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 .
Applicant Reply
“The claims may be amended by canceling particular claims, by presenting new claims, or by rewriting particular claims as indicated in 37 CFR 1.121(c). The requirements of 37 CFR 1.111(b) must be complied with by pointing out the specific distinctions believed to render the claims patentable over the references in presenting arguments in support of new claims and amendments. . . . The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. . . . An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” MPEP § 714.02. Generic statements or listing of numerous paragraphs do not “specifically point out the support for” claim amendments. “With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4, 83 USPQ2d 1373, 1376, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a ‘simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.’)” MPEP § 2163(II)(A).
Allowable Subject Matter under §§ 102 and 103
Claims 1-2, 4-9, 11-16, and 18-20 are non-obvious in view of the prior art.
During search, no documents were found teaching the following combination:
apply a deep learning variable importance method to the first portion to identify, from the variables associated with the forecast of the target variable, at least one salient variable that correlates to the forecast of the target variable, based on a correlation matrix that indicates strongly correlated features that are salient for predictions, wherein the at least one salient variable includes one or more of a code number associated with a medication, a package size of a medication, or and a dispensed quantity of a medication for a period
Claim 1 ll. 11-17.
No art was found using the specific combination of data used for predictions. Specifically, the claims recite a salient variable that correlates to a forecast of a target variable. The claims limit the salient variables to include “a code number associated with a medication, a package size of a medication, and a dispensed quantity of a medication for a period[.]” See Claim 1. No documents were found teaching the use of these three types of information in conjunction with a forecast. Therefore, the claimed subject matter is non-obvious under 35 U.S.C. § 103.
The following is a list of the closest prior art:
Kaushik (AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures, March 2020) teaches the use of an MLP and LSTM networks in combination, including in an ensemble, to predict drug pricing. Kaushik does not teach the combination including “apply a deep learning variable importance method to the first portion to identify, from the variables associated with the forecast of the target variable, at least one salient variable that correlates to the forecast of the target variable, based on a correlation matrix that indicates strongly correlated features that are salient for predictions, wherein the at least one salient variable includes one or more of a code number associated with a medication, a package size of a medication, or and a dispensed quantity of a medication for a period,” as substantially recited in all independent claims.
Mehta (2022/0108035; filed Oct 2020; different assignee) teaches operations associated with implementing machine learning on servers and other hardware. Mehta does not teach the combination including “apply a deep learning variable importance method to the first portion to identify, from the variables associated with the forecast of the target variable, at least one salient variable that correlates to the forecast of the target variable, based on a correlation matrix that indicates strongly correlated features that are salient for predictions, wherein the at least one salient variable includes one or more of a code number associated with a medication, a package size of a medication, or and a dispensed quantity of a medication for a period,” as substantially recited in all independent claims.
Kumar (Everything You Need To Know About Train/Dev/Test Split — What, How and Why, 2019) Kumar teaches way of splitting data for training testing and validation. Kumar does not teach the combination including “apply a deep learning variable importance method to the first portion to identify, from the variables associated with the forecast of the target variable, at least one salient variable that correlates to the forecast of the target variable, based on a correlation matrix that indicates strongly correlated features that are salient for predictions, wherein the at least one salient variable includes one or more of a code number associated with a medication, a package size of a medication, or and a dispensed quantity of a medication for a period,” as substantially recited in all independent claims.
Emert (US 2012/0185263) teaches various aspects of drug pricing, but does not teach using the specific type of data recited in the independent claims. Therefore, Emert does not teach the combination including “apply a deep learning variable importance method to the first portion to identify, from the variables associated with the forecast of the target variable, at least one salient variable that correlates to the forecast of the target variable, based on a correlation matrix that indicates strongly correlated features that are salient for predictions, wherein the at least one salient variable includes one or more of a code number associated with a medication, a package size of a medication, or and a dispensed quantity of a medication for a period,” as substantially recited in all independent claims.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) and the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
All claims are found to be directed to one of the four statutory categories, unless otherwise indicated in this Action.
Step 2A Prongs One and Two (Alice Step 1): According to Office guidance, claims that read on math do not recite an abstract idea at step 2A1, when the claims fail to refer to the math by name.1 The MPEP also equates “recit[ing] a judicial exception” with “state[ing]” or “describ[ing]” an abstract idea in the claims.2 Consistent with this guidance an abstract idea may be first recited in a dependent claim, even though the independent claims may read on the abstract idea. Claim limitations which recite any of the abstract idea groupings set forth in the manual are found to be directed, as a whole, to an abstract idea unless otherwise indicated.3 The claims do not recite additional elements that integrate the abstract ideas into a practical application.4 To confer patent eligibility to an otherwise abstract idea, claims may recite a specific means or method of solving a specific problem in a technological field.5
1. “A machine learning system for training a data model to predict data states, comprising: a first data warehouse system comprising a warehouse processor and a warehouse memory, the first data warehouse system further including a plurality of historical pharmaceutical data associated with one or more pharmaceuticals; a machine learning server in communication with the first data warehouse system, the machine learning server comprising a processor and a memory, wherein the machine learning server is configured to: (The claims are a whole are directed to mental processes. The limitations above are merely an instruction to implement the abstract idea using generic computer components. The use of “historical pharmaceutical data” merely limits to the data environment consistent with the field of use. Note that gathering and/or storing of data is mere insignificant extra solution activity.) receive a first portion of the plurality of historical pharmaceutical data, wherein the first portion includes variables associated with a forecast of a target variable; (Receiving and storing the recited types data is mere extra-solution activity. Limiting the type of data merely limits the claims to a field of use.) apply a deep learning variable importance method to the first portion to identify, from the variables associated with the forecast of the target variable, at least one salient variable that correlates to the forecast of the target variable, based on a correlation matrix that indicates strongly correlated features that are salient for predictions, wherein the at least one salient variable includes a code number associated with a medication, a package size of a medication, and a dispensed quantity of a medication for a period; (Applying a method to identify one of the listed “salient variable[s]” that correlates to one of the below listed “forecast[s] of the target variable,” reads on a mental process. Further, using a “correlation matrix” reads on both a mental process which can be implemented using a pencil and paper, and reads on a mathematical structure. Using “deep learning” to carry out this process is merely an instruction to apply the judicial exception using ordinary computer components.) generate a model generation algorithm that includes one or more of a long-short term memory algorithm, a multilayer perceptron algorithm; apply the model generation algorithm to the first portion and the at least one salient variable to generate a plurality of predictive models for the forecast of the target variable; (This is merely an instruction to apply the judicial exceptions recited in the claims using a combination of ordinary computer algorithms in no particular arrangement.) receive a second portion of the plurality of historical pharmaceutical data to test the plurality of predictive models, (Receiving data for testing a model is mere extra solution activity.) wherein the second portion includes the variables associated with the forecast of the target variable, including the at least one salient variable; (This merely limits the type of data to the field associated with a given forecast.) test the plurality of predictive models with the second portion; (Testing a model for potential selection (as indicated by the claim language below) reads on a mental process.) obtain a portion of current pharmaceutical data that includes the at least one salient variable; (Receiving data for testing a model is mere extra solution activity.) and apply the portion of current pharmaceutical data to a candidate predictive model to obtain a seasonality trend of the forecast of the target variable in response to the test of the plurality of predictive models, wherein the candidate predictive model is a most accurate predictive model of the plurality of predictive models.” (Using data to make a seasonality trend of the forecast in response to testing a model reads on a mental process. Note that using a model on a computer to determine the seasonality trend of the forecast based on data is merely an instruction to implement the abstract idea using ordinary computer components including a generic model.)
Claims 8 and 15 are rejected for the reasons given in the rejections of claim 1. Note that independent claims 8 and 15 do not include any claim elements that are not recited in claim 1.
Step 2B (Alice Step 2): The rejected claims do not recite additional elements that amount to significantly more than the judicial exception.
All additional limitations that do not integrate the claimed judicial exception into a practical application also fail to amount to significantly more, for the reasons given at step 2A2. All limitations found to be extra-solution activity at step 2A2 are found to be WURC, including limitations that read on mere data gathering, data storage, and data input/output/transfer.
Specifically, the independent claims substantially recite “receive a first portion of the plurality of historical pharmaceutical data,” “receive a second portion of the plurality of historical pharmaceutical data to test the plurality of predictive models,” and “obtain a portion of current pharmaceutical data that includes the at least one salient variable.” All three of these limitations read on mere data input/output/transfer, which is WURC as explained in the paragraph below.
This finding is based on cases which have recognized that generic input-output operations, repetitive processing operations, and storage operations are WURC.6 Other aspects of generic computing have also been found to be WURC.7 Further, the description itself may provide support for a finding that claim elements are WURC. The analysis under § 112(a) as to whether a claim element is “so well-known that it need not be described in detail in the patent specification” is the same as the analysis as to whether the claim element is widely prevalent or in common use.8 Similarly, generic descriptions in the Specification of claimed components and features has been found to support a conclusion that the claimed components were conventional.9 Improvements to the relevant technology may support a finding that the claims include a patent eligible inventive concept. But some mechanism that results in any asserted improvements must be recited in the claim, and the Specification must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing the improvement.10 This applies equally to the dependent claims rejected below.
Dependent Claims:
2. The machine learning system of Claim 1, wherein the machine learning server is further configured to: apply a grid search to obtain at least one hyperparameter associated with at least one of the plurality of predictive models. (Performing a grid search to obtain a hyperparameters reads on both a mental process and on a set of mathematical operations.)
4. The machine learning system of Claim 2, wherein the machine learning server is further configured to: apply the portion of the current pharmaceutical data to the candidate predictive model and to at least one hyperparameter that is associated with the candidate predictive model to obtain the forecast of the target variable. (This reads on using a model with a given hyperparameter limited to a field of use defined by the data environment of “pharmaceutical data.” Merely using the model is an instruction to implement the abstract idea using ordinary computer components.)
5. The machine learning system of Claim 1, wherein the machine learning server is further configured to: receive the first portion of the plurality of historical pharmaceutical data, (Receiving data is mere extra-solution activity and WURC.) wherein the first portion includes variables associated with a future price forecast; and (This limits the data to a field of use.) apply the deep learning variable importance method to the first portion to identify, from the variables associated with the future price forecast, at least one price salient variable that correlates to the future price forecast, (This reads on an instruction to implement an abstract idea using ordinary computer components. Note that identifying a price salient variable that correlates to the future price forecast reads on both a mental process and on a combination of mathematical operations and correlations.) wherein the at least one price salient variable includes one or more of an average wholesale price and a generic fill rate. (This merely limits the field of use to a particular data environment.)
6. The machine learning system of Claim 1, wherein the machine learning server is further configured to: receive the first portion of the plurality of historical pharmaceutical data, (Receiving data is mere extra-solution activity and WURC.) wherein the first portion includes variables associated with a factor rate forecast, wherein the factor rate forecast is a ratio of forecasted potential dispensed quantities of prescriptions to prior actual dispensed quantities of the prescriptions; and (This merely limits to a field of use corresponding to the claimed data environment.) apply the deep learning variable importance method to the first portion to identify, from the variables associated with the factor rate forecast, at least one salient variable that is correlates to the factor rate forecast. (Identifying a salient variable that correlates to a factor rate forecast from the variables associated with the factor rate forecast reads on a mental process. Specifically, this reads on determining which data is important in making a determination.)
7. (Currently Amended) The machine learning system of Claim 1, wherein the machine learning server is further configured to: apply at least one pre-processing step to the first portion to obtain a processed first portion; (This is mere extra-solution activity. Note that the operation of “pre-processing” reads on generic processing data using it in the model. Mere processing has been found to be WURC as indicated under step 2B.) apply the deep learning variable importance method to the processed first portion to identify the at least one salient variable; (Identifying a salient variable based on importance is a mental process.) and apply the model generation algorithm to the processed first portion and the at least one salient variable to generate the plurality of predictive models for the forecast of the target variable. (Note that “to generate . . .” is written as an intended use. Further, this reads on an instruction to implement the abstract idea, using a data environment that limits to a field of use, on ordinary computer components.)
Claims 9 and 11-14 are rejected for the reasons given in the rejections of claims 2 and 4-7, respectively. Note that independent claim 8 does not include any claim elements that are not recited in claim 1.
Claims 16 and 18-20 are rejected for the reasons given in the rejections of claims 2 and 4-6, respectively. Note that independent claim 15 does not include any claim elements that are not recited in claim 1.
All dependent claims are rejected as containing the material of the claims from which they depend.
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 1-2, 4-9, 11-16, 18-20 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 pre-AIA the applicant regards as the invention.
All independent claims recite “a correlation matrix that indicates strongly correlated features that are salient for predictions[.]” Without any objective measure of “strongly” the person of ordinary skill would be unable to determine whether a matrix that indicates of features which are salient for predictions, would read on the claim language. See MPEP §2173.05b.
All dependent claims are rejected as containing the limitations of the claims from which they depend.
Response to Arguments
Applicant's arguments filed 09/15/2025 have been fully considered but they are not persuasive.
Rejections under § 101
No specific arguments are offered.
Rejections under § 112:
No specific arguments are offered.
Rejections under § 103:
Applicant states that the art of record fails to teach the amended claims. No specific arguments are offered.
Conclusion
THIS ACTION IS MADE FINAL. 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 PAUL M KNIGHT whose telephone number is (571) 272-8646. The examiner can normally be reached Monday - Friday 9-5 ET.
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, Michelle Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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PAUL M. KNIGHTExaminerArt Unit 2148
/PAUL M KNIGHT/Examiner, Art Unit 2148
1 This distinction between claims which read on math and claims which recite an abstract idea is based on official USPTO Guidance. The 2019 Subject Matter Eligibility (SME) Examples instructs examiners that a claim reciting “training the neural network” where the background describes training as “using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network” “does not recite any mathematical relationships, formulas, or calculations.” See 2019 SME Example 39, PP. 8-9 (emphasis added). In this example, the plain meaning of “training the neural network” read in light of the disclosure reads on backpropagation using the gradient of a mathematical loss function. See MPEP § 2111.01. In contrast, the 2024 SME Examples instructs examiners that a claim reciting “training, by the computer, the ANN . . . wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” does recite an abstract idea because “[t]he plain meaning of [backpropagation algorithm and gradient descent algorithm] are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.” 2024 PEG Example 47, PP. 4-6. The Memorandum of August 4, 2025; Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, P. 3 also directs examiners that “training the neural network” recited in Example 39 merely “involve[s] . . . mathematical concepts” and contrasts claim 2 of example 47 as “referring to [specific] mathematical calculations by name[.]” (Emphasis added.)
2 “For instance, the claims in Diehr . . . clearly stated a mathematical equation . . . and the claims in Mayo . . . clearly stated laws of nature . . . such that the claims ‘set forth’ an identifiable judicial exception. Alternatively, the claims in Alice Corp. . . . described the concept of intermediated settlement without ever explicitly using the words ‘intermediated’ or ‘settlement.’” MPEP § 2106.04(II)(A).
3 “By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. . . . If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One.” MPEP § 2106.04(a). See also MPEP 2104(a)(2).
4 Step 2A prongs one and two are evaluated individually, consistent with the framework in the MPEP. Evaluation of relationships between abstract ideas and additional elements in one location promotes clarity of the record.
5 “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. . . . It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” MPEP 2106.04(d)(1). See also Koninklijke KPN N.V. v. Gemalto M2M GmbH, 942 F.3d 1143, 1150-1152 (Fed. Cir. 2019).
6 See MPEP § 2106.05(d)(II) listing operations including “receiving or transmitting data,” “storing and retrieving data in memory,” and “performing repetitive calculations” as WURC.
7 “But ‘[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of 'well-understood, routine, [and] conventional activities previously known to the industry.’ Content Extraction, 776 F.3d at 1347-48 (quoting Alice, 134 S. Ct at 2359). Here, the server simply receives data, ‘extract[s] classification information . . . from the received data,’ and ‘stor[es] the digital images . . . taking into consideration the classification information.’ See ‘295 patent, col. 10 ll. 1-17 (Claim 17). . . . These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (‘Nearly every computer will include a 'communications controller' and a 'data storage unit' capable of performing the basic calculation, storage, and transmission functions required by the method claims.’); Content Extraction, 776 F.3d at 1345, 1348 (‘storing information’ into memory, and using a computer to ‘translate the shapes on a physical page into typeface characters,’ insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324-25 (generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’ fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a ‘database’ and ‘a communication medium’ ‘are all generic computer elements’); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (‘That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.’).” TLI Commc'ns LLC v. AV Auto., LLC, 823 F.3d 607, 614 (Fed. Cir. 2016), Emphasis Added.
8 “The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph)[.]” MPEP § 2106.05(d)(I).
9 “Similarly, claim elements or combinations of claim elements that are routine, conventional or well-understood cannot transform the claims. (Citing BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290-1291 (Fed. Cir. 2018)). When the patent's specification ‘describes the components and features listed in the claims generically,’ it ‘support[s] the conclusion that these components and features are conventional.’ Weisner v. Google LLC, 51 F.4th 1073, 1083-84 (Fed. Cir. 2022); see also Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1357-58 (Fed. Cir. 2024).” Broadband iTV, Inc. v. Amazon.com, Inc., 113 F.4th 1359 (Fed. Cir. 2024)
10 “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” MPEP § 2106.05(a).