CTNF 18/016,507 CTNF 101744 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Status Claims 1-12 are currently pending and under examination herein. Claims 1-12 are rejected. Priority The instant application claims priority as a 371 to PCT/KR2021/006850 filed 02 June 2021 and foreign priority to KR10-2020-0086957 filed 14 July 2020. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. In this action, claims 1-12 are examined as though they had an effective filing date of 14 July 2020. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s). Information Disclosure Statement The information disclosure statement (IDS) submitted on 17 January 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Drawings The drawings filed on 17 January 2023 are accepted. 07-30-03-h AIA Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: an anesthetic state information calculation unit configured to calculate in claim 1 a policy model training unit configured to set target anesthetic state information pre-set in claim 1 a prediction model training unit configured to generate a prediction model in claim 1 a control unit configured to set the drug injection rate in claim 1 a prediction unit configured to predict expected anesthetic state information in claim 1 Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The units are being interpreted as a generic computing device, such as a computer, based on the structure shown in Figure 1 (no other structure was found within the disclosure). The algorithm information for the anesthetic state information calculation unit was found on Page 6, Paragraph 91-93. The algorithm information for the policy model training unit was found on Pages 6-7, Paragraphs 94-104. The algorithm information for the prediction model training unit was found on Page 7, Paragraphs 105-106. The algorithm information for the control unit was found on Page 7, Paragraphs 107-109. The algorithm information for the prediction unit was found on Page 7, Paragraphs 110-111. The pages and paragraphs cited for the algorithm information utilized the published disclosure of the application (US 20230293099 A1). If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f). Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. Claim 5-6 and 11-12 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. Claim 5 recites “based on a plurality of compensation values calculated from changes in different pieces of anesthetic state information, from arbitrary anesthetic state information”. It is unclear whether these phrases are modifying the calculating the expected value or some other part of the claim. The metes and bounds of the claim are therefore unclear, rendering the claim indefinite. For the purpose of examination this in interpreted to describe the inputs of the calculation. Claim 11 recites the same limitation and contains the same issue. Claims 6 and 12 depend on Claims 5 and 11, and thus contain the above issues due to said dependence. This rejection can be overcome by amending the claim to make it clear what specifically the plurality of compensation values and arbitrary anesthetic state information are referring to. Claim 6 recites “generate the policy model according to a drug injection rate in a change in the anesthetic state information”. It is unclear “in a change” indicates. The metes and bounds of this limitation is unclear, rendering the claim indefinite. Claim 12 recites the same limitation and thus contains the same issue. For the purpose of examination “in” is being interpreted as “related to”. This rejection can be overcome by amending the claim to make it clear what specifically “in a change” means. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 an abstract idea and a natural law without significantly more. In accordance with MPEP 2106, claims found to recite statutory subject matter ( Step 1 : YES ) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea or natural law ( Step 2A, Prong 1 ). Claims 1-6 are directed to a system and Claims 7-12 are directed to a method. In the instant application, the claims recite the following limitations that equate to an abstract idea or natural law: Claim 1 recites the limitation - calculate anesthetic state information of a patient; and calculate a compensation value according to a change in the anesthetic state information due to a drug injection rate set so that the anesthetic state information follows the target anesthetic state information. Based on the broadest reasonable interpretation, the calculations could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 1 also recites set target anesthetic state information pre-set for the anesthetic state information; generate a policy model by learning the compensation value; generate a prediction model, by learning the change in the anesthetic state information according to the drug injection rate; set the drug injection rate from the anesthetic state information, based on the policy model; and predict expected anesthetic state information from the set drug injection rate and a previously set drug injection rate, based on the prediction model. Based on the broadest reasonable interpretation, setting the information, generating the models, and predicting the information could practically be done by the human mind. This draws the limitations to a mental process, which classifies the limitation as an abstract idea. Additionally, the limitation predict expected anesthetic state information from the set drug injection rate and a previously set drug injection rate, based on the prediction model includes a natural correlation between the amount of an administered drug in the body and its effect. Therefore, the limitation is drawn to a natural law. Claim 2 recites the limitation - wherein the anesthetic state information calculation unit is configured to calculate an effect-site concentration and a plasma concentration based on the patient's fat-free mass and a drug model pre-provided for a drug injected into the patient, and calculate the anesthetic state information to indicate the patient's condition according to the effect-site concentration and the plasma concentration. Based on the broadest reasonable interpretation, the calculations could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitation calculate the anesthetic state information to indicate the patient's condition according to the effect-site concentration and the plasma concentration includes a natural correlation between the amount of an administered drug in the body and its effect. Therefore, the limitation is drawn to a natural law. Claim 3 recites the limitation - wherein the control unit is configured to, according to a difference between the anesthetic state information calculated from the patient's condition and the target anesthetic state information, an injection rate of remifentanil injected into the patient during a pre-set time interval, and an injection rate of propofol injected into the patient during the pre-set time interval, control the injection rate of remifentanil and the injection rate of propofol. Based on the broadest reasonable interpretation, the calculating could include equations and could practically be done by the human mind and the rate controlling could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 4 recites the limitation - wherein the policy model training unit is configured to calculate a compensation value according to the difference between the target anesthetic state information and the anesthetic state information calculated from the patient's condition changed after the pre-set time interval elapses, after the injection rate of remifentanil and the injection rate of propofol are controlled. Based on the broadest reasonable interpretation, the calculation could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 5 recites the limitation - wherein the policy model training unit is configured to, based on a plurality of compensation values calculated from changes in different pieces of anesthetic state information, from arbitrary anesthetic state information, calculate an expected value according to a plurality of compensation values matched to changes in anesthetic state information in a process where a time interval pre-set for a change in the anesthetic state information elapses several times. Based on the broadest reasonable interpretation, the calculations could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 6 recites the limitation - wherein the policy model training unit is configured to generate the policy model according to a drug injection rate in a change in the anesthetic state information matched to a compensation value selected so that the expected value is calculated as a maximum value. Based on the broadest reasonable interpretation, the calculation could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 7 recites the limitation - calculating anesthetic state information of a patient; and calculating a compensation value according to a change in the anesthetic state information due to a drug injection rate set so that the anesthetic state information follows the target anesthetic state information. Based on the broadest reasonable interpretation, the calculations could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 7 also recites setting target anesthetic state information pre-set for the anesthetic state information; generating a policy model by learning the compensation value; generating a prediction model, by learning the change in the anesthetic state information according to the drug injection rate; setting the drug injection rate from the anesthetic state information, based on the policy model; and predicting expected anesthetic state information from the set drug injection rate and a previously set drug injection rate, based on the prediction model. Based on the broadest reasonable interpretation, setting the information, generating the models, and predicting the information could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Additionally, the limitation predicting expected anesthetic state information from the set drug injection rate and a previously set drug injection rate, based on the prediction model describes a natural correlation between the amount of an administered drug in the body and its effect. Therefore, the limitation is drawn to a natural law. Claim 8 recites the limitation - wherein the calculating of the anesthetic state information comprises calculating an effect-site concentration and a plasma concentration based on the patient's fat-free mass and a drug model pre-provided for a drug injected into the patient, and calculating the anesthetic state information to indicate the patient's condition according to the effect-site concentration and the plasma concentration. Based on the broadest reasonable interpretation, the calculations could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Additionally, the limitation calculating the anesthetic state information to indicate the patient's condition according to the effect-site concentration and the plasma concentration relies on a natural correlation between the amount of an administered drug in the body and its effect. Therefore, the limitation is drawn to a natural law. Claim 9 recites the limitation - wherein the setting of the drug injection rate comprises, according to a difference between the anesthetic state information calculated from the patient's condition and the target anesthetic state information, an injection rate of remifentanil injected into the patient during a pre-set time interval, and an injection rate of propofol injected into the patient during the pre-set time interval, controlling the injection rate of remifentanil and the injection rate of propofol. Based on the broadest reasonable interpretation, the calculation could include equations and could practically be done by the human mind and the rate controlling could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 10 recites the limitation - wherein the generating of the policy model comprises calculating a compensation value according to the difference between the target anesthetic state information and the anesthetic state information calculated from the patient's condition changed after the pre-set time interval elapses, after the injection rate of remifentanil and the injection rate of propofol are controlled. Based on the broadest reasonable interpretation, the calculation could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 11 recites the limitation - wherein the generating of the policy model comprises, based on a plurality of compensation values calculated from changes in different pieces of anesthetic state information, from arbitrary anesthetic state information, calculating an expected value, according to the plurality of compensation values matched to changes in anesthetic state information in a process where a time interval pre-set for a change in the anesthetic state information elapses several times. Based on the broadest reasonable interpretation, the calculations could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 12 recites the limitation - wherein the generating of the policy model comprises generating the policy model according to a drug injection rate in a change in the anesthetic state information matched to a compensation value selected so that the expected value is calculated as a maximum value. Based on the broadest reasonable interpretation, the calculation could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. These limitations recite concepts of calculating values, setting or controlling or predicting information, and generating models that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, the limitations describe natural correlations between the concentration of a drug in the body and its effect on the body. This is similar to a correlation between the presence of myeloperoxidase in a bodily sample (such as blood or plasma) and cardiovascular disease risk ( Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017)) that the courts have identified as a law of nature. As such, claims 1-12 recite an abstract idea and law of nature ( Step 2A, Prong 1: YES ). Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not ( Step 2A, Prong 2 ). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP 2106.04(d)(1)) or a particular treatment (MPEP 2106.04(d)(2)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements: Claim 1 recites an anesthetic state information calculation unit; a policy model training unit; a prediction model training unit; a control unit; and a prediction unit. There are no limitations that indicate that the claimed calculating values, setting or controlling or predicting information, and generating models require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-12 are directed to an abstract idea and natural law ( Step 2A, Prong 2 : NO ). Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself ( Step 2B ). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities. As discussed above, there are no additional limitations to indicate that the claimed calculating values, setting or controlling or predicting information, and generating models require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea or natural law using a generic computer do not render an abstract idea or natural law eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Ilyas et al. (2017, BioMed Research International, Vol. 2017: 1-12) teaches the use of devices/computers for monitoring, predicting, and controlling anesthesia is well understood, routine, and conventional (Page 10, Column 1, Paragraph 3: General surgical procedures executed in well-equipped operation theatre are inclining towards automated drug delivery systems replacing manual anesthetics infusion. Recent research work on automated drug infusion signifies the importance of nonlinear and robust control strategies as compared to linear control schemes because they can cope well with nonlinearities and uncertainties occurring in natural phenomenon ; Page 7, Column 2, Paragraph 2: Ethicon Endo-Surgery Inc. introduced the first Computer-Assisted Personalized Sedation (CAPS) system named SEDASYS which is used for automating the administration of anesthesia to relatively healthy patients during colonoscopies. It also measures the oxygen saturation, blood pressure, capnometry, respiration, electrocardiography, patient responsiveness, and heart rate of the sedated patients ). The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No ). As such, Claims 1-12 are not patent eligible. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-4 and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez-Cava et al. (2020, Biomedical Signal Processing and Control, Vol. 59: 1-9), in view of Mertens et al. (2003, British Journal of Anaesthesia, Vol 90, No. 2: 132-141). Italicized text from reference art . The applicable claims include: Claim 1. A drug injection control device comprising: i. an anesthetic state information calculation unit configured to calculate anesthetic state information of a patient; ii. a policy model training unit configured to set target anesthetic state information pre-set for the anesthetic state information, calculate a compensation value according to a change in the anesthetic state information due to a drug injection rate set so that the anesthetic state information follows the target anesthetic state information, and generate a policy model by learning the compensation value; iii. a prediction model training unit configured to generate a prediction model, by learning the change in the anesthetic state information according to the drug injection rate; iv. a control unit configured to set the drug injection rate from the anesthetic state information, based on the policy model; and v. a prediction unit configured to predict expected anesthetic state information from the set drug injection rate and a previously set drug injection rate, based on the prediction model. Claim 2. The drug injection control device according to claim 1, wherein the anesthetic state information calculation unit is configured to i. calculate an effect-site concentration and a plasma concentration based on the patient's fat-free mass and a drug model pre-provided for a drug injected into the patient, and ii. calculate the anesthetic state information to indicate the patient's condition according to the effect-site concentration and the plasma concentration. Claim 3. The drug injection control device according to claim 1, wherein the control unit is configured to, according to a difference between the anesthetic state information calculated from the patient's condition and the target anesthetic state information, an injection rate of remifentanil injected into the patient during a pre-set time interval, and an injection rate of propofol injected into the patient during the pre-set time interval, control the injection rate of remifentanil and the injection rate of propofol. Claim 4. The drug injection control device according to claim 3, wherein the policy model training unit is configured to calculate a compensation value according to the difference between the target anesthetic state information and the anesthetic state information calculated from the patient's condition changed after the pre-set time interval elapses, after the injection rate of remifentanil and the injection rate of propofol are controlled. Claim 7. A drug injection control method using a drug injection control device using reinforcement learning, the drug injection control method comprising: i. calculating anesthetic state information of a patient; ii. setting target anesthetic state information pre-set for the anesthetic state information, calculating a compensation value according to a change in the anesthetic state information due to a drug injection rate set so that the anesthetic state information follows the target anesthetic state information, and generating a policy model by learning the compensation value; iii. generating a prediction model, by learning the change in the anesthetic state information according to the drug injection rate; iv. setting the drug injection rate from the anesthetic state information, based on the policy model; and v. predicting expected anesthetic state information from the set drug injection rate and a previously set drug injection rate, based on the prediction model. Claim 8. The drug injection control method according to claim 7, wherein the calculating of the anesthetic state information comprises i. calculating an effect-site concentration and a plasma concentration based on the patient's fat-free mass and a drug model pre-provided for a drug injected into the patient, and ii. calculating the anesthetic state information to indicate the patient's condition according to the effect-site concentration and the plasma concentration. Claim 9. The drug injection control method according to claim 7, wherein the setting of the drug injection rate comprises, according to a difference between the anesthetic state information calculated from the patient's condition and the target anesthetic state information, an injection rate of remifentanil injected into the patient during a pre-set time interval, and an injection rate of propofol injected into the patient during the pre-set time interval, controlling the injection rate of remifentanil and the injection rate of propofol. Claim 10. The drug injection control method according to claim 9, wherein the generating of the policy model comprises calculating a compensation value according to the difference between the target anesthetic state information and the anesthetic state information calculated from the patient's condition changed after the pre-set time interval elapses, after the injection rate of remifentanil and the injection rate of propofol are controlled. Regarding Claims 1 and 7 , Gonzalez-Cava et al. teach (Claim 1.i) calculate anesthetic state information of a patient (Page 5, Column 1, Paragraph 1: For assessing the hypnotic level of the patients, a BIS A2000 VISTA monitor was used. A BIS noninvasive Quatro four-electrode sensor was applied to the patient’s forehead to collect the raw EEG data during the surgery ; Page 3, Column 2, Paragraph 2: The variables in (7) can be interpreted as the ones presented in (6), in which BIS is now the clinical variable to be modelled (see equation 7 for a BIS formula)). Gonzalez-Cava et al. also teach (Claim 1.ii) set target anesthetic state information pre-set for the anesthetic state information (Page 1, Column 2, Paragraph 2: BIS values between 40 and 60 are recommended for general anesthesia ; Page 5, Column 1, Paragraph 1: The anesthesiologist varied the propofol infusion rate manually to reach a BIS target of 50 ), calculate a compensation value according to a change in the anesthetic state information due to a drug injection rate set so that the anesthetic state information follows the target anesthetic state information (Page 4, Column 2, Paragraph 2: The induction phase includes data from the beginning of the drug infusion to the instant at which the BIS of the patient reaches 50 (i.e. when the target is reached). The BIS 0 parameter in equation 7 is computed in this phase as the mean of the BIS values before the induction. This stage provides the first parameterization of the model before starting the predictions during the maintenance phase (i.e. generating the initial values of the variables in equation 7 during model parameterization is synonymous to generating a compensation value), and generate a policy model by learning the compensation value. Generating the parametrized model of equation 7 (see above in Claim 1.ii) is interpreted to be synonymous with the generation of the policy model (i.e. learning the value). Gonzalez-Cava et al. also teach (Claim 1.iii) generate a prediction model, by learning the change in the anesthetic state information according to the drug injection rate (Page 4, Column 1, Paragraph 3: The variable time delay introduced by the monitor is identified for the model characterization. Thus, the parameters to be determined during the optimization are equation 8. Where p and r represent propofol and remifentanil, respectively, and L represents the time delay introduced by the BIS monitor. The goal is to minimize the error between the real evolution of the BIS observed during clinical practice and the BIS evolution predicted by the model ; Page 4, Column 2, Paragraph 3: Once the maintenance phase starts, the algorithm predicts the evolution of the BIS considering the model previously identified. This model is kept until the values of the predicted BIS, F([Symbol font/0x71], u p , u r ) (u p = propofol infusion rate, u r = remifentanil infusion rate) , and the real BIS from the clinical data differ by>10% for 5 min ). The prediction model is interpreted to be synonymous with the parameterized algorithm generated during the maintenance phase. Gonzalez-Cava et al. also teach (Claim 1.iv) relate the drug injection rate from the anesthetic state information, based on the policy model (Page 5, Column 1, Paragraph 1: A BIS noninvasive Quatro four-electrode sensor was applied to the patient’s forehead to collect the raw EEG data during the surgery. The anesthesiologist varied the propofol infusion rate manually to reach a BIS target of 50 ; Page 5, Column 2, Paragraph 2: During the surgery, the BIS, propofol infusion rate and remifentanil infusion rate were automatically registered every 5 s using software. All the data recorded during the surgeries were saved as mat files for the analysis ). Gonzalez-Cava et al. also teach (Claim 1.v) predict expected anesthetic state information from the set drug injection rate and a previously set drug injection rate, based on the prediction model (Page 4, Column 2, Paragraph 3: Here, BIS p5 and BIS 5 represent the predicted and real BIS values, respectively, for the last 5 min. If error > 10%, the identification algorithm is run again, considering the more recent information of the clinical data for [Symbol font/0x71] identification. Thus, an updated model for BIS prediction is obtained. The data considered for the new identification of the model include (at least) the evolution of the BIS for the last 5 min. If no change in the infusion rates has been observed in the last 5 min, the algorithm increases the time range considered until the last variation of any of the drugs (remifentanil and propofol infusion rates) is included in the identification ). Additionally, Gonzalez-Cava et al. teach their methods are conducted by computer (Page 5, Column 2, Paragraph 2: During the surgery, the BIS, propofol infusion rate, and remifentanil infusion rate were automatically registered every 5 s using software. An application for this was previously developed in MATLAB. A laptop ran the application during the surgeries. The BIS monitor, as well as both infusion pumps, were connected to the laptop via RS232 interfaces. Finally, all the data recorded during the surgeries were saved for the analysis ), which read on the units (anesthetic state information calculation unit, policy model training unit, prediction model training unit, a control unit, prediction unit) recited in claim 1 (see Claim Interpretation: 112(F)). Claim 7 recites the limitations of claim 1 directed to a method. Regarding Claims 2 and 8 , Gonzalez-Cava et al. teach (Claim 2.i) calculate an effect-site concentration and a plasma concentration based on the patient's fat-free mass and a drug model pre-provided for a drug injected into the patient (Page 2, Column 2, Paragraph 3: Pharmacokinetic models quantitatively describe the effects of the drug dose in the plasmatic concentration of the different compartments that belong to the compartmental model. The effect concentration of the drug on a clinical variable, Ec, can be virtually represented by an additional virtual effect compartment ; Page 3, Column 2, Paragraph 2: The variables in equation 7 can be interpreted as the ones presented in equation 6, in which BIS is now the clinical variable to be modelled. In this study, E cr and E cp are obtained from the Minto and Schnider models, respectively, according to the values presented in Table 1 ; Page 3, Column 1, Paragraph 4: For remifentanil modelling, the Minto model has become popular, as itis applicable to a wide range of patient characteristics, such as age, gender, and lean body mass (LBM) ). Fat-free mass is interpreted to be equivalent to lean body mass. Gonzalez-Cava et al. also teach (Claim 2.ii) calculate the anesthetic state information to indicate the patient's condition according to the effect-site concentration and the plasma concentration (Page 2, Column 2, Paragraph 3: Pharmacokinetic models quantitatively describe the effects of the drug dose in the plasmatic concentration of the different compartments that belong to the compartmental model. The effect concentration of the drug on a clinical variable, Ec, can be virtually represented by an additional virtual effect compartment ). BIS is calculated using equation 7 (Page 3, Column 2) which contains variables for the effect site concertation (Ecr and Ecp). These values are calculated using equations 2-5 (Page 3, Column 1,) using the plasma concentration (Page 3, Column 1, Paragraph 2: Considering the volume of each compartment, the evolution of the concentrations in the different compartments can be mathematically modelled using equations 2-5 ). Claim 8 recites the limitations of claim 2 directed to a method. Regarding Claim 4 and 10 , Gonzalez-Cava et al. teach calculate a compensation value according to the difference between the target anesthetic state information and the anesthetic state information calculated from the patient's condition changed after the pre-set time interval elapses, after the injection rate of remifentanil and the injection rate of propofol are controlled (Page 4, Column 2, Paragraph 3: Once the maintenance phase starts (i.e., post injection of the drugs ), the algorithm predicts the evolution of the BIS considering the model previously identified. This model is kept until the values of the predicted BIS, F([Symbol font/0x71], u p , u r ), and the real BIS from the clinical data differ by>10% for 5 min. If error > 10%, the identification algorithm is run again, considering the more recent information of the clinical data for [Symbol font/0x71] identification. Thus, an updated model for BIS prediction is obtained ). Calculating a compensation value is interpreted to be synonymous with updating the parameters of the model. Claim 10 recites the limitations of claim 4 directed to a method. Gonzalez-Cava et al. does not teach set the drug injection rate from the anesthetic state information (Claims 1.iv and 7.iv). Gonzalez-Cava et al. also does not teach according to a difference between the anesthetic state information calculated from the patient's condition and the target anesthetic state information, an injection rate of remifentanil injected into the patient during a pre-set time interval, and an injection rate of propofol injected into the patient during the pre-set time interval, control the injection rate of remifentanil and the injection rate of propofol (Claims 3 and 9). Regarding Claims 1 and 7 , Mertens et al. teach (Claim 1.iv) set the drug injection rate from the anesthetic state information, based on a model (Page 133, Column 2, Paragraph 2: Whereas the target propofol concentration was maintained constant during the entire surgical procedure, the target remifentanil concentration was changed in response to the presence or absence of signs of inadequate anaesthesia ; Page 133, Column 2, Paragraph 1: A palm-top computer was provided with three-compartment pharmacokinetic data for remifentanil to control a syringe pump for the infusion of remifentanil using the algorithm ). It would be obvious to swap the model of Mertens et al. with the models of Gonzalez-Cava et al. (see reason to combine). It would also be obvious to control the rate of both drugs given the models of Gonzalez-Cava et al. consider the interaction between the drugs (Page 6, Column 2, Paragraph 3: The proposed model structure together with an optimization-based identification algorithm led to adaptive models capable of dealing with patient variabilities, propofol–remifentanil interactions, and the variable time delay introduced by the BIS monitor ). Claim 7 recites the limitations of claim 1 directed to a method. Regarding Claims 3 and 9 , Mertens et al. teach according to a difference between the anesthetic state information calculated from the patient's condition and the target anesthetic state information, an injection rate of remifentanil injected into the patient during a pre-set time interval, and an injection rate of propofol injected into the patient during the pre-set time interval, control the injection rate of remifentanil and the injection rate of propofol (Page 133, Column 2, Paragraph 2: Whereas the target propofol concentration was maintained constant during the entire surgical procedure, the target remifentanil concentration was changed in response to the presence or absence of signs of inadequate anaesthesia ; Page 133, Column 2, Paragraph 1: A palm-top computer was provided with three-compartment pharmacokinetic data for remifentanil to control a syringe pump for the infusion of remifentanil using the algorithm described by Hull. The same computer, provided with three-compartment pharmacokinetic data for propofol, was used to control another syringe pump for the infusion of propofol. The control algorithm checked and adjusted the infusion rates every 5 s. ). It would be obvious to swap the model of Mertens et al. with the models of Gonzalez-Cava et al. which incorporate with difference between BSIs (see reason to combine). It would also be obvious to control the rate of both drugs given the models of Gonzalez-Cava et al. consider the interaction between the drugs (see regarding Claim 1.iv above). Claim 9 recites the limitations of claim 3 directed to a method. It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Mertens et al. with Gonzalez-Cava et al. because Gonzalez-Cava et al. teach a novel and adaptable model to meet patient needs within the context of administering anesthesia, which is a major focus of Mertens et al. (Page 2, Column 1, Paragraph 4: The main objective of the present study was to develop an identification algorithm based on optimization techniques to model the hypnotic level of patients in propofol–remifentanil anesthesia. The final structure is based on a parametric pharmacokinetic–pharmacodynamic model that is capable of dealing with (i) interpatient and intrapatient variabilities (through an iterative identification algorithm to adapt the model parameters), (ii) propofol–remifentanil interactions, and (iii) variable time delays introduced by the BIS monitor. To the best of our knowledge, this is the first strategy in which all the potential influencing factors are included in a model simultaneously ). Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the methods from the references indicated above. Furthermore, one of ordinary skill in the art would predict that the methods taught by Gonzalez-Cava et al. could be readily added to the method of Mertens et al. with a reasonable expectation of success because both are within the same technical field - modeling and predicting the effects of anesthesia on a patient utilizing similar input data. Accordingly, Claims 1-4 and 7-10 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103 . 07-21-aia AIA Claim s 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez-Cava et al., as applied to Claims 1-4 and 7-10 above, in view of Mertens et al., as applied to Claims 1-4 and 7-10 above, and in further view of Padmanabhan et al. (2015, Biomedical Signal Processing and Control, Vol. 22: 54-64). Italicized text from reference art . The applicable claims include: Claims 1-4 and 7-10 are presented above Claim 5. The drug injection control device according to claim 1, wherein the policy model training unit is configured to, based on a plurality of compensation values calculated from changes in different pieces of anesthetic state information, from arbitrary anesthetic state information, calculate an expected value according to a plurality of compensation values matched to changes in anesthetic state information in a process where a time interval pre-set for a change in the anesthetic state information elapses several times. Claim 6. The drug injection control device according to claim 5, wherein the policy model training unit is configured to generate the policy model according to a drug injection rate in a change in the anesthetic state information matched to a compensation value selected so that the expected value is calculated as a maximum value. Claim 11. The drug injection control method according to claim 7, wherein the generating of the policy model comprises, based on a plurality of compensation values calculated from changes in different pieces of anesthetic state information, from arbitrary anesthetic state information, calculating an expected value, according to the plurality of compensation values matched to changes in anesthetic state information in a process where a time interval pre-set for a change in the anesthetic state information elapses several times. Claim 12. The drug injection control method according to claim 11, wherein the generating of the policy model comprises generating the policy model according to a drug injection rate in a change in the anesthetic state information matched to a compensation value selected so that the expected value is calculated as a maximum value. Regarding Claims 1-4 and 7-10, these are taught by Gonzalez-Cava et al. and Mertens et al. above Regarding Claims 5 and 11, Padmanabhan et al. teach based on a plurality of compensation values calculated from changes in different pieces of anesthetic state information, from arbitrary anesthetic state information, calculate an expected value according to a plurality of compensation values matched to changes in anesthetic state information in a process where a time interval pre-set for a change in the anesthetic state information elapses several times (Page 56, Column 1, Paragraph 4: The Q-learning algorithm can train an agent to control the states of the Reinforcement learning (RL) environment without knowledge of the system state x(t), t ≥ 0; only the data measured along the system trajectories at time steps is required. Specifically, at each time step k, the agent observes the system to determine the current the current state from the set of states and selects an action from the action sequence. In response, the system stochastically transitions to a new state with a numerical reward. The agent seeks to maximize the reward it receives over an infinite horizon. A common objective is to choose each action so as to maximize the expected value of the discounted return given by equation 4, where E[.] denotes expectation ). The calculating an expected value according to values calculated for optimizing an algorithm (i.e., compensation values) over multiple time steps (when t > 1) (i.e., where a time interval pre-set for a change in the anesthetic state information elapses several times) utilizes BIS and mean arterial pressure (MAP) (i.e., changes in the anesthetic state information) (see Figure 2, page 57). The model functioning through stochastic transition is interpreted to be synonymous with the implementation of arbitrary data. Additionally, it would be obvious to implement the reinforming leaning approach utilized by Padmanabhan et al. with the modeling of Gonzalez-Cava et al. (see reason to combine). Claim 11 recites the limitations of claim 5 directed to a method. Regarding Claim 6 and 12 , Padmanabhan et al. teach generate the policy model according to a drug injection rate in a change in the anesthetic state information matched to a compensation value selected so that the expected value is calculated as a maximum value (Page 56, Column 2, Paragraph 1: A common objective is to choose each action ak so as to maximize the expected value of the discounted return, given by equation 4 ). See Regarding Claims 5 and 11 for relationship of the cited section to anesthetic state information matched to a compensation values. Padmanabhan et al. teach this is related to a drug injection rate (Page 60, Column 1, Paragraph 2: After the learning process identifies the best control policy as the learned Q table, that is, the best sequence of infusion rates required for each state to reach the desired goal, the performance of the learned agent is evaluated over individual patients ). Claim 12 recites the limitations of claim 6 directed to a method. It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to modify Mertens et al. and Gonzalez-Cava et al. with Padmanabhan et al. because Padmanabhan et al. teach their methods of reinforcement learning (RL) are well adapted for anesthesia control due its ability to adapt to unforeseen circumstances (Page 55, Column 1, Paragraph 4: since the controller (RL agent) design is performed by interacting with the system, unknown and time-varying dynamics as well as changing performance requirements can be accounted for by the controller. RL exploits the computational efficiency and speed of digital computers to stochastically employ all possible control actions and assesses a best or optimal action ; Page 55, Column 2, Paragraph 1: In this study, RL demonstrated patient specific control of anesthesia administration marked by improved control accuracy as compared to performance metrics of other studies reported in the literature ). Therefore, it would have been obvious to someone of ordinary skill in the art at the time of the effective filling date to combine the methods from the references indicated above. Furthermore, one of ordinary skill in the art would predict that the methods taught by Padmanabhan et al. could be readily added to the methods of Mertens et al. and Gonzalez-Cava et al. with a reasonable expectation of success because both are within the same technical field - modeling and predicting the effects of anesthesia on a patient utilizing similar input data. Accordingly, Claims 1-12 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103. Double Patenting No double patenting was identified. Conclusion No Claims are allowed. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BLAKE H ELKINS whose telephone number is (571)272-2649. The examiner can normally be reached Monday-Friday 8-5PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /B.H.E./Examiner, Art Unit 1687 /Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687 Application/Control Number: 18/016,507 Page 2 Art Unit: 1687 Application/Control Number: 18/016,507 Page 3 Art Unit: 1687 Application/Control Number: 18/016,507 Page 4 Art Unit: 1687 Application/Control Number: 18/016,507 Page 5 Art Unit: 1687 Application/Control Number: 18/016,507 Page 6 Art Unit: 1687 Application/Control Number: 18/016,507 Page 7 Art Unit: 1687 Application/Control Number: 18/016,507 Page 8 Art Unit: 1687 Application/Control Number: 18/016,507 Page 9 Art Unit: 1687 Application/Control Number: 18/016,507 Page 10 Art Unit: 1687 Application/Control Number: 18/016,507 Page 11 Art Unit: 1687 Application/Control Number: 18/016,507 Page 12 Art Unit: 1687 Application/Control Number: 18/016,507 Page 13 Art Unit: 1687 Application/Control Number: 18/016,507 Page 14 Art Unit: 1687 Application/Control Number: 18/016,507 Page 15 Art Unit: 1687 Application/Control Number: 18/016,507 Page 16 Art Unit: 1687 Application/Control Number: 18/016,507 Page 17 Art Unit: 1687 Application/Control Number: 18/016,507 Page 18 Art Unit: 1687 Application/Control Number: 18/016,507 Page 19 Art Unit: 1687 Application/Control Number: 18/016,507 Page 20 Art Unit: 1687 Application/Control Number: 18/016,507 Page 21 Art Unit: 1687 Application/Control Number: 18/016,507 Page 22 Art Unit: 1687 Application/Control Number: 18/016,507 Page 23 Art Unit: 1687 Application/Control Number: 18/016,507 Page 24 Art Unit: 1687 Application/Control Number: 18/016,507 Page 25 Art Unit: 1687 Application/Control Number: 18/016,507 Page 26 Art Unit: 1687 Application/Control Number: 18/016,507 Page 27 Art Unit: 1687 Application/Control Number: 18/016,507 Page 28 Art Unit: 1687 Application/Control Number: 18/016,507 Page 29 Art Unit: 1687 Application/Control Number: 18/016,507 Page 30 Art Unit: 1687
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