DETAILED ACTION
Notices to Applicant
This communication is a First Action Non-Final on the merits. Claims 1-20 as filed 08/29/2025, are currently pending and have been considered below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
The application claims the benefit of and priority to U.S. Application No. 63/688,599, filed on 08/29/2024
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Claims 1-13 and 19 are drawn to a computer-implemented method (CIM) for neurobiological diagnosis of a subject, which is within the four statutory categories (i.e. method).
Independent Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites (additional elements bolded:
1. A computer-implemented method (CIM) for neurobiological diagnosis of a subject, optionally followed by an individual devising a treatment regimen, the method comprising:
using one or more adaptive diagnostic algorithms configured to (i) process the subject's clinical information, cognitive information, and/or behavioral information to obtain a first score and (ii) compute and/or output a probability of the subject having a subtype of psychosis by matching the first score with corresponding scores of other subjects with a known subtype of psychosis, wherein the one or more adaptive diagnostic algorithms have been trained on a second set of clinical information, cognitive information, and/or behavioral information to recognize one or more subtypes of psychosis, optionally wherein training of the one or more adaptive diagnostic algorithms occurs on a computing device,
wherein the one or more adaptive diagnostic algorithms are operably linked to one or more processors capable of executing the one or more adaptive diagnostic algorithms, and optionally wherein the psychosis is idiopathic.
The claim limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people through rules or instructions but for the recitation of generic computer components. That is, other than reciting the above bolded limitations, for example “on a computing device,” and “one or more processors,” nothing in the claim precludes the limitations from reciting rules or instructions for managing personal behavior or interactions between people. For example, but for the above bolded language, using one or more adaptive diagnostic algorithms configured to (i) process the subject's clinical information, cognitive information, and/or behavioral information to obtain a first score and (ii) compute and/or output a probability of the subject having a subtype of psychosis by matching the first score with corresponding scores of other subjects with a known subtype of psychosis, wherein the one or more adaptive diagnostic algorithms have been trained on a second set of clinical information, cognitive information, and/or behavioral information to recognize one or more subtypes of psychosis, and optionally wherein the psychosis is idiopathic in the context of this claim encompasses rules or instructions for managing the personal behavior or interactions between people for using one or more diagnostic algorithms for neurobiological diagnosis of a subject. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people through rules or instructions but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements, for example, “optionally wherein training of the one or more adaptive diagnostic algorithms occurs on a computing device, wherein the one or more adaptive diagnostic algorithms are operably linked to one or more processors capable of executing the one or more adaptive diagnostic algorithms,” each of which, when viewed individually and as a whole, amount to generally linking the use of the abstract idea to a particular technological environment or field of use. See MPEP 2106.05(h). That is, the additional elements generally link the training of the algorithms as occurring on a computing device and does not actively recite the computing device performing or having performed the training. Similarly, the additional element of the algorithms as operably linked to one or more processors capable of executing the one or more algorithms does not recite an active performance or having performed the algorithm by the one or more processors, but rather, generally links the algorithms to the one or more processors as being capable of being executed thereon. to perform the collecting, analyzing, storing, and generating limitations. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements, for example, “optionally wherein training of the one or more adaptive diagnostic algorithms occurs on a computing device, wherein the one or more adaptive diagnostic algorithms are operably linked to one or more processors capable of executing the one or more adaptive diagnostic algorithms,” each of which, when viewed individually and as a whole, amount to generally linking the use of the abstract idea to a particular technological environment or field of use. See MPEP 2106.05(h). That is, the additional elements generally link the training of the algorithms as occurring on a computing device and does not actively recite the computing device performing or having performed the training. Similarly, the additional element of the algorithms as operably linked to one or more processors capable of executing the one or more algorithms does not recite an active performance or having performed the algorithm by the one or more processors, but rather, generally links the algorithms to the one or more processors as being capable of being executed thereon. The claim is not patent eligible.
Dependent claims 2-13 and 19 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the identified abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Dependent claim 2 recites the additional element of “wherein the one or more adaptive diagnostic algorithms are operably linked to (i) a device configured to present audio data, visual data, audio-visual data, optical data, electrical data, magnetic data, electromagnetic data, mechanical data or a combination thereof, related to neurobiological diagnosis,” however, this amounts to generally linking the use of the abstract idea to a particular technological environment or field of use. See MPEP 2106.05(h). Dependent claim 11 recites the additional element of “causing a recommendation of one or more treatment regiments based on the subtype of psychosis to be presented at a user interface comprising a display of a computing device, an electro-mechanical acoustic system, or a combination thereof,” however, the elements of a user interface, a computing device, an electro-mechanical acoustic system, or a combination thereof amounts to using generic computer components or other machinery in their ordinary capacity as a tool for performing the abstract idea of presenting a recommendation of one or more treatment regiments. See MPEP 2106.05(f)(2); (Application Specification at [0051], [0134], [0137]). Therefore, the dependent claims are rejected under 35 U.S.C. § 101.
Claims 14-18 and 20 are drawn to a non-transitory computer-readable medium (CRM) for neurobiological diagnosis of a subject, which is within the four statutory categories (i.e. manufacture).
Independent Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 14 recites (additional elements bolded:
14. A non-transitory computer-readable medium (CRM) with one or more computer-executable instructions stored thereon executed by one or more processors, wherein the one or more computer-executable instructions comprise one or more adaptive diagnostic algorithms configured to (i) process a subject's clinical information, cognitive information, and/or behavioral information to obtain a first score and (ii) compute and/or output a probability of the subject having a subtype of psychosis by matching the first score with corresponding scores of other subjects with a known subtype of psychosis, wherein the one or more adaptive diagnostic algorithms have been trained on a second set of clinical information, cognitive information, and/or behavioral information to recognize one or more subtypes of psychosis, optionally wherein training of the one or more adaptive diagnostic algorithms occurs on a computing device, and
wherein the one or more computer-executable instructions are operably linked to the one or more processors.
The claim limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people through rules or instructions but for the recitation of generic computer components. That is, other than reciting the above bolded limitations, nothing in the claim precludes the limitations from reciting rules or instructions for managing personal behavior or interactions between people. For example, but for the above bolded language, process a subject's clinical information, cognitive information, and/or behavioral information to obtain a first score and (ii) compute and/or output a probability of the subject having a subtype of psychosis by matching the first score with corresponding scores of other subjects with a known subtype of psychosis, wherein the one or more adaptive diagnostic algorithms have been trained on a second set of clinical information, cognitive information, and/or behavioral information to recognize one or more subtypes of psychosis in the context of this claim encompasses rules or instructions for managing the personal behavior or interactions between people for using one or more diagnostic algorithms for neurobiological diagnosis of a subject. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people through rules or instructions but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above bolded additional elements, for example, a “non-transitory computer-readable medium (CRM) with one or more computer-executable instructions stored thereon executed by one or more processors, wherein the one or more computer-executable instructions comprise one or more adaptive diagnostic algorithms configured,” and “wherein the one or more computer-executable instructions are operably linked to the one or more processors,” are recited to perform the claim limitations. These additional elements are recited at a high level of generality such that they amount to using generic computer components for performing the abstract idea. (i.e. one or more processors, a non-transitory computer-readable medium, a computing device, as they relate to generic computer components (Application Specification at [0051], [0134], [0136], [0137]). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Further, the above bolded additional elements, for example “optionally wherein training of the one or more adaptive diagnostic algorithms occurs on a computing device,” when viewed individually and as a whole, amounts to generally linking the use of the abstract idea to a particular technological environment or field of use. See MPEP 2106.05(h). That is, the additional elements generally link the training of the algorithms as occurring on a computing device and does not actively recite the computing device performing or having performed the training. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements, for example, a “non-transitory computer-readable medium (CRM) with one or more computer-executable instructions stored thereon executed by one or more processors, wherein the one or more computer-executable instructions comprise one or more adaptive diagnostic algorithms configured,” and “wherein the one or more computer-executable instructions are operably linked to the one or more processors,” are recited to perform the claim limitations. These additional elements are recited at a high level of generality such that they amount to using generic computer components for performing the abstract idea. (i.e. one or more processors, a non-transitory computer-readable medium, a computing device, as they relate to generic computer components (Application Specification at [0051], [0134], [0136], [0137]). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Further, the above bolded additional elements, for example “optionally wherein training of the one or more adaptive diagnostic algorithms occurs on a computing device,” when viewed individually and as a whole, amounts to generally linking the use of the abstract idea to a particular technological environment or field of use. See MPEP 2106.05(h). That is, the additional elements generally link the training of the algorithms as occurring on a computing device and does not actively recite the computing device performing or having performed the training. The claim is not patent eligible.
Dependent claims 15-18 and 20 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the identified abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Dependent claim 15 recites the additional element of “wherein the one or more computer-executable instructions are operably linked to a device configured to present audio data, visual data, audio-visual data, optical data, electrical data, magnetic data, electromagnetic data, mechanical data or a combination thereof, related to neurobiological diagnosis,” however, this amounts to generally linking the use of the abstract idea to a particular technological environment or field of use. See MPEP 2106.05(h). Therefore, the dependent claims are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 9-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2009/0136423 A1 (hereinafter “Sahakian et al.”) in view of Koen, Joshua D et al. “Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP).” Scientific reports vol. 13,1 12980. 10 Aug. 2023 (hereinafter “Koen et al.”).
RE: Claim 1 Sahakian et al. teaches the claimed:
1. A computer-implemented method (CIM) for neurobiological diagnosis of a subject, optionally followed by an individual devising a treatment regimen, the method comprising: using one or more adaptive diagnostic algorithms configured to (i) process the subject's clinical information, cognitive information, and/or behavioral information to obtain a first score ((Sahakian et al., [0054]-[0055]) (The severity and/or prognosis of the psychosis may be expressed as a grade, which is produced by the processor from the visuospatial associative learning score of the individual, for example using a predictive algorithm or by comparison with a threshold value stored in the processor or data storage means; a predictive algorithm may be generated from the visuospatial associative learning scores of a population of individuals, for example in a database, as described above)) and
(ii) compute and/or output a probability of the subject having […] psychosis by matching the first score with corresponding scores of other subjects with a known […] psychosis, wherein the one or more adaptive diagnostic algorithms have been trained on a second set of clinical information, cognitive information, and/or behavioral information to recognize […] psychosis, optionally wherein training of the one or more adaptive diagnostic algorithms occurs on a computing device ((Sahakian et al., [0021]-[0024]) (A method of producing a predictive psychosis algorithm or model may comprise; assessing the visuospatial associative learning ability of a sample of individuals having a first episode psychosis, to produce visuospatial associative learning scores for each member of said sample; monitoring the progress of the psychosis in each of said members over a time course to determine the clinical outcome for each of said members, and; relating scores, age and IQ of each of said individuals with the clinical outcomes of the psychosis in said individuals to produce a predictive algorithm which relates said test scores, age and IQ to said clinical outcomes));
wherein the one or more adaptive diagnostic algorithms are operably linked to one or more processors capable of executing the one or more adaptive diagnostic algorithms, and optionally wherein the psychosis is idiopathic ((Sahakian et al., [0049]) (a computer system may comprise a processor adapted to determine the visuospatial associative learning ability of an individual having a first episode of psychosis, produce a visuospatial associative learning score for the individual, and correlate said score with the severity and/or prognosis of psychosis in said individual)).
Sahakian et al. fails to explicitly teach, but Koen et al. teaches the claimed:
the subject having a subtype of psychosis ((Koen et al. pgs. 1 and 4) (supervised machine learning classification of psychosis biotypes)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the supervised machine learning classification of psychosis biotypes as taught by Koen et al. within the method and system for assessing psychotic disorders as taught by Sahakian et al. with the motivation of using brain-based biomarkers to capture biologically-informed psychosis constructs (Koen et al. at Pg. 1).
RE: Claim 2 Sahakian et al. and Koen et al. teach the claimed:
2. The CIM of claim 1, wherein the one or more adaptive diagnostic algorithms are operably linked to (i) a device configured to present audio data, visual data, audio-visual data, optical data, electrical data, magnetic data, electromagnetic data, mechanical data or a combination thereof, related to neurobiological diagnosis ((Sahakian et al., [0011]) (Visuospatial associative learning ability is preferably assessed using a single test, for example a paired associates learning test, preferably a non-verbal paired associates learning test. CANTAB PAL is a precisely defined cognitive test which is well-known in the art. It involves the sequential display of 1, 2, 3, 6 or 8 patterns in boxes on a display)).
RE: Claim 3 Sahakian et al. and Koen et al. teach the claimed:
3. The CIM of claim 1, wherein the one or more adaptive diagnostic algorithms are configured to process the subject's clinical information ((Sahakian et al., [0021]-[0024]) (relating scores, age and IQ of each of said individuals with the clinical outcomes of the psychosis in said individuals to produce a predictive algorithm which relates said test scores, age and IQ to said clinical outcomes)).
RE: Claim 4 Sahakian et al. and Koen et al. teach the claimed:
4. The CIM of claim 1, wherein the one or more adaptive diagnostic algorithms are configured to process the subject's clinical information and cognitive information ((Sahakian et al., [0021]-[0024]) (A method of producing a predictive psychosis algorithm or model may comprise; assessing the visuospatial associative learning ability of a sample of individuals having a first episode psychosis, to produce visuospatial associative learning scores for each member of said sample; monitoring the progress of the psychosis in each of said members over a time course to determine the clinical outcome for each of said members, and; relating scores, age and IQ of each of said individuals with the clinical outcomes of the psychosis in said individuals to produce a predictive algorithm which relates said test scores, age and IQ to said clinical outcomes)).
RE: Claim 5 Sahakian et al. and Koen et al. teach the claimed:
5. The CIM of claim 1, wherein the one or more adaptive diagnostic algorithms are configured to process the subject's clinical information, cognitive information, and behavioral information ((Sahakian et al., [0034], [0054]-[0055]) (The severity and/or prognosis of the psychosis may be expressed as a grade, which is produced by the processor from the visuospatial associative learning score of the individual, for example using a predictive algorithm or by comparison with a threshold value stored in the processor or data storage means; a predictive algorithm may be generated from the visuospatial associative learning scores of a population of individuals, for example in a database, as described above; the individual may display one or more symptoms or behaviours characteristic of psychosis)).
RE: Claim 9 Sahakian et al. and Koen et al. teach the claimed:
9. The CIM of claim 1, wherein using the subject's cognitive information to compute the probability is performed after using the subject's clinical information ((Sahakian et al., [0054]-[0055]) (The severity and/or prognosis of the psychosis may be expressed as a grade, which is produced by the processor from the visuospatial associative learning score of the individual, for example using a predictive algorithm or by comparison with a threshold value stored in the processor or data storage means; a predictive algorithm may be generated from the visuospatial associative learning scores of a population of individuals, for example in a database, as described above)).
RE: Claim 10 Sahakian et al. and Koen et al. teach the claimed:
10. The CIM of claim 1, wherein the subtype of psychosis comprises a neural dysregulation Biotype (BT2), a neural vigor Biotype (BT1), and/or a stimulus salience Biotype (BT3) ((Koen et al., at pg 2) (Biotype1 (B1), characterized by poor cognitive and low sensorimotor function; Biotype2 (B2), with moderately impaired cognition and exaggerated sensorimotor reactivity; and Biotype3 (B3), with near normal cognitive and sensorimotor functions)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the Biotypes B1-B3 characterizing psychosis subtypes as taught by Koen et al. within the method and system for assessing psychotic disorders as taught by Sahakian et al. with the motivation of using brain-based biomarkers to capture biologically-informed psychosis constructs (Koen et al. at Pg. 1).
RE: Claim 11 Sahakian et al. and Koen et al. teach the claimed:
11. The CIM of claim 1, further comprising: causing a recommendation of one or more treatment regiments based on the subtype of psychosis to be presented at a user interface comprising a display of a computing device, an electro-mechanical acoustic system, or a combination thereof ((Sahakian et al., [0031], [0074]-[0075]) (An individual identified as having a severe psychosis and/or a psychosis with a negative prognosis using a method of the invention may be targeted or prioritised for cognitive enhancement, psychological, rehabilitative, or other therapeutic treatment; Following, identification of psychosis, the individual may be treated using anti-psychotic, cognitive enhancing or other ( e.g. psychological) therapies. Suitable therapies are well known in the art; a computer comprising a display)).
RE: Claim 12 Sahakian et al. and Koen et al. teach the claimed:
12. The CIM of claim 1, wherein the CIM provides or is capable of providing neurobiological diagnosis in real-time ((Sahakian et al., [0049]) (a computer system may comprise a
processor adapted to perform a method of the invention)).
RE: Claim 13 Sahakian et al. and Koen et al. teach the claimed:
13. The CIM of claim 1, comprising an individual devising a treatment regimen, wherein the treatment regimen comprises antipsychotic medications (typical and atypical), mood stabilizers, anxiolytics, antidepressants; cognitive behavioral therapy for psychosis; supportive therapy; insight- oriented therapy; family therapy; social skills training; vocational rehabilitation; case management; hospitalization; electroconvulsive therapy (ECT); sleep hygiene; exercise; dietary adjustments; mindfulness; relaxation techniques; or any combinations thereof ((Sahakian et al., [0031], [0074]-[0075]) (An individual identified as having a severe psychosis and/or a psychosis with a negative prognosis using a method of the invention may be targeted or prioritised for cognitive enhancement, psychological, rehabilitative, or other therapeutic treatment; Following, identification of psychosis, the individual may be treated using anti-psychotic, cognitive enhancing or other ( e.g. psychological) therapies. Suitable therapies are well known in the art; a computer comprising a display)).
RE: Claim 14 Sahakian et al. teaches the claimed:
14. A non-transitory computer-readable medium (CRM) with one or more computer-executable instructions stored thereon executed by one or more processors, wherein the one or more computer-executable instructions comprise one or more adaptive diagnostic algorithms configured to (i) process a subject's clinical information, cognitive information, and/or behavioral information to obtain a first score ((Sahakian et al., [0046], [0054]-[0055]) (a compute rprogram product which can be read and accessed directly by a computer; The severity and/or prognosis of the psychosis may be expressed as a grade, which is produced by the processor from the visuospatial associative learning score of the individual, for example using a predictive algorithm or by comparison with a threshold value stored in the processor or data storage means; a predictive algorithm may be generated from the visuospatial associative learning scores of a population of individuals, for example in a database, as described above)) and
(ii) compute and/or output a probability of the subject having […] psychosis by matching the first score with corresponding scores of other subjects with a known […] psychosis, wherein the one or more adaptive diagnostic algorithms have been trained on a second set of clinical information, cognitive information, and/or behavioral information to recognize […] psychosis, optionally wherein training of the one or more adaptive diagnostic algorithms occurs on a computing device ((Sahakian et al., [0021]-[0024]) (A method of producing a predictive psychosis algorithm or model may comprise; assessing the visuospatial associative learning ability of a sample of individuals having a first episode psychosis, to produce visuospatial associative learning scores for each member of said sample; monitoring the progress of the psychosis in each of said members over a time course to determine the clinical outcome for each of said members, and; relating scores, age and IQ of each of said individuals with the clinical outcomes of the psychosis in said individuals to produce a predictive algorithm which relates said test scores, age and IQ to said clinical outcomes)); and
wherein the one or more computer-executable instructions are operably linked to the one or more processors ((Sahakian et al., [0049]) (a computer system may comprise a processor adapted to determine the visuospatial associative learning ability of an individual having a first episode of psychosis, produce a visuospatial associative learning score for the individual, and correlate said score with the severity and/or prognosis of psychosis in said individual)).
Sahakian et al. fails to explicitly teach, but Koen et al. teaches the claimed:
the subject having a subtype of psychosis ((Koen et al. pgs. 1 and 4) (supervised machine learning classification of psychosis biotypes)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the supervised machine learning classification of psychosis biotypes as taught by Koen et al. within the method and system for assessing psychotic disorders as taught by Sahakian et al. with the motivation of using brain-based biomarkers to capture biologically-informed psychosis constructs (Koen et al. at Pg. 1).
RE: Claim 15 Sahakian et al. and Koen et al. teach the claimed:
15. The non-transitory CRM of claim 14, wherein the one or more computer-executable instructions are operably linked to a device configured to receive or output audio data, visual data, audio-visual data, optical data, electrical data, magnetic data, electromagnetic data, mechanical data, or a combination thereof, related to neurobiological diagnosis ((Sahakian et al., [0011]) (Visuospatial associative learning ability is preferably assessed using a single test, for example a paired associates learning test, preferably a non-verbal paired associates learning test. CANTAB PAL is a precisely defined cognitive test which is well-known in the art. It involves the sequential display of 1, 2, 3, 6 or 8 patterns in boxes on a display)).
RE: Claim 16 Sahakian et al. and Koen et al. teach the claimed:
16. The non-transitory CRM of claim 14, wherein the one or more adaptive diagnostic algorithms are configured to process: (i) the subject's clinical information, (ii) the subject's clinical information and cognitive information, or (iii) the subject's clinical information and cognitive information ((Sahakian et al., [0021]-[0024]) (relating scores, age and IQ of each of said individuals with the clinical outcomes of the psychosis in said individuals to produce a predictive algorithm which relates said test scores, age and IQ to said clinical outcomes)).
RE: Claim 19 Sahakian et al. and Koen et al. teach the claimed:
19. A method of treating a patient diagnosed with psychosis using the CIM of claim 1, wherein the treatment comprises any one or more of antipsychotic medications (typical and atypical), mood stabilizers, anxiolytics, antidepressants, cognitive behavioral therapy for psychosis, supportive therapy, insight-oriented therapy, family therapy, social skills training, vocational rehabilitation, case management, hospitalization, electroconvulsive therapy (ECT), sleep hygiene, exercise, dietary adjustments, mindfulness, relaxation techniques, or any combinations thereof ((Sahakian et al., [0031], [0074]-[0075]) (An individual identified as having a severe psychosis and/or a psychosis with a negative prognosis using a method of the invention may be targeted or prioritised for cognitive enhancement, psychological, rehabilitative, or other therapeutic treatment; Following, identification of psychosis, the individual may be treated using anti-psychotic, cognitive enhancing or other ( e.g. psychological) therapies. Suitable therapies are well known in the art; a computer comprising a display)).
RE: Claim 20 Sahakian et al. and Koen et al. teach the claimed:
20. A method of treating a patient diagnosed with psychosis using a device comprising the non-transitory CRM of claim 14, wherein the treatment comprises any one or more of antipsychotic medications (typical and atypical), mood stabilizers, anxiolytics, antidepressants, cognitive behavioral therapy for psychosis, supportive therapy, insight-oriented therapy, family therapy, social skills training, vocational rehabilitation, case management, hospitalization, electroconvulsive therapy (ECT), sleep hygiene, exercise, dietary adjustments, mindfulness, relaxation techniques, or any combinations thereof ((Sahakian et al., [0031], [0074]-[0075]) (An individual identified as having a severe psychosis and/or a psychosis with a negative prognosis using a method of the invention may be targeted or prioritised for cognitive enhancement, psychological, rehabilitative, or other therapeutic treatment; Following, identification of psychosis, the individual may be treated using anti-psychotic, cognitive enhancing or other ( e.g. psychological) therapies. Suitable therapies are well known in the art; a computer comprising a display)).
Claims 6-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over US 2009/0136423 A1 (hereinafter “Sahakian et al.”) in view of Koen, Joshua D et al. “Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP).” Scientific reports vol. 13,1 12980. 10 Aug. 2023 (hereinafter “Koen et al.”), and further in view of US 2024/0099623 A1 (hereinafter “Chang”),
RE: Claim 6 Sahakian et al. and Koen et al. teach the claimed:
6. The CIM of claim 1,
Sahakian et al. and Koen et al. fail to explicitly teach, but Chang teaches the claimed:
wherein the one or more adaptive diagnostic algorithms comprise a randomized ensemble of classifiers ((Chang, [0031], [0036]) (As shown in the figure, ExtraTreesClassifier yielded the highest accuracy rate of 81.49%, followed by RandomForestClassifier of 80.33%, LGBMCIassifier of 75.77%, SVC of 74.06%, DecisionTreeClassifier 58.72%, KNeighborsClassifier 54.43%, Logistic Regression 53.21%, and GaussianNB 20.78%. ExtraTreesClassifier and RandomForestClassifier are both ensemble machine learning algorithms based on a decision tree; The system and method according to the present disclosure enable machine learning algorithms, such as the Extra Tree algorithm, to diagnose ADHD disease and even classify the level of ADHD)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the random forest classifier for diagnosing ADHD disease as taught by Chang within the method and system for assessing psychotic disorders as taught by Sahakian et al. and the supervised machine learning classification of psychosis biotypes as taught by Koen et al. with the motivation of providing a simple diagnostic method for detecting ADHD faster and more accurately than currently available diagnostic methods (Chang at [0003]).
RE: Claim 7 Sahakian et al. and Koen et al. teach the claimed:
7. The CIM of claim 1,
Sahakian et al. and Koen et al. fail to explicitly teach, but Chang teaches the claimed:
wherein the one or more adaptive diagnostic algorithms comprise an adaptive decision tree algorithm ((Chang, [0031], [0036]) (As shown in the figure, ExtraTreesClassifier yielded the highest accuracy rate of 81.49%, followed by RandomForestClassifier of 80.33%, LGBMCIassifier of 75.77%, SVC of 74.06%, DecisionTreeClassifier 58.72%, KNeighborsClassifier 54.43%, Logistic Regression 53.21%, and GaussianNB 20.78%. ExtraTreesClassifier and RandomForestClassifier are both ensemble machine learning algorithms based on a decision tree; The system and method according to the present disclosure enable machine learning algorithms, such as the Extra Tree algorithm, to diagnose ADHD disease and even classify the level of ADHD)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the decision tree based algorithms for diagnosing ADHD disease as taught by Chang within the method and system for assessing psychotic disorders as taught by Sahakian et al. and the supervised machine learning classification of psychosis biotypes as taught by Koen et al. with the motivation of providing a simple diagnostic method for detecting ADHD faster and more accurately than currently available diagnostic methods (Chang at [0003]).
RE: Claim 8 Sahakian et al. and Koen et al. teach the claimed:
8. The CIM of claim 1,
Sahakian et al. and Koen et al. fail to explicitly teach, but Chang teaches the claimed:
wherein the one or more adaptive diagnostic algorithms comprise an extra-trees classifier ((Chang, [0036]) (The system and method according to the present disclosure enable machine learning algorithms, such as the Extra Tree algorithm, to diagnose ADHD disease and even classify the level of ADHD)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the extra tree decision tree based algorithms for diagnosing ADHD disease as taught by Chang within the method and system for assessing psychotic disorders as taught by Sahakian et al. and the supervised machine learning classification of psychosis biotypes as taught by Koen et al. with the motivation of providing a simple diagnostic method for detecting ADHD faster and more accurately than currently available diagnostic methods (Chang at [0003]).
RE: Claim 17 Sahakian et al. and Koen et al. teach the claimed:
17. The non-transitory CRM of claim 14,
Sahakian et al. and Koen et al. fail to explicitly teach, but Chang teaches the claimed:
wherein the one or more adaptive diagnostic algorithms comprise a randomized ensemble of classifiers (As shown in the figure, ExtraTreesClassifier yielded the highest accuracy rate of 81.49%, followed by RandomForestClassifier of 80.33%, LGBMCIassifier of 75.77%, SVC of 74.06%, DecisionTreeClassifier 58.72%, KNeighborsClassifier 54.43%, Logistic Regression 53.21%, and GaussianNB 20.78%. ExtraTreesClassifier and RandomForestClassifier are both ensemble machine learning algorithms based on a decision tree; The system and method according to the present disclosure enable machine learning algorithms, such as the Extra Tree algorithm, to diagnose ADHD disease and even classify the level of ADHD)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the random forest classifier for diagnosing ADHD disease as taught by Chang within the method and system for assessing psychotic disorders as taught by Sahakian et al. and the supervised machine learning classification of psychosis biotypes as taught by Koen et al. with the motivation of providing a simple diagnostic method for detecting ADHD faster and more accurately than currently available diagnostic methods (Chang at [0003]).
RE: Claim 18 Sahakian et al. and Koen et al. teach the claimed:
18. The non-transitory CRM claim 14.
Sahakian et al. and Koen et al. fail to explicitly teach, but Chang teaches the claimed:
wherein the one or more adaptive diagnostic algorithms comprise an adaptive decision tree algorithm (As shown in the figure, ExtraTreesClassifier yielded the highest accuracy rate of 81.49%, followed by RandomForestClassifier of 80.33%, LGBMCIassifier of 75.77%, SVC of 74.06%, DecisionTreeClassifier 58.72%, KNeighborsClassifier 54.43%, Logistic Regression 53.21%, and GaussianNB 20.78%. ExtraTreesClassifier and RandomForestClassifier are both ensemble machine learning algorithms based on a decision tree; The system and method according to the present disclosure enable machine learning algorithms, such as the Extra Tree algorithm, to diagnose ADHD disease and even classify the level of ADHD)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the decision tree based algorithms for diagnosing ADHD disease as taught by Chang within the method and system for assessing psychotic disorders as taught by Sahakian et al. and the supervised machine learning classification of psychosis biotypes as taught by Koen et al. with the motivation of providing a simple diagnostic method for detecting ADHD faster and more accurately than currently available diagnostic methods (Chang at [0003]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. 2022/0130518 A1 teaches methods and systems for monitoring the mental health of a subject (Abstract);
Miranda, Lucas et al. “Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping.” Frontiers in psychiatry vol. 12 665536. 22 Oct. 2021 teaches functional MRI for psychiatric disease subtyping (Pg. 2);
US 2024/0285206 A1 teaches dynamically determining psychotic disorder subtype and providing intervention based on the subtype ([0022]; and
US 20140066461 A1 teaches a diagnostic test to identify a specific patient subtype(s) of psychotic disorders, e.g., SCZ, that have common genetic, clinical, metabolic, and/or prognostic features ([0150]).
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/A.M.B./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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