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 Status
Claims 1-2, 4-12, 14-22, 24-32, and 34-40 are pending and under examination.
Claims 1-2, 4-12, 14-22, 24-32, and 34-40 are rejected.
Claims 3, 13, 23, and 33 are canceled.
Claims 1, 11, 20-21, 31, and 40 are independent.
No claims are withdrawn or new.
Office Action Outline
Rejections applied
Abbreviations
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
X
102, 103
JE
Judicial Exception
X
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed in the 03/31/2021 filing receipt, this application claims priority to Provisional Application No. 63/053,941, filed 07/20/2020.
Claims 1-2, 4-10, 21-22, and 24-30 are being examined with an effective filing date of 07/20/2020.
Claims 11-12, 14-20, 31-32, and 34-40 are being examined with an effective filing date of 11/06/2021, and are not granted the claim to the benefit of priority to U.S. Provisional Application No. 63/053,941, as claims 11-12, 14-20, 31-32, and 34-40 recite similar limitations which include the term "request," including "receiving a request," "received request," or "receive a request," which are not supported by the disclosure of Provisional App. No. 63/053,941.
Overview of Withdrawal/Revision of Objections/Rejections
In view of the amendment and remarks received 11/07/2025:
• The 112(b) rejections are withdrawn.
• The 101 rejection is maintained with revision.
• The 103 rejection is maintained for claim 20 for reasons given below in the 103 section of this Office action.
• The 103 rejection is withdrawn for claims 1-2, 4-12, 14-19, 21-22, 24-32, and 34-40 for reasons given below in the 103 section of this Office action.
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, 4-12, 14-22, 24-32, and 34-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 details the following framework to analyze Subject Matter Eligibility:
• Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? (see MPEP § 2106.03)
• Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (see MPEP § 2106.04(a)). Note, the MPEP at 2106.04(a)(2) & 2106.04(b) further explains that abstract ideas and laws of nature are defined as:
mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
laws of nature and natural phenomena are naturally occurring principles/ relations that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature.
• Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application? (see MPEP § 2106.04(d))
• Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05)
Step 1:
Claims 1-2, 4-12, 14-19, 21-22, 24-32, and 34-39 are directed to a 101 process, here a method. Claims 20 and 40 are directed to a 101 machine or manufacture, here a system. As such, claims 1-2, 4-12, 14-22, 24-32, and 34-40 are directed to a related method and system, which fall under categories of statutory subject matter. (See MPEP § 2106.03). (Step 1: Yes.)
Step 2A, Prong One:
The claims recite judicial exceptions (JEs) of mental processes and mathematical concepts as follows:
Independent claims 1 and 21 recite mental processes and mathematical concepts of: predicting efficacy of a predictive measure based on the received data set; adjusting values associated with an attribute in the data set based on a scaling factor; training one or more machine learning models; deploying the trained one or more machine learning models; and recommending preventative measures to implement in response to a pathogen.
Additionally, independent claims 1 and 21 recite a mental process of: considering the information of the data set, which includes a plurality of records, each respective record including at least information identifying a preventative measure, and an efficacy of the preventative measure; (including "information of a pathogen against which the preventative measure is targeted, and information about a built environment in which the preventative measure is installed" in claim 21).
Claims 2 and 22 recite mental processes and mathematical concepts of: generating a training data set by featurizing the received data set by assigning, for each attribute, a value which indicates classification of the attribute into a category (also considered a mathematical concept); machine learning models are trained; and considering the information of the generated training data set.
Claims 4 and 24 recite a mental process of: replacing null values for attributes in the data set indicating that the attributes do not apply to the preventative measure.
Claims 5 and 25 recite a mental process of: considering the information of the efficacy of the preventative measure comprises a reduction in persons contracting an illness caused by the pathogen ("relative to an estimated number of persons contracting the illness if no preventative measures were taken" in claim 5; and "over a time window after implementation of the preventative measure relative to a number of persons contracting the illness in the time window prior to implementation of the preventative measure" in claim 25.)
Claims 6 and 26 recite mental processes of: considering the information of the data set records aggregated from external data sources.
Claims 7 and 27 recite mental processes of: considering the information of the external data sources which comprise secure medical records and other data source(s).
Claims 8 and 28 recite mental processes of: considering the information of the other data source(s) including a physical activity records data source or a patient medicine usage data source.
Claims 9 and 29 recite a mathematical concept of: a clustering based machine learning models (also considered a mental process).
Claims 10 and 30 recite a mathematical concept of: probabilistic machine learning models in which preventative measure efficacy is represented by a probability distribution over a plurality of preventative measures associated with similar pathogens (also considered a mental process).
Independent claims 11 and 31 recite mental processes and mathematical concepts of: considering the information of the request which includes at least an identification of the pathogen, (and information of a built environment in which a preventative measure is to be implemented in claim 31), and information of the identified preventative measures; identify measures based on…trained machine learning models; generating a probability score as a weighted average of efficacy probabilities generated by each of the one or more trained machine learning models, each trained model associated with a weighting value to assign to a predicted efficacy of the preventative measures; and identifying the recommended preventative measures based on at least the identification of the pathogen.
Claims 12 and 32 recite a mathematical concept of: a probabilistic model trained to generate a probability distribution over a plurality of efficacy categories (also considered a mental process).
Claims 14 and 34 recite mathematical concepts of: a clustering model trained to identify a set of preventative measures undertaken in response to at least the identified pathogen (also considered a mental process).
Claims 15 and 35 recite mental processes of: grouping the identified set of preventative measures into subgroups; calculating an average efficacy of the associated specific preventative measure (also considered a mathematical concept); and selecting a measures having a calculated average efficacy above a threshold value.
Claims 16 and 36 recite mathematical concepts of: the trained machine learning models comprise a probabilistic model to output a probability distribution, and a clustering model to identify a set of preventative measures; and identifying the recommended preventative measures is based on a weighted average of probability scores in the probability distribution and average efficacy of preventative measures in the identified set of preventative measures.
Claims 17 and 37 recite a mental process of: considering the information of the received request for identifying preventative measures that have already been implemented.
Claims 18 and 38 recite a mental process of: selecting a preventative measure having a predicted efficacy exceeding a predicted efficacy associated with the preventative measures that have already been implemented.
Claims 19 and 39 recite mental processes of: considering the information of the received request about a physical environment; and identifying the recommended preventative measures further based on the information about the physical environment.
Independent claims 20 and 40 recite a mental processes and mathematical concepts of: identifying the recommended preventative measures based on at least the identification of the pathogen; and identify measures based on…trained machine learning models.
Step 2A Prong One Summary: The claims recite abstract ideas, characterized as mental processes and mathematical concepts. As examples, considering the broadest reasonable interpretation (BRI) of the claims, there are mental processes recited in independent claims 1 and 21 (e.g., for predicting efficacy; recommending preventative measures), in claims 11, 20, 31, and 40 (e.g., for identifying preventative measures), etc. Under the BRI of the claims, these steps can be performed mentally, or with pen and paper, as the steps of predicting, recommending, and identifying, etc., encompass mental observations or evaluations that can be practically performed in the human mind. Regarding the mathematical concepts recited in claims 1, 2, 9, 10, 12, 14, 16, 21, 22, 29, 30, 32, 34, and 36 (e.g., for training, clustering-based machine learning models; probabilistic models; generating/outputting a probability distribution; generating a probability score as a weighted average, etc.); these limitations inherently recite mathematical concepts such as those disclosed in Specification paragraphs [0033, 0036, 0047-0049, 0056]. When considering the broadest reasonable interpretation (BRI) of the limitations for machine learning, including training and deploying, these includes embodiments requiring mathematical concepts to arrive at the mathematical function that acts as the machine learning model; this is especially seen at Specification para. [0056]. Further, under the BRI of the claims, these mathematical concepts encompass processes that can be performed mentally, or with pen and paper; as the claim does not limit the size of the records, nor the number of attributes, etc. Such analysis performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea. Therefore, the claims recite elements that constitute a judicial exception in the form of an abstract idea. (Step 2A, Prong One: Yes.)
Step 2A, Prong Two:
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). Here at Step 2A, Prong Two, any remaining steps and/or elements not identified as JEs are therefore in addition to the identified JE(s), and are considered additional elements. Because the claims have been interpreted as being directed to judicial exceptions (abstract ideas in this instance) then Step 2A, Prong Two provides that the claims be examined further to determine whether the judicial exception is integrated into a practical application [see MPEP § 2106.04(d)]. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
MPEP § 2106.04(d)(I) lists the following five example considerations for evaluating whether a judicial exception is integrated into a practical application:
(1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
(2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2).
(3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b).
(4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c).
(5) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The claims recite additional elements as follows:
Additional elements of data gathering, inputting, and outputting steps: Claims 1, 11, 20, 21, 31, and 40 recite the additional elements of receiving data. Claims 11, 20, 31, and 40 recite the additional elements of outputting data. Data gathering steps are additional elements which perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed, nor do they provide an improvement to technology [see MPEP § 2106.04(d)(I)].
Additional elements of computer components: Claims 20 and 40 recite the additional elements of a processor and memory. The claims require only generic computer components, which do not improve computer technology, and do not integrate the recited judicial exception into a practical application (see MPEP § 2106.04(d)(1) and MPEP § 2106.05(f)).
Step 2A Prong Two summary: The claims have been further analyzed with respect to Step 2A, Prong Two, and no additional elements have been found, alone or in combination, that would integrate the judicial exception into a practical application. (Step 2A, Prong Two: No).
Step 2B:
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are well-understood, routine, and conventional. Those additional elements are as follows:
Additional elements of data gathering, inputting, and outputting steps: Additional elements of receiving data (claims 1, 11, 20, 21, 31, and 40), and outputting data (claims 11, 20, 31, and 40), do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network and storing and retrieving information in memory, [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as extra-solution activity. Thus, the data gathering steps are shown to be routine, well-understood, and conventional, and do not provide an inventive concept needed to amount to significantly more than the judicial exception.
Additional elements of computer components: Claims 20 and 40 recite the additional elements of a processor and a memory. These are conventional computer components; therefore, the additional elements of a processor and a memory do not cause the claims to rise to the level of significantly more than the judicial exception as they do not provide an inventive concept.
Further regarding the conventionality of additional elements, the MPEP at 2106.05(b) and 2106.05(d) presents several points relevant to conventional computers and data gathering steps in regard to Step 2A Prong 2 and Step 2B, including:
• A general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, does not qualify as a particular machine (see 2106.05(b)(I)), as in the case of claims 20 and 40, which are interpreted to recite conventional computer components.
• Integral use of a machine to achieve performance of a method may integrate the recited judicial exception into a practical application or provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more (see 2106.05(b)(II). In the instant claims, the recited processor and memory are used in recommending preventative measures; and act only as tools to perform the steps of recommending preventative measures, and do not integrate the exception into a practical application or provide significantly more.
• Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more (see 2106.05(b)(III). The processor and memory of claims 20 and 40 used in recommending preventative measures does not impose meaningful limitations on the claims.
• The courts have recognized "receiving or transmitting data over a network," "performing repetitive calculations," and "storing and retrieving information in memory," as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). The storing of receiving and outputting of data in claim 1, 11, 20, 21, 31, and 40 is recited in a generic manner.
All the limitations of claims 1-40 have been analyzed with respect to Step 2B. Considering these elements both alone and in combination, the additional elements do not provide an inventive concept that transforms the judicial exception into a patent eligible application of the exception, and the claims do not amount to significantly more than the judicial exception itself. (Step2B: NO.)
Therefore, the claims, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Applicant Arguments - 35 USC § 101
The Applicant's arguments, filed 11/07/2026, have been fully considered but they are not persuasive.
Regarding Step 2A (Prong One and Two combined; no arguments were received for Step 2B):
The Applicant asserts, on p.12-13:
• "the Office Action indicates that the machine learning elements that were
recognized as "additional elements" in the previous Office Action "have been recharacterized as abstract ideas of mathematical concepts and mental processes." (p.12, ¶3)
• "the recharacterization of the machine learning elements...is directly contrary to recent guidance from the USPTO...Each of the...elements should be considered an "additional element" and the associated claims should be found to be directed to eligible subject matter under the Alice framework." (p.12, ¶4)
• (Applicant points to) "USPTO Subject Matter Eligibility Example 39 as an
example of an AI-focused claim that is not directed to any judicial exception...
explained by the USPTO, "[t]he claim limitation 'training the neural network in a first stage using the first training set' of example 39 does not recite a judicial exception... the memorandum expressly states that the training step limitation above does not recite any judicial exception... consistent with the USPTO's view that the "mental process" grouping excludes processes that cannot be practically performed in the human mind or with pen and paper." (p.13, ¶1)
• AI training methods vary depending on the type of model... initial pre-processing operations ( e.g., encoding, vectorization, normalization, etc.), and the subsequent training of the AI/ML model (e.g., the repeated adjustment of model weights) cannot be practically performed in the human mind or with pen and paper." (p.13, ¶1)
• "none of the above-identified limitations of independent claims 1 or 21 should be considered to be directed to a "mental process" or "mathematical concept" at Step 2A of the eligibility analysis, and the eligibility analysis should end at this stage." (p.13, ¶1)
The arguments are not yet persuasive for the following reasons:
First because in a BRI, the training and/or deploying/using the machine learning models as claimed in the independent claims recite a mental process due to the lack of required complexity, as well as inherently recite mathematical concepts in the clustering and probabilistic models discussed at Specification [0036, 0048, 0049, 0056], and recited in dependent claims 9-10, 12, 14, 16, 29-30, 32, 34, and 36. Additionally, if the steps of training, deploying, and using the trained machine learning model were characterized as an additional elements, they would not integrate the JEs at Step 2A Prong Two, nor would they provide significantly more at Step 2B.
Further, regarding the "2025 AI Memo" (The Kim Memo, 08/04/2025) and the discussion of Example 39, the fact pattern differs between Example 39 (method for training a neural network for facial detection) and the instant application (training a model for use in predicting efficacy of preventative measures to mitigate spread of a pathogen or illness), and further because the judicial exceptions identified at Eligibility Step 2A, Prong One are not yet integrated into a practical application at Eligibility Step 2A, Prong Two. Further, the claims recite additional elements (for data gathering and computer components) that do not amount to significantly more than the judicial exceptions at Eligibility Step 2B when those additional elements are evaluated individually and in combination to determine whether they contribute an inventive concept. Additionally, Example 39 from the Subject Matter Eligibility Examples: Abstract Ideas is one examples that is a teaching tool using certain fact-specific scenarios, but is not the same as the guidance provided in the MPEP.
Reciting a practical application at Step 2A Prong Two is one path to overcoming the 101 rejection; MPEP § 2106.04(d)(I) lists five example considerations (listed below) for evaluating whether a judicial exception is integrated into a practical application at Step 2A Prong Two. Of course, one or two considerations will likely apply more than others. Applicant is encouraged to request an interview if it may be helpful.
(1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
(2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2).
(3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b).
(4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c).
(5) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Hirsch, (US 2019/0096526, published 28 March 2019; cited on the 09/05/2024 form PTO-892), in view of Kucharski, (The Lancet infectious diseases, vol. 20(10), pages 1151-1160 (published 16 June 2020); cited on the 09/05/2024 form PTO-892).
Independent claim 20 recites a system comprising a memory and processor to execute instructions to receive a request for recommended preventative measures, including the identification of a pathogen; identify the recommended preventative measures based on the pathogen identification and a trained machine learning model; and output information of the identified preventative measures.
Hirsch shows a machine learning method for advanced analytics performed by a disclosed Health Science Decision Support System (HSDSS) useful in making health science recommendations ([0006], fig.4). Hirsch shows at [0024] data and/or metadata can be derived from any electronic medical record (EMR) system (for example, EPIC, Cerner, Allscript, government databases, etc.), and at [0025, plus table] shows data which includes Individual Medical Information (Table 1), Hospital Information (table 5), Pharmaceutical Information (Table 7), Health Science Outputs (including Recommendations) (Table 8). Hirsch et al. shows training (of machine learning models), and grouping input data on statistical properties and clustering [0172]. This shows receiving data from a plurality of records, and making recommendations, a trained machine learning model of claim 20.
Hirsch shows a processor and system processor storage (memory) at fig.3 and [0017, 0023, 0027]. Hirsch shows the advanced analytics method 200, performed by the system processor 128, can implement system commands that can allow selection (i.e., a request) of any number of sets or subsets of the data contained in Tables 1 through 9 by entities, sub-entities, attributes, and/or characteristics, in various manual approaches [0112]. This shows the request of claim 20; the processor and memory of claim 20; and outputting data of claim 20.
Hirsch does not show the identified recommended preventative measures of claim 20.
Kucharski shows a model looking at effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 (p.1151, title), using the BBC Pandemic dataset, collected in 2017-18 (i.e., a dataset that would including preventative measures associated with similar pathogens) which included 40,162 UK participants with recorded social contacts (p.1152, col.1-2) . Kucharski shows first generating the number of secondary cases without any control measures in place, and second, randomly sampling the proportion of these secondary cases that were either successfully traced and quarantined, and hence removed from the potentially infectious pool, or averted through isolation of the primary case (p.1154, col.2); Kucharski shows the difference between these two values gave the overall number of secondary cases that would contribute to further transmission, the effective R (Reff; figure 1C, D) (bridging p.1154-1155, text and table 3). Kucharski shows a scenario with a limit on daily contacts made in other settings (i.e., physical environmental preventative measures) (with the baseline limit being four contacts, equal to the mean number reported by adults in the BBC Pandemic data) p.1155, col.2); and lists "other settings" (i.e., physical environments, also built environments) as "outside the home, work, and school" (p.1156, col.2) . Kucharski shows "Mean reduction in Reff" (right column of Table 3, p.1155); Reff is interpreted as average efficacy, because the reduction in transmission value (R) reflects the efficacy of preventative measures. This shows the identified recommended preventative measures of claim 20.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning methods and systems for determining health science recommendations thru data analysis of electronic medical records and other medical related data of Hirsch, with the method for modelling effectiveness of preventative measures in reducing transmission of SARS-CoV-2, of Kucharski, because Kucharski shows using their model considered self-isolation both within and outside the household; and their analysis suggests that a combination of self-isolation, contact tracing, and physical distancing might be required to maintain Reff (the effective transmission rate) lower than 1 (p.1159, col.1). One would be motivated to combine, because Hirsch states "the advanced analytics performed by a disclosed Health Science Decision Support System (HSDSS) include improvements to existing techniques and algorithms"[0006]. One of ordinary skill would have had a reasonable expectation of success, as Hirsch shows their methods "can be optimized for detection, characterization, and exploitation of obscure structures, patterns and information in the data" [0006], and would have understood how to and would have been motivated to combine the teachings of Hirsch and Kucharski, and as such, the combination would have been obvious.
Response to Applicant Arguments - 35 USC § 103
The Applicant's arguments, filed 11/07/2025, have been fully considered, and they are persuasive regarding claims 1-2, 4-12, 14-19, 21-22, 24-32, and 34-40. However, the arguments are not persuasive regarding independent claim 20, which has no dependents, and was not amended. Reasons for maintaining/withdrawing are as follow:
Reasons for maintaining 103 rejection for Independent claim 20:
Applicant did not assert anything regarding independent claim 20, which has not been amended. Claim 20 may have been inadvertently left out of the amending of the independent claims, as all other independent claims were amended to include limitations from now-canceled claims 3, 13, 23, 33 (which were found to be free of the prior art at last Office action). Should claim 20 be amended similar to the other independent claims, the 103 would likely be withdrawn.
Reasons for withdrawing 103 rejection for Claims 1-2, 4-12, 14-19, 21-22, 24-32, and 34-40:
Claims 1-2, 4-12, 14-19, 21-22, 24-32, and 34-40 are free of the obvious art at least because close art, e.g., Hirsch, (US 2019/0096526, published 03/28/2019; cited on the 09/05/2024 form PTO-892), in view of Kucharski, (The Lancet infectious diseases, vol. 20(10), pages 1151-1160, published 06/16/2020; cited on the 09/05/2024 form PTO-892), either individually or in obvious combination, does not teach at least the recited combination of "adjusting values associated with an attribute in the data set based on a scaling factor associated with an accuracy of a source from which the value was obtained" (of independent claims 1 and 21), nor does the combination of Hirsch in view of Kucharski teach "wherein identifying the preventative measures comprises generating a probability score as a weighted average of efficacy probabilities generated by each of the one or more trained machine learning models, each model of the one or more trained learning model being associated with a weighting value to assign to a predicted efficacy of the preventative measures" (of independent claims 11, 31, and 40). Additionally, Applicant's 11/07/2025 remarks at pp.10-11 supported the withdrawal of the 103 rejection.
Conclusion
No claims are allowed.
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 Meredith A Vassell whose telephone number is (571)272-1771. The examiner can normally be reached 8:30 - 4:30.
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, KARLHEINZ SKOWRONEK can be reached at (571)272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.A.V./Examiner, Art Unit 1687
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686