DETAILED ACTION
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 .
Claims 1-12 have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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:
Claim 1
a processor, … configured to:
use a plurality of machine learning models …;
perform a prediction process …; and
input the prediction data …
Claim 3
the processor is configured to:
input prescription information …
Claim 4
the processor is configured to:
input the reliability degree …
Claim 5
the processor is configured to:
input a feature amount …
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph (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) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “the machine learning model” 3 times in lines 11-14. Claim 3 recites “the machine learning model” in line 4. Claim 4 recites “the machine learning model” twice in lines 2 and 6. Claim 5 recites “the machine learning model” in line 4. There is insufficient antecedent basis for this limitation. While line 4 recites “a plurality of machine learning models,” a recitation of a particular, singular machine learning model is not present. For the purpose of further examination this limitation will be interpreted as “the machine learning models.”
Claims 5 and 9-10 recite the limitation "the protein" in lines 3, 3 and 2, respectively. There is insufficient antecedent basis for this limitation. For the purpose of further examination the limitation will be interpreted as “a protein.”
Claims 2-10 are each rejected for carrying the limitations of a rejected base claim.
Claims 11-12 include limitations similar to claim 1 and are rejected for the same reasons indicated above.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 5 and 7-12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5 and 8-11 of copending Application No. 18618765 in view of U.S. Patent Application Publication 20220293223 by Ren et al. (“Ren”) and U.S. Patent Application Publication 20210183522 by Lintereur et al. (“Lintereur”).
Regarding claim 1, 18618765 claims:
1. A pharmaceutical support device comprising: a processor, wherein the processor is configured to: 18618765 claim 1: “A pharmaceutical support device comprising: a processor, wherein the processor is configured to:”
use a … machine learning [model] that output prediction data indicating preservation stability of a candidate preservation solution, which is a candidate for a preservation solution for a biopharmaceutical, … 18618765 claim 1: “a prescription of a candidate preservation solution that is a candidate for a preservation solution for a biopharmaceutical … confirming preservation stability of the candidate preservation solution … use a machine learning model that outputs prediction data.”
18618765 does not expressly claim a plurality of machine learning models. However, this is taught by Ren. See Ren, Fig. 3, elements 128-1, 128-2 and 128-3, depicting prediction models. Also see ¶ 0018, “train machine learning models, and use those models to predict viscosity and/or other protein formulation properties based on different sets of formulation descriptors (including product molecule/protein descriptors, excipient descriptors, and/or solution descriptors).” Also ¶ 0031, “Generally, however, models 126, 128 may collectively include one or more types of models.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to Ren’s models with the prediction data of 18618765 in order to assist in predicting a relatively broad range of protein formulation property values as suggested by Ren (see ¶ 0035).
… at a future time point and that are provided for a plurality of types of the preservation stability, respectively; 18618765 claim 1: “use a machine learning model that outputs prediction data at a future time point of first measurement data, which is at least one type of the plurality of types of measurement data; and”
perform a prediction process of inputting prescription information related to a prescription of a candidate preservation solution to be predicted and measurement data obtained by actually measuring the preservation stability of a candidate preservation solution actually prepared to the machine learning model 18618765 claim 1: “acquire prescription information related to a prescription of a candidate preservation solution that is a candidate for a preservation solution for a biopharmaceutical containing a protein; acquire a plurality of types of measurement data actually measured in a test for confirming preservation stability of the candidate preservation solution; … input the prescription information, the first measurement data, and at least one type of second measurement data other than the first measurement data among the plurality of types of measurement data to the machine learning model.”
such that the prediction data is output from the machine learning model … 18618765 claim 1: “output the prediction data from the machine learning model.”
18618765 does not expressly claim:
output … in stages using the plurality of machine learning models; and input the prediction data obtained in the prediction process in a previous stage to the machine learning model in the prediction process in a subsequent stage. Lintereur teaches this. See Lintereur, ¶ 0138, “ Stacking works in two phases: multiple base classifiers are used to predict the class, and then a new learner is used to combine their predictions with the aim of reducing the generalization error.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Lintereur’s stacking with the output of 18618765 in order to obtain better predictive performance and reduce generalization error as suggested by Lintereur (see ¶ 0128 and 0138).
Regarding claim 2, 18618765 also claims:
2. The pharmaceutical support device according to claim 1, wherein the plurality of machine learning models provided for the plurality of types of preservation stability, respectively, are models corresponding to at least two of preservation stability of a protein included in the biopharmaceutical against aggregation, preservation stability of the protein against temperature, and preservation stability of the protein against temporal deterioration. 18618765 claim 4: “wherein the measurement data includes data for confirming preservation stability of the protein with respect to aggregation and data for confirming preservation stability of the protein with respect to a temperature.”
Regarding claim 3, 18618765 also claims:
3. The pharmaceutical support device according to claim 1, wherein the processor is configured to: input prescription information of the candidate preservation solution actually prepared to the machine learning model. 18618765 claim 1: “acquire a plurality of types of measurement data actually measured in a test for confirming preservation stability of the candidate preservation solution;”
Regarding claim 5, 18618765 also claims:
5. The pharmaceutical support device according to claim 1, wherein the processor is configured to: input a feature amount derived on the basis of protein information related to the protein included in the biopharmaceutical to the machine learning model. 18618765 claim 8: “wherein the processor is configured to also input a feature amount derived based on protein information related to the protein to the machine learning model.”
Regarding claim 7, 18618765 also claims:
7. The pharmaceutical support device according to claim 1, wherein the measurement data is time-series data measured at at least two time points. 18618765 claim 2: “wherein the measurement data is time-series data measured at least at two time points.”
Regarding claim 8, 18618765 also claims::
8. The pharmaceutical support device according to claim 1, wherein the prescription information related to the prescription of the candidate preservation solution to be predicted includes at least one of a type of each of a buffer solution, an additive, and a surfactant included in the candidate preservation solution, a concentration of each of the buffer solution, the additive, and the surfactant, or a hydrogen ion exponent of the candidate preservation solution. 18618765 claim 3: “wherein the prescription information includes at least one of a type of each of a buffer solution, an additive, or a surfactant contained in the candidate preservation solution, a concentration of each of the buffer solution, the additive, or the surfactant, or a hydrogen ion exponent of the candidate preservation solution.”
Regarding claim 9, 18618765 also claims:
9. The pharmaceutical support device according to claim 1, wherein the measurement data includes at least one of aggregation analysis data of sub-visible particles of the protein in the candidate preservation solution included in the biopharmaceutical, analysis data of the protein in the candidate preservation solution by a dynamic light scattering method, analysis data of the protein in the candidate preservation solution by size exclusion chromatography, or analysis data of the protein in the candidate preservation solution by differential scanning calorimetry. 18618765 claim 5: “wherein the measurement data is any of aggregation analysis data of a sub-visible particle of the protein in the candidate preservation solution, analysis data of the protein in the candidate preservation solution by a dynamic light scattering method, analysis data of the protein in the candidate preservation solution by size exclusion chromatography, or analysis data of the protein in the candidate preservation solution by differential scanning calorimetry.”
Regarding claim 10, 18618765 also claims:
10. The pharmaceutical support device according to claim 1, wherein the protein included in the biopharmaceutical is an antibody. 18618765, claim 9, “wherein the protein is an antibody.”
Regarding claim 11, 18618765 claims:
11. A method for operating a pharmaceutical support device, the method comprising: 18618765 claim 10: “An operation method of a pharmaceutical support device, comprising:”
Claim 11 is essentially similar to claim 1. 18618765 claim 10 is essentially similar to 18618765 claim 1. Further limitations of claim 11 are similar to those of 18618765 claim 10 in the same manner as indicated in the above rejection of claim 1.
Regarding claim 12, 18618765 claims::
12. A non-transitory computer-readable storage medium storing a program for operating a pharmaceutical support device, the program causing a computer to execute a process comprising: 18618765 claim 11: “A non-transitory computer-readable storage medium storing an operation program of a pharmaceutical support device causing a computer to execute a process comprising: …”
Claim 12 is essentially similar to claim 1. 18618765 claim 11 is essentially similar to 18618765 claim 1. Further limitations of claim 12 are similar to those of 18618765 claim 11 in the same manner as indicated in the above rejection of claim 1.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 4 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18618765 in view of Ren, Lintereur and U.S. Patent Application Publication 20190311807 by Anitha et al. (“Anitha”).
Regarding claim 4, parent claim 1 is addressed above.
18618765 does not expressly claim:
4. The pharmaceutical support device according to claim 1, wherein the machine learning model outputs a reliability degree of the prediction data together with the prediction data, and the processor is configured to: input the reliability degree obtained in the prediction process in the previous stage to the machine learning model in the prediction process in the subsequent stage.
This is taught by Anitha. See Anitha, ¶ 0019, “an ensemble arbitrator chooses a response out of the possible responses or a collection of responses that is best for the user or circumstance given a match or a mismatch between the possible responses, and a value and confidence each of the first, second, third, and fourth diagnosis engines expresses in its corresponding response. The ensemble arbitrator learns a weight to use for each possible response from each of the first, second, third, and fourth diagnosis engines based upon history.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Anitha’s confidence weight with the models of 18618765 in order to utilize the best match as suggested by Anitha.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 6 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 8 of copending Application No. 18618765 in view of Ren, Lintereur and cited art of record U.S. Patent Application Publication 20170239355 by Sharma et al. (“Sharma”).
Regarding claim 6, parent claim 5 is addressed above.
18618765 does not expressly claim:
6. The pharmaceutical support device according to claim 5, wherein the feature amount includes at least one of a solvent accessible surface area of the protein, a spatial aggregation propensity of the protein, a space charge map of the protein, or an indicator showing compatibility between the protein and an additive included in the candidate preservation solution.
This is taught by Sharma. See Sharma ¶ 0005, “Solvent-accessible surface area (SASA) is a measure of the surface area of a biomolecule (e.g. amino acid residue) that is accessible to a solvent. … SASA could therefore be a useful parameter for determining the suitability of including antioxidants in a given protein formulation.” Also ¶ 0059, “Instability may involve any one or more of: aggregation, deamidation (e.g. Asn deamidation), oxidation (e.g. Met oxidation and/or Trp oxidation), …” Also ¶ 0066, “A “solvent-accessible surface area” or “SASA” of a biomolecule in a solvent is the surface area of the biomolecule that is accessible to the solvent.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Sharma’s stability features with the model of 18618765 in order to determine suitability as suggested by Sharma (see at least ¶ 0005).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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.
Claims 1-3, 5, 7 and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over cited art of record U.S. Patent Application Publication 20220293223 by Ren et al. (“Ren”) in view of U.S. Patent Application Publication 20210183522 by Lintereur et al. (“Lintereur”).
Regarding claim 1, Ren discloses:
1. A pharmaceutical support device comprising: a processor, wherein the processor is configured to: See Ren, Fig. 2 elements 110 and 140 depicting processors.
use a plurality of machine learning models that output prediction data indicating preservation stability of a candidate preservation solution, which is a candidate for a preservation solution for a biopharmaceutical, at a future time point and … See Ren, Fig. 3, elements 128-1, 128-2 and 128-3, depicting prediction models. Also see ¶ 0001 “formulation development for protein-based biologics.” Also ¶ 0007, “protein formulation optimization for drug product development.” Also see ¶ 0018, “train machine learning models, and use those models to predict viscosity and/or other protein formulation properties based on different sets of formulation descriptors (including product molecule/protein descriptors, excipient descriptors, and/or solution descriptors).” Also ¶ 0031, “Generally, however, models 126, 128 may collectively include one or more types of models.” Also ¶ 0032, “… outputs a predicted protein formulation property (e.g., viscosity, or a stability metric such as a predicted size exclusion chromatography (SEC) peak percentage, etc.)” Also ¶ 0059, “A “stable” formulation is one in which the protein therein essentially retains its physical stability and/or chemical stability and/or biological activity upon storage.”
… that are provided for a plurality of types of the preservation stability, respectively; Ren, ¶ 0052, “a stability metric such as a size-exclusion chromatography (SEC) reading (e.g., SEC main peak percentage, SEC low molecular weight peak percentage, or SEC high molecular weight peak percentage, including absolute readings and the rate of change as a function of time or temperature).”
Even if Ren fails to fully disclose a plurality of models provided for a plurality of types of the preservation stability, respectively, this is taught by Lintereur. See Lintereur, ¶ 0138, “ Stacking works in two phases: multiple base classifiers are used to predict the class, and then a new learner is used to combine their predictions with the aim of reducing the generalization error.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Lintereur’s stacking with Ren’s stability metrics in order to obtain better predictive performance and reduce generalization error as suggested by Lintereur (see ¶ 0128 and 0138).
Ren also discloses:
perform a prediction process of inputting prescription information related to a prescription of a candidate preservation solution to be predicted and measurement data obtained by actually measuring the preservation stability of a candidate preservation solution actually prepared to the machine learning [models] … See Ren, ¶ 0047 and Fig. 6A depicting a GUI for input of formulation information. Also ¶ 0032, “By operating user input device 152, the user may enter a set of formulation descriptors (e.g., product molecule identifier or properties, pH level, concentration, excipient type or properties, etc.) via the GUI, or may upload a file that includes the set of formulation descriptors.” Also see ¶ 0037, “Conversely, each of prediction models 128-1 through 128-3 may be trained using training data sets that are specific to the value range of the corresponding group. For example, if group 206-1 corresponds to a viscosity value range of 0-20 cP, prediction model 128-1 may be trained using numerous training data sets that each include (1) a formulation descriptor set similar to descriptor set 204 (or a subset thereof that prediction model 128-1 operates upon), and (2) a label indicating the actual/historical measured value, falling within the range of 0 to 20 cP, for that formulation descriptor set.”
… such that the prediction data is output from the machine learning [models]… in stages using the plurality of machine learning models; and See Ren, Fig. 3, element 210, depicting prediction output. Also see Fig. 7, depicting staged output, along with ¶ 0007, “To increase the accuracy of a given prediction, a two-stage approach is used. For the first stage, a machine learning model classifies a set of formulation descriptors as one of a predetermined number of “groups,” with each group corresponding to a different range of likely or expected values of a given property (e.g., viscosity, or a chromatography-based stability metric, etc.). For the second stage, a second machine learning model predicts a value of the property (e.g., a viscosity value in units of centipoise, or cP).”
input the prediction data obtained in the prediction process in a previous stage to the machine learning [models] in the prediction process in a subsequent stage. As noted above, Ren discloses staged output (see Ren Fig. 7 and associated text). However, even if Ren fails to fully disclose staged output of respective models, Lintereur teaches this as also noted above. See Lintereur, ¶ 0138, “ Stacking works in two phases: multiple base classifiers are used to predict the class, and then a new learner is used to combine their predictions with the aim of reducing the generalization error.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Lintereur’s stacking with Ren’s stability metrics in order to obtain better predictive performance and reduce generalization error as suggested by Lintereur (see ¶ 0128 and 0138).
Regarding claim 2, Ren also discloses:
2. The pharmaceutical support device according to claim 1, wherein the plurality of machine learning models provided for the plurality of types of preservation stability, respectively, are models corresponding to at least two of preservation stability of a protein included in the biopharmaceutical against aggregation, preservation stability of the protein against temperature, and preservation stability of the protein against temporal deterioration. Ren ¶ 0040, “predict temporal/thermal changes in SEC and CEX (cation exchange chromatography) main peak percentage for formulation descriptor set 204.” Also see ¶ 0052, “The protein formulation property may be viscosity, for example, or a stability metric such as a size-exclusion chromatography (SEC) reading (e.g., SEC main peak percentage, SEC low molecular weight peak percentage, or SEC high molecular weight peak percentage, including absolute readings and the rate of change as a function of time or temperature) or a CEX reading, etc.”
Regarding claim 3, Ren also discloses:
3. The pharmaceutical support device according to claim 1, wherein the processor is configured to: input prescription information of the candidate preservation solution actually prepared to the machine learning [models]. Ren, ¶ 0007, “historical data corresponding to those descriptors is used to train machine learning models to predict values of one or more specific protein formulation properties”
Regarding claim 5, Ren also discloses:
5. The pharmaceutical support device according to claim 1, wherein the processor is configured to: input a feature amount derived on the basis of protein information related to [a] protein included in the biopharmaceutical to the machine learning [models]. Ren, ¶ 0007, “In particular, useful formulation descriptors (which can include product molecule/protein descriptors, excipient descriptors, and/or solution descriptors) are identified, and historical data corresponding to those descriptors is used to train machine learning models to predict values of one or more specific protein formulation properties, such as viscosity and product quality attributes.”
Regarding claim 7, Ren also discloses:
7. The pharmaceutical support device according to claim 1, wherein the measurement data is time-series data measured at at least two time points. Ren, ¶ 0052, “The protein formulation property may be viscosity, for example, or a stability metric such as a size-exclusion chromatography (SEC) reading (e.g., … including absolute readings and the rate of change as a function of time or temperature) or a CEX reading, etc.”
Regarding claim 9, Ren also discloses:
9. The pharmaceutical support device according to claim 1, wherein the measurement data includes at least one of aggregation analysis data of sub-visible particles of [a] protein in the candidate preservation solution included in the biopharmaceutical, analysis data of the protein in the candidate preservation solution by a dynamic light scattering method, analysis data of the protein in the candidate preservation solution by size exclusion chromatography, or analysis data of the protein in the candidate preservation solution by differential scanning calorimetry. Ren, ¶ 0032, “outputs a predicted protein formulation property (e.g., viscosity, or a stability metric such as a predicted size exclusion chromatography …”
Regarding claim 10, Ren also discloses:
10. The pharmaceutical support device according to claim 1, wherein [a] protein included in the biopharmaceutical is an antibody. See Ren, Fig. 6A elements 402 and 404 along with ¶ 0047, e.g. “antibody.”
Regarding claim 11, Ren discloses:
11. A method for operating a pharmaceutical support device, the method comprising: See Ren, at least Fig. 7, generally depicting a method.
All further limitations of claim 11 have been addressed in the rejection of claim 1 above.
Regarding claim 12, Ren discloses:
12. A non-transitory computer-readable storage medium storing a program for operating a pharmaceutical support device, the program causing a computer to execute a process comprising: See Ren, Fig. 2, elements 114 and 144, depicting storage media for program storage.
All further limitations of claim 11 have been addressed in the rejection of claim 1 above.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Lintereur as applied above, and further in view of U.S. Patent Application Publication 20190311807 by Anitha et al. (“Anitha”).
Regarding claim 4, Ren does not expressly disclose:
4. The pharmaceutical support device according to claim 1, wherein the machine learning [models] outputs a reliability degree of the prediction data together with the prediction data, and the processor is configured to: input the reliability degree obtained in the prediction process in the previous stage to the machine learning [models] in the prediction process in the subsequent stage.
This is taught by Anitha. See Anitha, ¶ 0019, “an ensemble arbitrator chooses a response out of the possible responses or a collection of responses that is best for the user or circumstance given a match or a mismatch between the possible responses, and a value and confidence each of the first, second, third, and fourth diagnosis engines expresses in its corresponding response. The ensemble arbitrator learns a weight to use for each possible response from each of the first, second, third, and fourth diagnosis engines based upon history.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Anitha’s confidence weight with Ren’s models in order to utilize the best match as suggested by Anitha.
Claims 6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Ren in view of Lintereur as applied above, and further in view of cited art of record U.S. Patent Application Publication 20170239355 by Sharma et al. (“Sharma”).
Regarding claim 6, Ren does not expressly disclose:
6. The pharmaceutical support device according to claim 5, wherein the feature amount includes at least one of a solvent accessible surface area of the protein, a spatial aggregation propensity of the protein, a space charge map of the protein, or an indicator showing compatibility between the protein and an additive included in the candidate preservation solution.
This is taught by Sharma. See Sharma ¶ 0005, “Solvent-accessible surface area (SASA) is a measure of the surface area of a biomolecule (e.g. amino acid residue) that is accessible to a solvent. … SASA could therefore be a useful parameter for determining the suitability of including antioxidants in a given protein formulation.” Also ¶ 0059, “Instability may involve any one or more of: aggregation, deamidation (e.g. Asn deamidation), oxidation (e.g. Met oxidation and/or Trp oxidation), …” Also ¶ 0066, “A “solvent-accessible surface area” or “SASA” of a biomolecule in a solvent is the surface area of the biomolecule that is accessible to the solvent.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Sharma’s stability features with Ren’s models in order to determine suitability as suggested by Sharma (see at least ¶ 0005).
Regarding claim 8, Ren also discloses:
8. The pharmaceutical support device according to claim 1, wherein the prescription information related to the prescription of the candidate preservation solution to be predicted includes at least one of a type of each of a buffer solution, an additive, and a surfactant included in the candidate preservation solution, See Ren, ¶ 0023, “For example, a set of formulation descriptors may include descriptors of a candidate product molecule, descriptors of a candidate excipient, and descriptors of a candidate solution.” Also see ¶ 0032, “excipient type.” Also ¶ 0039, “surfactant … buffer type.”
a concentration of each of the buffer solution, the additive, and the surfactant, or See Ren, ¶ 0023 as cited above. Also see ¶ 0039, “a target concentration of the protein molecule in the solution.”
… a hydrogen ion exponent of the candidate preservation solution.
Note that while Ren generally discloses information regarding type and concentration, Ren does not expressly address type/concentration for each of the buffer solution, the additive, and the surfactant. However, this is taught at least by Sharma. See Sharma ¶ 0028, “In some embodiments, the formulation further comprises one or more excipients selected from the group consisting of a stabilizer, a buffer, a surfactant, and a tonicity agent.” Also see ¶ 0071, “’Pharmaceutically acceptable’ excipients …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Sharma’s formulation with Ren’s type and concentration descriptors in order to provide a formulation that can reasonably be administered to a subject to provide an effective dose of the active ingredient employed and that are nontoxic to the subject being exposed thereto at the dosages and concentrations employed as suggested by Sharma (see ¶ 0071).
Conclusion
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/James D. Rutten/Primary Examiner, Art Unit 2121
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