DETAILED ACTION
Applicant’s response filed on 07/02/2026 has been fully considered. The following rejections are either reiterated or withdrawn. They constitute the complete set presently being applied to the instant application.
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 Status
Claims 1-2, 4-8, and 12-15 are currently pending and under exam herein.
Claim 3 is cancelled.
Claim 1-2, 4-8 and 12-15 are rejected.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Taiwanese Application No. 111108870 filed on March 10th 2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Thus, the effective filing date of Claims 1-2, 4 and 12-15 are March 10th 2022.
Specification
The Specification amendment filed on 07/02/2026 is accepted and has corrected the specification objection. Therefore, the objection to the Specification is withdrawn and the Specification filed on 07/02/2026 is accepted.
Claim Objections
The claim amendment for claim 13 has corrected the claim objection. Therefore, the objection to claim 13 is withdrawn in light of the claim amendments.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The claim amendment for claim 8 has resolve the claim indefiniteness. Therefore, the 35 U.S.C. 112(b) rejection to claim 8, and its dependent claims 12-14 are withdrawn in light of the claim amendments.
Response to Arguments
Applicant’s arguments, see page 2 under Discussion of Claim Rejection under 35 U.S.C. 112(b), filed 07/02/2026 with respect to claims 8 and 12-14 have been fully considered. The 112(b)-indefiniteness rejection of 8 and 12-14 have been withdrawn due to amendments.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 1-2, 4-8 and 12-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The rejection of claims 1-2, 4-8, and 12-15 are maintained and reiterate in view of the claim amendments and applicant arguments. Please see below for Response to Argument for more details.
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/law of nature/natural phenomenon:
Claim 1 recites a method of checking drug interactions. The method starts by generating a drug combination set (3 drug combinations) from medical records, in which the first drug combination and the second drug combination share a first drug, while the first drug combination and third drug combination share a second drug (Abstract idea; mental process). Next, the method generates odds ratios (3 odds ratios) that correlates each drug combination with a hospitalization event based on mathematical counts and Equation (1) in the Specification of the instant application (Abstract idea; mathematical concepts and/or mental process). And based off of these odds ratios, the method then generates a first fraction (risk combination fraction) and a second fraction (normal combination fraction) based on Equation (2) and Equation (3) in the Specification (Abstract Idea; mathematical concepts and/or mental process). Lastly, the method outputs the first drug combinations in response to the first odds ratio being greater than a first threshold, the sum of the fractions (first and second) being greater than a second threshold, and the quotient of the fractions being less than a third threshold (Abstract idea; mathematical process and/or mental math). To summarized, the claim limitations are merely collecting patient data, and attempting to find high risk drug combinations that may lead to hospitalization events through statistical analysis and mathematical relations, which constitutes as an abstract idea. In addition, the mathematical equations and relations are simple enough to be also performed in the human mind, or with pen and paper as shown in the Specification of the instant application, such that these limitations would also fall under mental process in abstract ideas. Please see MPEP § 2106.04(a)(2) for more details. In addition, the amended claim 1 also recites executing a screening process to generate a “first unique drug combination set” by initially generating K (just a number) topic vectors with a latent Dirichlet allocation (LDA) model where each topic vector contains probability distributions of all drug combinations (Abstract Idea; mathematical concept and/or mental process). Topic modeling is just a natural language processing technique that can process text datasets to product a summary set of terms that represent themes. LDA modeling is a specific topic modeling approach, that assumes words that occur together are likely part of similar topics and generates topic distributions (lists of keywords with respective probabilities) based on word frequency and co-occurrences. In the broadest reasonable interpretation, the process of LDA modeling utilizes mathematical concepts to calculate probabilities of correlation, and in the simplest of scenarios could be done in the human mind. After generating the K number of topic vectors, the method goes on to select the drug combinations with the max probabilities in the first topic vector, to generate a “first important drug combination set” (Abstract Idea; mathematical concepts and/or mental process). Finally, the method uses the “first important drug combination set” to generate the “first unique drug combination set” and then uses the unique drug combination set to generate the final drug combination set (Abstract Idea; mental process).
Claim 2 recites the method of generating the first fraction, in claim 1, corresponding to the first drug. The method starts by comparing the second odds ratio to a risk threshold, and marking the second drug combination if the ratio is greater than the risk threshold (Abstract idea; mathematical concepts and/or mental process). The method then goes on to generate a third fraction, which is equivalent to (the number of drug combinations comprising the first drug but not the second drug, and is marked in the drug combination set) divided by (the number of drug combinations comprising the first drug but not the second drug), which is also the verbal equivalent of Equation (2) in the Specification of the instant application (Abstract Idea; mathematical concepts and/or mental process). Finally, the first fraction can then be obtained by subtracting the third fraction from 1, the verbal equivalent of Equation (3) in the Specification of the instant application (Abstract Idea; mathematical concepts and/or mental process). The limitations above are merely using mathematical relations to calculate fractions pertaining to the drug sets, and in the broadest reasonable interpretation, is simple enough to also be performed in the human mind. Hence, the claim limitations constitute an abstract idea.
Claim 4 further defines the method of forming the “first unique drug combination set” from “first important drug combination set” based in claim 1. The method does this by deleting drug combinations that are in the “first important drug combination set” and a “second important drug combination set” generated from the second topic vector (Abstract Idea; mathematical concept and/or mental process).
Claim 5 further defines the method of generating a drug combination set based in claim 1, by generating a “first stable drug combination set”. The method first repeatedly generates a plurality of unique drug combination sets based on the methods in claim 3 and 4 above (Abstract Idea: Mathematical process and/or mental process). Then, the method generates the “first stable drug combination set” with drug combinations that show up a selected number of times (pass a threshold) in the plurality of unique drug combination sets (Abstract Idea; mathematical concepts and/or mental process). Finally, the method uses the “first stable drug combination set” to form the drug combination set.
Claim 6 further defines the method of generating a drug combination set based on the “first stable drug combination set” in claim 5. The method starts by generating a plurality of medical record vectors with LDA modeling, where each medical record vector comprises of probability distribution of the K topics (Abstract Idea; mathematical concepts and/or mental process). Then, the method determines a medical record set corresponding to the first topic based on the probability distribution (Abstract Idea; mathematical process and/or mental process). Next, the method calculates a ratio of at least one medical record to the medical record set, where the at least one medical record represent a medical record that has at least one drug combination in the “first stable drug combination set” (Abstract Idea: mathematical process and/or mental process). Finally, the drug combination set is generated from the “first stable drug combination set” if the ratio is above a threshold (Abstract Idea; mathematical concept and/or mental process).
Claim 7 further defines the method of determining a medical record set corresponding to the first topic, in claim 6. The method does this by picking the medical records in the medical record vectors that have a max probability in the first topic (Abstract Idea; mathematical concepts and/or mental process)
Claim 8 adds onto the method of claim 1, by generating a first index corresponding to the first topic number and a second index corresponding to a second topic number based on the medical records and LDA model (Abstract Idea; mathematical concept). Then the indexes are compared before selecting one index as K, the first topic number in claim 3 (Abstract Idea; mathematical concepts and/or mental process).
Claim 12 further defines the method in claim 8, with a method to generate the first index. The method starts by generating a plurality of medical record vectors based on the medical records and the first topic number using the LDA model, wherein that the medical record vectors comprise a probability distribution of the K topics (Abstract Idea; mathematical concept and/or mental process). Then the method divides the medical records into K groups according to the probability distribution, where the K groups correspond to the K topics (Abstract Idea; mathematical concept and/or mental process). Next, the method calculates a first statistical value representing the inter-group distances and a second statistical value representing the intra-group distances (Abstract Idea; mathematical concepts and/or mental process). Finally, the method calculates a ratio of the first statistical value to the second statistical value as the first index (Abstract Idea; mathematical concepts and/or mental process).
Claim 13 further defines the method of calculating inter-group distances in claim 12. The method starts by calculating a plurality of distances between the K topic vectors (Abstract Idea; mathematical concepts and/or mental process). Then, it adds all the distances together to get the first statistical value representing the inter-group distances (Abstract Idea; mathematical concepts and/or mental process).
Claim 14 further defines the method of calculating intra-group distances in claim 12. The method starts by calculating a plurality of distances between elements in the first group to generate a sum of intra-group distances corresponding to the first group (Abstract Idea; mathematical concepts and/or mental process). Then the method calculates a second sum of intra-group distance corresponding to a second group (Abstract Idea; mathematical concepts and/or mental process). Finally, the method adds the first sum of intra-group distances to the second sum of intragroup distances to obtain the second statistical value representing the intra-group distances (Abstract Idea; mathematical concepts and/or mental process).
Claim 15 an electronic device that is capable of checking drug interactions. The device starts by generating a drug combination set (3 drug combinations) from medical records, in which the first drug combination and the second drug combination share a first drug, while the first drug combination and third drug combination share a second drug (Abstract idea; mental process). Next, the device generates odds ratios (3 odds ratios) that correlates each drug combination with a hospitalization event based on mathematical counts and Equation (1) in the Specification of the instant application (Abstract idea; mathematical concepts and/or mental process). And based off of these odds ratios, the method then generates a first fraction (risk combination fraction) and a second fraction (normal combination fraction) based on Equation (2) and Equation (3) in the Specification (Abstract Idea; mathematical concepts and/or mental process). Lastly, the method outputs the first drug combinations in response to the first odds ratio being greater than a first threshold, the sum of the fractions (first and second) being greater than a second threshold, and the quotient of the fractions being less than a third threshold (Abstract idea; mathematical process and/or mental math). To summarized, the claim limitations are merely collecting patient data, and attempting to find high risk drug combinations that may lead to hospitalization events through statistical analysis and mathematical relations, which constitutes as an abstract idea. In addition, the mathematical equations and relations are simple enough to be also performed in the human mind, or with pen and paper as shown in the Specification of the instant application, such that these limitations would also fall under mental process in abstract ideas. Please see MPEP § 2106.04(a)(2) for more details. In addition, the amended claim 15 also recites executing a screening process to generate a “first unique drug combination set” by initially generating K (just a number) topic vectors with a latent Dirichlet allocation (LDA) model where each topic vector contains probability distributions of all drug combinations (Abstract Idea; mathematical concept and/or mental process). Topic modeling is just a natural language processing technique that can process text datasets to product a summary set of terms that represent themes. LDA modeling is a specific topic modeling approach, that assumes words that occur together are likely part of similar topics and generates topic distributions (lists of keywords with respective probabilities) based on word frequency and co-occurrences. In the broadest reasonable interpretation, the process of LDA modeling utilizes mathematical concepts to calculate probabilities of correlation, and in the simplest of scenarios could be done in the human mind. After generating the K number of topic vectors, the method goes on to select the drug combinations with the max probabilities in the first topic vector, to generate a “first important drug combination set” (Abstract Idea; mathematical concepts and/or mental process). Finally, the method uses the “first important drug combination set” to generate the “first unique drug combination set” and then uses the unique drug combination set to generate the final drug combination set (Abstract Idea; mental process).
The limitations regarding generating high risk drug combinations based on patient medical records utilize probabilities, statistical analysis, and mathematical concepts to analyze and correlate data, making them a mathematical concept. In addition, based on the broadest reasonable interpretation, there is no additional limit on the data being process, such that the mathematical calculations cannot be performed in the human mind, hence these limitations also constitute a mental process. These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 15 recite performing some aspects of the analysis with “an electronic device”, there are no additional limitations that indicate that this device 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 1-8 and 12-15 are 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 in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claim 1 recites obtaining a plurality of medical records through a transceiver coupled to a processor, wherein at least one of the plurality of medical records indicates whether a patient taking a first drug combination has a hospitalization event. The processor coupled to a transceiver recited broadly is equated to a generic computer/computing environment, while the data obtaining is mere data gathering.
Claim 1 recites outputting the first drug combination in response to a number of thresholds being met, which is mere data outputting at a generic computer, a known and conventional process.
Claims 15 recites an electronic device with a transceiver and a processor coupled to the transceiver, which again just equates to a generic computer/computing environment.
Claim 15 recites obtain, through transceiver, a plurality of medical records, wherein at least one of the plurality of medical records indicates whether a patient taking a first drug combination has a hospitalization event, which is mere data gathering.
Claim 15 recites outputting, through the transceiver, the first drug combination in response to a number of thresholds being met, which is mere data outputting at a generic computer, a known and conventional process.
There are no limitations that indicate that the claimed method of obtaining and outputting data along with the electronic device with a transceiver and processor 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. In general, linking the use of an abstract idea to a particular technological environment, such as a computer, does not integrate the abstract idea into a practical application based on MPEP 2106.05(h). As such, claims 1-2, 4-8 and 12-15 are directed to an abstract idea as the additional elements do not integrate the judicial exceptions into a practical application (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). In claims 1-2, 4-8 and 12-15, there are no additional elements or limitations that would indicate anything other than carrying out the mathematical analysis on a generic computer. According to MPEP 2106.05(d), courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amount to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). 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 1-2, 4-8 and 12-15 are not patent eligible.
Response to Arguments
Applicant's arguments on page 3 under Discussion of Claim Rejections under 35 U.S.C. 101, filed on 07/02/2026 have been fully considered but are not considered persuasive.
Applicant argues that the multi-stage screening elements tie the method to a practical technological implementation rather than an abstract idea. Specifically, the applicant claims that claim 1 recites executing a specialized screening processing using a LDA model, and that the multi-dimensional, large-scale statistical framework cannot be practically performed in the human mind. However, the multi-stage screening elements in claim 1 (generating, selecting, determining) are judicial exceptions (Abstract Idea; Mental Processes) themselves and not additional elements that can tie the abstract idea to a practical use. The consideration of integration of a judicial exception into a practical application only applies to whether the additional elements integrate the judicial exception in a practical application. Please see MPEP 2106.04(d) for more details. In addition, as claimed, the LDA model qualifies as a mathematical concept under Abstract Ideas for its use of probabilities and mathematical operations for analysis, and not just a mental process. Therefore, it would still qualify as a judicial exception under Abstract Ideas. However, in considering the LDA model as a mental process, there are no claim limitations specifying that the framework requires “mathematical processing that goes far beyond conventional mental steps or generic data manipulation”. In the simplest broadest reasonable interpretation, a human can go through at least one medical record where a patient has taken a drug combination and has a hospitalization event and count the number of times a certain drug combination shows up in their charts to determine a probability distribution for the drug combination. Although, it might take a while, or need to be done with pen and paper/computer, there is no limitations hindering the calculation of probability distributions and correlations in the human mind. Therefore, in the simplest sense the LDA model would also qualify as a mental process.
Applicant also argues that even if the claims are assumed to involve mathematical concepts, they integrate these claims into a practical technological application that improves computer-assisted drug interaction analysis. However, as seen above, the additional elements in the claim merely serve to link the abstract idea to a generic computing environment. In general, linking the use of an abstract idea to a particular technological environment, such as a computer, does not integrate the abstract idea into a practical application based on MPEP 2106.05(h). Furthermore, applicant is reminded that the consideration of integration of a judicial exception into a practical application only applies to whether the additional elements integrate the judicial exception in a practical application based on MPEP 2106.04(d).
Finally, the applicant argues the claimed invention describes a specific hardware-bound (processor and transceiver) data pipeline designed to solve a distinct technological problem in healthcare data processing. In general, linking the use of an abstract idea to a particular technological environment, such as a computer, does not integrate the abstract idea into a practical application based on MPEP 2106.05(h).
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The previous rejection of claims 1-2, and 15 are withdrawn in view of the claim amendments.
Response to Arguments
Applicant’s arguments, see page 4 under Discussion of Claim Rejection under 35 U.S.C. 103), filed 07/02/2026 with respect to claims 1-2 and 15 have been fully considered. The 103 rejection of 1-2 and 15 have been withdrawn due to amendments.
Claims Free From Prior ArtClaims 1-2, 4-8 and 12-15 are currently free from the prior art.
The following is an examiner’s statement over the prior art:
Claims 1-2, 4-8 and 12-14 pertain to the generation of drug combinations through topic modeling in the form of a latent Dirichlet allocation (LDA) model. In the most basic sense, topic modeling works to categorize singular words in a document into topics of similar themes. LDA modeling is a specific type of topic modeling that defines each topic by a probability distribution over words, where words that often appear together are likely in the same topic. The instant application generates K topic vectors through a LDA model, where each topic comprises a probability distribution of all drug combinations (claim 1). Then the instant application screens the drug combinations by topic vectors, picking a plurality of drug combinations with a range of max probabilities in each vector to generate an important drug combination set for each vector (claim 1). Then, the important drug combination sets are screened out for duplicate drug combinations that appear in different vectors to create unique drug combination sets (claim 4). And the process is repeatedly executed before a stable drug combination set is picked out based on certain drug combinations appearing more than a certain amount of times (claim 5). Lastly, the instant application makes sure there are enough medical records displaying certain drug combination in the topic vector (claim 7) and picking the drug combinations that have a certain ratio of medical records to create the final drug combination set (claim 6). The instant application then goes backwards to specify a method of picking the optimal number for K, which represents the number of topics for the LDA model, from a first index number and a second index number (claim 8). The instant application implies that after an initial run of the LDA model with an initial K, the medical records should then be split into K groups (claim 12). Next, the inter-group distances between the groups are calculated and summed to get a first statistical value (claim 13). Then, the intra-group distances between groups are calculated and summed to get a second statistical value. Finally, the ratio of the first statistical value to the second statistical value would be the first index (claim 12).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bisgin, H., Liu, Z., Fang, H. et al. Mining FDA drug labels using an unsupervised learning technique - topic modeling. BMC Bioinformatics 12 (Suppl 10), S11 (2011).
H. Huang et al., "Discovering Medication Patterns for High-Complexity Drug-Using Diseases Through Electronic Medical Records," in IEEE Access, vol. 7, pp. 125280-125299, 2019
S. Park et al., Journal of Biomedical Informatics 75 (2017) Pgs. 35–47
LePendu P, Iyer SV, Bauer-Mehren A, et al. Pharmacovigilance using clinical notes. Clin Pharmacol Ther. 2013;93(6):547-555.
Gan J, Qi Y. Selection of the Optimal Number of Topics for LDA Topic Model-Taking Patent Policy Analysis as an Example. Entropy (Basel). 2021 Oct 3;23(10):1301.
Burke et al. US 2019/0005019 A1 Contextual Pharmacovigilance System
Dey et al. WO2019/171187 A1, Published Sep 12 2019, Adverse drug reaction analysis
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 WENYU YANG whose telephone number is (571)272-0035. The examiner can normally be reached 8:30am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia 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.
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/W.Y./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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