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 .
Claim Objections
Claim 14 is objected to because of the following informalities: “the material composition” should be followed with “of the material to be learned” for consistency and to avoid confusion with the material composition of the material to be predicted. 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 11-13 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites that the calculation unit is configured to add a value “when calculating the logarithmic value of the phase fraction”, however the examiner believes Applicant intends to recite that this process occurs for any logarithmic value of the phase fraction that may be calculated. This will be assumed for examination purposes, and the above quote will be replaced with “when calculating any logarithmic value of the phase fraction”.
Claim 12 recites dividing the predetermined temperature range into a specific range and a non-specific range then recites that in doing so the training apparatus “thereby [calculates] a loss function for the specific range and a loss function for the non-specific range separately”. However, dividing one range into two ranges does not imply that a loss function must be calculated for each range separately. At issue is the word “thereby”, which is used to assert that the former actions cause whatever follows. To fix this issue, the examiner will assume the following language for claim 12 for examination purposes:
The training apparatus according to claim 8, wherein the calculation unit is configured to divide the predetermined temperature range into a specific range including a plurality of phase formations and a plurality of phase disappearances and into a non-specific range, which is a range excluding the specific range, and to calculate a loss function for the specific range and a loss function for the non-specific range separately.
Claim 13 recites determining an amount “of each element”. The term “element” has not yet been used, therefore this represents proper antecedent basis because it refers to a set of elements which have not been recited. The examiner assumes Applicant intends to recite that, for each element of a set of elements comprising the material to be learned, the amount of that element to be included in the material is selected at random from a given range. The examiner recommends the following rewrite of claim 13, which will be used for examination purposes:
The training apparatus according to claim 6, wherein the material composition of the material to be learned included in the training data is determined by selecting at random an amount of a set of elements comprising the material to be learned.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At Step 1 of the 101 analysis, all claims are directed to one of the statutory categories of invention.
Claim 1 is rejected in response to the following analysis:
At Step 2A Prong One, the judicial exceptions are bolded in the copy of claim 1 below:
A prediction apparatus comprising:
a trained model trained by using training data in which a material composition of a material to be learned is associated with a phase fraction of the material to be learned at each temperature within a predetermined temperature range, the trained model being configured to predict a phase fraction at an i+1-th temperature by using a phase fraction predicted by the trained model for one or more temperatures up to an i-th temperature within the predetermined temperature range (where i is an integer of 1 or more), wherein
the prediction apparatus is configured to input a material composition of a material to be predicted into the trained model, thereby predicting a phase fraction of the material to be predicted at each temperature within the predetermined temperature range.
The descriptions of what the trained model is configured to perform represent mental or mathematical steps. The claim does not recite a method step of training a machine learning model; rather, it recites an apparatus, which is only limited by its structure (see MPEP § 2114).
At Step 2A, Prong Two, the additional element is a trained model which receives material composition data and outputs a phase fraction of a material to be predicted over a range of temperatures. The “material” can be interpreted broadly, as can the “phase fraction”. Is one predicting liquid vs. solid, hard vs. soft, amorphous vs. crystalline, superconducting vs. non-superconducting, etc.? All of these examples are consistent with the claim language not just of claim 1 but of all the claims. Therefore, the additional elements do not integrate the judicial exceptions into a practical application.
At Step 2B, when considered as a whole, the claim does not amount to significantly more than the judicial exceptions for the reasons given above.
Claims 2-5 recite further limitations but do not change the reasons for ineligibility given in claim 1, therefore these claims are also ineligible.
Claim 15 recites a method of executing, by a general-purpose computer, the trained model. However, this does not recite a method of training the model, and indeed uses the same language as claim 1 and so is ineligible for the same reasons.
Claim 17 recites a non-transitory computer-readable storage medium that stores a prediction program for causing a computer to execute the trained model of claim 1. This is ineligible for the same reasons as claim 1.
Claim 6 recites a training apparatus comprising a model which is trained. As was the case with claim 1, claim 6 is an apparatus claim and is therefore only limited by its structure. The structure of claim 6 is essentially the same as claim 1 and is therefore ineligible for the same reasons.
Claims 7-14 recite further limitations but do not change the reasons for ineligibility given in claim 6, therefore these claims are also ineligible.
Claims 16 and 18 relate to claim 6 in the same way that claims 15 and 17 relate to claim 1, and are rejected for the same reasons.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Okuno (US 20210406433 A1).
Regarding claims 1-14, Okuno discloses a prediction apparatus (Abstract: “A prediction device for predicting a thermodynamic equilibrium state of a target material”) comprising a trained model (Fig. 1, model 2). An apparatus claim is only limited by its structure (see MPEP § 2114), and the structure implied by claims 1-14 is indistinguishable from Okuno’s invention, which outputs a phase fraction of a material to be predicted at each temperature in a predetermined temperature range (see at least Fig. 3(c)-(d)).
Regarding claims 15-16, these claims are written as method claims, but instead of reciting a method of training a machine learning model, these method claims merely recite the execution of the apparatus of claims 1 and 6, and therefore are also anticipated by Okuno.
Regarding claims 17-18, these claims are anticipated by Okuno for the same reasons as claims 15-16.
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 factual inquiries 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.
Claims 1-8 and 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Okuno (US 20210406433 A1) in view of Pang (US 20230394283 A1).
Regarding claim 1, Okuno discloses a prediction apparatus (Abstract: “A prediction device for predicting a thermodynamic equilibrium state of a target material”) comprising:
a trained model (Fig. 1, model 2) trained by using training data (see Fig. 2 representing the training data, and ¶30) in which a material composition of a material to be learned (Fig. 2, the “explanatory variables” give a percent weight of components and an associated temperature) is associated with a phase fraction of the material to be learned (Fig. 2, “Target variables” give phase fractions) at each temperature within a predetermined temperature range (¶31: “Values of the temperature of the explanatory variable are a group of values selected within a predetermined temperature range (e.g., 0 to 1000° C.)”), the trained model being configured to predict a phase fraction at an i+1-th temperature within the predetermined temperature range (where i is an integer of 1 or more) (see Figs. 3(c)-(d) and 5(b), and ¶35: the model outputs a phase diagram over the whole range of temperature values included in the input data), wherein
the prediction apparatus is configured to input a material composition of a material to be predicted into the trained model, thereby predicting a phase fraction of the material to be predicted at each temperature within the predetermined temperature range (¶52: “With this configuration, appropriate predictive target variables with respect to unknown inputs that are different from the explanatory variables of the training data can be output by using a generalization capability of the trained model 2, thereby accurately predicting the thermodynamic equilibrium state.”).
Okuno does not explicitly disclose that the trained model predicts a phase fraction at an i+1-th temperature by using a phase fraction predicted by the trained model for one or more temperatures up to an i-th temperature. However, Okuno does disclose that the model is a multi-layer neural network (¶27), and further teaches that other supervised learning methods may be used (¶59).
Pang teaches a method of inputting environmental conditions and chemical properties of input components of a fluid mixture into an encoder-decoder machine learning model, then predicting the phase of the mixture (Abstract). The input data is ordered in a sequence (Abstract). The encoder-decoder may be a recurrent neural network (¶33), which works by operating on a sequence and using its own output as input for subsequent steps (¶34).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Pang with the invention of Okuno by causing the trained model to include an encoder-decoder recurrent neural network (RNN). Since the temperature values are naturally sequential, it would have been obvious to input the data into the encoder-decoder as a temperature sequence. By so doing, the encoder-decoder would predict a phase fraction at an i+1-th temperature by using a phase fraction predicted by the trained model for one or more temperatures up to an i-th temperature. Doing the above would implement a known multi-layer neural network method of performing phase predictions from material composition input.
Claims 15 and 17 recite a prediction method implementing a computer to execute the limitations of claim 1, and a non-transitory computer-readable storage medium that stores therein a prediction program for causing a computer to execute the limitations of claim 1. Both of these claims are rejected for the same reasons as claim 1.
Claim 6 recites a training apparatus implementing the method of training the trained model of claim 1 and is rejected for the same reasons as claim 1.
Claims 16 and 18 recite a training method implementing a computer to execute the limitations of claim 6, and a non-transitory computer-readable storage medium that stores therein a training program for causing a computer to execute the limitations of claim 6. Both of these claims are rejected for the same reasons as claim 6.
Regarding claim 2, Okuno in view of Pang teaches the limitations of claim 1, and further teaches that the trained model is applied with an architecture capable of calculating time series data that is data for each of predetermined time intervals (an encoder-decoder RNN is such an architecture), and the trained model predicts a phase fraction for each of predetermined temperature intervals based on the material composition of the material to be predicted (see rejection of claim 1 and at least Fig. 3 of Okuno as discussed above).
Regarding claim 3, Okuno in view of Pang teaches the limitations of claim 2, and further teaches that the trained model can be any one of an RNN, a gated recurrent unit (GRU), or a long short term memory (LSTM) (See rejection of claim 1 and Pang, ¶33: “The encoder and decoder may be neural networks, such as a gated recurrent network (GRU), and recurrent neural networks (RNN).” ¶34: “Further, the GRU or RNN may be a long short-term memory (LSTM) network”).
Regarding claim 4, Okuno in view of Pang teaches the limitations of claim 3, and further teaches that the trained model includes an encoder configured to output a feature, upon input of the material composition of the material to be predicted (the “encoder” portion of the encoder-decoder RNN; Pang, ¶36: “The encoder (204) may encode the input data (202) as a context vector. The context vector is a vector representation of the input data (202).”); and a decoder configured to predict the phase fraction at the i+1-th temperature, upon input of the output feature and the phase fraction predicted for the temperature up to the i-th temperature (the “decoder” portion of the encoder-decoder RNN; Pang, ¶37: “The decoder (208) may generate the output data (210) from the context vector.” From the rejection of claim 1 and ¶34: “A GRU and a RNN are networks that operate on a sequence and uses its own output as input for subsequent steps”. Based on the rejection of claim 1, in this context the “steps” are temperature sequences.).
Regarding claim 5, Okuno in view of Pang teaches the limitations of claim 1, and further teaches that the phase fraction is a phase fraction at thermodynamic equilibrium (see rejection of claim 1 and Abstract of Okuno).
Regarding claim 7, Okuno in view of Pang teaches the limitations of claim 6, and further teaches that the model includes an encoder configured to output a feature, upon input of the material composition of the material to be learned (the “encoder” portion of the encoder-decoder RNN; Pang, ¶36: “The encoder (204) may encode the input data (202) as a context vector. The context vector is a vector representation of the input data (202).”); and a decoder configured to output the output data corresponding to the phase fraction at the i+1-th temperature, upon input of the output feature and the ground truth data of the phase fraction for the temperature up to the i-th temperature (the “decoder” portion of the encoder-decoder RNN; ¶37: “The decoder (208) may generate the output data (210) from the context vector.” From the rejection of claim 1 and ¶34: “A GRU and a RNN are networks that operate on a sequence and uses its own output as input for subsequent steps”. Based on the rejection of claim 1, in this context the “steps” are temperature sequences.).
Regarding claim 8, Okuno in view of Pang teaches the limitations of claim 6, and further teaches a calculation unit (part of the model training unit 4 of Okuno; see ¶37) configured to, upon input of the material composition of the material to be learned, compare the output data output by the model with the phase fraction at each temperature within the predetermined temperature range that is associated with the material composition of the material to be learned (Okuno, ¶37: “The machine learning performed by the model training unit 4 on the model 2 may be configured to be performed so as to reduce the output error of each data set of the training data, or may be configured to be performed so that the phase diagram based on the outputs of the model 2 illustrated in FIG. 3(c) or FIG. 5(b) approaches the phase diagram of the training data illustrated in FIG. 3(a) or FIG. 5(a).”), thereby calculating a loss function (reducing the output error or causing the output phase diagram to approach the test data’s phase diagram imply calculation of a “loss function”).
Regarding claim 12, Okuno in view of Pang teaches the limitations of claim 8. Furthermore, one with the apparatus of Okuno in view of Pang would not be able to distinguish from the phase fraction output by the training apparatus (see Okuno, at least Fig. 3(c)-(d)) whether or not the calculation unit divided the predetermined temperature range into a specific range including a plurality of phase transformations and a plurality of phase disappearances and into a non-specific range, which is a range excluding the specific range, and calculated a loss function for the specific range and a loss function for the non-specific range separately. Therefore, Okuno in view of Pang and Piatt teaches the same structure as is implied by claim 12.
Regarding claim 13, Okuno in view of Pang teaches the limitations of claim 6, and further teaches that the material composition of the material to be learned included in the training data is determined by selecting an amount between a lower limit and an upper limit of an amount of each element of a set of elements comprising the material to be learned (Okuno, ¶31: “the value groups of the respective elements are generated by selecting values of the respective elements within predetermined ranges.”).
Okuno in view of Pang does not explicitly teach that the selection is random, however it would have been obvious to one of ordinary skill in the art practicing the invention of Okuno in view of Pang to cause the selection to be random in order to seek to avoid creating training data with statistical correlations which would bias the trained model, making the trained model less generalizable.
Regarding claim 14, Okuno in view of Pang teaches the limitations of claim 6, and further teaches that the material composition of the material to be learned includes any one of a chemical composition indicating a ratio of chemical components contained in a material; or an alloy composition indicating a ratio of metallic elements or non-metallic elements included in an alloy (see Figs. 2 and 4 of Okuno, where the input includes a percent weight (equivalent to a ratio) of components in a material. Also in Pang, ¶31, the input data includes “the molar fractions of the input components in the input fluid mixture”).
While Okuno in view of Pang does not explicitly state that the material composition of the material to be learned includes each chemical component contained in a material, or each metallic element or each non-metallic element included in an alloy, it would have been obvious to do so in order for the input data to completely describe the composition of the material to be learned.
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Okuno (US 20210406433 A1) in view of Pang (US 20230394283 A1), and further in view of Piatt (US 20220311380 A1).
Regarding claim 9, Okuno in view of Pang teaches the limitations of claim 8 but does not explicitly disclose the limitations of claim 9. However, it would have been obvious to configure the calculation unit to calculate the loss function and to output a loss in order to report a numerical measure of similarity between the model’s output and the expected output.
Piatt discloses a method for bifacial solar modeling (Abstract), and calculates a loss function to compare an expected bifacial gain with an actual bifacial gain, where the loss function can be a mean squared error (MSE) loss function or a mean squared logarithmic error (MSLE) loss function or a mean absolute error (MAE) loss function (¶5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Piatt with the invention of Okuno in view of Pang by causing the loss to include at least any one of:
a second addition result obtained by adding, for the predetermined temperature range, an error between a phase fraction at each temperature included in the output data and a phase fraction at each temperature included in the training data;
a third addition result obtained by adding, for the predetermined temperature range, an error between a logarithmic value of the phase fraction at each temperature included in the output data and a logarithmic value of the phase fraction at each temperature included in the training data; or
a fifth addition result obtained by adding, for the predetermined temperature range, an error between a ratio of the phase fraction at each temperature included in the output data and a ratio of the phase fraction at each temperature included in the training data.
The second and fifth addition results would be encompassed by MSE (MSE involves dividing the error by the mean, which could be incorporated into the phase fraction values to obtain ratios of the phase fraction values), and the third addition result would be encompassed by MSLE. Doing so would apply known error calculation methods to generate a quantitative loss.
Regarding claim 10, Okuno in view of Pang and Piatt teaches the limitations of claim 9. Furthermore, one with the apparatus of Okuno in view of Pang and Piatt would not be able to distinguish from the phase fraction output by the training apparatus (see Okuno, at least Fig. 3(c)-(d)) whether or not the calculation unit performed a weighted addition of the first addition result to the fifth addition result. Therefore, Okuno in view of Pang and Piatt teaches the same structure as is implied by claim 10.
Regarding claim 11, Okuno in view of Pang and Piatt teaches the limitations of claim 9 but does not explicitly teach the limitations of claim 11. However, consider that for all
a
,
log
a
x
<
0
if x is less than 1, and furthermore
lim
x
→
0
log
a
x
=
-
∞
. When calculating MSLE it would be useful to keep the arguments of the log functions equal to or greater than 1, to avoid the unbounded behavior of logarithms over
(
0
,
1
)
. Phase fractions can be any value from 0 to 1; to avoid a log argument in
(
0
,
1
)
, then, one would need to add a value to the argument unless the phase fraction is 1. Therefore, it would have been obvious to one of ordinary skill in the art practicing the invention of Okuno in view of Pang and Piatt to configure the calculation unit to add a value according to a decimal place of the phase fraction when calculating any logarithmic value of the phase fraction, thereby making the logarithmic value of the phase fraction non-negative. This would ensure that the log functions in the MSLE calculations never have an argument in the range
(
0
,
1
)
, thereby avoiding the unbounded behavior near the origin.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 6, and 13-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of copending Application No. 18/859,209 in view of Okuno (US 20210406433 A1) and Pang (US 20230394283 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations of the present application not found in copending Application No. 18/859,209 would have been obvious over Okuno in view of Pang as argued above.
Claims 2-5 and 8 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 3-6 and 9, respectively, of copending Application No. 18/859,209 in view of Okuno (US 20210406433 A1) and Pang (US 20230394283 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations of the present application not found in copending Application No. 18/859,209 would have been obvious over Okuno in view of Pang as argued above.
Claims 7 and 12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 5 and 9, respectively, of copending Application No. 18/859,209 in view of Okuno (US 20210406433 A1) and Pang (US 20230394283 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations of the present application not found in copending Application No. 18/859,209 would have been obvious over Okuno in view of Pang as argued above.
Claims 9-11 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 10-12, respectively, of copending Application No. 18/859,209 in view of Okuno (US 20210406433 A1) and Pang (US 20230394283 A1) and Piatt (US 20220311380 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations of the present application not found in copending Application No. 18/859,209 would have been obvious over Okuno in view of Pang and Piatt as argued above.
This is a provisional nonstatutory double patenting rejection.
Conclusion
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ETHAN WESLEY EDWARDS
Examiner
Art Unit 2857
/E.W.E./ Examiner, Art Unit 2857
/ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights