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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/05/2026 has been entered.
Status of Claims
Claim 1-5, 7-8, and 10-15 are pending and under examination.
Claims 6 and 9 have been canceled.
Response to Amendment
Claim the amendments have overcome the claim objections previously set forth in the Non-Final Rejection. Therefore, the claim objections have been withdrawn. However, based on the claim amendments, new claim objections have been set forth.
The 112(f) claim interpretation(s) have been maintained.
Based on the amended claims, the 112(a) and 112(d) rejection(s) have been withdrawn.
The 101 rejection(s) have been modified and maintained to address the amended claims.
Based on the amended claims and remarks, the previous prior art rejection over Simakova has been modified (see below).
Claim Objections
Claims 10 and 13 are objected to because of the following informalities:
Claim 10 lines 16-17 recites “prior to and without synthesis of the candidate material, prior to and without synthesis of the candidate material” which appears to be a grammatical/clerical mistake. The examiner requests applicant remove the redundant language.
Claim 13 line 6 recites “the set of polymers_at least two of their respective characteristic properties” and lines 15-16 recite “the at least two characteristic material_properties”. The examiner requests applicant replace the underlined portion (i.e. “_”) with a space between each word.
Appropriate correction is required.
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 11 and 13 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 pre-AIA the applicant regards as the invention.
Claim 11 recites “a control unit”. Claim 10 line 31 has been amended to recite “a control unit”. It is unclear if applicant is referent to a second control unit or the control unit of claim 10. Perhaps applicant is intending to recite “the control unit”?
Claim 13 lines 15 and 19 recite “the processing device”. There is insufficient antecedent basis for this term in the claims and it is unclear what applicant is referring to as the processing device. Claims 14-15 are also rejected by their dependency from claim 13.
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:
“an obtaining unit configured to receive a digital representation” in claim 10.
“a model unit configured to provide a data driven model” in claim 10.
“a pareto unit configured to provide a provisional pareto front” in claim 10.
“a property determination unit configured to determine the at least two characteristic material properties” in claim 10.
“a validation unit configured to compare the determined at least two characteristic material properties” in claim 10.
“a providing unit configured to, based on the comparison provide a control file” in claim 10.
Claim element “an obtaining unit” is a limitation that invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. A review of the specification states “An obtaining unit 510 is configured for receiving a digital representation of a material. In this example the obtaining unit is a client device communicatively coupled to a property determination unit 520.”. Accordingly, the examiner interpreting claim limitation “obtaining unit” as any computer, display, or personal electronic device configured to communicate with a processor.
Claim element “a model unit” is a limitation that invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. A review of the specification states “A model unit 530, is configured to provide a data driven model trained based on digital representations of previously presented materials and at least two of their respective characteristic properties. In this example the model unit 530 comprises a database with the stored data driven model”. See paragraph [0134] of applicant’s printed publication. Accordingly, the examiner interpreting claim limitation “model unit” as a database or storage medium with a model.
Claim element “a pareto unit” is a limitation that invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. A review of the specification states “A pareto unit 532 is configured to provide a provisional pareto front to the validation unit 522. In this example, the pareto unit comprises a database storing the provisional pareto front”. Accordingly, the examiner interpreting claim limitation “pareto unit” as a database or storage medium that stores a pareto front.
Claim element “a property determination unit” is a limitation that invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. A review of the specification states “In this example the obtaining unit is a client device communicatively coupled to a property determination unit 520. The obtaining unit may be communicatively coupled to the property determination unit by a bus system, wired, wirelessly as well as over via an internet protocol … The property determination unit 520 is configured to determine the at least two characteristic material properties of the candidate material based on the digital representation and the data driven model.”. Further, fig. 5 shows property determination unit 520 as a processor. Accordingly, the examiner interpreting claim limitation “property determination unit” as a processor in communication with a computer, display, or personal electronic device and a database.
Claim element “a validation unit” is a limitation that invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. A review of the specification states “A pareto unit 532 is configured to provide a provisional pareto front to the validation unit 522 … The validation unit compares the preliminary praetor front with the determined at least two characteristic properties of the candidate material.”. Accordingly, the examiner interpreting claim limitation “validation unit” as any computer component or processor configured to compare data.
Claim element “a providing unit” is a limitation that invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. A review of the specification states “Control unit 540 is communicatively coupled to a providing unit 524 and is configured to control the synthesis equipment, 550, 560, 570, 580, 590 … The control unit 540 controls the synthesis equipment according to the control file provided by the providing unit 524.”. Accordingly, the examiner interpreting claim limitation “providing unit” as any computer component or processor configured to transmit or send data.
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-8 and 10-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is directed toward a method. Claim 10 is directed toward an apparatus. Claim 13 is directed toward a method.
Step 2A, Prong One: Identify the law of nature/natural phenomenon/abstract ideas.
Claim 1 recites the abstract ideas “providing a data driven model trained based on digital representations of previously presented materials and at least two of their respective characteristic properties”, “determining, prior to and without synthesis of the candidate material, the at least two characteristic material properties of the candid material based on the data driven model and the digital representation, wherein the at least two characteristic properties comprise at least two of adsorption free energy, dimer free energy barrier, and radius of gyration, and wherein the data driven model is configured to predict characteristic properties of candidate polymers based on their digital representation”, “comparing the determined at least two characteristic material properties with a provisional pareto front, wherein the comparing comprises determining if the determined characteristic material properties overlap with the provisional pareto front”.
Claim 10 recites the abstract ideas “provide a data driven model trained based on digital representations of previously presented materials and at least two of their respective characteristic properties, wherein the at least two characteristic properties comprise at least two of adsorption free energy, dimer free energy barrier, and radius of gyration, and wherein the data driven model is configured to predict characteristic properties of candidate polymers based on their digital representation”, “a pareto unit configured to provide a provisional pareto front associated with the at least two characteristic material properties for the previously presented materials”, “a property determination unit configured to determine the at least two characteristic material properties of the candidate material based on the data driven model and the digital representation”, “a validation unit configured to compare the determined at least two characteristic material properties with the provisional pareto front to determine if the determined characteristic material properties overlap with the provisional pareto front”.
Claim 13 recites the abstract ideas “providing a data driven model trained based on the digital representation of each of the polymers of the subset of polymers and at least two of their respective characteristic properties”, “wherein the data driven model is configured to predict characteristic properties of polymers based on their digital representation”, “predicting with the processing device the at least two characteristic material properties of remaining polymers from the set of polymers based on the data driven model prior to and without synthesis of the remaining polymers”, “determining with the processing device from the set of polymers a predicted pareto optimum for the at least two of the characteristic material properties”, “providing via the communication interface a determined provisional pareto front”.
These abstract ideas are mental processes and/or mathematical concepts that could be performed by a human person or by pen and paper or by a generic computer. The computer in claim 1, the pareto unit, property determination unit, and validation unit in claim 10, and processing device in claim 13, are merely a general-purpose computer for which to apply the abstract ideas, but does not preclude the steps from being considered an abstract idea. See MPEP 2106.04(a)(2) subsections (I) and (III).
Step 2A, Prong Two: Has the abstract idea been integrated into a particular practical application?
No. These judicial exceptions are not integrated into a practical application because the additional elements recited in the claims do not impose any meaningful limits on practicing the abstract ideas. After determining whether there is an overlap in the comparison or not, the control until controls synthesis equipment. However, these actions are interpreted as mere instructions to implement the abstract idea or other exception on a computer, and insignificant extra-solution activity as data gathering and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception. Accordingly, these elements do not integrate a judicial exception or provide significantly more and cannot integrate the judicial exception into a practical application.
Claim 1 additionally recites providing a digital representation of a candidate polymer material, providing a data driven model based on previously presented materials, the at least two characteristic properties comprise adsorption free energy, dimer free energy barrier, and radius of gyration, and providing a control file for controlling synthesis equipment if an overlap is determined.
Claim 10 additionally recites an obtaining unit to receive a digital representation of a candidate polymer material, a modeling unit to provide a data driven model based on previously presented materials, the at least two characteristic properties comprise adsorption free energy, dimer free energy barrier, and radius of gyration, and a providing unit to provide a control file for controlling synthesis equipment if an overlap is determined.
Claim 13 additionally recites providing a digital representation of a set of polymer and material properties via a communication interface, providing the at least two characteristic properties comprise adsorption free energy, dimer free energy barrier, and radius of gyration, providing a data driven model based on digital representation of a subset of polymers, providing a provisional pareto front via the communication interface.
However, data gathering and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception do not integrate the judicial exception into a particular practical application because data gathering is merely insignificant extra-solution activity. See MPEP § 2106.05(g), Insignificant Extra-Solution Activity and MPEP § 2106.05(h), Field of Use and Technological Environment. Furthermore, controlling equipment to perform the synthesis based on the abstract idea is merely instructions to apply the exception. See MPEP § 2106.05 (f), Mere Instructions To Apply an Exception.
Step 2B: Does the claim recite any elements which are significantly more than the abstract idea?
Claim 1 further recites synthesis equipment comprising one or more valves regulating flow of ingredients into a reactor and a heating or cooling element.
Claim 10 further recites synthesis equipment comprising one or more valves regulating flow of ingredients into a reactor and a heating or cooling element for controlling flow rates of the ingredients and reaction temperatures.
Claim 13 further recites a provisional pareto front, a communication interface and a processing device.
These additional elements do not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more than well-understood, routine, and conventional activities previously known to the industry (See MPEP § 2106.05(c), Particular Transformation and MPEP § 2106.05(d), Well-Understood, Routine, Conventional Activity) as evidenced by Simakova et al. (US 2020/0093930 – hereinafter “Simakova”) and Schaad (US Patent No. 2,517,720). Simakova and Robb disclose a method for synthesizing a polymer synthesis equipment comprising one or more valves regulating flow of ingredients into a reactor and a heating or cooling element for controlling flow rates of the ingredients and reaction temperatures; (Simakova; [0045-0050] and Schaad; fig. 1, col.5 lines 10-75) and a provisional pareto front, a communication interface and a processing device (Simakova; [0047, 0096, 0106, 0115]). Lastly, data gathering in conjunction with the abstract ideas (mental processes and mathematical concepts) is not enough to transform the nature of the claim into "significantly more" than the judicial exception.
Claim 2 further limits the synthesis specification of the candidate material as comprising a list of ingredients and machine-readable instructions for synthesizing material. However, these additional elements are interpreted as mere instructions to implement the abstract idea or other exception on a computer, and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception. Accordingly, these elements do not integrate a judicial exception or provide significantly more and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(f), Mere Instructions To Apply An Exception, and MPEP §2106.05(h), Field of Use and Technological Environment). Simakova additionally disclose selecting candidate materials from a list of ingredients and performing the synthesis according to an algorithm with an automated device; [0009-0011, 0014, 0047, 0053-0081].
Claims 3 further limits the digital representation as being provided by a client device and the control file as being received by a client device. However, these additional elements do not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more than well-understood, routine, and conventional activities previously known to the industry (See MPEP § 2106.05(c), Particular Transformation and MPEP § 2106.05(d), Well-Understood, Routine, Conventional Activity). Furthermore, receiving, transmitting, or providing data over a network has been recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity to apply an exception. See MPEP § 2106.05(d), Well-Understood, Routine, Conventional Activity. Simakova disclose the self-learning algorithm is implemented on a high-throughput device using automated equipment programmed with the algorithm and thus comprise a client device; [0096, 0100, 0106].
Claim 4 further limits the abstract idea of the pareto front as comprising a measure for uncertainty. However, the pareto front implemented on a computer is merely a general-purpose computer for which to apply the abstract ideas, but does not preclude the steps from being considered an abstract idea. See MPEP 2106.04(a)(2) subsections (I) and (III). Further, these additional elements are interpreted as mere instructions to implement the abstract idea or other exception on a computer, and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception. Accordingly, these elements do not integrate a judicial exception or provide significantly more and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(f), Mere Instructions To Apply An Exception, and MPEP §2106.05(h), Field of Use and Technological Environment). Simakova disclose the numerical ranges and parameters setting forth the broad scope of the invention are approximations which inherently contains error necessarily resulting from the standard deviation found in its underlying respective testing measurements; [0130].
Claim 5 further limits the abstract idea of the characteristic properties as comprising a measure for uncertainty. However, determining properties based on the data model implemented on a computer is merely a general-purpose computer for which to apply the abstract ideas, but does not preclude the steps from being considered an abstract idea. See MPEP 2106.04(a)(2) subsections (I) and (III). Further, these additional elements are interpreted as mere instructions to implement the abstract idea or other exception on a computer, and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception. Accordingly, these elements do not integrate a judicial exception or provide significantly more and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(f), Mere Instructions To Apply An Exception, and MPEP §2106.05(h), Field of Use and Technological Environment). Simakova disclose determining, for example, a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics, and the numerical ranges and parameters setting forth the broad scope of the invention are approximations which inherently contains error necessarily resulting from the standard deviation found in its underlying respective testing measurements; [0106, 0130].
Claim 7 further limits the abstract idea of the comparing step as determining an overlap, and providing the control file if an overlap is determined. However, comparing values using the pareto front implemented on a computer is merely a general-purpose computer for which to apply the abstract ideas, but does not preclude the steps from being considered an abstract idea. See MPEP 2106.04(a)(2) subsections (I) and (III). Further, these additional elements are interpreted as mere instructions to implement the abstract idea or other exception on a computer, and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception. Accordingly, these elements do not integrate a judicial exception or provide significantly more and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(f), Mere Instructions To Apply An Exception, and MPEP §2106.05(h), Field of Use and Technological Environment). Simakova disclose a self-learning algorithm programmed into a high-throughput device for generating synthesis conditions and generating libraries of biomolecule-polymer conjugates under controlled conditions until an optimal biomolecule-polymer conjugate for a particular application is obtained; [0004, 0008, 0012, 0047, 0096, 0100, 0106].
Claim 8 further limits the method by controlling flow rates of ingredients and reaction temperatures. However, these additional elements do not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more than well-understood, routine, and conventional activities previously known to the industry (See MPEP § 2106.05(c), Particular Transformation and MPEP § 2106.05(d), Well-Understood, Routine, Conventional Activity). Simakova disclose controlling flow rates and temperatures; [0011, 0014].
Claim 11 further limits the apparatus as comprising a control unit for controlling the synthesis. However, these additional elements do not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more than well-understood, routine, and conventional activities previously known to the industry (See MPEP § 2106.05(c), Particular Transformation and MPEP § 2106.05(d), Well-Understood, Routine, Conventional Activity). Simakova disclose an automated device configured to deliver one or more of a reactant, solve or catalyst to each reaction chamber; [0011, 0014].
Claim 12 recites a non-transitory computer readable medium comprising instructions for controlling the synthesis of the polymer on computing devices according to claim 1. However, a general-purpose computer for which to apply the abstract ideas, but does not preclude the steps from being considered an abstract idea. See MPEP 2106.04(a)(2) subsections (I) and (III). Further, these additional elements are interpreted as mere instructions to implement the abstract idea or other exception on a computer, and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception.
Claim 14 further limits the method of claim 13 as comprising a processor and a memory. However, these additional elements do not effectively transform or reduce the system to a different state or thing beyond such that the claims recite significantly more than well-understood, routine, and conventional activities previously known to the industry (See MPEP § 2106.05(c), Particular Transformation and MPEP § 2106.05(d), Well-Understood, Routine, Conventional Activity). Simakova disclose the method is performed on a high-throughput device programmed with the self-learning algorithm; [0100, 0106].
Claim 15 recites a non-transitory computer readable medium comprising instructions for a computer to perform the method of claim 13. However, a general-purpose computer for which to apply the abstract ideas, but does not preclude the steps from being considered an abstract idea. See MPEP 2106.04(a)(2) subsections (I) and (III). Further, these additional elements are interpreted as mere instructions to implement the abstract idea or other exception on a computer, and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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.
Claims 1-5, 7-8 and 10-15 are rejected under 35 U.S.C. 103 as being unpatentable over Simakova et al. (US 2020/0093930; already of record – hereinafter “Simakova”) in view of Robb, Ian D., (Comprehensive Polymer Science, 1989, Elsevier Ltd, ScienceDirect eBooks, Chapter 24 - Adsorption, pp. 733-754 – hereinafter “Robb”).
Regarding claim 1, Simakova disclose a computer implemented method for controlling synthesis of a material (Simakova disclose a method for synthesizing biomolecule-polymer conjugates using a high-throughput system programmed with self-learning algorithms; [0005, 0008, 0047, 0096, 0100, 0106]), the method comprising:
- providing a digital representation associated with a synthesis specification of a candidate material, wherein the candidate material is a polymer (Simakova disclose synthesizing a biomolecule-polymer conjugate for a particular application; [0096, 0100, 0106])
- providing a data driven model trained based on digital representations of previously presented materials and at least two of their respective characteristic properties (Simakova disclose a self-learning algorithm that analyzes results from a first generation of synthesized conjugates, selects for a particular feature or functionality – for example, polymers of a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics, and then uses this information to generate new parameters to introduce into the second generation of synthetic conditions; [0096, 0100, 0106]),
- determining, prior to and without synthesis of the candidate material, the at least two characteristic material properties of the candidate material based on the data driven model and the digital representation, and wherein the data driven model is configured to predict characteristic properties of candidate polymers based on their digital representation (Simakova disclose a self-learning algorithm that analyzes results from a first generation of synthesized conjugates, selects for a particular feature or functionality – for example, polymers of a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics, and then uses this information to generate new parameters to introduce into the second generation of synthetic conditions; [0096, 0100, 0106]. After multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047-0050, 0096, 0100, 0106]),
- comparing the determined at least two characteristic material properties with a provisional pareto front, wherein the comparing comprises determining if the determined characteristic material properties overlap with the provisional pareto front (Simakova disclose after multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047-0050, 0096, 0100, 0106]),
- based on the comparison, when the determined at least two characteristic material properties overlap with the provisional pareto front, providing a control file that initiates for controlling the synthesis of the candidate material if the comparing step determines an overlap, when the determined at least two characteristic material properties do not overlap with the provisional pareto front, providing a stop signal to a synthesis apparatus that prevents synthesis of the material that would not improve the provisional pareto front (Simakova disclose generating synthesis conditions and generating libraries of biomolecule-polymer conjugates under controlled conditions to act as a feedback loop in an effort to Pareto optimize conjugate synthesis for a given application until a conjugate that cannot be further improved is selected. An automated system is configured to synthesize a plurality of biomolecule-polymer conjugates, then determine and optimize advantageous properties by varying synthetic conditions. The automated system screens for optimized conditions, allowing one to quickly focus on a particular robust set of conditions with a specific function while eliminating reaction conditions that produce conjugates which are unstable or unsuitable for a given application; [0004, 0008, 0012, 0047-0050, 0095-0096, 0100, 0106]), and
- controlling, by a control unit that receives the control file, synthesis equipment comprising one or more valves regulating flow of ingredients into a reactor and a heating or cooling element, by controlling flow rates of the ingredients and reaction temperatures (Simakova disclose an automated system is configured to synthesize a plurality of biomolecule-polymer conjugates, then determining and the optimizing advantageous properties by varying synthetic conditions. The automated system comprise a plurality of reaction chambers and an automated device configured to deliver one or more of a reactant, solvent, or catalyst to each reaction chamber. The system further comprise a temperature control module, such as a cooling module to maintain the plurality of reaction chambers at a specified temperature; [0014, 0048-0049]).
Simakova does not teach wherein the at least two characteristic properties comprise at least two of adsorption free energy, dimer free energy barrier, and radius of gyration.
However, Robb teach the analogous art of providing a data driven model for determining characteristic properties of polymers (Robb disclose models and studies of adsorbed polymers; Section 24.1, “Introduction”, pp. 733-737), wherein the determined characteristic properties comprise at least adsorption free energy (Robb; Section 24.2.4, “Influence of Solution Conditions”, p. 739), and radius of gyration (Robb; Section 24.5, “Polymers Between Two Surfaces”, pp. 749-750).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the at least two characteristic properties of Simakova with the at least two properties comprising adsorption free energy and radius of gyration, as taught by Robb, because Robb teach the at least two properties can be used to determine the likelihood of success that a polymer will successfully adsorb onto a surface having an adsorbed layer thickness; Sections 24.2.4, 24.3.3 & 24.5, pp. 739, 746-747, & 749-750).
Regarding claim 2, modified Simakova disclose the method according to claim 1 above, wherein the synthesis specification of the candidate material comprises a list of ingredients and machine-readable instructions for synthesizing material (Simakova disclose selecting candidate materials from a list of ingredients and performing the synthesis according to an algorithm with an automated device [0009-0011, 0014, 0047, 0053-0081]).
Regarding claim 3, modified Simakova disclose the method according to claim 1 above, wherein the digital representation is provided by a client device and the control file is received by the client device (Simakova disclose a self-learning algorithm that analyzes results from a first generation of synthesized conjugates, selects for a particular feature or functionality – for example, polymers of a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics, and then uses this information to generate new parameters to introduce into the second generation of synthetic conditions. The self-learning algorithm is implemented on a high-throughput device programmed with the algorithm and thus comprise a client device; [0096, 0100, 0106]).
Regarding claim 4, modified Simakova disclose the method according to claim 1 above, wherein the provisional pareto front comprises a measure for uncertainty (Simakova disclose the numerical ranges and parameters setting forth the broad scope of the invention are approximations which inherently contains error necessarily resulting from the standard deviation found in its underlying respective testing measurements; [0130]).
Regarding claim 5, modified Simakova disclose the method according to claim 1 above, wherein the at least two determined characteristic properties comprise a measure for uncertainty (Simakova disclose determining, for example, a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics, and the numerical ranges and parameters setting forth the broad scope of the invention are approximations which inherently contains error necessarily resulting from the standard deviation found in its underlying respective testing measurements; [0106, 0130]).
Regarding claim 7, modified Simakova disclose the method of claim 1 above, wherein the control file is provided if the comparing step determines an overlap (Simakova disclose a self-learning algorithm programmed into a high-throughput device for generating synthesis conditions and generating libraries of biomolecule-polymer conjugates under controlled conditions until an optimal biomolecule-polymer conjugate for a particular application is obtained; [0004, 0008, 0012, 0047, 0096, 0100, 0106]).
Regarding claim 8, modified Simakova disclose the method of claim 1 above, wherein the method further comprises controlling the synthesis by controlling flow rates of ingredients and reaction temperatures (Simakova; [0011, 0014]).
Regarding claim 10, modified Simakova disclose an apparatus for controlling synthesis of a material (Simakova disclose a method for synthesizing biomolecule-polymer conjugates using a high-throughput device programmed with self-learning algorithms; [0005, 0008, 0047, 0096, 0100, 0106]), the apparatus comprising at least:
- an obtaining unit configured to receive a digital representation of a candidate material, wherein the candidate material is a polymer (Simakova disclose the high-throughput device programmed with the algorithm for synthesizing a biomolecule-polymer conjugate for a particular application; [0096, 0100, 0106]),
- a model unit configured to provide a data driven model trained based on digital representations of previously presented materials and at least two of their respective characteristic properties, and wherein the data driven model is configured to predict characteristic properties of candidate polymers based on their digital representation (Simakova disclose the self-learning algorithm analyzes results from a first generation of synthesized conjugates, selects for a particular feature or functionality – for example, polymers of a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics, and then uses this information to generate new parameters to introduce into the second generation of synthetic conditions; [0096, 0100, 0106]. After multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047, 0096, 0100, 0106]; [0106]),
- a pareto unit configured to provide a provisional pareto front associated with the at least two characteristic material properties for the previously presented materials (Simakova disclose after multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047, 0096, 0100, 0106]),
- a property determination unit configured to determine, prior to and without synthesis of the candidate material, prior to and without synthesis of the candidate material, the at least two characteristic material properties of the candidate material based on the data driven model and the digital representation (Simakova disclose determining, for example, a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics; [0106]. A self-learning algorithm that analyzes results from a first generation of synthesized conjugates, selects for a particular feature or functionality and then uses this information to generate new parameters to introduce into the second generation of synthetic conditions; [0096, 0100, 0106]. After multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047-0050, 0096, 0100, 0106]),
- a validation unit configured to compare the determined at least two characteristic material properties with the provisional pareto front to determine if the determined characteristic material properties overlap with the provisional pareto front (Simakova disclose after multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047, 0096, 0100, 0106]),
- a providing unit configured to, based on the comparison providing a control file, control the synthesis of the candidate material if the validation unit determines an overlap and prevents synthesis of the materials that would not improve the provisional pareto front, provide a control file that initiates synthesis of the candidate material when the validation unit determines that the determined at least two characteristic material properties overlap with the provisional pareto front, and provide a stop signal to a synthesis apparatus that prevents synthesis of the candidate material when the validation unit determines no overlap (Simakova disclose generating synthesis conditions and generating libraries of biomolecule-polymer conjugates under controlled conditions to act as a feedback loop in an effort to Pareto optimize conjugate synthesis for a given application until a conjugate that cannot be further improved is selected. An automated system is configured to synthesize a plurality of biomolecule-polymer conjugates, then determine and optimize advantageous properties by varying synthetic conditions. The automated system screens for optimized conditions, allowing one to quickly focus on a particular robust set of conditions with a specific function while eliminating reaction conditions that produce conjugates which are unstable or unsuitable for a given application; [0004, 0008, 0012, 0047-0050, 0095-0096, 0100, 0106]), and
- a control unit configured to receive the control file and to control synthesis equipment comprising one or more valves regulating flow of ingredients into a reactor and a heating or cooling element, by controlling flow rates of the ingredients and reaction temperatures (Simakova disclose an automated system is configured to synthesize a plurality of biomolecule-polymer conjugates, then determining and the optimizing advantageous properties by varying synthetic conditions. The automated system comprise a plurality of reaction chambers and an automated device configured to deliver one or more of a reactant, solvent, or catalyst to each reaction chamber. The system further comprise a temperature control module, such as a cooling module to maintain the plurality of reaction chambers at a specified temperature; [0014, 0048-0049]).
Simakova does not teach wherein the at least two characteristic properties comprise at least two of adsorption free energy, dimer free energy barrier, and radius of gyration.
However, Robb teach the analogous art of providing a data driven model for determining characteristic properties of polymers (Robb disclose models and studies of adsorbed polymers; Section 24.1, “Introduction”, pp. 733-737), wherein the determined characteristic properties comprise at least adsorption free energy (Robb; Section 24.2.4, “Influence of Solution Conditions”, p. 739), and radius of gyration (Robb; Section 24.5, “Polymers Between Two Surfaces”, pp. 749-750).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the at least two characteristic properties of Simakova with the at least two properties comprising adsorption free energy and radius of gyration, as taught by Robb, because Robb teach the at least two properties can be used to determine the likelihood of success that a polymer will successfully adsorb onto a surface having an adsorbed layer thickness; Sections 24.2.4, 24.3.3 & 24.5, pp. 739, 746-747, & 749-750).
Regarding claim 11, modified Simakova disclose the apparatus of claim 10 above, communicatively coupled to a control unit for controlling the synthesis (Simakova disclose an automated device configured to deliver one or more of a reactant, solvent or catalyst to each reaction chamber; [0011, 0014]).
Regarding claim 12, modified Simakova disclose a non-transitory computer readable medium for controlling synthesis of the polymer comprising instructions, which, when executed on computing devices of a computing environment, is configured to carry out the steps of the method of controlling according to claim 1 (Simakova disclose a method for synthesizing biomolecule-polymer conjugates using a high-throughput device programmed with self-learning algorithms; [0005, 0008, 0047, 0096, 0100, 0106]).
Regarding claim 13, Simakova disclose a computer implemented method for determining a provisional pareto front associated with characteristic material properties of polymers in particular for screening comprising providing via a communication interface a set of polymers, wherein each of the polymers of the set of polymers is described by their digital representation, providing via the communication interface for each of the polymers of a subset of the set of polymers at least two of their respective characteristic properties (Simakova disclose a method for synthesizing biomolecule-polymer conjugates using a high-throughput system programmed with self-learning algorithms, and pareto optimization for screening and determining polymers for a particular application; [0005, 0008, 0047, 0096, 0100, 0106]. The method uses automated equipment and generates synthesis conditions and generating libraries of biomolecule-polymer conjugates under controlled conditions until an optimal biomolecule-polymer conjugate for a particular application is obtained; [0004, 0008, 0011-0012, 0014, 0047, 0096, 0100, 0106]);
providing a data driven model trained based on the digital representation of each of the polymers of the subset of polymers and at least two of their respective characteristic properties, wherein the data driven model is configured to predict characteristic properties of polymers based on their digital representations (Simakova disclose a self-learning algorithm that analyzes results from a first generation of synthesized conjugates, selects for a particular feature or functionality – for example, polymers of a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics, and then uses this information to generate new parameters to introduce into the second generation of synthetic conditions; [0096, 0100, 0106]. After multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047, 0096, 0100, 0106]; [0106]);
predicting with the processing device the at least two characteristic material properties of remaining polymers from the set of polymers based on the data driven model prior to and without synthesis of the remaining polymers (Simakova disclose a self-learning algorithm that analyzes results from a first generation of synthesized conjugates, selects for a particular feature or functionality – for example, polymers of a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics, and then uses this information to generate new parameters to introduce into the second generation of synthetic conditions; [0096, 0100, 0106]. After multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047, 0096, 0100, 0106]);
providing via the communication interface for each of the set of polymers their at least two characteristic properties (Simakova disclose a self-learning algorithm that analyzes results from a first generation of synthesized conjugates, selects for a particular feature or functionality – for example, polymers of a specific length that yield higher conjugate stability or a particular polymer density that yields rapid enzyme kinetics, and then uses this information to generate new parameters to introduce into the second generation of synthetic conditions; [0096, 0100, 0106]. After multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047, 0096, 0100, 0106]);
determining with the processing device from the set of polymer a predicted pareto optimum for the at least two of the characteristic properties (Simakova disclose after multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047, 0096, 0100, 0106]);
providing via the communication interface a determined provisional pareto front (Simakova disclose after multiple iterations of synthetic refinement and Pareto optimization, an optimal biomolecule-polymer conjugate for a particular application is obtained; [0047, 0096, 0100, 0106]).
Simakova does not teach wherein the at least two characteristic properties comprise at least two of adsorption free energy, dimer free energy barrier, and radius of gyration.
However, Robb teach the analogous art of providing a data driven model for determining characteristic properties of polymers (Robb disclose models and studies of adsorbed polymers; Section 24.1, “Introduction”, pp. 733-737), wherein the determined characteristic properties comprise at least adsorption free energy (Robb; Section 24.2.4, “Influence of Solution Conditions”, p. 739), and radius of gyration (Robb; Section 24.5, “Polymers Between Two Surfaces”, pp. 749-750).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the at least two characteristic properties of Simakova with the at least two properties comprising adsorption free energy and radius of gyration, as taught by Robb, because Robb teach the at least two properties can be used to determine the likelihood of success that a polymer will successfully adsorb onto a surface having an adsorbed layer thickness; Sections 24.2.4, 24.3.3 & 24.5, pp. 739, 746-747, & 749-750).
Regarding claim 14, modified Simakova disclose a computing apparatus including a processor and a memory storing instructions that, when executed by the processor, configure the apparatus to perform the method of claim 13 (Simakova disclose the method is performed on a high-throughput device programmed with the self-learning algorithm; [0100, 0106]).
Regarding claim 15, modified Simakova disclose a non-transitory computer readable medium including instructions that, when processed by a computer, configure the computer to perform the method of claim 13 (Simakova disclose the method is performed on a high-throughput device programmed with the self-learning algorithm; [0100, 0106]).
Response to Arguments
Applicants arguments filed on 08/05/2026 have been fully considered.
Applicant’s arguments and claim amendments, see pages 6-7 of their remarks, towards the 112(a) written-description rejection(s) over claims 1, 10, and 13 were found persuasive by the examiner. Accordingly, the 112(a) written-description rejection(s) have been withdrawn.
Applicant argues, see pages 7-8 of their remarks, towards the 101 rejection(s) over claims 1, 10, and 13, that the amended limitation of the affirmative control action to directing the synthesis equipment provide a practical application of the claims, and that the comparison step serves as a transformative step by initiating or preventing physical synthesis of a material based on the computational analysis. The examiner respectfully disagrees. The additional elements including the synthesis equipment (i.e. valves, reactor, heating/cooling element) do not impose any meaningful limits on practicing the abstract ideas. After determining whether there is an overlap in the comparison or not, the control until controls synthesis equipment according to a control file. However, these actions are interpreted as mere instructions to implement the abstract idea or other exception on a computer, and insignificant extra-solution activity as data gathering and generally linking the use of a judicial exception to a particular technological environment or field of use in which to apply the judicial exception. Accordingly, these elements do not integrate a judicial exception or provide significantly more and cannot integrate the judicial exception into a practical application. Data gathering in conjunction with the abstract ideas (mental processes and mathematical concepts) is not enough to transform the nature of the claim into "significantly more" than the judicial exception. Lastly, the additional elements of the synthesis equipment are well-known, routine, and conventional in the art as evidenced by Simakova and Schaad. See Simakova paragraphs [0045-0050, 0096, 0106, 0115] and Schaad; col. 5 lines 10-75.
Applicant argues, see pages 9 of their remarks towards the 103 rejection(s) over claims 1, 10, and 13, that the cited prior art does not teach or suggest the amended limitation “at least two of adsorption free energy, dimer free energy barrier and radius of gyration”. The examiner agrees with applicant and notes that applicant’s arguments are towards the amended limitations and do not apply to the current grounds of rejection. Further, upon additional search and consideration, the examiner has withdrawn the previous prior art rejection and set forth a new prior art rejection over Simakova in view of Robb which the examiner contends teach the amended language comprising “at least two of adsorption free energy, dimer free energy barrier, and radius of gyration”.
Applicant argues on pages 9-10 of their remarks that Simakova is directed to an iterative process where materials are first synthesized, the evaluated and then feedback is used to optimize further synthesis conditions whereas the claimed invention uses a data driven model to predict characteristic material properties of a candidate polymer before synthesis occurs and determines whether to provide a control file for synthesis based on whether those predicted properties overlap with the provisional pareto front. The examiner respectfully disagrees with applicant’s argument. The iterative process where materials are first synthesized in Simakova is equivalent to the claimed digital representations of previously presented materials and at least two of their respective characteristic properties. Simakova also disclose an automated system is configured to synthesize a plurality of biomolecule-polymer conjugates, then determine and optimize advantageous properties by varying synthetic conditions. The automated system screens for optimized conditions, allowing one to quickly focus on a particular robust set of conditions with a specific function while eliminating reaction conditions that produce conjugates which are unstable or unsuitable for a given application; [0004, 0008, 0012, 0047-0050, 0095-0096, 0100, 0106]. Accordingly, Simakova disclose using a data driven model to predict characteristic material properties for polymers before synthesis occurs.
Regarding applicant’s argument on page 10 towards the previous remarks, the examiner notes that the material synthesized in Simakova is used to train the model and thus part of the training data for the model used to predict properties of unsynthesized candidate materials while eliminating conditions which are unstable or unsuitable for a given application.
Citations to art
In the above citations to documents in the art, an effort has been made to specifically cite representative passages, however rejections are in reference to the entirety of each document relied upon. Other passages, not specifically cited, may apply as well.
Other References Cited
The prior art of made of record and not relied upon is considered pertinent to Applicant’s disclosure include:
Mei et al. (US 2018/0178180) disclose a polymer synthesis reactor comprising thermal control and valves.
Zheng et al. (US 2004/0230411) disclose an apparatus and method for predicting properties of material.
Schaad (US Patent No. 2,517,720) disclose synthesis equipment for creating a polymer material.
Conclusion
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/C.A.T./Examiner, Art Unit 1798
/BENJAMIN R WHATLEY/Primary Examiner, Art Unit 1798
