DETAILED ACTION
This Office action is in reply to correspondence filed 20 July 2026 in regard to application no. 18/557,588. Claims 20-36 have been cancelled. Claims 1-19 and 37 are pending and are considered below.
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 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 1-19 and 37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims lie within statutory categories of invention, as each is directed to a method (process), non- transitory computer readable medium (manufacture), or a system (machine). The claim(s) recite(s) gathering data (the feeding step, which is data gathering from the point of view of the system receiving the data), and providing an estimate in no particular manner. The machine learning and random sampling limitations are within a “wherein” clause which may describe events which happened before and outside the scope of the claimed process (in each embodiment), and are not further considered.
This recites human mental work; in the absence of computers, these steps can be practically performed in the human mind. A person can gather data, by observation or by examining paper records, and can set parameters mentally. None of this presents any practical difficulty and none requires any technology at all.
This judicial exception is not integrated into a practical application because aside from the bare inclusion of a generic computer, discussed below, nothing is done beyond what was set forth above, which does not go beyond using a generic computer as a tool to implement the abstract idea. See MPEP § 2106.05(f).
As the claims only manipulate numerical data used as parameters for models and, apparently, date related to output of the models, they do not improve the "functioning of a computer" or of "any other technology or technical field". See MPEP § 2106.05(a). They do not apply the abstract idea "with, or by use of a particular machine", MPEP § 2106.05(b), as the below-cited Guidance is clear that a generic computer is not the particular machine envisioned.
They do not effect a "transformation or reduction of a particular article to a different state or thing", MPEP § 2106.05(c). First, such data, being intangible, are not a particular article at all. Second, the claimed manipulation is neither transformative nor reductive; as the courts have pointed out, in the end, data are still data.
They do not apply the abstract idea "in some other meaningful way beyond generally linking [it] to a particular technological environment", MPEP § 2106.05(e), as the lack of technical and algorithmic detail in the claims is so as not to go beyond such a general linkage.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional claim limitations, considered individually and as an ordered combination, are insufficient to elevate an otherwise-ineligible claim.
Taking claims 19 and 37 together, they include a processor and memory which, apparently, stores instructions. These elements are recited at a high degree of generality and the specification does not meaningfully limit them, such that a generic computer will suffice and is encompassed. It only performs generic computer functions of storing and receiving information and nondescriptly manipulating the information. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea.
The type of information being manipulated does not impose meaningful limitations or render the idea less abstract. Referring to an algorithm as a "machine learning process", without more, is considered mere labeling and given no patentable weight. The training limitations of the independent claims are considered but given no patentable weight as explained above; even if it were otherwise, in light of Recentive1, the use of known machine learning techniques where the only modification is in the type of data used or produces is not, per se, sufficient.
The claim elements when considered as an ordered combination - a generic computer performing a chronological sequence of abstract steps - do nothing more than when they are analyzed individually. The other independent claims are simply different embodiments but are likewise directed to a generic computer performing, essentially, the same process.
The dependent claims further do not amount to significantly more than the abstract idea: claims 2, 3 and 9-11 are simply further descriptive of the type of information being manipulated. Claims 4 and 13-18 simply and broadly recite well-known machine learning techniques. Claim 5 requires nothing more than storing information. Claims 6 and 7 purport to limit processes outside the scope of the claimed method, and claims 8 and 12 simply recite further, abstract manipulation of data.
The claims are not patent eligible. The Examiner, as mentioned previously, has thoroughly considered the originally-filed application in its entirety and finds nothing likely sufficient to overcome this rejection.
For further guidance please see MPEP § 2106.03 – 2106.07(c) (formerly referred to as the “2019 Revised Patent Subject Matter Eligibility Guidance”, 84 Fed. Reg. 50, 55 (7 January 2019, revised October 2019)).
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.
Claim(s) 1-13, 19 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Hubbell et al. (U.S. Publication No. 2020/0105374) in view of Gunes et al. (U.S. Publication No. 2019/0370684).
In-line citations are to Hubbell.
With regard to Claim 1:
Hubbell teaches: A method for estimating parameters of a model that is non-linear and is more complex than a log-linear model, the method comprises:
feeding measured observations and sampling coordinates of the measured observations to a machine learning process; [0115; vectors are used; 0019; they may represent observed data based on training samples; 0102; the information is used to train models] and
processing the measured observations and the sampling coordinates of the measured observations, by the machine learning process, to provide an estimate of the parameters of the model; wherein the model is indicative of a relationship between the measured observations and the sampling coordinates... [0129; a re-estimation of dispersion parameters is performed by the machine learning engine]
Hubbell does not explicitly teach the machine learning process is trained during a training period in which the machine learning process is fed by training observations and training sampling coordinates, or some of the training sampling coordinates are generated by randomly biasing initial training sampling coordinates to provide randomly biased training sampling coordinates, and though it is of no patentable significance as explained above, it is known in the art.
Gunes teaches a system for tuning a machine learning model. [title] It produces a “trained model for use in predicting” a “value for an observation vector”. [0016] The training data may include sampled data, [0035] and may compute biases by using a “stochastic gradient descent [] algorithm that minimizes a root mean square error” criterion. [0042] A “performance measure value” is computed, [0003] which may be based on a comparison of observed data with data in the training dataset. [0026] It may use a “neural network”. [0013] Gunes and Hubbell are analogous art as each is directed to the use of machine learning in statistical analysis.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Gunes with that of Hubbell in order to improve accuracy of a machine-learning model, as taught by Gunes; [0002] further, it is simply a substitution of one known part for another with predictable results, simply making Gunes’ computations in place of, or in addition to, those of Hubbell; the substitution produces no new and unexpected result.
With regard to Claim 2:
The method according to claim 1 wherein the model is a multi-exponential model. [0118; the model may be based on an "exponentially-modulated poser law"]
This claim is not patentably distinct from claim 1 as it consists entirely of nonfunctional, descriptive language, purporting to limit the type of model, but which imparts neither structure nor functionality to the claimed method. The reference is provided for the purpose of compact prosecution.
With regard to Claim 3:
The method according to claim 1 wherein the model is a diffusion weighted magnetic resonance imaging model.
This claim is not patentably distinct from claim 1 as it consists entirely of nonfunctional, descriptive language, purporting to limit the type of model, but which imparts neither structure nor functionality to the claimed method.
With regard to Claim 4:
The method according to claim 1, further comprising training. [Hubbell, as cited above in regard to claim 1]
With regard to Claim 5:
The method according to claim 1, further comprising responding to the estimate of the parameters of the model, where the responding comprises at least one of applying the model, storing the model, sending the model, uploading the model to a cloud computing environment, or generating MRI results. [0109; multiple models are stored]
With regard to Claim 6:
The method according to claim 1 wherein some of the training observations represent estimated training observations obtained at the randomly biased trained sampling coordinates.
This claim is not patentably distinct from claim 1, as it consists entirely of nonfunctional, descriptive language which discloses merely human interpretation of data but which imparts neither structure nor functionality to the claimed method.
With regard to Claim 7:
The method according to claim 1 wherein some of the training observations are simulated.
This claim is not patentably distinct from claim 1. As no claim from which this claim depends performs any creation of training observations, the manner of their creation purports to limit a step outside the scope of the claimed process.
With regard to Claim 8:
The method according to claim 1 comprising training the machine learning during a training period, wherein the training comprises feeding the machine learning process by training observations and training sampling coordinates. [0019; 0102 as cited above in regard to claim 1; any training necessarily occurs during a training period]
With regard to Claim 9:
The method according to claim 8 comprising generating some of the training sampling coordinates by randomly biasing initial training sampling coordinates to provide randomly biased training sampling coordinates.
This claim is not patentably distinct from claim 8. As claim 8 does not perform any generating, and this claim simply gives details about how generating would proceed if it were performed, this claim is not patentably distinct from claim 8 as it purports to limit a step outside the scope of the claimed process. The phrase "to provide randomly biased training sample coordinates" consists entirely of manner-of-use language which is considered but given no patentable weight.
With regard to Claim 10:
The method according to claim 9 comprising obtaining some of the training observations at the randomly biased trained sampling coordinates. [0115 as cited above; vectors necessarily have a first tuple; referring to an X axis coordinate as "randomly biased", without more, is considered mere labeling and given no patentable weight]
With regard to Claim 11:
The method according to claim 9 wherein some of the training observations are simulated. [0026; a prediction reads on a simulation]
This claim is not patentably distinct from claim 9 as it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of numbers but which imparts neither structure nor functionality to the claimed method. The reference is provided for the purpose of compact prosecution.
With regard to Claim 12:
The method according to claim 1 comprising feeding the measured observations and sampling coordinates of the measured observations to an input stage of the machine learning process. [0102 as cited above in regard to claim 1]
With regard to Claim 13:
The method according to claim 1 wherein the machine learning process is implemented by a neural network. [Gunes, 0013 as cited above in regard to claim 1]
With regard to Claim 19:
Hubbell teaches: A non-transitory computer readable medium for estimating parameters of a model [0032; "a computer processor and a memory" storing "instructions" for the processor to execute] that is non-linear and is more complex than a log- linear model, the non-transitory computer readable medium comprises:
feeding measured observations and sampling coordinates of the measured observations to a machine learning process; [0115; vectors are used; 0019; they may represent observed data based on training samples; 0102; the information is used to train models] and
processing the measured observations and the sampling coordinates of the measured observations, by the machine learning process, to provide an estimate of the parameters of the model; wherein the model is indicative of a relationship between the measured observations and the sampling coordinates... [0129; a re-estimation of dispersion parameters is performed by the machine learning engine]
Hubbell does not explicitly teach the machine learning process is trained during a training period in which the machine learning process is fed by training observations and training sampling coordinates, or some of the training sampling coordinates are generated by randomly biasing initial training sampling coordinates to provide randomly biased training sampling coordinates, and though it is of no patentable significance as explained above, it is known in the art.
Gunes teaches a system for tuning a machine learning model. [title] It produces a “trained model for use in predicting” a “value for an observation vector”. [0016] The training data may include sampled data, [0035] and may compute biases by using a “stochastic gradient descent [] algorithm that minimizes a root mean square error” criterion. [0042] A “performance measure value” is computed, [0003] which may be based on a comparison of observed data with data in the training dataset. [0026] Gunes and Hubbell are analogous art as each is directed to the use of machine learning in statistical analysis.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Gunes with that of Hubbell in order to improve accuracy of a machine-learning model, as taught by Gunes; [0002] further, it is simply a substitution of one known part for another with predictable results, simply making Gunes’ computations in place of, or in addition to, those of Hubbell; the substitution produces no new and unexpected result.
With regard to Claim 37:
Hubbell teaches: A computerized system that comprises one or more processing circuits and a memory, [0032; "a computer processor and a memory" storing "instructions" for the processor to execute] wherein the one or more processing circuits are configured to:
feed measured observations and sampling coordinates of the measured observations to a machine learning process; [0115; vectors are used; 0019; they may represent observed data based on training samples; 0102; the information is used to train models] and
process the measured observations and the sampling coordinates of the measured observations, by the machine learning process, to provide an estimate of the parameters of the model; wherein the model is indicative of a relationship between the measured observations and the sampling coordinates, [0129; a re-estimation of dispersion parameters is performed by the machine learning engine] wherein the model is non-linear and is more complex than a log-linear model…
Hubbell does not explicitly teach the machine learning process is trained during a training period in which the machine learning process is fed by training observations and training sampling coordinates, or some of the training sampling coordinates are generated by randomly biasing initial training sampling coordinates to provide randomly biased training sampling coordinates, and though it is of no patentable significance as explained above, it is known in the art.
Gunes teaches a system for tuning a machine learning model. [title] It produces a “trained model for use in predicting” a “value for an observation vector”. [0016] The training data may include sampled data, [0035] and may compute biases by using a “stochastic gradient descent [] algorithm that minimizes a root mean square error” criterion. [0042] A “performance measure value” is computed, [0003] which may be based on a comparison of observed data with data in the training dataset. [0026] Gunes and Hubbell are analogous art as each is directed to the use of machine learning in statistical analysis.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Gunes with that of Hubbell in order to improve accuracy of a machine-learning model, as taught by Gunes; [0002] further, it is simply a substitution of one known part for another with predictable results, simply making Gunes’ computations in place of, or in addition to, those of Hubbell; the substitution produces no new and unexpected result.
Claim(s) 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hubbell et al. in view of Gunes et al. further in view of Georgescu et al. (U.S. Publication No. 2016/0174902).
With regard to Claim 14:
The method according to claim 13 wherein the neural network is a feed-forward back-propagation deep neural network.
Hubbell and Gunes teach the method of claim 14 but do not explicitly teach this type of neural network, but it is known in the art. Georgescu teaches a deep neural network system [title] that makes predictions within a search space. [0004] The neural network may comprise a "fully connected network" of layers and may use "feed- forward" with "back-propagation" for efficiency. [0044] The network may be trained by a supervised training phase after an unsupervised pre-training phase. [0049] Georgescu and Hubbell are analogous art as each is directed to electronic means for using machine learning to make predictions.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Georgescu with that of Hubbell and Gunes in order to improve efficiency, as taught by Georgescu; further, it is simply a substitution of one known part for another with predictable results, simply implementing a neural network in the manner of Georgescu rather than that of Gunes; the substitution produces no new and unexpected result.
With regard to Claim 15:
The method according to claim 13 wherein the neural network comprises multiple fully connected layers. [Georgescu, 0044 as cited above in regard to claim 14]
With regard to Claim 16:
The method according to claim 1 wherein the machine learning process is trained by a supervised training process. [Georgescu, 0049 as cited above in regard to claim 14]
With regard to Claim 17:
The method according to claim 1 wherein the machine learning process is trained by an un-supervised training process. [Georgescu, 0049 as cited above in regard to claim 14]
With regard to Claim 18:
The method according to claim 1 wherein the machine learning process is trained by a combination of a supervised training process and an un- supervised training process. [Georgescu, 0049 as cited above in regard to claim 14]
Response to Arguments
Applicant's arguments filed 20 July 2026 in regard to rejections made under 35 U.S.C. § 101 have been fully considered but they are not persuasive. The present amendment has rendered moot the objections previously made, and those are withdrawn. In regard to § 101, the Examiner does not see any improvement to machine learning in any claim; rather, at most, known machine learning techniques are used in a new data environment which, in view of Recentive (cited above), is not, per se, sufficient.
Including two types of data as input is not an “architecture”; in computer terms, an architecture is an arrangement of physical components, and no unusual arrangement of physical components is claimed, nor can it be. Better generalization of a model is a mathematical rather than a technical improvement.
The applicant states in conclusory fashion that the claims “amount to significantly more” but does not identify any additional, that is, non-abstract component at all, and the Examiner finds none. The claims are not patent eligible and the rejection is maintained.
Applicant’s arguments with respect to claim(s) 1-19 and 37 in regard to rejections made under 35 U.S.C. § 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The arguments focus on language added by amendment and for which the teaching of Gunes has been incorporated herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT C ANDERSON whose telephone number is (571)270-7442. The examiner can normally be reached M-F 9:00 to 5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bennett Sigmond can be reached at (303) 297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SCOTT C ANDERSON/ Primary Examiner, Art Unit 3694
1 Recentive Analytics, Inc. v. Fox Corp. et al., 134 F.4th 1205, 1216 (Fed. Cir. 2025)
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