DETAILED ACTION
Comments
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-15 are pending and examined in the instant Office action.
Information Disclosure Statement
The IDSs filed have been considered.
Claim Interpretation – Means plus function
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.
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: “phase diagram image scanning processing module,” “physical properties and phase classification module,” “design integration module,” “machine learning bootstrapping submodule,” and “expected improvement submodule” in claims 1-8.
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.
Paragraphs 53-54 of the specification gives structural support for each processing module as comprising hardware and/or software.
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.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon without significantly more. Claims 1-8 are drawn to a system comprising a processor, and claims 9-15 are drawn to methods.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
The independent claims recite the mental step of obtaining a binary phase diagram for each material to be used as a component of a high-entropy alloy.
The independent claims recite the mental step of generating a feature that is representative of a probability that the HEA will exhibit a solid solution phase and/or an intermetallic phase.
The independent claims recite the mental step of generating physics-based feature that is representative of a factor favoring formation of a desired intermetallic phase.
The independent claims recite the mental step of encoding the primary feature and/or the adaptive feature with thermodynamic data associated with formation of HEA alloy phases.
The independent claims recite the mental step of generating an output representation of the HEA alloy phases for a material under analysis.
Claims 2 and 10 recite the mental step of data feedback.
Claim 3 recites the mental step of determining an engineering feature which is a mathematical variant of the prior features.
Claim 4 recites the mental step of including machine learning in the analysis.
Claim 5 recites the mental step of a physical properties model interacting with the phase classification model.
Claim 6 recites the mental steps of using regression to improvement modeling and then updating the model with the improvements.
Claims 7 and 13 recite the mental step of constraining the type of improvement.
Claim 8 recites the mental steps of the HEA composition and phase of the predicted materials meeting a material design criterion and characterizing structural and functional properties of a material via Gaussian regression.
Claim 11 recites the mental step of the interacting between two machine learning models.
Claim 12 recites the mental step of determining an improvement in material processes. Claims 14 and 15 recite the mental step and mathematical limitation of the improvement including a regression technique and a Gaussian regression technique.
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claim(s) 1-15 recite(s) an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1 : YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment.
There are no limitations that indicate that the claimed analysis engine or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-15 is/are directed to an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 2 : NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
The prior art documents of Guillemin et al. [US PGPUB 2003/0086721 A1; on IDS] and Mori [US PGPUB 2017/0094093; on IDS] teach that it is routine and obvious to use computer scanners to scan images printed on paper.
As discussed above, there are no additional limitations to indicate that the claimed analysis engine requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B : No). As such, claims 1-15 is/are not patent eligible.
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.
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.
35 U.S.C. 103 Rejection #1:
Claim(s) 9-10 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi et al. [Scientific Reports, volume 9, 2019, article 15501; 10 pages; on IDS] in view of Gibbs Energy [2014, 4 pages; on IDS].
Claim 9 is drawn to a method for predicting thermodynamic phase of a material. The method comprises obtaining a binary phase diagram for each material to be used as a component of a HEA. The method comprises using machine learning for generating a primary feature that is representative of a probability that the HEA with exhibit a solid solution phase and/or an intermetallic phase. The method comprises generating an adaptive feature that is representative of a factor favoring formation of a desired intermetallic HEA phase. The method comprises encoding the primary feature and/or adaptive feature with thermodynamic data associated with formation of HEA alloy phases. The method comprises generating an output representation of the HEA alloy phases for the material under analysis.
The document of Qi et al. studies high entropy alloys mined from binary phase diagrams [title]. Figures 5 and 6 of Qi et al. illustrate binary phase diagrams of materials used as components of a HEA. Figures 1 and 2 of Qi et al. illustrate the primary features of the PFPx and PSP. The “Calculating PSP” section bridging the seventh and eighth pages of Qi et al. relate phase separation parameters to phase separation percentages. The abstract of Qi et al. teaches encoding HEA structural stability within the phase diagrams.
While Qi et al. does not directly teach physics-based features, the second paragraph on page 8 of Qi et al. teaches relating mixing enthalpies and mixing entropies to Gibbs free energies for forming alloy solutions. The Machine learning section on page 8 of Qi et al. teaches machine learning.
The document of Gibbs Energy teaches the equation that relates mixing enthalpy (DH), temperature (T), and mixing entropy (DS) to Gibbs energy (DG) to determine the favorable thermodynamics threshold for combination (DG < 0).
Page 1 of Gibbs Energy teaches that the equation is DG = DH – TDS.
With regard to claims 10 and 12, the paragraph bridging pages 490-491 of Tancret et al. teaches using GP models to give feedback to understanding and improving to high entropy alloys.
With regard to claim 13, the section bridging the seventh and eighth pages of Qi et al. teach the chemical properties of phase separation parameters and phase separation percentages.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the thermodynamic analysis using enthalpies, temperatures, and entropies of Qi et al. by use of the thermodynamic relationships of Gibbs Energy wherein the motivation would have been that Gibbs Energies mathematical integrates thermodynamic parameters to determine overall favorability of combination [page 1 of Gibbs Energy].
35 U.S.C. 103 Rejection #2:
Claim(s) 11 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi et al. in view of Gibbs Energy as applied to claims 9-10 and 12-13 above, in further view of Tancret et al. [Materials and Design, volume 115, 2017, pages 486-497].
Claim 11 is drawn to implementation of plural machine learning models, wherein at least one machine learning model interacts with another machine learning model via the one or more active learning machine learning techniques.
Claim 14 is further limiting requiring use of regression.
Claim 15 is further limiting requiring use of Gaussian regression.
Qi et al. and Gibbs Energy teach using machine learning to understand the properties of high entropy alloys, as discussed above. On page 8 of Qi et al, Qi et al. teaches random forest as the machine learning technique.
Qi et al. and Gibbs Energy do not teach a plurality of machine learning models interacting with one another, regression, or Gaussian regression.
The document of Tancret et al. studies designing high entropy alloys by employing thermodynamics and Gaussian process statistical analysis [title]. The second full paragraph in the left column on page 492 of Tancret et al. teaches use of machine learning to understand high entropy alloys by use of GP models. The paragraph bridging pages 490-491 of Tancret et al. teach that GP models are Gaussian Process models that employ regression as a machine learning technique.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the thermodynamic analysis using enthalpies, temperatures, and entropies of Qi et al. and the thermodynamic relationships of Gibbs Energy by use of other forms of machine learning of Tancret et al. wherein the motivation would have been that other forms of machine learning is an additional tool that facilitates understanding of high entropy alloys [paragraph bridging pages 490-491 of Tancret et al.].
35 U.S.C. 103 Rejection #3:
Claim(s) 1-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi et al. in view of Gibbs Energy as applied to claims 9-10 and 12-15 above, in further view of Mori [US PGPUB 2017/0094093 A1; on IDS].
Claim 1 is drawn to similar subject matter as claim 16, except claim 1 is drawn to a system comprising a processor and image scanner with constraints on the types of primary and adaptive features.
The document of Qi et al. studies high entropy alloys mined from binary phase diagrams [title]. Figures 5 and 6 of Qi et al. illustrate binary phase diagrams of materials used as components of a HEA. Figures 1 and 2 of Qi et al. illustrate the primary features of the PFPx and PSP. The “Calculating PSP” section bridging the seventh and eighth pages of Qi et al. relate phase separation parameters to phase separation percentages. The abstract of Qi et al. teaches encoding HEA structural stability within the phase diagrams.
While Qi et al. does not directly teach adaptive features, the second paragraph on page 8 of Qi et al. teaches relating mixing enthalpies and mixing entropies to Gibbs free energies for forming alloy solutions. Qi et al. does not teach the computer and image scanner limitations of the claims.
The document of Gibbs Energy teaches the equation that relates mixing enthalpy (DH), temperature (T), and mixing entropy (DS) to Gibbs energy (DG) to determine the favorable thermodynamics threshold for combination (DG < 0).
Page 1 of Gibbs Energy teaches that the equation is DG = DH – TDS.
Qi et al. and Gibbs Energy do not teach the computer and image scanner limitations of the claims.
The document of Mori studies scanner and non-transitory computer readable medium storing [title]. Figures 2 and 7 of Mori illustrate the computer and image scanner.
With regard to claims 2-4, the machine learning section on page 8 of Qi et al. teaches updating HEA data after optimization and mathematical variation of parameters.
With regard to claim 5, the machine learning section on page 8 of Qi et al. teaches using random forest models to further understand the chemical properties of HEAs.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the thermodynamic analysis using enthalpies, temperatures, and entropies of Qi et al. by use of the thermodynamic relationships of Gibbs Energy wherein the motivation would have been that Gibbs Energies mathematical integrates thermodynamic parameters to determine overall favorability of combination [page 1 of Gibbs Energy].
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the thermodynamic analysis using enthalpies, temperatures, and entropies of Qi et al. and the thermodynamic relationships of Gibbs Energy by use of the computers and image scanners of Mori wherein the motivation would have been that it is obvious to automate a manual activity [Figures 2 and 7 of Mori].
35 U.S.C. 103 Rejection #4:
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi et al. in view of Gibbs Energy in view of Mori et al. as applied to claims 1-5 above, in further view of Tancret et al. [Materials and Design, volume 115, 2017, pages 486-497].
Claim 6 is drawn to analysis of HEAs using regression.
Claim 7 is drawn to optimizing chemical properties of the HEAs.
Claim 8 is drawn to analysis of HEAs using Gaussian regression.
Qi et al., Gibbs Energy, and Mori et al. teach using machine learning to understand the properties of high entropy alloys, as discussed above. On page 8 of Qi et al, Qi et al. teaches random forest as the machine learning technique to optimize chemical properties of HEAs.
Qi et al., Gibbs Energy, and Mori et al. do not teach a plurality of machine learning models interacting with one another, regression, or Gaussian regression.
The document of Tancret et al. studies designing high entropy alloys by employing thermodynamics and Gaussian process statistical analysis [title]. The second full paragraph in the left column on page 492 of Tancret et al. teaches use of machine learning to understand high entropy alloys by use of GP models. The paragraph bridging pages 490-491 of Tancret et al. teach that GP models are Gaussian Process models that employ regression as a machine learning technique.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the thermodynamic analysis using enthalpies, temperatures, and entropies of Qi et al., the thermodynamic relationships of Gibbs Energy, and the computers and image scanners of Mori et al. by use of other forms of machine learning of Tancret et al. wherein the motivation would have been that other forms of machine learning is an additional tool that facilitates understanding of high entropy alloys [paragraph bridging pages 490-491 of Tancret et al.].
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.
Double Patenting Rejection #1:
Claims 1, [4 or 5], and 9 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 11, and [16 or 19] of copending Application No. 17/380,549 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are analogously drawn to using features and binary phase diagrams to predict phase behavior of high-entropy alloys using machine learning. While the claims of ‘549 recite additional limitations relative to the instantly rejected claims, the claims of ‘549 anticipate the instantly rejected claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Double Patenting Rejection #2:
Claims [2, 3, 4, or 5] and [9, 10, 12, or 13] are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 9, respectively, of copending Application No. 17/380,549 (reference application) in view of Qi et al. Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are analogously drawn to using features and binary phase diagrams to predict phase behavior of high-entropy alloys. The claim of ‘549 does not teach the limitations of the dependent claims.
With regard to claims 2-4, the machine learning section on page 8 of Qi et al. teaches updating HEA data after optimization and mathematical variation of parameters.
With regard to claim 5, the machine learning section on page 8 of Qi et al. teaches using random forest models to further understand the chemical properties of HEAs.
With regard to claims 10 and 12, the paragraph bridging pages 490-491 of Tancret et al. teaches using GP models to give feedback to understanding and improving to high entropy alloys.
With regard to claim 13, the section bridging the seventh and eighth pages of Qi et al. teach the chemical properties of phase separation parameters and phase separation percentages.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the claims of ‘549 to use the machine learning and optimization techniques of Qi et al. wherein the motivation would have been that these mathematical techniques facilitate analysis of high-entropy alloy phase behavior [pages 7-8 of Qi et al.].
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Double Patenting Rejection #3:
Claims [6, 7, or 8] and 11 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 9, respectively, of copending Application No. 17/380,549 (reference application) in view of Qi et al. in view of Tancret et al. Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are analogously drawn to using features and binary phase diagrams to predict phase behavior of high-entropy alloys. The machine learning section on page 8 of Qi et al. teaches using random forest models to further understand the chemical properties of HEAs.
The claim of ‘549 does not teach the limitations of the dependent claims.
The document of Tancret et al. studies designing high entropy alloys by employing thermodynamics and Gaussian process statistical analysis [title]. The second full paragraph in the left column on page 492 of Tancret et al. teaches use of machine learning to understand high entropy alloys by use of GP models. The paragraph bridging pages 490-491 of Tancret et al. teach that GP models are Gaussian Process models that employ regression as a machine learning technique.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the claims of ‘549 and the thermodynamic analysis using enthalpies, temperatures, entropies, and machine learning of Qi et al. by use of other forms of machine learning of Tancret et al. wherein the motivation would have been that other forms of machine learning is an additional tool that facilitates understanding of high entropy alloys [paragraph bridging pages 490-491 of Tancret et al.].
Related Prior Art
The prior art document of Vecchio et al. [US PGPUB 2018/0172611] studies methods of selecting material compositions and designing materials having a target property [title]. The cover figure of Vecchio et al. illustrates a phase diagram comprising alloys. The prior art document of Park et al. [US PGPUB 2017/0233855 A1; on IDS] studies properties of high-entropy alloys (i.e. twin-induced plasticity and transformation-induced plasticity) that facilitates manufacturing of the alloys [abstract].
E-mail Communications Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
No claim is allowed.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Russell Negin, whose telephone number is (571) 272-1083. This Examiner can normally be reached from Monday through Thursday from 8 am to 3 pm and variable hours on Fridays.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Larry Riggs, Supervisory Patent Examiner, can be reached at (571) 270-3062.
/RUSSELL S NEGIN/Primary Examiner, Art Unit 1686 24 August 2026
Prosecution Insights