ([0036] “For example, the patient personalization system 12 displays the quantitative evaluation and comparison of the choices of treatment and pathways including a comparison of alternative choices on the same measure, such as allowing the patients to adjust for lifestyle regime and preferences, outcome parameters, patient pathways, QALYs, desired probability of an overall outcome or of a specific outcome parameter, and the like.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Pecora’s techniques for clinical outcome tracking and analysis and Villazón-Terrazas’ techniques for context-based patient similarity with Zhang’s techniques for a patient-centric decision support tool. The motivation for the combination of Pecora, Villazón-Terrazas, and Zhang is to modify the system of Pecora and Villazón-Terrazas to incorporate determination and outputting of a solution to a first information gap as in Zhang in order to make it possible to select a treatment most suitable for a patient (See Zhang, Background).
Regarding claim 9, Pecora in view of Villazón-Terrazas does not explicitly disclose however Zhang teaches wherein the attributes of the target patient include a first factor that is able to be controlled by the target patient ([0048] “the main lifestyle changes and other adverse effects (e.g. dietary, sleep, tiredness, sex life, etc.).”)
and a second factor that is unable to be controlled by the target patient ([0024] “demographic information”)
and the processor further estimates a future living situation of the target patient on the basis of the attributes of the target patient whose first factor has been adjusted when the first information gap has been generated ([0015] “receive adjustments to the patient's lifestyle values and preferences, re-generate patient pathway and treatment options from the patient data and the patient's lifestyle values and preferences”)
determines whether or not a second information gap has been generated between the first living situation and a third living situation, the third living situation being a future living situation of the target patient estimated when the first factor has been adjusted ([0081] “In the event that the patient preferences 66 have changed, the patient's physician may be alerted as to the inconsistency between past preferences and current preferences at 636, enabling the physician to schedule additional consultation with the patient. The change in preferences 66 may also alert the physician to adverse side effects of past treatment, degradation in mental facilities of the patient, change in lifestyle, and the like.”)
determines that there is a solution to the first information gap when the second information gap has not been generated ([0033] “The shared decision support system 18 also enables patients to compare alternative choices on the same measure, such as allowing the patients to adjust for lifestyle regime and preferences, outcome parameters, patient pathways, quality-adjusted life years (QALYs), desired probability of an overall outcome or of a specific outcome parameter, and the like. […] The present application, via the storage of patient preferences 66 and rationales 68, as well as physician rationales 70, simplifies the shared decision making process for the patient and clinician, reduces patient's stress, increases the patient's satisfaction of their decisions, ensures and improves decision quality and continuity, reduces clinician's workload, increases quality and efficiency of the education provided to patients, increases clinician's confidence, and reduces overall healthcare costs.”)
Note: by a patient able to adjust their preferences, a second information gap is not generated since it leads to a solution of the first information gap through balancing of patient preferences & recommended treatment resulting benefits such as reduced patient stress, increased patient satisfaction, increase in clinician’s confidence, etc.
and outputs there being an adjustment of the first factor in the solution to the first information gap via the output unit ([0048] “In one embodiment, the shared decision support system 18 generates a graphical tool that allows patients to visualize the tailored patient pathway(s) or treatment option(s) that were generated based on the input as described above […] In a further embodiment, the patient is able to have control and further personalize the graphical tool by graphically adjusting any one of the above parameters to visualize the effect of that change on the trends of the other outcome parameters and on the patient pathway.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Pecora’s techniques for clinical outcome tracking and analysis and Villazón-Terrazas’ techniques for context-based patient similarity with Zhang’s techniques for a patient-centric decision support tool. The motivation for the combination of Pecora, Villazón-Terrazas, and Zhang is to modify the system of Pecora and Villazón-Terrazas to incorporate determination of a solution to a second information gap and outputting an adjustment of the first factor for a first information gap as in Zhang in order to make it possible to select a treatment most suitable for a patient (See Zhang, Background).
Regarding claim 10, Pecora discloses a medical information processing method using a computer ([0101] “A computing device embodied fully or in part as computing system 205 and/or user computing device 210 may be capable of sending or receiving signals, such as via a wired or wireless network, or may be capable of processing or storing signals, such as in memory as physical memory states.”)
estimating a future living situation of a target patient for each treatment method to be applied to the target patient on the basis of attributes of the target patient ([0086] “The treatment plan information may include information regarding treatment plans and/or treatment strategies specific to all of the treatment relevant variables or attributes of the patient.” [0110] “In some embodiments, the COTA module 220 tracks outcomes, such as overall survival (OS) […] expected prognosis-related outcome specific to the patient”)
outputting information based on the living situation via an output unit ([0040] “where it is determined that the one or more outcomes for the patient of interest are trending away from the standard, to send an alert to a health care provider or health payer of the patient of interest including information regarding the one or more outcomes that are trending away from the standard.”)
acquiring a first distribution representing attributes of each of a plurality of groups stratified from a plurality of patients on the basis of diagnostic information of the plurality of patients to which the same treatment method as that for the target patient has been applied ([0114] “In one embodiment, the COTA module 220 performs sorting/grouping 310, which sorts, groups, and/or identifies patients satisfying one or more parameters. […] The parameters may be a simple indicator (e.g., positive, negative, not accessed), a numerically based parameter (e.g., tumor size), a standards based parameter (e.g., tumor grade), etc. In some embodiments, the parameters may be received by the COTA module 220 as a user selected input. […] In some embodiments, the COTA module 220 may provide individual/and or collective information regarding patients in the group of patients having the same treatment relevant parameters as the identified patient without providing any personally identifiable information regarding patients in the group.”)
acquiring a second distribution representing the attributes of the target patient ([0007] “accessing or receiving a second set of data including updated and/or additional personal health information associated with the patient of interest at a second time”)
calculating a degree of similarity between the first distribution and the second distribution ([0213] “based on the outcomes of many patients whose similarity is determined based on the provisional nodal address or the refined nodal address assigned to the patient.” [0241] “The content 2015 may include the patient data associated with the alert 2010, a comparison, or any other relevant content. In one embodiment, the comparison may be, e.g., […] between one physician's patients and the whole patient population, [….]. The comparison may be based on a trending analysis to show where treatment is trending and if it is going off course (i.e., results are not as good as the standard).”)
and estimating a prognosis of the target patient on the based on the degree of similarity ([0114] “In some embodiments, a user selected input may identify a patient (e.g. a patient of interest) and the sorting, grouping, or identification identifies a group of patients having the same prognosis or outcome relevant parameters as the identified patient (e.g., a prognosis or outcome based group of patients). In some embodiments, the COTA module 220 may provide individual/and or collective information regarding patients in the group of patients having the same prognosis or outcome parameters as the identified patient without providing any personally identifiable information regarding patients in the group.”)
Pecora does not explicitly disclose however Villazón-Terrazas teaches wherein the first distribution is a first two-dimensional statistical chart with the attribute as a dimension ([0030] “inputting patient data including historical clinical data for a population of patients; and inputting open data, and using these inputs to create a patient clinical object, PCO representing each patient in the form of a graph” [0118] “The full patient graph is composed as follows: a vertex is created for each patient. The patient vertex contains attributes, e.g., ID numbers. Symptoms, medications, treatments, and diseases are key entities in the domain of discourse and are also modeled as vertices in the graph.”)
and the second distribution is a second two-dimensional statistical chart with the attribute as a dimension ([0119] “Effectively, a complete patient egocentric network (also known as PCO) presents a patient profile as a subgraph within the full graph: it is part of the graph representing the domain of discourse. So patient vertices have links to other types of vertices in the graph, such as instances of doctors who treat the patient, and instances of hospitals where the patient is treated.” [0117] “FIG. 5 illustrates fragments of a patient's ego-net or PCO: a subgraph including the subject patient and all those vertices directly related to the patient (or direct neighboring vertices). Key-value pairs such as “Gender: Female” and “Age: 58” are attributes of the patient vertex (labeled as 22242).”)
and wherein the medical information processing method further comprises calculating the degree of similarity between a graphic shape of the first two-dimensional statistical chart and a graphic shape of the second two-dimensional statistical chart ([0151] “Similarity of vertices of the same category can be computed using for instance string similarity algorithms as σc2(a, b)=jaro_winkler_distance(a, b).”)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Pecora’s techniques for clinical outcome tracking and analysis with Villazón-Terrazas’ techniques for context-based patient similarity. The motivation for the combination of Pecora and Villazón-Terrazas is to modify the system of Pecora to incorporate statistical charts and calculating degree of similarity as in Villazón-Terrazas to improve efficiency in prescribing treatments (See Villazón-Terrazas, Background).
Pecora in view of Villazón-Terrazas does not explicitly disclose however Zhang teaches displaying a screen on a terminal device used by the target patient or a family for the target patient or the family to input a first living situation, the first living situation being a future living situation of the target patient desired by the target patient or the family ([0036] “The patient personalization system 12 enables the patient to input the patient values, lifestyle regimes, and preferences 66 related to diagnosis and treatment of the patient from a patient's perspective” [0037] “Examples of patient personalization systems 12 include, but are not limited to, a software application that could be accessed and/or displayed on a personal computer, web-based applications, tablets, mobile devices, cellular phones, and the like.”)
in response to the first living situation being input on the screen, determines whether or not a first information gap has been generated between the first living situation and a second living situation, the second living situation being the estimated future living situation of the target patient, ([0036] “the patient to input the patient values, lifestyle regimes, and preferences 66 related to diagnosis and treatment of the patient from a patient's perspective […]the patient personalization system 12 displays the quantitative evaluation and comparison of the choices of treatment and pathways including a comparison of alternative choices on the same measure.”)
and outputting different information to the output unit between a case in which the first information gap has been generated or the first information gap has not been generated ([0037] “display the evaluation and/or comparison of choices” [0054] “Using patient reported outcome data, the system can visually portray differences between the actual trends of recovery and side effects, and the expected trends based on available evidence.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Pecora’s techniques for clinical outcome tracking and analysis and Villazón-Terrazas’ techniques for context-based patient similarity with Zhang’s techniques for a patient-centric decision support tool. The motivation for the combination of Pecora, Villazón-Terrazas, and Zhang is to modify the system of Pecora and Villazón-Terrazas to incorporate displaying a living situation, determining a first information gap, and outputting different information as in Zhang in order to make it possible to select a treatment most suitable for a patient (See Zhang, Background).
Regarding claim 13, Pecora in view of Villazón-Terrazas does not explicitly disclose however Zhang teaches wherein the processor, when it is determined that the first information gap has been generated, virtually adjusts at least one control factor related to as least one of the attributes of the patient ([0069] “A ‘processor’ as used herein encompasses an electronic component that is able to execute a program or machine executable instruction.” [0048] “In a further embodiment, the patient is able to have control and further personalize the graphical tool by graphically adjusting any one of the above parameters to visualize the effect of that change on the trends of the other outcome parameters and on the patient pathway.”)
re-estimates the future living situation based upon the virtual adjustment of the at least one control factor ([0015] “re-generate patient pathway and treatment options from the patient data and the patient's lifestyle values and preferences”)
and outputs information to the terminal identifying the at least one of the attributes and an indication of a change in the future living situation based upon the adjustment ([0015] “a graphical tool to evaluate and compare the chosen pathway and treatment option before the first treatment stage and the re-generated patient pathway and treatment options” [0048] “The graphical tool portrays visually the personalized patient pathway(s) and visual trends on the health outcome for each (or the selected) pathway or treatment option, including the time of recovery, the consequences (e.g. physical, mental, emotional), the frequency and regime of the treatment, the main lifestyle changes and other adverse effects (e.g. dietary, sleep, tiredness, sex life, etc.).”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Pecora’s techniques for clinical outcome tracking and analysis and Villazón-Terrazas’ techniques for context-based patient similarity with Zhang’s techniques for a patient-centric decision support tool. The motivation for the combination of Pecora, Villazón-Terrazas, and Zhang is to modify the system of Pecora and Villazón-Terrazas to incorporate displaying a living situation, determining a first information gap, and outputting different information as in Zhang in order to make it possible to select a treatment most suitable for a patient (See Zhang, Background).
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Pecora et al. (US20210082573A1) in view of Villazón-Terrazas et al. (US20180098737A1), Zhang at al. (US20170124268A1), and further in view of Nash et al. (US20210217516A1).
Regarding claim 4, Pecora in view of Villazón-Terrazas and Zhang does not explicitly disclose however Nash teaches wherein the processor stratifies the plurality of patients into the plurality of groups on the basis of both a first index value regarding a temporal or economic cost of each of the plurality of patients ([0011] “In another aspect of the disclosure, a treatment system is provided for blending known population-based treatment effects with N-of-1 science for displaying intervention insights and group clusters.” [0004] “The otherwise expensive cost of random control trials is amortized across a large number of patients”)
Note: large number of patients in the patient population.
and a second index value regarding treatment effects of each treatment method applied to each of the plurality of patients ([0010] “In one aspect of the disclosure, a treatment system is provided for blending known population-based treatment effects (group averages) with N-of-1 measures (of the individual patient).”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Pecora’s techniques for clinical outcome tracking and analysis, Villazón-Terrazas’ techniques for context-based patient similarity, and Zhang’s techniques for a patient-centric decision support tool with Nash’s techniques for treating a patient. The motivation for the combination of Pecora, Villazón-Terrazas, Zhang, and Nash is to modify the system of Pecora, Villazón-Terrazas, and Zhang to incorporate determination of a first index value and a second index value as in Nash in order to measure treatment effects to help select a treatment suitable for a patient (See Nash, Background).
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Pecora et al. (US20210082573A1) in view of Villazón-Terrazas et al. (US20180098737A1), Zhang at al. (US20170124268A1), Nash et al. (US20210217516A1), and further in view of Rao et al. (US20030126101A1).
Regarding claim 5, Pecora in view of Villazón-Terrazas, Zhang, and Nash does not explicitly disclose however Rao teaches wherein the processor determines at least one of the first index value and the second index value regarding the target patient as the prognosis of the target patient on the basis of a comparison between the first distribution and the second distribution ([0054] “Basically, the system evaluates a number of possible future treatment options (one of which is “do nothing”) and projects the disease state into the future, e.g., if we put the patient on Drug 1 then what will happen”.” [0055] “Drug 1 costs $5,000,000, the system might recommend against giving Drug 1. Similarly, the system will look at quality of life metrics, where if Drug 1 has severe side effects and only improves survival by 1%”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Pecora’s techniques for clinical outcome tracking and analysis, Villazón-Terrazas’ techniques for context-based patient similarity, Zhang’s techniques for a patient-centric decision support tool, and Nash’s techniques for treating a patient with Rao’s techniques for projecting patient state. The motivation for the combination of Pecora, Villazón-Terrazas, Zhang, Nash, and Rao is to modify the system of Pecora, Villazón-Terrazas, Zhang, and Nash to incorporate determining at least one of the first index value and the second index value as in Rao to help in determination of a suitable intervention for the target patient (See Rao, Background).
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Pecora et al. (US20210082573A1) in view of Villazón-Terrazas et al. (US20180098737A1), Zhang at al. (US20170124268A1), Nash et al. (US20210217516A1), Rao et al. (US20030126101A1), and further in view of Hayakawa (US20150227714A1).
Regarding claim 6, Pecora discloses wherein the processor calculates the degree of similarity between the second distribution and the first distribution of each of the plurality of groups ([0213] “based on the outcomes of many patients whose similarity is determined based on the provisional nodal address or the refined nodal address assigned to the patient.” [0241] “The content 2015 may include the patient data associated with the alert 2010, a comparison, or any other relevant content. In one embodiment, the comparison may be, e.g., […] between one physician's patients and the whole patient population, [….]. The comparison may be based on a trending analysis to show where treatment is trending and if it is going off course (i.e., results are not as good as the standard).”)
Pecora in view of Villazón-Terrazas, Zhang, Nash, and Rao does not explicitly disclose however Hayakawa teaches and estimates the first index value of the first distribution having the highest degree of similarity to the second distribution as the first index value regarding the target patient, or estimates the second index value of the first distribution having a highest degree of similarity to the second distribution as the second index value regarding the target patient ([0135] “In addition, the degree of similarity between each of the plurality of record notes included in the medical record of a target patient and each of the plurality of record notes included in the medical record of each of the other patients is calculated, the total degree of similarity is calculated for each of the other patients, and a pre