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 .
Status of Claims
This action is in response to the reply received 7/9/2026.
Claims 1 and 17 were amended 7/09/2026.
Claims 1-4, 14-42 are currently pending and have been examined.
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 17-24 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) without significantly more.
Claims 17-24 are drawn to a method which is a statutory category of invention (Step 1: YES).
Independent claims 17 recites: accessing a set of tokenized electronic health record documents wherein each document is tokenized to exclude patient-identifying information; applying to extract structured medical fact tokens from each document, each token comprising an ordered triple of a fact, a relationship, and a term; generating database queries using a rule-based comprising a first plurality of expert-system rules with predicates configured to match values of the extracted tokens; retrieving identifiers of medical-literature publications based on matched predicates; computing document-similarity scores between each tokenized document and each retrieved publication using a term-weighted similarity metric; ranking the publications and selecting a subset above a relevance threshold; transmitting the ranked subset and not transmitting identifiers below the relevance threshold; wherein the first plurality of expert-system rules and a second plurality of expert-system rules correspond to quantification over a plurality of sets including the set of patients, the set of tokenized electronic health record documents, and the set of identifiers.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, between a user and a patient as reflected in the specification, which states that “A recommender system delivers a selection of relevant publications from the medical literature (e.g., medical journal articles, clinical studies, guidelines, presentations, videos, podcasts, blog postings, etc.) to a user such as a healthcare professional (HCP), patient, or patient caregiver. The selection of publications is relevant to the HCP user because it may be based in part on information extracted from a database of the HCP's patients' electronic health records (EHRs)” (see: specification paragraph 8). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a situation where “it can be very difficult for an HCP to stay informed about research that is relevant to the HCP's patients. There is therefore a need for systems to help make the growing medical literature more accessible to HCPs” (see: specification paragraph 3). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “first data store”, “trained machine learning model”, “rule-based expert system”, “second data store”, are recited at a high level of generality (e.g., that the extracting, retrieving, determining and displaying is performed using generic computer components with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 2, Figure 7, Figure 10 and
Paragraph 9, where “An embodiment system might comprise a network interface, a computing system, and at least one computing device configured to implement one or more services, wherein the one or more services are configured to access, over a network using the network interface, a set of EHRs relating to a set of patients from at least a first data store, use a first set of AI techniques to analyze the contents of the set of EHRs to extract a set of extracted medical facts, use a second set of AI techniques to formulate a set of database queries based on the set of extracted medical facts that are used to retrieve, over a network using the network interface, a set of resource locators, each resource locator for a retrieved medical-literature publication from at least a second data store that are relevant to the set of extracted medical facts, use a third set of AI techniques to determine a subset of the set of resource locators to present to a user, and present the subset of the set of the resource locators to a user via the computing system's display”
Paragraph 30, where “Some systems might use machine learning, which uses processes and statistical models that computer systems can use to perform a specific task without requiring explicit instructions, instead perhaps relying on pattern matching and inference. More probably, artificial intelligence (AI) might be used. Machine learning processes can be used to build a mathematical model of sample or training data, in order to make predictions or decisions about input data without being explicitly programmed to perform the task.”
Paragraph 62, where “In artificial intelligence, an expert or rule-based system is a computer system that emulates human-expert decision making. Expert systems represent knowledge and actions explicitly as if-then rules: if a condition holds, then an action is taken. An inference module selects which rules to apply and in which order. Referring again to FIG. 2, a medical-literature query module (208) is configured to use expert-system methods to automatically formulate search queries that will retrieve a selection of medical-literature publications (e.g., joumal articles, guidelines, presentations, videos, pod casts, blog postings, websites, etc.) from a medical-literature database (212). The automatically formulated search queries retrieve a selection of medical-literature publications that relate to the diagnosis and treatment facts extracted by the EHR analysis module (206). These diagnosis and treatment. The automatically formulated search queries might also use the metadata associated with the extracted diagnosis and treatment facts to further refine the selection of medical-literature publications (e.g., by prioritizing recent diagnoses and treatments, or by prioritizing patients with high-risk conditions, etc.). The automatically formulated search queries might also reflect a patient's comorbidities and adjuvant therapies (rather than consider each diagnosis and treatment in isolation), as well as a patient's genomic profile in its entirety (rather than consider individual genomic markers in isolation). In an embodiment, the medical-literature query module (208) is implemented via a logic-programming language (as an example, the Prolog language) that supports dynamic assertion and manipulation of facts (e.g., to record user feedback), second-order predicates that permit logical statements over all EHR documents (e.g., set of and bag of in Prolog), and the ability to interface with external modules (e.g., to use ML libraries written in other languages).”
Paragraph 70, where “The system may incorporate relevance filtering via the
relevance_percentile(T, I, C) predicate that is included in each of the presentation rules (600): this predicate considers all the candidate publication items C retrieved by the query module (208) and ranks them by relevance to the text file document T, and then returns the rank of the particular publication item I in that sorted order. When the text file Tis the EHR document for a patient, this predicate gives a rank measure of relevance to patient P for publication item I from the candidate set of publication items C. The relevance_percentile(T, I, C) predicate may compute a score using a document-similarity metric in which stop words are removed, word stemming is applied, and then common terms in the two documents are counted after they have been weighted by term frequency within the documents, and inversely weighted by term frequency in a representative corpus of the medical literature. In addition, the document-similarity approach can be augmented by the artificial inclusion in the document of words and phrases like "therapy," "treatment," "drug trial," "review," "study," and "guideline."”
Paragraph 76, where “[According to one embodiment, the techniques described herein are implemented by one or generalized computing systems programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Special-purpose computing devices may be used, such as desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques”
Paragraph 80, where “Computer system (1000) may be coupled via bus (1002) to a display (1012), such as a computer monitor, for displaying information to a computer user.”
Paragraph 82, where “The term "storage media" as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operation in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device (1010). Volatile media includes dynamic memory, such as main memory (1006). Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge”
Paragraph 84, where “Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor (1004) for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a network connection. A modem or network interface local to computer system (1000) can receive the data. Bus (1002) carries the data to main memory (1006), from which processor (1004) retrieves and executes the instructions. The instructions received by main memory (1006) may optionally be stored on storage device (1010) either before or after execution by processor (1004).”
Paragraph 86, “Network link (1020) typically provides data communication through one or more networks to other data devices. For example, network link (1020) may provide a connection through local network (1022) to a host computer (1024) or to data equipment operated by an Internet Service Provider (ISP) (1026). ISP (1026) in tum provides data communication services through the world wide packet data communication network now commonly referred to as the "Internet" (1028). Local network (1022) and Internet (1028) both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link (1020) and through communication interface (1018), which carry the digital data to and from computer system (1000), are example forms of transmission media.”
The claims recite additional elements for extra-solution activity, as recited above, each of which amounts to mere post-solution activity concerning an insignificant application. The specification (e.g., as excerpted above) does not indicate that the additional element(s) provide anything other than well‐understood, routine, and conventional functions when claimed in a merely generic manner (as they are here). See: MPEP 2106.05(g).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 18-24 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claims 18-24 further recite extracting and applying rule-based calculations to data using the generic recited machine learning model implemented on the generic computing system of its independent claim as recited above. These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Allowable Subject Matter
Claims 1-4 and 14-16, 25-42 are allowed as the rejection to independent claim 1 has been withdrawn.
Response to Arguments
The arguments filed 7/9/2026 have been fully considered.
Regarding the 112 rejections, these arguments are persuasive. The amendments to Claims 1 and 17 overcome the 112(b) and 112(f) rejections and they have been withdrawn.
Regarding the 101 rejection regarding claims 17-24, these arguments are not persuasive. The claimed invention does not have limitations relating to AI techniques that are structurally and functionally integrated into the system to create privacy-preserving architecture that would provide a practical application as in independent claim 1 and is dissimilar from Desjardins as it does not claim an improvement to machine learning. The claimed invention recites a trained machine learning model that implements the input/output of data calculations. The machine learning model recited is a generic machine learning model as shown in paragraph 30 of the specification and does not provide a practical application of an improvement of the machine learning model.
The calculations of thresholds to filter data on a generic computing device does not provide a practical application to overcome the abstract idea and is dissimilar to Enfish. An improvement to technology creates a practical application, not the input/output of data using threshold techniques. The functions argued are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
Further, not every claim that recites concrete, tangible components escapes the reach of the abstract-idea inquiry. (See, e.g., Alice, 134). It is well-settled that mere recitation of concrete, tangible components that are generic is insufficient to confer patent eligibility to an otherwise abstract idea. In order to amount to an inventive concept, the components must involve more than performance of “’well-understood, routine, conventional activities’ previously known to the industry.” (Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)). The originally filed specification was investigated and found to support this conclusion.
The dependent claims rely on the arguments of the independent claims and are rejected for the reasons stated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Krayer (US 9,905,112 B1) teaches analyzing patient medical data using machine learning using thresholds and rules, but does not explicitly tokenize the electronic health record documents.
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.
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/KIMBERLY A. SASS/Examiner, Art Unit 3686
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