DETAILED ACTION
Applicant's response, filed 07/08/2026, has been fully considered. The following rejections
and/or objections are either reiterated or newly applied. They constitute the complete set presently
being applied to the instant application.
Claim Status
Claims 5 and 10 were cancelled.
Claims 1-4 and 6-9 are rejected.
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 .
Priority
This application is a 371 of PCT/JP2021/01544. The certified copy was appropriately filed. This application claims foreign priority to Japanese application #JP2020-079793, filed 4/28/2020. Foreign priority is acknowledged. Therefore, the effective filing date of claims 1-10 is 4/28/2020.
Drawings
The drawings submitted on 10/20/2022 have been accepted.
Claim Rejections - 35 USC § 112
The rejections under 112 have been withdrawn in light of the claim amendments.
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 1-4 and 6-9 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.
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:
1, 6. generate a low-dimensional descriptor including the predetermined number of elements for each of multiple materials, predict each of the characteristic values of the multiple materials from the low-dimensional descriptor using the first machine learning model
1, 6. select a part of materials from the multiple materials based on the characteristic value
1, 6. generate a high-dimensional descriptor having the number of elements larger than the predetermined number for each of the part of the materials
1, 6. predict each of the characteristic values of the part of the materials from the high-dimensional descriptor using the second machine learning model
1, 6. wherein parameters of the first machine learning model are updated based on an error between the predicted value of the characteristic value and the characteristic value from the experimented material database to optimize the first machine learning model
2, 7. The system according to claim 1, wherein types of all elements of the low-dimensional descriptor are included in types of elements of the high-dimensional descriptor.
3, 8. calculate importance to be placed on characteristic value prediction of the type of the element of the high-dimensional descriptor by implementing a prescribed method
3, 8. select the type of the element of the low-dimensional descriptor from the type of the element of the high-dimensional descriptor based on the importance
4, 9. execute training of the first machine learning model using first training data selected from the measurement data, and learning of the second machine learning model using second training data larger in size than the first training data selected from the measurement data
The limitations for “generating,” “predicting,” “selecting”, “calculating”, and to “execute training” are directed to a mental process and mathematical relationship. These limitations are using verbal equivalents to mathematical calculations in order to describe an algorithm that could be performed practically by a human being using a pen and paper. Applicant states that some limitations are performed by a machine learning model. Applicant’s specification states: “Arbitrary regression algorithms may be utilized by the low-dimensional material property prediction model and the high-dimensional material property prediction model. Those algorithms may be the same or different from each another. An arbitrary algorithm may be selected from various types of regression algorithms including the random forest, support vector machine, Gaussian process regression…” (¶ 31). Linear regression can be practically performed by a human being using a pen and paper. Calculating an importance or weight can also be practically performed by a human being using a pen and paper. All of the limitations are directed to the descriptors, so they form part of the mathematical relationship and mental process.
While claims 1-4 and 6-9 recite performing some aspects of the analysis with a “system”, there are no additional limitations that indicate that this system requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. 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, claims 1-4 and 6-9 recite an abstract idea ( 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 effect 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. Specifically, the claims recite the following additional elements:
1, 6. A system for predicting a material property value, the system including one or more processors and one or more storage devices
1, 6. receive experimental data indicating characteristic values of various materials from a user through an input device, and store the data in an experimented material database,
4, 9. the storage device stores measurement data including characteristic measured values of the multiple materials
1, 6. the one or more processors output the part of the materials, and the characteristic value predicted by the second machine learning model on a monitor
There are no limitations that indicate that the claimed “system” 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.
The limitation of claims 1 and 6 to display the results of claim 1 on a monitor is a type of “apply it” limitation that generally recites the idea of an outcome without providing details, similar to Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017).
The additional elements of claims 1, 4, 6, and 9 do not add a meaningful limitation to the abstract idea because they amount to mere data gathering or output steps that would be required for the claimed mental processes. These limitations serve to gather or output data that is used as input or output for the abstract idea and there is no indication that the abstract idea has any impact on those data gathering or output steps. The courts have indicated that mere data gathering or output activity is insignificant extra-solution activity that does not provide a practical application (see MPEP 2106.05(g)).
As such, claims 1-4 and 6-9 are directed to an abstract idea ( 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 instant claims recite additional elements listed above, in the previous section on step 2A.
The additional elements of claims 1, 4, 6, and 9 do not provide an inventive concept. Data storage and output of information on a monitor are well-known, not related to the invention, and amount to necessary data gathering and output, as determined in step 2A. These limitations are similar to Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 respectively.
As discussed above, there are no additional limitations to indicate that the claimed system 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 1-4 and 6-9 are not patent eligible.
Response to Arguments - 35 USC § 101
Applicant's arguments filed 07/08/2026 have been fully considered but they are not persuasive.
Applicant asserts that “the human mind is not practically capable of updating a machine learning model to optimize the model.” (pg 10 ¶ 2) This argument is not persuasive. Machine learning, when recited broadly as it is in the current claim sets, encompasses a multitude of mathematical models, some of which are quite simple and able to be performed by a human being on a limited data set, such as linear regression. Updating the parameters of the machine learning model would entail tallying the differences between the predicted values and the database values, then incorporating that information back into the parameters of the regression, which could be performed by a human being (see MPEP 2106.04.II.A.1).
Next (pg 11 ¶ 1), applicant asserts that the link to a database of material properties provides a “direct link to physical reality.” This argument is not persuasive. The claims only stipulate importing data from an input into the computer, which could be accomplished by a generic computer, such as with a USB drive. Therefore, this limitation is mere data gathering (See MPEP 2106.05(g)).
Next (pg 12 ¶ 1-2), applicant asserts that the improvement to virtual screening is an improvement to technology because it reduces computational effort, and that it applies the abstract idea in a meaningful way. The improvements to the virtual screening process were determined to be mathematical processes and not the additional elements in step 2A. These limitations are using verbal equivalents to mathematical calculations in order to describe an algorithm that could be performed practically by a human being using a pen and paper, so they cannot provide a practical application or apply the abstract idea in a particular way (see MPEP 2106.04.II.A.1).
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.
The previous rejection was withdrawn due to the claim amendments.
Claims 1-4 and 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Petinrin et al. (IEEE Access, vol. 7, pp. 153952-153957, 2019, IDS Reference, previously cited) in view of Vogt et al (J. Chem. Inf. Model. 2007, 47, 39-46, henceforth “Vogt”, previously cited) and Davis et al. (AMIA Annu Symp Proc. 2020 Mar 4;, newly cited, henceforth “Davis”).
Regarding claim 1, the invention is directed to predicting a value for a property of a material. Petinrin discloses a method for predicting the bioactivity value of a molecular compound (abstract). Petinrin achieves this through a stacked ensemble of machine learning models, made up of multiple base classifiers (abstract), thus having a first and second machine learning model. Algorithm 1 and Figure 1 of Petinrin, on page 3, describe the stacked ensemble model.
Petinrin is silent as to the low- and high-dimensional descriptors.
Vogt teaches a function called DACCS, where low-dimensional descriptors are used to generate a high-dimensional descriptor which is used to predict the activity of compounds (Vogt pg 39 left col ¶ 1).
Petinrin is silent as to the computer implementation of the algorithm. However, its implementation on a computer is an obvious automation, see In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
MPEP 2121: “When the reference relied on expressly anticipates or makes obvious all of the elements of the claimed invention, the reference is presumed to be operable. Once such a reference is found, the burden is on applicant to rebut the presumption of operability." The Petinrin reference is presumed to be implemented on a generic computer, as the reference points to the implementation (abstract). Implementation of the system on a generic computer would include outputting the values on a display.
Petinrin suggests applying the model to new datasets of materials (pg 5 left col ¶ 3). Uploading data to a computer is part of the normal functions of a generic computer.
Davis teaches updating the parameters of a machine learning model based on error between predicted values and database values (abstract).
For claim 2, Vogt’s DACCS algorithm takes 2D compounds as an input (Vogt pg 39 right col ¶ 2) and uses a distance function to place them in a higher-dimensional “active subspace” (Vogt pg 40 left col ¶ 1). Therefore, the types of the elements of the low-dimensional descriptor are included in the higher-dimensional descriptor.
With respect to claim 3, Petinrin is silent as to the stacked ensemble algorithm giving “importance” or weight to the base classifiers or the meta-classifier. Vogt’s DACCS method gives each compound a unit weighting in the distance function (Vogt pg 40 left col ¶ 1). Vogt goes on to detail BDACCS, a search method which uses log-odds to minimize the distance from the “activity center” and states: “In addition to considering active molecules, the log-odds formulation explicitly takes the descriptor value distributions of database compounds into account.” (Vogt pg 40 right col ¶ 4)
Moving on to claim 4, Algorithm 1 of Petinrin shows that the meta-classifier is trained on Ds, which is larger than D because it contains all the predictions of the base classifiers.
Petinrin and Vogt are silent as to the computer implementation. Regarding claims 1 and 6, displaying the output of the algorithm on a generic computer would be a part of the automation of the manual activity of writing the result of the algorithm, see In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958).
Claims 6-9 recite the exact same limitations as claims 1-4, only differing in that they are directed to “a method implemented by a system” rather than the system itself. The same arguments against claims 1-4 apply against claims 6-9, mutatis mutandis.
Regarding claims 1-4, 6-9, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use low and high dimensional descriptors with weighting in the text of Vogt (Vogt pg 39 left col ¶ 1) (Vogt pg 40 left col ¶ 1). There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as there is no technical problem blocking the ordinary artisan from adding the low and high dimensional descriptors with weighting to the algorithm. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the algorithm of Petinrin by adding the low-and-high dimensional descriptor process, weighting and a computer implementation, in order to find more compounds proximal to an “active subspace” (Vogt pg 40 left col ¶ 2).
Regarding claims 1-4, 6-9, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use parameter optimization for machine learning in the text ofDavis, in order to reduce errors. There would be a reasonable expectation of success in making this combination to a person of ordinary skill in the art, as both methods are machine learning value prediction methods. Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the system of Petinrin and Vogt by implementing the optimization of Davis, in order to reduce errors (abstract).
Response to Arguments - 35 USC § 103
Applicant’s arguments, see page pg 12 ¶ 4 – pg 16 ¶ 3, filed 07/08/2026, with respect to the rejections of claims 1-4 and 6-9 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection has been made in view of Davis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACELYN M HILL whose telephone number is (571)272-9871. The examiner can normally be reached Monday-Friday 8:30-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia M. Wise, can be reached at 571-272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G.M.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
Prosecution Insights