DETAILED ACTION
Notice to Applicant
This communication is in response to the application submitted January 23, 2025. The present application is a continuation of PCT Application No. PCT/JP2023/022147, filed on June 14, 2023, which claims priority to Japanese Patent Application No. 2022-118143, filed on July 25, 2022. Claims 1 – 12 are pending.
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 – 5 and 9 – 12 of the present application are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 5 and 14 – 17 of Application Number 19/035,718. The differences between the claims are outlined in boldface in the table below.
Claim
Application 19/035,631
Claim
Application 19/035,718
1
A non-transient computer readable medium comprising a computer program that causes a computer to execute processing comprising:
acquiring pain related information regarding pain of a patient;
inputting the acquired pain related information into a learning model that outputs pain prediction information, and acquiring, from the learning model, pain prediction information of the patient;
generating pain treatment assistance information for the patient based on the acquired pain prediction information; and
outputting the generated pain treatment assistance information.
1
A non-transient computer readable medium comprising a computer program that causes a computer to execute processing comprising:
acquiring pain related information regarding pain of a patient;
inputting the acquired pain related information into a learning model that outputs a pain evaluation result, and acquiring, from the learning model, the pain evaluation result of the patient;
generating coping information for coping with the pain based on the acquired pain evaluation result; and
outputting the generated coping information.
2
The non-transient computer readable medium according to claim 1, wherein:
the pain related information includes at least one of an evaluation index used to evaluate pain, vital data, medication information, and a physical condition.
2
The non-transient computer readable medium according to claim 1, wherein:
the pain related information includes at least one of an evaluation index used to evaluate pain, vital data, medication information, and a physical condition.
3
The computer program according to claim 1, wherein the computer program causes the computer to execute processing further comprising:
acquiring medical information regarding medical care of the patient; and
inputting the acquired medical information into the learning model that outputs the pain prediction information, and acquiring, from the learning model, the pain prediction information of the patient.
3
The non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising:
acquiring medical information regarding medical care of the patient; and
inputting the acquired medical information into the learning model that outputs the pain evaluation result, and acquiring, from the learning model, the pain evaluation result of the patient.
4
The non-transient computer readable medium according to claim 3, wherein:
the medical information includes at least one of diagnosis information, examination information, treatment information, prescription information, and disease prognosis prediction information.
4
The non-transient computer readable medium according to claim 3, wherein:
the medical information includes at least one of diagnosis information, examination information, treatment information, prescription information, and disease prognosis prediction information.
5
The non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising:
acquiring literature information regarding a pain treatment; and
inputting the acquired literature information into the learning model that outputs the pain prediction information, and acquiring, from the learning model, the pain prediction information of the patient.
5
The non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising:
acquiring literature information regarding a pain treatment; and
inputting the acquired literature information into the learning model that outputs the pain evaluation result, and acquiring, from the learning model, the pain evaluation result of the patient.
9
A control unit programmed to execute steps comprising:
acquiring pain related information regarding pain of a patient;
inputting the acquired pain related information into a learning model that outputs pain prediction information, and acquiring, from the learning model, pain prediction information of the patient;
generating pain treatment assistance information for the patient based on the acquired pain prediction information; and
outputting the generated pain treatment assistance information.
14
A control unit programmed to execute steps comprising:
acquiring pain related information regarding pain of a patient;
inputting the acquired pain related information into a learning model that outputs a pain evaluation result, and acquiring, from the learning model, the pain evaluation result of the patient;
generating coping information for coping with the pain based on the acquired pain evaluation result; and
outputting the generated coping information.
10
An information processing method comprising:
acquiring pain related information regarding pain of a patient;
inputting the acquired pain related information into a learning model that outputs pain prediction information, and acquiring, from the learning model, pain prediction information of the patient;
generating pain treatment assistance information for the patient based on the acquired pain prediction information; and
outputting the generated pain treatment assistance information.
15
An information processing method comprising:
acquiring pain related information regarding pain of a patient;
inputting the acquired pain related information into a learning model that outputs a pain evaluation result, and acquiring, from the learning model, the pain evaluation result of the patient;
generating coping information for coping with the pain based on the acquired pain evaluation result; and
outputting the generated coping information.
11
A learning model generation method comprising:
acquiring first training data including pain related information regarding pain of a plurality of patients and pain prediction information of the plurality of patients; and
generating a learning model that receives pain related information of a patient and outputs pain prediction information of the patient based on the acquired first training data.
16
A learning model generation method comprising:
acquiring first training data including pain related information regarding pain of a plurality of patients and pain evaluation results of the plurality of patients; and
generating a learning model that receives pain related information of a patient and outputs a pain evaluation result of the patient based on the acquired first training data.
12
The learning model generation method according to claim 11, further comprising:
acquiring second training data further including medical information of the plurality of patients, literature information regarding a pain treatment, and the pain prediction information of the plurality of patients; and
generating the learning model so as to output the pain prediction information based on the acquired second training data.
17
The learning model generation method according to claim 16, further comprising:
acquiring second training data further including medical information of the plurality of patients, literature information regarding a pain treatment, and the pain evaluation results of the plurality of patients; and
generating the learning model so as to output the pain evaluation result based on the acquired second training data.
Furthermore, an obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but an examined claim is either anticipated by, or would been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In reLongi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985). Although the conflicting claims are not identical, they are not patentably distinct from each other because claim 1, for example, is generic to the method recited in claim 1 of Application Number 19/035,718. That is, claim 11 of the present application falls entirely with scope of claim 1 of Application Number 19/035,718, or in other words claim 1 is substantially the same and would have been obvious to one of ordinary skill in the art as described by claim 1 of Application Number 19/035,718.
Claim Objections
Claim 3 is objected to because of the following informalities: Claim 3 recited is the preamble “computer program” rather than “non-transient computer readable medium”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 12 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.
Step One
Claims 1 – 12 are drawn to a non-transient computer-readable medium, a system, and method, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1 and 9 - 10 recite acquiring pain related information regarding pain of a patient, acquiring pain prediction information of the patient, generating pain treatment assistance information for the patient based on the acquired pain prediction information, and outputting the generated pain treatment assistance information.
Independent claim 11 recites acquiring pain related information regarding pain of a plurality of patients and pain prediction information of the plurality of patients, and outputs pain prediction information of the patient.
The respective dependent claims 2 – 8 and 12, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that “present invention is to provide a non-transient computer-readable medium, an information processing device, an information processing method, and a learning model generation method that can assist an appropriate pain treatment” (paragraph 5 of the published specification). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a need “to perform an accurate pain treatment according to the patient” (paragraph 4 of the published specification). This problem is addressed by “include[ing] at least one of recommended prescription information and recommended treatment information used to assist a doctor who examines the patient” (paragraph 11 of the published specification). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claim 1: “A non-transient computer readable medium comprising a computer program that causes a computer to execute processing”, “inputting the acquired pain related information into a learning model that outputs pain prediction information”
Claims 2, 4, 6 - 7: “non-transient computer readable medium”
Claim 3: “computer program”, “the computer program causes the computer to execute processing”, “learning model”
Claim 5: “non-transient computer readable medium”, “the computer program causes the computer to execute processing”, “learning model”
Claim 8: “non-transient computer readable medium”, “the computer program causes the computer to execute processing”
Claim 9: “control unit programmed to execute steps”, “inputting the acquired pain related information into a learning model that outputs pain prediction information”
Claim 10: “information processing method”, “inputting the acquired pain related information into a learning model that outputs pain prediction information”
Claims 11 – 12: “learning model generation”, “training data”, “learning model”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, 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.
Hence, the 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. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B
The claims do 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, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The published specification supports this conclusion as follows:
[0039] The patient terminal device 10 is a terminal device carried or held by a patient, and the assistant terminal device 30 is a terminal device carried or held by an assistant such as a family member or a caregiver of the patient. The caregiver may be a family member or a caregiver at a care provider. The patient terminal device 10 and the assistant terminal device 30 can include a smartphone, a tablet, a personal computer, or the like including a display panel, an operation panel, a microphone, a speaker, or the like. In the patient terminal device 10 and the assistant terminal device 30, an app (application) for using the pain treatment assistance system is introduced. For example, a patient who is suffering from cancer or the like and periodically visits a medical institution while being treated at home is assumed. However, the patient is not limited to such a patient. Note that the app may be a Web app or a downloaded app.
[0041] The doctor terminal device 40 is a terminal device used by a doctor in a medical institution or the like. The doctor terminal device 40 can include a personal computer or the like including a display panel, an operation panel, or the like. A medical worker such as a public health nurse or a nurse may use the doctor terminal device 40 under guidance of the doctor. An application (for example, Web app or the like) for using the pain treatment assistance system is introduced in the doctor terminal device 40.
[0044] The control unit 51 may be configured by incorporating a required number of central processing units (CPUs), micro-processing units (MPUs), graphics processing units (GPUs), or the like. The control unit 51 can execute processing defined by the computer program 60. That is, the processing executed by the control unit 51 corresponds to processing executed in accordance with the computer program 60. The control unit 51 can execute functions of the assistance information generation unit 58, by executing the computer program 60. The assistance information generation unit 58 may include hardware or software or may be implemented by a combination of hardware and software. The control unit 51 can execute processing using the learning model 61. Note that the control unit 51 has functions as a first acquisition unit, a second acquisition unit, a generation unit, and an output unit.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2 – 8 and 12 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 – 4, 6, and 8 – 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rezai et al., herein after Rezai (U.S. Publication Number 2023/0277122 A1) in view of Manteau-Rao et al., herein after Manteau-Rao (U.S. Publication Number 2020/0268324 A1).
Claim 1. Rezai teaches a non-transient computer readable medium (paragraph 40 discloses a non-transitory computer readable medium) comprising a computer program that causes a computer to execute processing comprising:
acquiring pain related information regarding pain of a patient (paragraph 23 discloses a plurality of portable monitoring devices that include sensors for monitoring systems tracking pain-relevant parameters for the user);
inputting the acquired pain related information into a learning model that outputs pain prediction information (paragraph 22 discloses a "predictive model" is a mathematical model or machine learning model that either predicts a future state of a parameter or estimates a current state of a parameter that cannot be directly measured), and acquiring, from the learning model, pain prediction information of the patient (paragraph 47 discloses the pain-related parameters predicted by the constituent models can include measured parameters such as heart rate and heart rate variability as well as symptoms such as sleep disruption and reduced activity).
Rezai fails to explicitly teach the following limitations met by Manteau-Rao as cited.
generating pain treatment assistance information for the patient based on the acquired pain prediction information (paragraph 45 discloses a treatment module having a recommendation module; paragraph 51 discloses the recommendation module of the treatment module is configured to determine a recommended dosage for the patient based on the migraine forecast prediction and/or the weighted input data. The recommendation module may implement supervised or unsupervised machine learning or other artificial intelligence to determine the recommended dosage, e.g., based on an individual patient's medication use patterns during migraine episodes); and
outputting the generated pain treatment assistance information (paragraph 52 discloses the recommendation module is also configured to transmit, via the network, the recommended dosage data to the patient device, causing the patient device to display the recommended dosage on the display in the patient application; paragraph 55 discloses the treatment module may determine which therapeutic content (i.e. recommended dosage data, dosage instruction) to deliver to the patient device, indicating outputting the treatment information to the patient).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Rezai to further include systems and methods implementing digital therapeutics for the treatment of symptoms associated with serious medical conditions, such as migraines as disclosed by Manteau-Rao.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Rezai in this way to include obtaining a plurality of data from (i) first sensors associated with a patient electronic device, (ii) second sensors associated with the patient, (iii) the patient via the patient electronic device, and (iv) a remote server, generating a migraine forecast prediction for the patient, and determining a recommended dosage of a migraine-treating medication for the patient based on the migraine forecast prediction (Manteau-Rao: paragraph 75).
Claim 2. Rezai and Manteau-Rao teach the non-transient computer readable medium according to claim 1. Rezai discloses wherein: the pain related information includes at least one of an evaluation index used to evaluate pain, vital data, medication information, and a physical condition (paragraph 19 discloses an index; paragraph 32 discloses evaluating pain can include the user's clinical state, the user's medical history (which would include medication data and vital data), employment information, and residential status; paragraph 40 discloses the computed index can be compared with self-reported pain and mood of the user to identify pain catastrophization).
Claim 3. Rezai and Manteau-Rao teach the computer program according to claim 1. Rezai teaches wherein the computer program causes the computer to execute processing further comprising: acquiring medical information regarding medical care of the patient (paragraph 23 discloses a plurality of portable monitoring devices that include sensors for monitoring systems tracking pain-relevant parameters for the user); and
inputting the acquired medical information into the learning model that outputs the pain prediction information (paragraph 22 discloses a "predictive model" is a mathematical model or machine learning model that either predicts a future state of a parameter or estimates a current state of a parameter that cannot be directly measured), and acquiring, from the learning model, the pain prediction information of the patient (paragraph 47 discloses the pain-related parameters predicted by the constituent models can include measured parameters such as heart rate and heart rate variability as well as symptoms such as sleep disruption and reduced activity).
Claim 4. Rezai and Manteau-Rao teach the non-transient computer readable medium according to claim 3. Rezai discloses wherein: the medical information includes at least one of diagnosis information, examination information, treatment information, prescription information, and disease prognosis prediction information (paragraph 48 discloses the prediction model can be used to facilitate a feedback strategy to the subject, participant, health care provider, care team, and other entities to facilitate the diagnosis of chronic pain, management of pain, return to work and function, and to improve the response to therapies for pain including medications).
Claim 6. Rezai and Manteau-Rao teach the non-transient computer readable medium according to claim 1.
Rezai fails to explicitly teach the following limitations met by Manteau-Rao as cited.
wherein: the pain treatment assistance information includes at least one of recommended prescription information and recommended treatment information used to assist a doctor who examines the patient paragraph 51 discloses the recommendation module of the treatment module is configured to determine a recommended dosage for the patient based on the migraine forecast prediction and/or the weighted input data. The recommendation module may implement supervised or unsupervised machine learning or other artificial intelligence to determine the recommended dosage, e.g., based on an individual patient's medication use patterns during migraine episodes).
The motivation to combine the teachings of Rezai and Manteau-Rao is discussed in the rejection of claim 1, and incorporated herein.
Claim 8. Rezai and Manteau-Rao teach the non-transient computer readable medium according to claim 1. Rezai discloses wherein the computer program causes the computer to execute processing further comprising: outputting a pain prediction pattern indicating a temporal transition of a degree of pain based on the pain prediction information of the patient (paragraph 47 discloses the predictive model can include a constituent model that predicts future values for pain-related parameters, such as a convolutional neural network that is provided with one or more two-dimensional arrays of wavelet transform coefficients as an input. The wavelet coefficients detect changes not only in time, but also in temporal patterns, and can thus reflect changes in the ordinary biological rhythms of the user).
Claim 9. Rezai teaches a control unit programmed to execute steps (paragraph 82 discloses the processing unit executes one or more computer executable instructions originating from the system memory and the memory devices) comprising:
acquiring pain related information regarding pain of a patient (paragraph 23 discloses a plurality of portable monitoring devices that include sensors for monitoring systems tracking pain-relevant parameters for the user);
inputting the acquired pain related information into a learning model that outputs pain prediction information (paragraph 22 discloses a "predictive model" is a mathematical model or machine learning model that either predicts a future state of a parameter or estimates a current state of a parameter that cannot be directly measured), and acquiring, from the learning model, pain prediction information of the patient (paragraph 47 discloses the pain-related parameters predicted by the constituent models can include measured parameters such as heart rate and heart rate variability as well as symptoms such as sleep disruption and reduced activity).
Rezai fails to explicitly teach the following limitations met by Manteau-Rao as cited.
generating pain treatment assistance information for the patient based on the acquired pain prediction information (paragraph 45 discloses a treatment module having a recommendation module; paragraph 51 discloses the recommendation module of the treatment module is configured to determine a recommended dosage for the patient based on the migraine forecast prediction and/or the weighted input data. The recommendation module may implement supervised or unsupervised machine learning or other artificial intelligence to determine the recommended dosage, e.g., based on an individual patient's medication use patterns during migraine episodes); and
outputting the generated pain treatment assistance information (paragraph 52 discloses the recommendation module is also configured to transmit, via the network, the recommended dosage data to the patient device, causing the patient device to display the recommended dosage on the display in the patient application; paragraph 55 discloses the treatment module may determine which therapeutic content (i.e. recommended dosage data, dosage instruction) to deliver to the patient device, indicating outputting the treatment information to the patient).
The motivation to combine the teachings of Rezai and Manteau-Rao is discussed in the rejection of claim 1, and incorporated herein.
Claim 10. Rezai teaches an information processing method (paragraph 82 discloses the processing unit executes one or more computer executable instructions originating from the system memory and the memory devices) comprising:
acquiring pain related information regarding pain of a patient (paragraph 23 discloses a plurality of portable monitoring devices that include sensors for monitoring systems tracking pain-relevant parameters for the user);
inputting the acquired pain related information into a learning model that outputs pain prediction information (paragraph 22 discloses a "predictive model" is a mathematical model or machine learning model that either predicts a future state of a parameter or estimates a current state of a parameter that cannot be directly measured), and acquiring, from the learning model, pain prediction information of the patient (paragraph 47 discloses the pain-related parameters predicted by the constituent models can include measured parameters such as heart rate and heart rate variability as well as symptoms such as sleep disruption and reduced activity).
Rezai fails to explicitly teach the following limitations met by Manteau-Rao as cited.
generating pain treatment assistance information for the patient based on the acquired pain prediction information (paragraph 45 discloses a treatment module having a recommendation module; paragraph 51 discloses the recommendation module of the treatment module is configured to determine a recommended dosage for the patient based on the migraine forecast prediction and/or the weighted input data. The recommendation module may implement supervised or unsupervised machine learning or other artificial intelligence to determine the recommended dosage, e.g., based on an individual patient's medication use patterns during migraine episodes); and
outputting the generated pain treatment assistance information (paragraph 52 discloses the recommendation module is also configured to transmit, via the network, the recommended dosage data to the patient device, causing the patient device to display the recommended dosage on the display in the patient application; paragraph 55 discloses the treatment module may determine which therapeutic content (i.e. recommended dosage data, dosage instruction) to deliver to the patient device, indicating outputting the treatment information to the patient).
The motivation to combine the teachings of Rezai and Manteau-Rao is discussed in the rejection of claim 1, and incorporated herein.
Claim 11. Rezai teaches a learning model generation method (Rezai: paragraph 22 discloses a "predictive model" is a mathematical model or machine learning model that either predicts a future state of a parameter or estimates a current state of a parameter that cannot be directly measured) comprising:
acquiring first training data including pain related information regarding pain of a plurality of patients (paragraph 41 discloses the training process of given classifiers; paragraph 48 discloses data can also be used to group the patient with patients who respond similarly to these parameters, with data fed back from patients within a given group used to better tailor the model to the patient, indicating a plurality of patients; paragraph 72 discloses a first pain-relevant parameter representing the user is monitored at an in-vivo sensing device over a defined period to produce a time series for the first pain-relevant parameter; .and pain prediction information of the plurality of patients).
Rezai fails to explicitly teach the following limitations met by Manteau-Rao as cited.
generating a learning model that receives pain related information of a patient (paragraph 22 discloses a "predictive model" is a mathematical model or machine learning model that either predicts a future state of a parameter or estimates a current state of a parameter that cannot be directly measured; paragraph 50 discloses the patient and/or the HCP may train the machine learning or other artificial intelligence); and
outputs pain prediction information of the patient based on the acquired first training data (paragraph 52 discloses the recommendation module is also configured to transmit, via the network, the recommended dosage data to the patient device, causing the patient device to display the recommended dosage on the display in the patient application; paragraph 55 discloses the treatment module may determine which therapeutic content (i.e. recommended dosage data, dosage instruction) to deliver to the patient device, indicating outputting the treatment information to the patient).
The motivation to combine the teachings of Rezai and Manteau-Rao is discussed in the rejection of claim 1, and incorporated herein.
Claim(s) 5 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rezai et al., herein after Rezai (U.S. Publication Number 2023/0277122 A1) in view of Manteau-Rao et al., herein after Manteau-Rao (U.S. Publication Number 2020/0268324 A1) further in view of Wager et al., herein after Wager (U.S. Publication Number 2016/0054409 A1).
Claim 5. Rezai and Manteau-Rao teach the non-transient computer readable medium according to claim 1.
Rezai and Manteau-Rao fail to explicitly teach the following limitations met by Wager as cited:
wherein the computer program causes the computer to execute processing further comprising:
acquiring literature information regarding a pain treatment (paragraph 82 discloses the signature development analysis consisted of feature selection: Voxels within an a priori mask of pain-related brain regions was selected based on prior literature and Machine learning: LASSO-PCR was run using those maps to predict pain reports; paragraph 83 discloses studies were included f they mentioned the word “pain” more than one time in 1000 words); and
inputting the acquired literature information into the learning model that outputs the pain prediction information (paragraph 82 discloses the signature development analysis consisted of feature selection: Voxels within an a priori mask of pain-related brain regions was selected based on prior literature and Machine learning: LASSO-PCR was run using those maps to predict pain reports), and
acquiring, from the learning model, the pain prediction information of the patient (paragraph 82 discloses LASSO-PCR was run using those maps to predict pain reports).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Rezai and Manteau-Rao to further include detecting pain in a subject by determining a neurological signature of physical pain as disclosed by Wager.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Rezai and Manteau-Rao in this way to include approaches to pain assessment that are sensitive and specific to physical pain and can provide objective measurements of pain (Wager: paragraph 6).
Claim 12. Rezai and Manteau-Rao teach the learning model generation method (Rezai: paragraph 22 discloses a "predictive model" is a mathematical model or machine learning model that either predicts a future state of a parameter or estimates a current state of a parameter that cannot be directly measured) according to claim 11, further comprising:
acquiring second training data further including medical information of the plurality of patients and the pain prediction information of the plurality of patients (paragraph 41 discloses the training process of given classifiers; paragraph 73 discloses a value for a second pain-relevant parameter for the user is obtained at first and second times in the defined period from the user via a portable computing device to provide respective first and second values for the second pain-relevant parameter).
Rezai fails to explicitly teach the following limitations met by Manteau-Rao as cited.
generating the learning model (paragraph 22 discloses a "predictive model" is a mathematical model or machine learning model that either predicts a future state of a parameter or estimates a current state of a parameter that cannot be directly measured; paragraph 50 discloses the patient and/or the HCP may train the machine learning or other artificial intelligence) so as to output the pain prediction information based on the acquired second training data (paragraph 52 discloses the recommendation module is also configured to transmit, via the network, the recommended dosage data to the patient device, causing the patient device to display the recommended dosage on the display in the patient application; paragraph 55 discloses the treatment module may determine which therapeutic content (i.e. recommended dosage data, dosage instruction) to deliver to the patient device, indicating outputting the treatment information to the patient).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Rezai to further include systems and methods implementing digital therapeutics for the treatment of symptoms associated with serious medical conditions, such as migraines as disclosed by Manteau-Rao.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Rezai in this way to include obtaining a plurality of data from (i) first sensors associated with a patient electronic device, (ii) second sensors associated with the patient, (iii) the patient via the patient electronic device, and (iv) a remote server, generating a migraine forecast prediction for the patient, and determining a recommended dosage of a migraine-treating medication for the patient based on the migraine forecast prediction (Manteau-Rao: paragraph 75).
Rezai and Manteau-Rao fail to explicitly teach the following limitations met by Wager as cited:
literature information regarding a pain treatment (paragraph 82 discloses the signature development analysis consisted of feature selection: Voxels within an a priori mask of pain-related brain regions was selected based on prior literature and Machine learning: LASSO-PCR was run using those maps to predict pain reports; paragraph 83 discloses studies were included f they mentioned the word “pain” more than one time in 1000 words).
The motivation to combine the teachings of Rezai, Manteau-Rao, and Wager is discussed in the rejection of claim 5, and incorporated herein.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rezai et al., herein after Rezai (U.S. Publication Number 2023/0277122 A1) in view of Manteau-Rao et al., herein after Manteau-Rao (U.S. Publication Number 2020/0268324 A1) further in view of Lotsch et al., herein after Lotsch (Lötsch J, Sipilä R, Tasmuth T, Kringel D, Estlander AM, Meretoja T, Kalso E, Ultsch A. Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy. Breast Cancer Res Treat. 2018 Sep;171(2):399-411. doi: 10.1007/s10549-018-4841-8. Epub 2018 Jun 6. PMID: 29876695; PMCID: PMC6096884).
Claim 7. Rezai and Manteau-Rao teach the non-transient computer readable medium according to claim 1.
Rezai and Manteau-Rao fail to explicitly teach the following limitations met by Lotsch as cited:
wherein: the pain treatment assistance information includes a coping method for assisting the patient (page 408, column 2, paragraph 2 discloses the level of psychological wellbeing at 6 months predicted persistent pain at 3 years, suggesting that better psychological coping after the acute phase might associate with lower risk of pain persistence. It might therefore be important to include routine assessment of psychological coping at this time point and initiate appropriate interventions as needed).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Rezai and Manteau-Rao to further include machine learning derived classifiers to predict the absence of pain after breast cancer surgery with high accuracy as disclosed by Lotsch.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Rezai and Manteau-Rao in this way to include identifying parameters from the data acquired before surgery and up to the sixth month after surgery, which could predict the presence or absence of persisting pain in the area operated on during the 3-year follow-up available (Wager: page 402, column 1, paragraph 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wasan et al. (U.S. Publication Number 2022/0328198 A1) discloses systems and methods are described for training a treatment prediction model using patient profiles.
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KRISTINE K. RAPILLO
Examiner
Art Unit 3626
/KRISTINE K RAPILLO/Examiner, Art Unit 3682