DETAILED ACTION
The present office action represents a nonfinal action on the merits.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/10/2025 has been entered.
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 .
Priority
This application claims priority to provisional application 63/279,738, filed on November 16, 2021.
Status of Claims
Claims 1, 11, 21, 31 are amended and Claims 1-40 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-40 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, claims 1, 11, 21, and 31 contain subject matter which is not described in the specification: claim 1 recites, “administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan“, claim 11 recites, “a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan”, claim 21 recites, “administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan”, and claim 31 recites, “a linkage that allows for administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan”. Claims 1, 11, 21, and 31, contain subject matter which is not described in the specification. Specifically, the specification does not disclose administering a specific treatment. The specification recites, “making a decision on how to address the to-be-treated person's disorder and identifying his/her initial treatment plan 40 based on the trajectory phenotype into which the person has been classified. This treatment plan will presumably be structured so as to help the person avoid the pitfalls that are predicted for him/her based on the trajectory phenotype 26 into which the person has been classified.” Specification page 7. The specification dose not disclose the administration of a specific treatment. The specification’s recitation of linkage includes, “a unique help button or linkage 19 where more detailed information can be found. This includes an optional expandable or pop-up panel when the help button is pushed or clicked that provides: (a) answers for commonly asked questions regarding the disorder, (b) probability estimates of the most likely trajectory phenotype, (c) how to interpret the personalized treatment recommendations, and (d) an input screen for feedback, comments, and suggestions.” Specification pages 8-9. The specification does not disclose a linkage to administer a specific treatment. The specification recites, “Upon enrollment, each participant or patient would begin daily treatment with methadone or buprenorphine and attend required clinic sessions five to seven days a week and provide two to three weekly urine samples under observation. These urine samples would be screened for substances such as opioids, cocaine, amphetamines, PCP, benzodiazepines, and cannabinoids. A participant's medication type would be determined by the participant's preference and clinical judgment of the study physician. Dosage would be optimized for each participant to minimize withdrawal symptoms and side effects and reduce illicit opioid use.” Specification page 10. Dependent claims 2-10 depend directly on claim 1 and are therefore rejected due to their dependency on claim 1. Dependent claims 12-20 depend directly on claim 11 and are therefore rejected due to their dependency on claim 11. Dependent claims 22-30 depend directly on claim 21 and are therefore rejected due to their dependency on claim 21. Dependent claims 32-40 depend directly on claim 31 and are therefore rejected due to their dependency on claim 31.
Claims 21-30 are further rejected because claim 21 recites, “a non-transitory, computer readable medium…administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan”. The specification includes "a non-transitory, computable readable medium”, which appears to be a generic computer. See Specification page 8. The specification fails to disclose, how a non-transitory, computer readable medium is administering a treatment. Dependent claims 22-30 depend directly on claim 21 and are therefore rejected due to their dependency on claim 21.
Claims 31-40 are further rejected because claim 31 recites, “user interface for the display of the computing device of a treatment professional…a linkage that allows for administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan”. The Specification includes "user interfaces or graphical user interfaces” and “user interfaces has a unique help button or linkage where more detailed information can be found”, which appears to be part of a generic computer. See Specification page 8. The specification fails to disclose how a user interface for the display of the computing device of a treatment professional has a linkage that allows for administering a treatment. Further, the specification fails to disclose how a linkage would allow for administering a treatment plan. Dependent claims 32-40 depend directly on claim 31 and are therefore rejected due to their dependency on claim 31.
The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention. The specification does not describe the limitations recited above. Accordingly, appropriate correction is requested.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 31-40 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 31 line 1 recites the limitation “the display”, line 1 recites the limitation “the computing device”. There is insufficient antecedent basis for these limitations in the claim because claim 31 is an independent claim and the terms are not previously referenced therein. Examiner is interpreting “the display” as “a display”. Examiner is interpreting “the computing device” as “a computing device”. Further, claim 31 line l recites, “A user interface for the display of the computing device”, however, with the use of “of”, it is unclear what is meant by the limitation. Examiner is interpreting “A user interface for the display of the computing device” as “A user interface for the display on the computing device”. Claims 32-40 depend directly on claim 31 and are therefore rejected due to their dependency on claim 31. Appropriate correction is requested.
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 31-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because there are no structural elements in the claims.
Claims 31-40 claim a user interface, which is not a statutory category and the claim doesn’t positively recite any hardware. Pursuant to MPEP 2106.03, a machine is a "concrete thing, consisting of parts, or of certain devices and combination of devices." Digitech, 758 F.3d at 1348-49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)). This category "includes every mechanical device or combination of mechanical powers and devices to perform some function and produce a certain effect or result." Nuijten, 500 F.3d at 1355, 84 USPQ2d at 1501 (quoting Corning v. Burden, 56 U.S. 252, 267, 14 L. Ed. 683, 690 (1854)). Here, the claims recite “a user interface” but fail to disclose any structural element, no computer or device is claimed, and therefore, is non-statutory.
Claims 1-40 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.
Claims 1-10 are drawn to a method for treating a patient having a specified disorder, which is within the four statutory categories (i.e., process). Claims 11-20 are drawn to system for treating a patient having a specified disorder, which is within the four statutory categories (i.e., machine). Claims 21-30 are drawn to a non-transitory, computer readable medium having program code recorded thereon, for execution on a computing device having a display, to enable a treatment professional to treat a patient having a specified disorder, which is within the four statutory categories (i.e., machine). Claims 31-40 are drawn to a user interface for a display of a computing device of a treatment professional that enables said treatment professional to treat a patient having a specified disorder, which is not within the four statutory categories; the claims may fall within a statutory category once amended by Applicant.
Claims 1-10 recite a method for treating a patient having a specified disorder in order to reduce the risk of said patient suffering an otherwise predictable, adverse occurrence in said patient's future, healthcare treatment for said specified disorder, said method comprising the steps of:
compiling a treatment records database for prior patients who have been treated for said disorder, and including therein a longitudinal trajectory for each of said prior patients,
applying a plurality of clustering algorithms that are configured to analyze each of said longitudinal trajectories to identify a plurality of trajectory phenotypes for said disorder, wherein any of said trajectory phenotypes is mathematically expressible as a continuous line, with a distinctive shape, on a two-dimensional graph,
using a plurality of mathematical and statistical model selection techniques, selected from the use of model fit indices, domain expertise, practical considerations, and other state-of-the-art model selection guidelines, to determine said number and shape for each of said identified trajectory phenotypes,
compiling an intake database for said prior patients who have been treated for said disorder, and including therein a plurality of background-characterizing factors for each of said patients at the time of their entry into treatment,
using on said intake database a plurality of analysis techniques that are configured to identify a means for classifying, expressible in terms of said background-characterizing factors, that predicts, for a to-be-treated patient with said disorder who has provided at intake information on said background-characterizing factors, which of said plurality of trajectory phenotypes will have a shape that is most similar to the real-time longitudinal trajectory that said to-be-treated patient will exhibit, and classifying said to-be-treated patient to said trajectory phenotype,
providing for said to-be-treated patient a treatment plan based on said 28 predicted trajectory phenotype of said to-be-treated patient, and
administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan.
Claims 11-20 recite system that enable a treatment professional to treat a patient having a specified disorder in order to reduce the risk of said patient suffering an otherwise predictable, adverse occurrence in said patient's future, healthcare treatment for said specified disorder, said system comprising:
a treatment records database for prior patients who have been treated for said disorder, and including therein a longitudinal trajectory for each of said prior patients,
a plurality of clustering algorithms that are configured to analyze each of said longitudinal trajectories to identify a plurality of trajectory phenotypes for said disorder, wherein any of said trajectory phenotypes is mathematically expressible as a continuous line, with a distinctive shape, on a two-dimensional graph,
a plurality of mathematical and statistical model selection techniques, selected from the use of model fit indices, domain expertise, practical considerations, and other state-of-the-art model selection guidelines, to determine said number and shape for each of said identified trajectory phenotypes,
an intake database for said prior patients who have been treated for said disorder, and including therein a plurality of background-characterizing factors for each of said patients at the time of their entry into treatment,
a plurality of analysis techniques that are configured to be used on said intake database to identify a means for classifying, expressible in terms of said background- characterizing factors, that predicts, for a to-be-treated patient with said disorder who has provided at intake information on said background-characterizing factors, which of said plurality of trajectory phenotypes will have a shape that is most similar to the real-time longitudinal trajectory that said to-be-treated patient will exhibit, and classifying said to-be-treated patient to said trajectory phenotype,
a treatment plan for said to-be-treated patient that is based on said predicted trajectory phenotype of said to-be-treated patient, and
a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan.
Claims 21-30 recite a non-transitory, computer readable medium having program code recorded thereon, for execution on a computing device having a display, to enable a treatment professional to treat a patient having a specified disorder in order to reduce the risk of said patient suffering an otherwise predictable, adverse occurrence in said patient's future, healthcare treatment for said specified disorder, said program code causing said computing device to perform the following method steps:
compiling a treatment records database for prior patients who have been treated for said disorder, and including therein a longitudinal trajectory for each of said prior patients,
applying a plurality of clustering algorithms that are configured to analyze each of said longitudinal trajectories to identify a plurality of trajectory phenotypes for said disorder, wherein any of said trajectory phenotypes is mathematically expressible as a continuous line, with a distinctive shape, on a two-dimensional graph,
using a plurality of mathematical and statistical model selection techniques, selected from the use of model fit indices, domain expertise, practical considerations, and other state-of-the-art model selection guidelines, to determine said number and shape for each of said identified trajectory phenotypes,
compiling an intake database for said prior patients who have been treated for said disorder, and including therein a plurality of background-characterizing factors for each of said patients at the time of their entry into treatment,
using on said intake database a plurality of analysis techniques that are configured to identify a means for classifying, expressible in terms of said background-characterizing factors, that predicts, for a to-be-treated patient with said disorder who has provided at intake information on said background-characterizing factors, which of said plurality of trajectory phenotypes will have a shape that is most similar to the real-time longitudinal trajectory that said to-be-treated patient will exhibit, and classifying said to-be-treated patient to said trajectory phenotype,
providing for said to-be-treated patient a treatment plan based on said predicted trajectory phenotype of said to-be-treated patient, and
administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan.
Claims 31-40 recite a user interface for a display on a computing device of a treatment professional that enables said treatment professional to treat a patient having a specified disorder in order to reduce the risk of said patient suffering an otherwise predictable, adverse occurrence in said patient's future, healthcare treatment for said specified disorder, said user interface comprising:
a treatment records database linkage that provides access to a treatment records database for prior patients who have been treated for said disorder, and including therein a longitudinal trajectory for each of said prior patients,
a clustering algorithms linkage that provides access to a plurality of clustering algorithms that are configured to analyze each of said longitudinal trajectories to identify a plurality of trajectory phenotypes for said disorder, wherein any of said trajectory phenotypes is mathematically expressible as a continuous line, with a distinctive shape, on a two-dimensional graph,
a modeling techniques linkage that provides access to a plurality of mathematical and statistical model selection techniques, selected from the use of model fit indices, domain expertise, practical considerations, and other state-of-the-art model selection guidelines, to determine said number and shape for each of said identified trajectory phenotypes,
an intake database linkage that provides access to an intake database for said prior patients who have been treated for said disorder, and including therein a plurality of background-characterizing factors for each of said patients at the time of their entry into treatment,
an analysis techniques linkage that provides access to a plurality of analysis techniques that are configured to be used on said intake database to identify a means for classifying, expressible in terms of said background-characterizing factors, that predicts, for a to-be-treated patient with said disorder who has provided at intake information on said background-characterizing factors, which of said plurality of trajectory phenotypes will have a shape that is most similar to the real-time longitudinal trajectory that said to-be-treated patient will exhibit, and classifying said to-be-treated patient to said trajectory phenotype,
a treatment plan linkage that provides access to a treatment plan for said to- be-treated patient that is based on said predicted trajectory phenotype of said to-be- treated patient, and
a linkage that allows for administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity- relating to fundamental economic practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people and math – relating to mathematical and statistical models, but for the recitation of generic computer components. The underlined limitations are not part of the identified abstract idea (the method of organizing human activity or math) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-10, 12-20, 22-30, and 32-40 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Specifically, claim 2 recites further comprising the step of: once said to-be-treated patient has begun treatment and is now a new patient, collecting a plurality of real-time patient data from said new patient that quantifies, at specified times, the status level of said disorder for said new patient, and adding said plurality of new patient data to said treatment records database, and redetermining said number and shape of said predicted trajectory phenotype for said new patient, claim 3 recites further comprising the step of: creating, on a display of a computing device of one using said method, a user interface that is configured to aid one in using said method, claim 4 recites further comprising the step of: creating, on a display of a computing device of one using said method, a user interface that is configured to aid one in using said method, claim 5 recites wherein: said specified disorder is an opioid-use disorder, claim 6 recites wherein: said specified disorder is an opioid-use disorder, claim 7 recites wherein: said specified disorder is an opioid-use disorder, claim 8 recites wherein: said user interface includes a storage linkage that allows said new patient to store both said real-time patient data and said plurality of background-characterizing factors for said new patient, claim 9 recites wherein: said user interface includes a treatment linkage that aids one treating a new patient to decide on a treatment plan for said new patient, claim 10 recites wherein: said user interface includes a trajectory linkage that displays both a real-time longitudinal trajectory and said predicted trajectory phenotype for said new patient, claim 12 recites further comprising: once said to-be-treated patient has begun treatment and is now a new patient, a plurality of real-time patient data from said new patient that quantifies, at specified times, the status of the level of disorder of said new patient, claim 13 recites further comprising: a user interface that is created on a display of a computing device of said treatment professional and configured to aid said treatment professional in using said method, claim 14 recites further comprising: a user interface that is created on a display of a computing device of said treatment professional and configured to aid said treatment professional in using said method, claim 15 recites wherein: said specified disorder is an opioid-use disorder, claim 16 recites wherein: said specified disorder is an opioid-use disorder, claim 17 recites wherein: said specified disorder is an opioid-use disorder, claim 18 recites wherein: said user interface includes a storage linkage that allows said new patient to collect and store both said real-time patient data and said plurality of background- characterizing factors for said new patient, claim 19 recites wherein: said user interface includes a treatment linkage that aids said treatment professional in deciding on a treatment plan for said new patient, claim 20 recites wherein: said user interface includes a trajectory linkage that displays both a real-time longitudinal trajectory and said predicted trajectory phenotype for said new patient, claim 22 recites said program code further causing said computing device to perform the method steps: once said to-be-treated patient has begun treatment and is now a new patient, collecting real-time patient data from said new patient that quantifies, at specified times, the status of the level of disorder of said new patient, and adding said new patient data to said treatment records database, and redetermining said number and shape of said predicted trajectory phenotype for said new patient, claim 23 recites said program code further causing said computing device to perform the method step: creating on said display a user interface that is configured to aid one in using said method, claim 24 recites said program code further causing said computing device to perform the method step: creating on said display a user interface that is configured to aid one in using said method, claim 25 recites wherein: said specified disorder is an opioid-use disorder, claim 26 recites wherein: said specified disorder is an opioid-use disorder, claim 27 recites wherein: said specified disorder is an opioid-use disorder, claim 28 recites wherein: said user interface includes a storage linkage that allows said new patient to collect and store both said real-time patient data and said plurality of background- characterizing factors for said new patient, claim 29 recites wherein: said user interface includes a treatment linkage that aids said treatment professional in deciding on a treatment plan for said new patient, claim 30 recites wherein: said user interface includes a trajectory linkage that displays both a real-time longitudinal trajectory and said predicted trajectory phenotype for said new patient, claim 32 recites further comprising: once said to-be-treated patient has begun treatment and is now a new patient, a real-time data linkage that allows said new patient to collect a plurality of real-time patient data from said new patient that quantifies, at specified times, the status level of said disorder of said new patient, claim 33 recites further comprising: a help linkage that provides said treatment professional with access to help in utilizing said method, claim 34 recites further comprising: a help linkage that provides said treatment professional with access to help in utilizing said method, claim 35 recites wherein: said specified disorder is an opioid-use disorder, claim 36 recites wherein: said specified disorder is an opioid-use disorder, claim 37 recites wherein: 1 said specified disorder is an opioid-use disorder, claim 38 recites further comprising: a storage linkage that allows said new patient to collect and store said plurality of background-characterizing factors for said new patient, claim 39 recites further comprising: a treatment linkage that aids said treatment professional in deciding on a treatment plan for said new patient, claim 40 recites further comprising: a trajectory linkage that displays both a real-time longitudinal trajectory and said predicted trajectory phenotype for said new patient, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 11, 21, and 31.
The additional elements from claims 1, 11, 21, and 31 include:
database (apply it, MPEP 2106.05(f)).
The additional elements from claims 1 include:
administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan (generally linking, MPEP 2106.05(h)).
The additional elements from claims 11 include:
system (apply it, MPEP 2106.05(f)).
The additional elements from claim 21 include:
non-transitory, computer readable medium having program code recorded thereon, for execution on a computing device (apply it, MPEP 2106.05(f)).
display (apply it, MPEP 2106.05(f)).
said program code causing said computing device to perform the following method steps (apply it, MPEP 2106.05(f)).
administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan (generally linking, MPEP 2106.05(h)).
The additional elements from claims 31 include:
user interface for a display on a computing device (apply it, MPEP 2106.05(f)).
user interface comprising (apply it, MPEP 2106.05(f)).
The dependent claims include additional elements not recited in the independent claims, including:
said user interface includes a storage linkage that allows said new patient to store (apply it, MPEP 2106.05(f)).
said user interface includes a storage linkage that allows said new patient to collect and store (apply it, MPEP 2106.05(f)) (insignificant extra-solution activity, MPEP 2106.05(g)).
a storage linkage that allows said new patient to collect and store (insignificant extra-solution activity, MPEP 2106.05(g)).
These additional elements, in the independent claims are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of, “database”, “display of a computing device”, “user interface”, “system”, “non-transitory, computer readable medium having program code recorded thereon”, “computing device”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see Specification Pages [7]-[9]. (See MPEP 2106.05(f)).
add insignificant extra-solution activity to the abstract idea – for example, the recitation of a storage linkage that allows said new patient to collect and store, which amounts to mere data gathering, which amounts to an insignificant application, see MPEP 2106.05(g).
generally link the abstract idea to a particular technological environment or field of use, for example, “administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan,” see MPEP 2106.05(h).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Specification Pages [7]-[9], disclose that the additional elements (i.e., interface, computing device, etc.) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e., perform steps and display data) that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Claims 8, 18, 28, 38, also include the additional element of collecting and storing data, e.g. see Versata Dev. Group Inc. v. SAP Am. Inc.– similarly, the current invention collects and stores patient data and display patient information.
Dependent claims 2-10, 12-20, 22-30, and 32-40 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than collecting and storing data (e.g., collect and store patient data claims 18 and 28.).
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves any other technology, and their collective functions merely provide conventional computer implementation.
The application, is an attempt to organize human activity, using a method to receive, analyze patient data for treating a patient having a specified disorder. The inventive concept is the means for categorizing and treating patient disorders, which is not patent eligible subject matter. Therefore, whether taken individually or as an ordered combination, claims 1-40 are nonetheless 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 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.
Claims 1-40 are rejected under 35 U.S.C. 103 as being unpatentable over Oleynik (U.S. Pub. No. 2015/0339442 A1) in view of Goldsmith (U.S. Pub. No. 2020/0350073 A1) and Wu (U.S. Pat. No. 10943301 B1).
Regarding claim 1, Oleynik discloses a method for treating a patient having a specified disorder in order to reduce the risk of said patient suffering an otherwise predictable, adverse occurrence in said patient's future, healthcare treatment for said specified disorder, said method comprising the steps of (Paragraphs [0006], [0057], and [0064] discuss a method for optimizing a patient treatment plan for a particular symptom or disease and the electronic medical records enable health care providers to develop treatment plans for patients, reduce misdiagnosis, improve quality of service, improve medical outcomes of patients and assessing the risk of a subject/patient in developing a disease or condition in the future or in having a disease or condition recur during or after treatment.):
compiling a treatment records database for prior patients who have been treated for said disorder, and including therein a longitudinal trajectory (Examiner interprets “longitudinal trajectory” as “the progression of a person’s state of disorder through time”, in accordance with the Specification Page 2 to include patterns of data change over time, for example observing patients over time to detect changes in variables.) for each of said prior patients (Paragraphs [0057] and [0070] discuss receiving, analyzing, correlating, and generating a large volume of patients' medical records and treatments, a central database and compiling and storing medical records and for utilizing the electronic medical records for identifying a course of treatment for a patient based on stored data for other patients as well as diagnosing, treating, and/or monitoring the patient's medical conditions and disease.),
applying a of machine learning algorithm that are configured to analyze each of said longitudinal trajectories to identify a plurality of trajectory phenotypes (Examiner is interpreting “trajectory phenotype” as observable characteristics of a phenotype as it changes over time.) for said disorder (Paragraph [0129] and FIG. 10 discuss an automated process in which the intelligent medical engine is configured to analyze and classify objective medical data for disease and treatment into a group that contains the same subset (or the same set) of clusters as the newly entered objective medical data into the central database and the learning module provides a machine-learning function to the overall automated process by constantly adjusting parameters and new data to improve the analyzing and classifying of groups of medical objective data.), wherein any of said trajectory phenotypes is mathematically expressible as a continuous line, with a distinctive shape, on a two-dimensional graph (Paragraphs [0008], [0017], and [0057] FIGS. 4D, 10 discuss degrouping methods can be implemented with respect to parameters over a period of time (on a two-dimensional graph) and classifying of groups of medical objective data including disease and treatment data, and diagrams of a patient’s provide over time assist a doctor in making treatment decision based on multiple different data points in a continuous line graph.),
using machine learning selection techniques, to determine said number and shape for each of said identified trajectory phenotypes (Paragraphs [0005]-[0006], [0072], [0017], [0102], [0109], [0113], [0129], and FIGS. 4D, 10 discuss the intelligent medical engine incorporates a learning module for interactively processing and learning of the patient's and other electronic medical records and the prescribed treatment plans over time for optimizing the recommended treatment protocol and an interactive machine learning function that constantly adjusts parameters of the degrouping process which uses data and new data to statistically improve the analyzing and classifying of groups of medical objective data and the outcomes treatments can be characterized by a single token, a number, or by a vector, representing different values at different points in time and this corresponds to the trajectory of a patient’s disease as the patient undergoes treatment and a graphical diagram illustrates how parameters change with treatment, showing multiple different data points in a continuous line graph depending on the treatment.),
compiling an intake database for said prior patients who have been treated for said disorder, and including therein a plurality of background-characterizing factors for each of said patients at the time of their entry into treatment (Paragraphs [0014], [0094]-[0095] discuss a global medical data analysis system for receiving and analyzing patients’ medical records and treatments and various factors and symptoms are inputted as parameters into the system and the disease and course of treatment for a patient is obtained based on data in the system which is obtained from other patients with similar medical history, symptoms, and conditions and their success and/or failure with a specific course of treatment and through the iterative process of comparison, classification, and degrouping of parameters inputted for the patient, the system provides a disease and course of treatment for the patient.),
using on said intake database a plurality of analysis techniques that are configured to identify a means for classifying, expressible in terms of said background-characterizing factors, that predicts, for a to-be-treated patient with said disorder who has provided at intake information on said background-characterizing factors, which of said plurality of trajectory phenotypes will have a shape that is most similar to the real-time longitudinal trajectory that said to-be-treated patient will exhibit, and classifying said to-be-treated patient to said trajectory phenotype (Paragraphs [0009], [0017], [0046], [0070]-[0071], and [0109] discuss a global medical data analysis system that is coupled to a central database coupled to a network for each hospital database and electronic medical records are fed into the intelligent medical engine for analysis and correlation, the analysis starts by classifying records into multiple levels of subgroups according to patient parameters, disease, treatments, etc. and a graphical diagram illustrating the dynamics of how the significant parameters change for two subgroups with the same treatment protocol.),
providing for said to-be-treated patient a treatment plan based on said predicted trajectory phenotype of said to-be-treated patient (Paragraphs [0072], [0114] discuss provides for ways to use these degroupings to find previous patients with the same or similar parameters to those of a new patient for whom the clinician wishes to determine one or more effective treatment options, the result of the comparison is to find the one or more treatment options that proved most effective with respect to desired outcomes for those patients in the subgroups whose parameters most closely match the parameters of the new patient.),
administering a specific treatment, selected from the use of a specific medication, medication dosage and counseling, according to said treatment plan (Paragraphs [0052], [0119], and FIG. 4E discuss recommended treatment protocols as a result of analyzing and choosing amount different selected treatment protocols and a continuous dynamic process as between a treatment protocol and corresponding parameters, the patient's parameters respond to the applied treatment plan (e.g., a surgery, medication, or both) and moves along the timelines, such as from timeline 1 to 2 or from timeline 3 to 1, indicating disease progression or recovery, as response to the applied treatment protocol.).
Oleynik does not explicitly disclose:
plurality of clustering algorithms;
using a plurality of mathematical and statistical model selection techniques selected from the use of model fit indices, domain expertise, practical considerations, and other state-of-the-art model selection guidelines.
Goldsmith teaches: plurality clustering algorithms (Paragraphs [0096] and [0100] discuss the dataset may be processed using one or more machine learning algorithms, including a clustering algorithm.) (Examiner interprets one or more to include plurality, therefore the limitation includes a plurality of clustering algorithms.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Oleynik to include, plurality of clustering algorithms, as taught by Goldsmith, in order to provide personalized medicine to a patient. (Goldsmith Paragraph [0010]).
Wu teaches: using a plurality of mathematical and statistical model selection techniques selected from the use of model fit indices, domain expertise, practical considerations, and other state-of-the-art model selection guidelines (Column 1 lines 35-50, Column 2 lines 6-8, and Column 3 lines 1-5 discuss data modeling is the process of creating one or more data models using statistical and data science techniques, predictive modeling is a type of data modeling that uses those techniques to forecast outcomes and incorporates various techniques, including naïve Bayes classifiers, k-nearest neighbor algorithms, majority classifiers, support vector machines, random forests, boosted trees, classification and regression trees (CARD), multivariate adaptive regression splines (MARS), neural networks, ordinary least squares, generalized linear models (GLM), logistic regression, generalized additive models (GAM), ensemble learning methods (ELM), robust regression, and semi-parametric regression, among others, the modeling output may include multiple model outputs, and the method may further include combining the multiple outputs using either an additive technique or a multiplicative statistical technique and relate to techniques for combining data models and selecting a “champion” data model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Oleynik to include, using a plurality of mathematical and statistical model selection techniques selected from the use of model fit indices, domain expertise, practical considerations, and other state-of-the-art model selection guidelines, as taught by Wu, in order to provide an opportunity to effectively and efficiently build and combine various types of models. (Wu Column 1 lines 59-62).
Regarding claim 2, Oleynik discloses further comprising the step of:
once said to-be-treated patient has begun treatment and is now a new patient, collecting a plurality of real-time patient data from said new patient that quantifies, at specified times, the status level of said disorder for said new patient (Paragraphs [0118]-[0119], [0120]-[0121], FIG. 4E discuss a method of monitoring a new patient’s disease and if necessary adjusts the course of treatment, the degrouping reflects a continuous dynamic process as between a treatment protocol and corresponding parameters and collecting real time medical data that is analyzed relative to the patient’s previously stored medical data.), and
adding said plurality of new patient data to said treatment records database, and redetermining said number and shape of said predicted trajectory phenotype for said new patient (Paragraphs [0072], [0114], [0119]-[0122] discuss provide patient’s real time parameters to the central database for degrouping processing by the intelligent medical engine and for ways to use these degroupings to find previous patients with the same or similar parameters to those of a new patient for whom the clinician wishes to determine one or more effective treatment options, the result of the comparison is to find the one or more treatment options that proved most effective with respect to desired outcomes for those patients in the subgroups whose parameters most closely match the parameters of the new patient.).
Regarding claim 3, Oleynik discloses further comprising the step of:
creating, on a display of a computing device of one using said method, a user interface that is configured to aid one in using said method (Paragraphs [0059], [0072], [0121], [0139], [0177] discuss a display module configured to display information and computer system outputs the disease and recommended course of treatment based on the entered information and the iterative process of comparing with stored objective medical data obtained for other patients and data can be displayed on a dashboard.).
Regarding claim 4, Oleynik discloses further comprising the step of:
creating, on a display of a computing device of one using said method, a user interface that is configured to aid one in using said method (Paragraphs [0059], [0121], [0130], FIGS. 9 and 12 discuss the system outputs the disease and recommended course of treatment based on the entered information and the iterative process of comparing with stored objective medical data obtained for other patients and data can be displayed on a dashboard.).
Regarding claims 5-7, 15-17, 25-27, and 35-37 Oleynik discloses wherein:
said specified disorder (Paragraph [0067] discusses methods used to diagnose, treat, identify a course of treatment for and monitor any medical disease or condition.).
Oleynik does not explicitly disclose:
wherein: said specified disorder is an opioid-use disorder.
Goldsmith teaches:
wherein: said specified disorder is an opioid-use disorder (Paragraphs [0065] and [0118] discuss opiate dependency is the disease or condition.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Oleynik to include, wherein: said specified disorder is an opioid-use disorder, as taught by Goldsmith, in order to provide personalized medicine to a patient. (Goldsmith Paragraph [0010]).
Regarding claim 8 Oleynik discloses wherein:
said user interface includes a storage linkage that allows said new patient to store both said real-time patient data and said plurality of background-characterizing factors for said new patient (Paragraphs [0030], [0057], [0059], [0063], [0072], and [0120] discuss a storage module, storing, compiling, analyzing key parameters in a patient’s template over time to assist a doctor in making a decision, the course of treatment, the information collected relating to a patient's condition at each visit to a health care provider's office is entered into the computer system and stored, degrouping is triggered when a plurality of objective medical data corresponding to new or existi