Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
Claims 1-17 are currently pending in this case and have been examined and addressed below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/23/2025, 01/29/2025, and 02/02/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 – 17 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.
Step 1: Claims 1-13 and 14 are drawn to a machine. Claims 15 and 16-17 is drawn to a process. As such, claims 1-17 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Independent Claim 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.
Independent Claim 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.
Independent Claim 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.
Independent Claim 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.
(Examiner notes: The above claim terms underlined are additional elements that fall under Step 2A - Prong Two analysis section detailed below)
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people). Therefore, acquiring pain related information regarding pain of a patient, inputting the acquired pain related information to output a pain evaluation result, acquiring the pain evaluation result of the patient, generating coping information for coping with the pain based in the acquired pain evaluation result, outputting the generated coping information, acquiring first training data including pain related information regarding pain of a plurality of patients and pain evaluation results of the plurality of patients, receive pain related information of a patient and outputs a pain evaluation result of the patient based on the acquired first training data are directed to managing personal interactions or personal behavior.
The dependent claim 2 is directed to the pain related information includes at least one of an evaluation index used to evaluate pain, vital data, medication information, and a physical condition.
The dependent claim 3 is directed to acquiring medical information regarding medical care of the patient; inputting the acquired medical information to output the pain evaluation result, and acquiring the pain evaluation result of the patient.
The dependent claim 4 is directed to the medical information includes at least one of diagnosis information, examination information, treatment information, prescription information, and disease prognosis prediction information
The dependent claim 5 is directed to acquiring literature information regarding a pain treatment, inputting the acquired literature information to output the pain evaluation result, and acquiring the pain evaluation result of the patient.
The dependent claim 6 is directed to the pain evaluation result includes a pain progress of the patient from the past to the future.
The dependent claim 7 is directed to receiving a situation of the patient as a result of performing the coping information and recommending examination by a doctor according to the received situation.
The dependent claim 8 is directed to receiving a situation of the pain of the patient as a result of performing a pain treatment based on the coping information, correcting the pain evaluation result based on the received situation of the pain, and using the corrected pain evaluation result.
The dependent claim 9 is directed to receiving an operation for correcting the coping information generated based on the pain evaluation result of the patient.
The dependent claim 10 is directed to outputting a question generated based on the pain evaluation result of the patient and outputting coping information according to an answer of the patient to the question.
The dependent claim 11 is directed to receiving consultation from the patient and outputting the coping information according to the received consultation.
The dependent claim 12 is directed to receiving arrangement of medicine or contracting a doctor, together with the coping information.
The dependent claim 13 is directed to controlling automatic administration of medicine based on the pain evaluation result of the patient.
The dependent claim 17 is directed to 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 output the pain evaluation result based on the acquired second training data.
Each of these steps of the preceding dependent claims 2-13 and 17 only serve to further limit or specify the features of independent claims 1 and 14-16 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
As such, the Examiner concludes that the preceding claims recite an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.”
Claims 1-13 recite the use of a non-transient computer readable medium comprising a computer program that causes a computer to execute processing, in this case to acquiring pain related information regarding pain of a patient; inputting the acquired pain related information to get an output of pain evaluation result, acquiring 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; the pain related information includes at least one of an evaluation index used to evaluate pain, vital data, medication information, and a physical condition; acquiring medical information regarding medical care of the patient; and inputting the acquired medical information to get an output of the pain evaluation result, and acquiring the pain evaluation result of the patient; the medical information includes at least one of diagnosis information, examination information, treatment information, prescription information, and disease prognosis prediction information; acquiring literature information regarding a pain treatment; and inputting the acquired literature information to output the pain evaluation result, acquiring the pain evaluation result of the patient, the pain evaluation result includes a pain progress of the patient from the past to the future; receiving a situation of the patient as a result of performing the coping information; and recommending examination by a doctor according to the received situation; receiving a situation of the pain of the patient as a result of performing a pain treatment based on the coping information; correcting the pain evaluation result based on the received situation of the pain; receiving an operation for correcting the coping information generated based on the pain evaluation result of the patient; outputting a question generated based on the pain evaluation result of the patient and outputting coping information according to an answer of the patient to the question; receiving consultation from the patient; and outputting the coping information according to the received consultation; receiving arrangement of medicine or contacting a doctor, together with the coping information; controlling automatic administration of medicine based on the pain evaluation result of the patient, only recites the non-transient computer readable medium comprising a computer program that causes a computer to execute processing as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claims 1 and 14-17 recite the use of a learning model, only as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 8 recites the use of a relearning the learning model, only as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 9 recites the use of a doctor terminal device, only as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claim 10-11 recites the use of a patient terminal device, only as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claim 13 recites the use of a medicine injection pump, only as being used in its ordinary capacity and is merely a tool to execute the abstract idea (MPEP § 2106.05(f)(2).
Claim 14 recites the use of a control unit, in this case to acquiring pain related information regarding pain of a patient; inputting the acquired pain related information to get an output of pain evaluation result, acquiring 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, only recites the control unit as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception.
As discussed above in “Step 2A – Prong 2”, the identified additional elements, such as the non-transient computer readable medium comprising a computer program that causes a computer to execute processing, learning model, relearning the learning model, doctor terminal device, patient terminal device, medicine injection pump, and control unit in independent claims 1, and 14-16, and dependent claims 2-13 and 17 are equivalent to adding the words “apply it” on a generic computer. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”). Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements are directed to generic computer component and functions being used to perform the abstract idea.
Applicant’s own disclosure in paragraphs [0010] and [0012] acknowledges that 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…and… The doctor terminal device 40 can include a personal computer, a tablet, or the like including a display panel, an operation panel, or the like”. Paragraphs [0014-0015] disclose that the “computer program 60 may be downloaded from an external device via the communication unit 52 and stored in the storage unit 59. Furthermore, the computer program 60 recorded in a recording medium (for example, optically readable disk storage medium such as CD- ROM) may be read by a recording medium reading unit and stored in the storage unit 59, or the computer program 60 may be read by the recording medium reading unit and developed in the memory 53… 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”. Additionally, paragraph [0033] discloses the “learning model 61 can use, for example, a support vector machine (SVM), a decision tree, a random forest, an adaboost, a neural network (for example, recurrent neural network (RNN), long short term memory (LSTM), a transformer, or the like), or the like". Furthermore, the specification acknowledges that paragraph [0065] discloses “Medicine 00 is administrated from pump”.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-17 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6 and 10-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lin et al.( US-20190362843-A1)[hereinafter Lin].
As per Claim 1, Lin discloses a non-transient computer readable medium comprising a computer program that causes a computer to execute processing in paragraph [0023] (one or more computer readable mediums that causes a computer to execute processing) comprising: acquiring pain related information regarding pain of a patient in paragraph [0025] (acquiring a measure of pain (synonymous to 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 in paragraphs [0025], [0028], [0032-0034], [0052], and claim 1 (input the measure of pain into an artificial intelligence model that outputs a pain evaluation result (Examiner notes that model capturing the pain perception of users and the relationships of the pain perception with the treatments indicates the model outputting a pain evaluation result)); generating coping information for coping with the pain based on the acquired pain evaluation result in paragraphs [0019], [0025], [0028], [0036], and claim 1 (generating personalized treatment (synonymous to coping information) for pain mitigation based on the pain perception); and outputting the generated coping information in paragraphs [0019] and [0025] (providing the generated personalized treatment).
As per Claim 2, Lin discloses the non-transient computer readable medium according to claim 1, Lin also 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 in paragraphs [0032-0034] (a measure of pain includes vital signs, medication information, and a physical condition (Examiner notes that a measure of pain indicates an evaluation index used to measure pain)).
As per Claim 3, Lin discloses the non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising: Lin also discloses acquiring medical information regarding medical care of the patient in paragraphs [0030-0031] (acquiring medical data regarding medical care of the user); 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 in paragraph [0031] (inputting the medical data into the artificial intelligence model that outputs a pain evaluation result).
As per Claim 4, Lin discloses the non-transient computer readable medium according to claim 3, Lin also discloses wherein: the medical information includes at least one of diagnosis information, examination information, treatment information, prescription information, and disease prognosis prediction information in paragraphs [0030-0031] (the medical data includes medical diagnoses, medical reports, medical therapies, medicine prescription (Examiner notes that the medical diagnoses, medical reports, medical therapies, and medicine prescription meet the "at least one of" limitation)).
As per Claim 5, Lin discloses the non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising: Lin also discloses acquiring literature information regarding a pain treatment in paragraph [0051] (acquiring data related to treatments (synonymous to literature information regarding pain treatment), wherein the data can be related to many users of a geographical region); 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 in paragraphs [0025], [0028], [0032-0034], [0049], [0051-0053], and claim 1 (input the data statistics related to treatments into an artificial intelligence model that outputs a pain evaluation result).
As per Claim 6, Lin discloses the non-transient computer readable medium according to claim 1, Lin also discloses wherein: the pain evaluation result includes a pain progress of the patient from the past to the future in paragraphs [0025], [0033], [0084] (the pain evaluation result includes temporal tracking (synonymous to pain progress of the patient from the past to the future)).
As per Claim 10, Lin discloses the non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising: Lin also discloses outputting a question generated based on the pain evaluation result of the patient to a patient terminal device in paragraphs [0041], [0050], [0069-0071], and Figure 3 (asking the user a question based on the pain evaluation result of the user on the user device (synonymous to a patient terminal device)); and outputting coping information to the patient terminal device according to an answer of the patient to the question in paragraphs [0041], [0069-0072], and Figure 3 (outputting suggested treatment to the user device according to the answer of the user to the question).
As per Claim 11, Lin discloses the non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising: Lin also discloses receiving consultation from the patient in paragraphs [0041], [0069-0071], and Figure 3 (receiving feedback from the patient); and outputting the coping information to a patient terminal device according to the received consultation in paragraphs [0041], [0069-0072], and Figure 3 (outputting the updated suggested treatment to the user device based on the feedback).
As per Claim 12, Lin discloses the non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising: Lin also discloses receiving arrangement of medicine or contacting a doctor, together with the coping information in paragraphs [0026-0029] (receiving medicine or notifying a medical professional (synonymous to a doctor), with receiving personalized treatment).
As per Claim 13, Lin discloses the non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising: Lin also discloses controlling automatic administration of medicine in a medicine injection pump based on the pain evaluation result of the patient in paragraphs [0020], [0026], and [0028] (controlling automatic delivery of a determined treatment, wherein the treatment includes medicine, using the treatment component (synonymous to a medicine injection pump) based on the pain perception of the user (Examiner notes that the treatment can include an intrathecal pump that can be used to deliver medication)).
As per Claim 14, Lin discloses a control unit programmed to execute steps in paragraphs [0023-0024] (a processor to perform the operations) comprising: acquiring pain related information regarding pain of a patient in paragraph [0025] (acquiring a measure of pain (synonymous to 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 in paragraphs [0025], [0028], [0032-0034], [0052], and claim 1 (input the measure of pain into a model that outputs a pain evaluation result (Examiner notes that model capturing the pain perception of users and the relationships of the pain perception with the treatments indicates the model outputting a pain evaluation result)); generating coping information for coping with the pain based on the acquired pain evaluation result in paragraphs [0019], [0025], [0028], [0036], and claim 1 (generating personalized treatment (synonymous to coping information) for pain mitigation based on the pain perception); and outputting the generated coping information in paragraphs [0019] and [0025] (providing the generated personalized treatment).
As per Claim 15, Lin discloses an information processing method in paragraphs [0022-0023] (an information processing method) comprising: acquiring pain related information regarding pain of a patient in paragraph [0025] (acquiring a measure of pain (synonymous to 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 in paragraphs [0025], [0028], [0032-0034], [0052], and claim 1 (input the measure of pain into a model that outputs a pain evaluation result (Examiner notes that model capturing the pain perception of users and the relationships of the pain perception with the treatments indicates the model outputting a pain evaluation result)); generating coping information for coping with the pain based on the acquired pain evaluation result in paragraphs [0019], [0025], [0028], [0036], and claim 1 (generating personalized treatment (synonymous to coping information) for pain mitigation based on the pain perception); and outputting the generated coping information in paragraphs [0019] and [0025] (providing the generated personalized treatment).
As per Claim 16, Lin discloses a learning model generation method in paragraphs [0049] and [0051-0052] (an artificial intelligence 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 in paragraphs [0032-0034], [0049], and [0051-0052] (obtain pooled statistics for many users, wherein the pooled statistics include pain measurements, pain evaluation results, and treatments of the many users (synonymous to first training data)); 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 in paragraphs [0025], [0028], [0032-0034], [0049], [0051-0053], [0059], and claim 1 (generating an artificial intelligence model that receives the measure of pain of an user and outputs a pain evaluation result based on the pooled statistics (Examiner notes that model capturing the pain perception of users and the relationships of the pain perception with the treatments indicates the model outputting a pain evaluation result)).
As per Claim 17, Lin discloses the learning model generation method according to claim 16, further comprising: Lin also discloses 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 in paragraph [0051] (obtain pooled statistics for many users, wherein the pooled statistics include medical records, data related to treatments (synonymous to literature information regarding pain treatment), and pain evaluation of the many users (synonymous to second training data)); and generating the learning model so as to output the pain evaluation result based on the acquired second training data in paragraphs [0025], [0028], [0032-0034], [0049], [0051-0053], [0059], and claim 1 (generating the artificial intelligence model to output a pain evaluation outputs a pain evaluation result based on the pooled statistics).
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.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lin et al.( US-20190362843-A1)[hereinafter Lin], in view of Saenz (US-20180160904-A1)[hereinafter Saenz].
As per Claim 7, Lin discloses the non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising.
Lin does not disclose the following limitations. However, Saenz discloses receiving a situation of the patient as a result of performing the coping information in paragraphs [0056-0057] and Figure 2 (the user will provide feedback after performing the recommended treatment); and recommending examination by a doctor according to the received situation in paragraphs [0056-0057] and Figure 2 (recommending the user for an in-office visit with a medical professional if there has not been any improvement).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a non-transient computer readable medium including a computer program to acquire pain related information, input the acquired pain related information into a learning model that outputs a pain evaluation result, generating coping information and outputting the generated coping information, as disclosed by Lin, to be combined with receiving a situation of the patient as a result of performing the coping information and recommending examination by a doctor according to the received situation, as disclosed by Saenz, for the purpose of determining and implementing a diagnosis and treatment efficiently and practically [0002-0009].
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Lin et al.( US-20190362843-A1)[hereinafter Lin], in view of REZAI (US-20230277122-A1)[hereinafter Rezai].
As per Claim 8, Lin discloses the non-transient computer readable medium according to claim 1, Lin also discloses wherein the computer program causes the computer to execute processing further comprising: receiving a situation of the pain of the patient as a result of performing a pain treatment based on the coping information in paragraphs [0038], [0040-0041] (receiving that the user is not responsive to further treatment and experiencing a pain that exceeds a threshold); and relearning the learning model in paragraphs [0025], [0075-0076] and [0087] (implementing a closed-loop adaptive model).
Lin discloses receiving a situation of the pain of the patient as a result of performing a pain treatment based on the coping information and the concept of relearning the learning model, but does not disclose correcting the pain evaluation result based on the received situation of the pain and relearning the learning model using the corrected pain evaluation result. However, Rezai discloses receiving a situation of the pain of the patient as a result of performing a pain treatment based on the coping information in paragraphs [0040] and [0048-0051] and [0075] (receive a pain level of the user after receiving pain treatment); correcting the pain evaluation result based on the received situation of the pain in paragraphs [0048-0051] (adjust the current pain level based on the accuracy of predictions in regards to pain level increasing or decreasing); and relearning the learning model using the corrected pain evaluation result in paragraphs [0048-0051] and [0075] (refine the predictive model using the adjusted pain level).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a non-transient computer readable medium including a computer program to acquire pain related information, input the acquired pain related information into a learning model that outputs a pain evaluation result, generating coping information and outputting the generated coping information, as disclosed by Lin, to be combined with receiving a situation of the pain of the patient as a result of performing a pain treatment based on the coping information, correcting the pain evaluation result based on the received situation of the pain, and relearning the learning model using the corrected pain evaluation result, as disclosed by Rezai, for the purpose of limiting complications in diagnosis and management of conditions and disorders that present chronic pain [0003].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Lin et al.( US-20190362843-A1)[hereinafter Lin], in view of Mason et al.( US-20210366587-A1)[hereinafter Mason].
As per Claim 9, Lin discloses the non-transient computer readable medium according to claim 1, wherein the computer program causes the computer to execute processing further comprising: Lin also discloses receiving an operation for correcting the coping information generated based on the pain evaluation result of the patient in paragraphs [0038], [0040-0041] (adapt the treatment based on the feedback measured from the user).
Lin discloses receiving an operation for correcting the coping information generated based on the pain evaluation result of the patient, but does not disclose the concept being done from a doctor terminal device. However, Mason discloses receiving an operation for correcting the coping information generated based on the pain evaluation result of the patient from a doctor terminal device in paragraphs [0039-0042] and [0071] (receiving an operation to modify the treatment plan based on the pain level exceeding a threshold level from the artificial intelligence engine (synonymous to a doctor terminal device) (Examiner notes that the specification defines a healthcare professional to include an artificial intelligent entity including a software program, integrated software and hardware or hardware alone)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a non-transient computer readable medium including a computer program to acquire pain related information, input the acquired pain related information into a learning model that outputs a pain evaluation result, generating coping information and outputting the generated coping information, as disclosed by Lin, to be combined with receiving an operation for correcting the coping information generated based on the pain evaluation result of the patient from a doctor terminal device, as disclosed by Mason, for the purpose of avoiding inefficiencies and inaccuracies in the treatment plan selection process [0032].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bargshady et al. (“Enhanced deep learning algorithm development to detect pain intensity from facial expression images”) (2020) teaches a deep neural network algorithm to detect pain intensity effectively.
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/K.N.W./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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