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 .
Notice to Applicant
Status of Claims
Claims 1, 3, 7, 12, 14-17 have been amended. Claims 2, 8, and 10 have been canceled. Claim 18 is new. Now, Claims 1, 3-7, 9, and 11-18 are pending.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1, 3-7, 9, and 11-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
4. Step 1 – Statutory Categories of Invention:
Claims 1, 3-7, 9, 11-13, and 18 are drawn to a device (machine), claims 14 and 15 are drawn to a method (process), and claims 16 and 17 are drawn to a computer-readable non-transitory storage medium (apparatus) which is one of the statutory categories of invention.
5. Step 2A – Judicial Exception Analysis, Prong 1:
Independent claims 1, 14, and 16 recite, in part, a device, a method, and a computer-readable non-transitory storage medium comprising the following steps:
Acquire a first attribute factor group which is a plurality of attribute factors relating to a disease of a target patient and which is a plurality of attribute factors at a first timing which is before treatment was applied on the target patient;
estimate a second attribute factor group which is a plurality of attribute factors at a second timing after the first timing on the basis of the first attribute factor group, the second timing being a timing after the treatment was applied on the target patient; and
output information including the first attribute factor group and the second attribute factor group before the second timing,
wherein is further configured to estimate the second attribute factor group on the basis of temporal changes in a third attribute factor group which is a plurality of attribute factors relating to a disease of other patients who were applied the treatment scheduled for the target patient.
These steps amount to functions performable in the mind or with pen and paper and are only concepts relating to organizing or analyzing information (i.e. acquiring data, estimating data, and outputting information) in a way that can be performed mentally or is analogous to human mental work (MPEP § 2106.04(a)(2)(III)(c)(2) citing the abstract idea grouping for mental processes in a computer environment).
These steps are also directed to estimating a user’s health based on treatment scheduled for the target patient, which amounts to certain methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people).
Independent claims 7, 15, and 17 recite, in part, a device, a method, and a computer-readable non-transitory storage medium comprising the following steps:
Acquire a plurality of attribute factors relating to a disease of a target patient;
Identify a related attribute factor that is an attribute factor highly relevant to the disease among the plurality of attribute factors, the related attribute factor being an attribute factor having a higher degree of influence on at least one of selection of a disease treatment method and improvement of the outcome of the target patient than the other attribute factors; and
output information based on the related attribute factor,
wherein is further configured to compare each of the plurality of attribute factors with a second attribute factor that is an attribute factor of another patient group having an improved outcome and identify the attribute factor having deviation from the second attribute factor greater than or equal to a threshold value among the plurality of attribute factors as the related attribute factor.
These steps amount to functions performable in the mind or with pen and paper and are only concepts relating to organizing or analyzing information (i.e. acquiring data, estimating data, and outputting information) in a way that can be performed mentally or is analogous to human mental work (MPEP § 2106.04(a)(2)(III)(c)(2) citing the abstract idea grouping for mental processes in a computer environment).
These steps are also directed to comparing attribute factors of a patient group having an improved treatment outcome, which amounts to certain methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people).
Dependent claim 3 recites, in part, wherein the treatment on the target patient is decided on the basis of treatment results of other patient groups suffering from the same disease as the target patient.
Dependent claim 4 recites, in part, wherein the treatment on the target patient is decided on the basis of treatment results of other patients whose previous treatment outcomes were improved among the other patient groups.
Dependent claim 5 recites, in part, wherein stratifies the other patient groups into a plurality of groups on the basis of outcomes of the other patient groups suffering from the same disease as the target patient and calculates an attribute distribution quantitatively expressing an attribute factor group which is the plurality of attribute factors of each group, and
wherein extracts a related attribute factor that is an attribute factor having a higher degree of influence on at least one of selection of a disease treatment method and improvement of the outcome than the other attribute factors from among the attribute factor groups on the basis of a result of comparing a first attribute distribution that is the attribute distribution of a first group with a second attribute distribution that is the attribute distribution of a second group having a better outcome than the first group.
Dependent claim 6 recites, in part, wherein the first attribute factor group includes a control factor that is the attribute factor capable of being controlled by the target patient and a non-control factor that is the attribute factor incapable of being controlled by the target patient, and
wherein calculates a third attribute distribution that is the attribute distribution of the target patient and estimates the second attribute factor group on the basis of the first attribute factor group in which the control factor is adjusted so that the third attribute distribution is close to the second attribute distribution.
Dependent claim 9 recites, in part, wherein compares each of the plurality of attribute factors with a reference value defined by a guideline or evidence, and
identifies the attribute factor having deviation from the reference value greater than or equal to a threshold value as the related attribute factor among the plurality of attribute factors.
Dependent claim 11 recites, in part, wherein estimates an outcome of the target patient on the basis of the attribute factor of the target patient.
Dependent claim 12 recites, in part, wherein calculates a first distribution quantitatively expressing an attribute factor of the target patient,
calculates a second distribution quantitatively expressing an attribute factor of each of a plurality of groups stratified from the other patient group on the basis of outcomes of other patient groups that were previously treated, and
estimates an outcome of the target patient on the basis of a result of comparing the first distribution with the second distribution.
Dependent claim 13 recites, in part, wherein the outcome includes a treatment fee of a treatment method scheduled to be applied to the target patient, and
wherein determines whether or not the treatment fee estimated as the outcome of the target patient is within a range of an insured amount, and
outputs the outcome of the target patient and the treatment method scheduled to be applied to the target patient via the output interface when the treatment fee is within the range of the insured amount.
Dependent claim 18 recites, in part, wherein the treatment includes direct treatment, pre-habilitation performed prior to the direct treatment, and rehabilitation performed after the direct treatment, and
wherein the direct treatment includes surgery, drug therapy, chemotherapy, or photoimmunotherapy.
Each of these steps of the preceding dependent claims 3-6, 9, 11-13, and 18 only serve to further limit or specify the features of independent claims 1, 7, 14, 15, 16, and 17 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements already analyzed in the expected manner.
6. Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Independent claims 1 and 7 recite, in part, by processing circuitry and an output interface. The specification defines by processing circuitry as includes, for example, an acquisition function 21, an output control function 22, and a communication control function 23. In the processing circuitry 20, for example, a hardware processor (a computer) executes a program stored in the memory 14 (storage circuit) to implement these functions (Specification in § 0046), and an output interface as the display 13a, a speaker 13b, and the like, (Specification in § 0042). The use of by processing circuitry and an output interface are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Independent claims 14 and 15 recites, in part, an output interface. The specification defines an output interface as the display 13a, a speaker 13b, and the like, (Specification in § 0042). The use of an output interface are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Independent claims 16 and 17 recites, in part, a computer-readable non-transitory storage medium and an output interface. The specification defines a computer-readable non-transitory storage medium as a DVD or a CD-ROM, and installed from the non-transitory storage medium to the memory 14 when the non-transitory storage medium is loaded to a drive device (not shown) of the terminal device, (Specification in § 0047), and an output interface as the display 13a, a speaker 13b, and the like, (Specification in § 0042). The use of a computer-readable non-transitory storage medium and an output interface are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Dependent claims 5, 6, 9, and 11-13 recite, in part, processing circuitry. The limitations are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
The above claims, as a whole, are therefore directed to an abstract idea.
7. Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Independent claims 1 and 7 recite, in part, by processing circuitry and an output interface. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as use of processing circuitry and an output interface to process and output data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception’).
Independent claims 14 and 15 recites, in part, an output interface. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as use of an output device to output data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception’).
Independent claims 16 and 17 recites, in part, a computer-readable non-transitory storage medium and an output interface. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as use of a computer-readable non-transitory storage medium and an output device to process, save, and output data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception’).
Dependent claims 5, 6, 9, and 11-13 recite, in part, processing circuitry. Each of these elements is only recited as a tool for performing steps of the abstract idea. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception’).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1, 3-7, 9, and 11-18 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
8. 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.
9. Claims 1, 3-7, 9, and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over by United States Patent Number 9,536,051, Kabir, et al., hereinafter Kabir in view of United States Patent Application Publication Number 2021/0319894, Sobol, et al., hereinafter Sobol.
10. Regarding claim 1, Kabir discloses a medical information processing device comprising processing circuitry configured to:
acquire a first attribute factor group which is a plurality of attribute factors relating to a disease of a target patient and which is a plurality of attribute factors at a first timing which is before treatment was applied on the target patient, (col. 5, lines 1-28, diagnosis module 110 may also process all the signs, symptoms or clinical findings associate with or related to a patient and generate a short list of high probability differential diagnoses. Generation of such a list will help reduce diagnostic error and improve patient safety. Diagnosis module 110 may also act as a reminder system for health care providers and can also act to avert diagnostic error by generating the short list of high probability diagnoses and col. 7, lines 11-40, input list 10 is created by the user/health care provider selecting or inputting inputs 24 during a patient interview or treatment); and
output information including the first attribute factor group and the second attribute factor group via an output interface before the second timing, (col. 7, lines 11-40, input list 10 is created by the user/health care provider selecting or inputting inputs 24 during a patient interview or treatment and col. 8, lines 29-36, output table 34 may include at least two lists: the ranked high probability differential diagnosis list 26 and the ranked low probability differential diagnosis list).
Kabir does not explicitly disclose estimate a second attribute factor group which is a plurality of attribute factors at a second timing after the first timing on the basis of the first attribute factor group, the second timing being a timing after the treatment was applied on the target patient; and
wherein the processing circuitry is further configured to estimate the second attribute factor group on the basis of temporal changes in a third attribute factor group which is a plurality of attribute factors relating to a disease of other patients who were applied the treatment scheduled for the target patient.
However, Sobol teaches estimate a second attribute factor group which is a plurality of attribute factors at a second timing after the first timing on the basis of the first attribute factor group, the second timing being a timing after the treatment was applied on the target patient, (para. 322, after initiation of treatment programs tracking the influence of pharmacological and nonpharmacological interventions); and
wherein the processing circuitry is further configured to estimate the second attribute factor group on the basis of temporal changes in a third attribute factor group which is a plurality of attribute factors relating to a disease of other patients who were applied the treatment scheduled for the target patient, (para. 239, grouping the acquired data (from either the wearable electronic device 100 or elsewhere, such as lookup table based on known prior data of a particular patient or group of patients with similar health demographics) can be through an unsupervised clustering model; such an approach may be particularly good at segmenting the data into several different groups. In one form, this baseline data 1700—which may correspond to so-called normal conditions associated with a particular individual's health, location, movement or other measurable parameter—may be annotated for use in training-based activity, behavior or related parametric information that can be compared to real-time (i.e., presently-acquired) data in turn can be operated upon by one or more of the machine learning models discussed herein, and para. 285, four general groups of attributes may be measured, corresponding to the various forms of LEAP data).
One having ordinary skill in the art at the time the invention was filed would combine the method of Kabir with the method of Sobol with the motivation of efficiently delivering necessary data and ensuing health care analysis and insight in real-time (Sobol, para. 7).
11. Regarding claim 3, Kabir discloses the device of claim 1 as described above. Kabir further discloses wherein the treatment on the target patient is decided on the basis of treatment results of other patient groups suffering from the same disease as the target patient, (col. 6, lines 35-60, generate and store reports in a database that allow health care providers to track the most prevalent disease or condition existing among patients of a certain age, race, and gender for a particular geographic region during a particular time or year).
12. Regarding claim 4, Kabir discloses the device of claims 1-3 as described above. Kabir further discloses wherein the treatment on the target patient is decided on the basis of treatment results of other patients whose previous treatment outcomes were improved among the other patient groups, (col. 5, lines 29-46, Such patient data may include various types of data regarding a patient, such as name, date of birth, weight, height, race, gender, current medical conditions, current medications, surgery history, family medical history, current signs and symptoms, blood pressure, and the like. Any data input by a user/health care provider will be managed and handled by data sub-module).
13. Regarding claim 5, Kabir discloses the device of claim 1 as described above. Kabir further discloses wherein the processing circuitry stratifies the other patient groups into a plurality of groups on the basis of outcomes of the other patient groups suffering from the same disease as the target patient and calculates an attribute distribution quantitatively expressing an attribute factor group which is the plurality of attribute factors of each group, (col. 5, lines 29-46, Such patient data may include various types of data regarding a patient, such as name, date of birth, weight, height, race, gender, current medical conditions, current medications, surgery history, family medical history, current signs and symptoms, blood pressure, and the like. Any data input by a user/health care provider will be managed and handled by data sub-module), and
wherein the processing circuitry extracts a related attribute factor that is an attribute factor having a higher degree of influence on at least one of selection of a disease treatment method and improvement of the outcome than the other attribute factors from among the attribute factor groups on the basis of a result of comparing a first attribute distribution that is the attribute distribution of a first group with a second attribute distribution that is the attribute distribution of a second group having a better outcome than the first group, (col. 5, lines 1-46, Diagnosis module 110 may analyze the signs, symptoms, or clinical findings (SSF) related to a patient and then compare this patient data to a database of signs, symptoms or clinical findings that are linked to multiple diseases (called differential diagnoses) to ultimately provide a differential diagnosis for the health care provider.).
14. Regarding claim 6, Kabir discloses the device of claims 1 and 5 as described above. Kabir further discloses wherein the first attribute factor group includes a control factor that is the attribute factor capable of being controlled by the target patient and a non-control factor that is the attribute factor incapable of being controlled by the target patient, (col. 6, lines 6-34, diagnosis generation sub-module 113 will analyze patient data input via data input sub-module 111 and interface and gather disease or medical condition data from disease sub-module 112 so that a differential diagnosis may be generated), and
wherein the processing circuitry calculates a third attribute distribution that is the attribute distribution of the target patient and estimates the second attribute factor group on the basis of the first attribute factor group in which the control factor is adjusted so that the third attribute distribution is close to the second attribute distribution, (col. 7, line 50-col. 8, line 5, the rank order of the differential diagnosis/potential diseases DD, such as the listing of potential DD entries 16 in symptoms table list 22 will be based on epidemiological distribution (disease prevalence) of the diseases in the general population).
15. Regarding claim 7, Kabir discloses a medical information processing device comprising processing circuitry configured to:
Acquire a plurality of attribute factors relating to a disease of a target patient, (col. 5, lines 1-28, diagnosis module 110 may also process all the signs, symptoms or clinical findings associate with or related to a patient and generate a short list of high probability differential diagnoses. Generation of such a list will help reduce diagnostic error and improve patient safety. Diagnosis module 110 may also act as a reminder system for health care providers and can also act to avert diagnostic error by generating the short list of high probability diagnoses);
output information based on the related attribute factor via an output interface, (col. 8, lines 29-36, output table 34 may include at least two lists: the ranked high probability differential diagnosis list 26 and the ranked low probability differential diagnosis list).
Kabir does not explicitly disclose identify a related attribute factor that is an attribute factor highly relevant to the disease among the plurality of attribute factors ,the related attribute factor being an attribute factor having a higher degree of influence on at least one of selection of a disease treatment method and improvement of the outcome of the target patient than the other attribute factors; and
wherein the processing circuitry is further configured to compare each of the plurality of attribute factors with a second attribute factor that is an attribute factor of another patient group having an improved outcome and identify the attribute factor having deviation from the second attribute factor greater than or equal to a threshold value among the plurality of attribute factors as the related attribute factor.
However, Sobol teaches identify a related attribute factor that is an attribute factor highly relevant to the disease among the plurality of attribute factors ,the related attribute factor being an attribute factor having a higher degree of influence on at least one of selection of a disease treatment method and improvement of the outcome of the target patient than the other attribute factors, (para. 249, the analytics associated with these feature vectors may be performed in order to ascertain classification-based results (for example, whether the sensed parameter or attribute is less than, equal to or greater than a threshold that may itself be based on a known relative baseline, absolute baseline or other measure of interest), or to perform a regression in order to determine whether the sensed parameter or its attribute can be correlated to the likelihood of an event outcome. Within the present disclosure, a feature vector could be a summary of one or more of a patient's kinematic data (which may form indicia of activity) and related location data, physiological data, or environmental data such that the ensuing clinical observation of symptoms may lead to an enhanced diagnosis of a particular condition, ); and
wherein the processing circuitry is further configured to compare each of the plurality of attribute factors with a second attribute factor that is an attribute factor of another patient group having an improved outcome and identify the attribute factor having deviation from the second attribute factor greater than or equal to a threshold value among the plurality of attribute factors as the related attribute factor, (para. 361, at least some of the acquired LEAP data may be analyzed (such as by one or more of the machine learning models discussed herein) in order to identify condition changes proactively at the threshold PH.sub.3t of these dips D (rather than retroactively at the end PH.sub.3e) so that suitable medical intervention may be undertaken in enough time in order to avoid or mitigate the effect of these acute events. In one form, this may be part of a disease state management (DSM, also referred to as disease management) protocol to allow a holistic approach to healthcare monitoring, intervention and communication for a person P who exhibits one or more of these health conditions).
One having ordinary skill in the art at the time the invention was filed would combine the method of Kabir with the method of Sobol with the motivation of efficiently delivering necessary data and ensuing health care analysis and insight in real-time (Sobol, para. 7).
16. Regarding claim 9, Kabir discloses the device of claim 7 as described above. Kabir further discloses wherein the processing circuitry compares each of the plurality of attribute factors with a reference value defined by a guideline or evidence, (col. 7, line 51-col. 9, line 5, Differential diagnosis data table 12 may be configured to include one or more sub-tables 14. Sub-tables 14 are data tables also known as the signs/symptoms/findings (SSF)—differential diagnosis (DD) sub-table 14 or the SSF-DD sub-table 14. SSF-DD sub-table 14 is a data table that includes a symptoms table list 22 for each particular input 24. Symptoms table list 22 includes a listing of one or more potential diseases or conditions 16 or differential diagnosis (DD) 16 that are associated with the particular input 24. In a preferred embodiment, the rank order of the differential diagnosis/potential diseases DD, such as the listing of potential DD entries 16 in symptoms table list 22 will be based on epidemiological distribution (disease prevalence) of the diseases in the general population), and
identifies the attribute factor having deviation from the reference value greater than or equal to a threshold value as the related attribute factor among the plurality of attribute factors, (col. 12, lines 4-45, the DDs that are linked to the greatest number of symptoms (SSFs) will be ranked ahead of those DDs that are linked to a lower number of symptoms (SSFs)).
17. Regarding claim 11, Kabir discloses the device of claim 7 as described above. Kabir further discloses wherein the processing circuitry estimates an outcome of the target patient on the basis of the attribute factor of the target patient, (col. 10, lines 13-50, the specific potential disease or differential diagnosis DD (e.g., X) becomes ranked number one in the DD outcome section for high probability differential diagnosis because it is associated with three potential differential diagnosis (DD) and has a total weight of three associated with being linked to all three inputs).
18. Regarding claim 12, Kabir discloses the device of claim 7 as described above. Kabir further discloses wherein the processing circuitry calculates a first distribution quantitatively expressing an attribute factor of the target patient, (col. 8, lines 37-56, the potential DD entries 16 have been extracted from symptoms table lists 22 and populated into the high probability differential diagnosis list 26 and the low probability differential diagnosis list),
calculates a second distribution quantitatively expressing an attribute factor of each of a plurality of groups stratified from the other patient group on the basis of outcomes of other patient groups that were previously treated, (col. 8, lines 37-56, the potential DD entries 16 have been extracted from symptoms table lists 22 and populated into the high probability differential diagnosis list 26 and the low probability differential diagnosis list), and
estimates an outcome of the target patient on the basis of a result of comparing the first distribution with the second distribution, (col. 6, lines 35-60, output sub-module 114 may also create disease history reports that keep track of the various disease and/or conditions that were generates as a potential patient disease or condition so that health care providers can access the data for later use including, but not limited to, keep track of various trends related to diseases or conditions).
19. Regarding claim 13, Kabir discloses the device of claims 7 and 12 as described above. Kabir further discloses wherein the outcome includes a treatment fee of a treatment method scheduled to be applied to the target patient, (col. 2, lines 3-16, all the available differential diagnoses linked to a given sign, symptom or finding during every patient encounter, the time and costs associated with such a visit would increase leading to increased health care costs, and increasing the risk of diagnostic error and creating an ineffective health care system), and
wherein the processing circuitry determines whether or not the treatment fee estimated as the outcome of the target patient is within a range of an insured amount, (col. 2, lines 3-16, all the available differential diagnoses linked to a given sign, symptom or finding during every patient encounter, the time and costs associated with such a visit would increase leading to increased health care costs, and increasing the risk of diagnostic error and creating an ineffective health care system), and
outputs the outcome of the target patient and the treatment method scheduled to be applied to the target patient via the output interface when the treatment fee is within the range of the insured amount, (col. 2, lines 3-16, all the available differential diagnoses linked to a given sign, symptom or finding during every patient encounter, the time and costs associated with such a visit would increase leading to increased health care costs, and increasing the risk of diagnostic error and creating an ineffective health care system).
20. Regarding claims 14-17, these claims are rejected for the same reason as claims 1 and 7 as set forth above. Kabir further discloses an output interface and a computer-readable non-transitory storage medium storing a program for causing a computer to perform a series of steps, (col. 6, line 61-col. 7, line 10).
21. Regarding claim 18, Kabir discloses the device of claim 1 as described above. Kabir does not explicitly disclose wherein the treatment includes direct treatment, pre-habilitation performed prior to the direct treatment, and rehabilitation performed after the direct treatment, and wherein the direct treatment includes surgery, drug therapy, chemotherapy, or photoimmunotherapy.
However, Sobol teaches wherein the treatment includes direct treatment, pre-habilitation performed prior to the direct treatment, and rehabilitation performed after the direct treatment, and wherein the direct treatment includes surgery, drug therapy, chemotherapy, or photoimmunotherapy, (para. 237, this ordered sequence may be used to perform an action plan so that it can provide guidance on changes in medication dosages, changes in dietary or activity protocols, changes in occupational or physical therapy plans or the like, and para. 328, an example of which is the Milliman Criteria that set forth targets in order to help individual patients attain certain outcome metrics following any type of intervention or procedure (such as surgery).).
One having ordinary skill in the art at the time the invention was filed would combine the method of Kabir with the method of Sobol with the motivation of efficiently delivering necessary data and ensuing health care analysis and insight in real-time (Sobol, para. 7).
Response to Arguments
22. Applicant's arguments filed October 14, 2025 have been fully considered but they are not persuasive.
A. Applicant argues that the elements of Claim I, 14, and 16 integrate any judicial exception into the practical application of evaluating affected as well as unaffected organs of a patient and that claims 7, 15, and 17 are believed to both provide an improvement in the technical field and thus are integrated into a practical application of improved patient outcomes along with better collection and analysis of attributes.
In response, Examiner respectfully disagrees. As explained above, the claims set forth receiving, analyzing, outputting data, estimating a user’s health based on treatment scheduled for the target patient and/or comparing attribute factors of a patient group having an improved treatment outcome, for the purpose of solving “selecting treatment strategies that improve outcomes.” (see page 1, line 25 of the filed Specification). The Examiner asserts that receiving, analyzing, outputting data, estimating a user’s health based on treatment scheduled for the target patient and/or comparing attribute factors of a patient group having an improved treatment outcome are clearly organizing or analyzing information in a way that can be performed mentally or is analogous to mental human work and can also be managing human behavior. That features are recited relating to computer technologies does not negate such a conclusion. Accordingly, such arguments are not persuasive.
The claims do not integrate the abstract idea into a practical application, and does not include additional elements that provide an inventive concept (are sufficient to amount to significantly more than the abstract idea). (Digitech Image Tech., LLC v. Electronics for Imaging, Inc. (Fed. Cir. 2014)). The claims do not recite any unconventional computer functions. There are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, and the claims are properly rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. The claim is silent on any computer operation and specific technological implementation that would move the claim beyond a general link to a technological environment.
Further, in order for an alleged application of an abstract idea to be considered eligible, it must amount to significantly more than the abstract idea (i.e., pass step 2B of the Mayo test). As shown in the rejection above, the claims set forth receiving, analyzing, outputting data, estimating a user’s health based on treatment scheduled for the target patient and/or comparing attribute factors of a patient group having an improved treatment outcome, for the purpose of solving “selecting treatment strategies that improve outcomes.” (see page 1, line 25 of the filed Specification). Accordingly, it does not amount to significantly more, and the application of the abstract idea is therefore not eligible.
Accordingly, it does not amount to significantly more, and the application of the abstract idea is therefore not eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DATA BASED CANCER RESEARCH AND TREATMENT SYSTEMS AND METHODS (US 20210090694 A1) teaches obtaining and employing data related to physical and genomic patient characteristics as well as diagnosis, treatments and treatment efficacy to provide a suite of tools to healthcare providers, researchers and other interested parties.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amber A. Misiaszek whose telephone number is (571) 270-1362. The examiner can normally be reached on M-Th 7:30-5, F 7:30-4, every other Friday Off.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached on 571-270-5096. The fax phone numbers for the organization where this application or proceeding is assigned are (571) 273-8300.
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/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682