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 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 1-13 are pending and examined in the instant Office action.
While the claims recite judicial exceptions, the core limitations of the claims involve the use of neural networks to analyze chemical structure and property data. These limitations are too complex to be carried out in the human mind. Consequently, the claims are subject matter eligible.
Information Disclosure Statements
The IDSs filed have been considered.
Claim Rejections - 35 USC § 112(b) - Indefiniteness
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 5 and 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Each of claims 5 and 11 recite calculating a “center point” in the image of the plurality of expression regions based on the coordinate information and obtaining a pixel value of the center point. In this limitation, it is unclear as to whether the “center point” is the center pixel in the image, the pixel locates at the center of mass of the structure, the pixel happening to be in the middle of the particular display, or any other measure. For the purpose of examination, it is interpreted that the “center point” can be represented by any pixel within the image.
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) 1-2, 4-8, and 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoo et al. [US PGPUB 2017/0124482] in view of Lim et al. [Journal of Cheminformatics, volume 10, 11 July 2018, 9 pages; on IDS] in view of Smellie et al. [US PGPUB 2014/0173475 A1].
Claim 1 is drawn to a method of generating a chemical structure by using a neural network apparatus. The method comprises inputting an image of a chemical structure to a trained neural network that generates a property value of a property of the chemical structure, the image of the chemical structure, and the property of the chemical structure being a characteristic possessed by the chemical structure. The method comprises determining an expression region for expressing the property in the image wherein the expression region comprises one or more pixels in the image. The method comprises generating a new chemical structure by modifying a partial structure in the chemical structure wherein the partial structure comprises the expression region.
Claim 7 is drawn to similar subject matter as claim 1, except claim 7 is drawn to an apparatus.
Claim 13 is drawn to similar subject matter as claim 1, except claim 13 is drawn to a non-transitory computer readable medium.
The document of Yoo et al. studies a method and device for searching new material [title]. Figure 6 of Yoo et al. illustrates the computer limitations of the claims. Figure 2 of Yoo et al. teaches inputting a model for learning an integration of a physical property to determine a new candidate model. Figure 2 of Yoo et al. also teaches verification steps. Figure 3 of Yoo et al. illustrates an example of a structural model. Claim 3 of Yoo et al. teaches that the analysis pertains to partial structures.
Yoo et al. does not teach neural networks, pixelated images, and expression regions of the models.
The document of Lim et al. studies molecular generative models based on conditional variational autoencoder for de novo molecular design [title]. The abstract of Lim et al. teaches generating designs of molecules with specific drug-like properties. Figure 1 and column 1 on page 2 of Lim et al. teaches that on addition to the advantages of using the latent space, the algorithm can incorporate the information of molecular properties in the encoding process and manipulate them in the decoding process. Column 1 on page 2 of Lim et al. teaches using CVAE as a molecular generator as one of the most popular generative models which generates objects similar to but not identical to a given dataset. Page 3, column 1 of Lim et al. teaches that the latent vector concatenated with the condition vector becomes an input of the decoder at each time step of the RNN cell. Page 2, column 2 of Lim et al. teaches that the control was represented as a control vector where structure and properties are controlled independently. Lim et al. illustrates structures that are expression regions in Figures 3-6.
Yoo et al. and Lim et al. do not teach pixelated structural images.
Paragraph 86 of Smellie et al. teaches that images of chemical structures visualized as pixelated images with selectable pixels.
With regard to claims 2 and 8, Figure 1 of Lim et al. illustrates using CVAE to determine whether a property value is expressed by the structure in a chemical structure. Claim 3 of Yoo et al. teaches that the analysis pertains to partial structures.
With regard to claims 4-5 and 10-11, the first paragraph on page 4 of Lim et al. teaches adding Gaussian noise to images to compare structural models within a given error range (i.e. the center point within Aspirin and/or Tamiflu). Figure 3 of Lim et al. teaches the result of other molecules picked as a result of the algorithm in the presence of Gaussian noise. Paragraph 86 of Smellie et al. teaches that images of chemical structures visualized as pixelated images with selectable pixels.
With regard to claims 6 and 12, Figure 6a of Lim et al. illustrates storing structures with a LogP larger than 5.5, and Figure 6b of Lim et al. illustrates storing structures with TPSA larger than 165.
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 partial structural and functional modeling of Yoo et al. by use of the neural networks and expression region analysis of Lim et al. wherein the motivation would have been that Lim et al. adds machine learning and analysis tools that facilitate structural and functional analysis of chemicals [abstract and figures of Lim et al.].
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 partial structural and functional modeling of Yoo et al. and the neural networks and expression region analysis of Lim et al. by use of the pixelated images of Smellie et al. wherein the motivation would have been that Smellie et al. teaches use of selectable pixels as a tool for fine image analysis [paragraph 86 of Smellie et al.].
There would have been a reasonable expectation of success in combining Yoo et al., Lim et al., and Smellie et al. because all three studies are analogously applicable to understanding structural models of small chemical compounds.
35 U.S.C. 103 Rejection #2:
Claim(s) 3 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lim et al. as applied to claim 1-2, 4-8, and 10-13 above, in further view of Bach et al. [WO 2016/150472 A1; on IDS].
Claims 3 and 9 are further limiting comprising determining the expression region for expressing the property in the descriptor by applying LRP technique to the trained neural network. The claims require the activation function applied to a node of the trained neural network to be designated as a linear function applied to apply the LRP technique to the trained neural network and a MSE to be designated for optimization.
Yoo et al., Lim et al., and Smellie et al. make obvious use CVAE to design molecules with structural and functional properties, as discussed above.
Yoo et al., Lim et al., and Smellie et al. do not teach LRP or MSE in optimization.
Bach et al. teaches improved efficiency of generating, using LRP on a trained neutral network, wherein an activation function applied to a node of the trained neural network, wherein an activation function applied to a node of the trained neural network is designated is designated as a linear function to apply the LRP technique to the trained neural network, and an MSE is designated for optimization. Figure 13, page 58, lines 24-27; page 21, lines 1-4; page 51, line 29; of Bach et al. at least suggest applying an LRP analysis to the neural network and using MSE for optimization.
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 structural and functional modeling of Yoo et al. and Lim et al. and the pixelated imaged of Smellie et al. to include the LRP and MSE of Bach et al. wherein the motivation would have been that LRP and MSE are additional mathematical tools that facilitates structure and function analysis [Figure 13, page 58, lines 24-27; page 21, lines 1-4; page 51, line 29 of Bach 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, 2, 4, 5, or 6] and [7, 8, 10, 11, 12, or 13] are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 13, respectively, of U.S. Patent No. 10,957,419 B2 in view of Yoo et al. in view of Lim et al. in view of Smellie et al.
Both sets of claims are analogously drawn to integrating structural and function chemical models into machine learning algorithms to arrive at a new structure. The claims of ‘419 do not teach partial structures, neural networks, or pixelated images.
Claim 3 of Yoo et al. teaches applying the analysis to partial structures.
Page 3, column 1 of Lim et al. teaches that the latent vector concatenated with the condition vector becomes an input of the decoder at each time step of the RNN cell. Page 2, column 2 of Lim et al. teaches that the control was represented as a control vector where structure and properties are controlled independently. Lim et al. illustrates structures that are expression regions in Figures 3-6.
Paragraph 86 of Smellie et al. teaches that images of chemical structures visualized as pixelated images with selectable pixels.
With regard to claims 2 and 8, Figure 1 of Lim et al. illustrates using CVAE to determine whether a property value is expressed by the structure in a chemical structure. Claim 3 of Yoo et al. teaches that the analysis pertains to partial structures.
With regard to claims 4-5 and 10-11, the first paragraph on page 4 of Lim et al. teaches adding Gaussian noise to images to compare structural models within a given error range (i.e. the center point within Aspirin and/or Tamiflu). Figure 3 of Lim et al. teaches the result of other molecules picked as a result of the algorithm in the presence of Gaussian noise. Paragraph 86 of Smellie et al. teaches that images of chemical structures visualized as pixelated images with selectable pixels.
With regard to claims 6 and 12, Figure 6a of Lim et al. illustrates storing structures with a LogP larger than 5.5, and Figure 6b of Lim et al. illustrates storing structures with TPSA larger than 165.
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 structural and functional machine learning algorithm and models of the claims of ‘419 by use of the partial structural and functional modeling of Yoo et al. wherein the motivation would have been that Yoo et al. enables narrowing down the analysis to the region of interest on the compound [claim 3 of Yoo et al.].
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 partial structural and functional modeling of the claims of ‘419 and Yoo et al. by use of the neural networks and expression region analysis of Lim et al. wherein the motivation would have been that Lim et al. adds machine learning and analysis tools that facilitate structural and functional analysis of chemicals [abstract and figures of Lim et al.].
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 partial structural and functional modeling of the claims of ‘419 and Yoo et al. and the neural networks and expression region analysis of Lim et al. by use of the pixelated images of Smellie et al. wherein the motivation would have been that Smellie et al. teaches use of selectable pixels as a tool for fine image analysis [paragraph 86 of Smellie et al.].
There would have been a reasonable expectation of success in combining the claims of ‘419, Yoo et al., Lim et al., and Smellie et al. because all four studies are analogously applicable to understanding structural models of small chemical compounds.
Double Patenting Rejection #2:
Claims 3 and 9 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 13, respectively, of U.S. Patent No. 10,957,419 B2 in view of Yoo et al. in view of Lim et al. in view of Smellie et al., as applied to Double Patenting Rejection #1 above, in further view of Bach et al.
Both sets of claims are analogously drawn to integrating structural and function chemical models into machine learning algorithms to arrive at a new structure. The claims of ‘419 do not teach LRP and MSE analysis of the compounds.
Bach et al. teaches improved efficiency of generating, using LRP on a trained neutral network, wherein an activation function applied to a node of the trained neural network, wherein an activation function applied to a node of the trained neural network is designated is designated as a linear function to apply the LRP technique to the trained neural network, and an MSE is designated for optimization. Figure 13, page 58, lines 24-27; page 21, lines 1-4; page 51, line 29; of Bach et al. at least suggest applying an LRP analysis to the neural network and using MSE for optimization.
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 structural and functional modeling of the claims of ‘419, Yoo et al., and Lim et al. and the pixelated imaged of Smellie et al. to include the LRP and MSE of Bach et al. wherein the motivation would have been that LRP and MSE are additional mathematical tools that facilitates structure and function analysis [Figure 13, page 58, lines 24-27; page 21, lines 1-4; page 51, line 29 of Bach et al.].
Related Prior Art
The document of De Winter et al. [US PGPUB 2010/0010946 A1; on IDS] studies a method for evolving molecules [title]. Paragraph 13 of De Winter et al. teaches using a combination of SMILES and genetic algorithms to more effectively enable the design of molecules. The genetic algorithms of De Winter et al. are an additional mathematical tool that facilitates structure and function analysis.
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 22 September 2026
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