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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/26/2025 has been entered.
Status of Claims
This action is in response to the RCE filed 11/26/2025.
Claims 17-33 were amended 11/26/2025.
Claims 1-4 and 14-42 are currently pending and have been examined.
Claim Objections
Claim 1 is objected to because of the following informalities: “the processor being physically configure” on page 3, line 7 of claim 1 is grammatically incorrect. Appropriate correction is required.
Claim 4 is objected to because of the following informalities: “tothat consider citation” (line8), “tothat consider user-supplied” (line 10) and “tothat apply pedagogical” (line15) are grammatically incorrect. Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 and 17 and therefore their dependent claims 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “the processor is physically configure to analyze the health record tokens for information about a patient to be used to find relevant information without analyzing the identity of the patient” in lines 14-16 and “the processor is physically configure to analyze the health record tokens for information about a patient to be used to find relevant information without analyzing the identity of the patient” in lines 21-24 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The database extracts data from multiple formats and uses a generic processor to filter the data (paragraphs 27 and 77). The specification is silent on the processor being physically configured in a specific way to provide security of patient data. Specifically, the specification is silent on AI techniques on a processor being physically configured in a specific, non-generic way, to create privacy-preserving architecture as argued that the claim limitations purport. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 17 recites the limitations "the set identifiers that are under the threshold" and “and the set of identifiers” in the last paragraph of claim 17. It is unclear whether the set identifiers and the set of identifiers are referring to the “retrieving identifiers” as previously claimed and refer to the same identifiers that are being retrieved, or if there is a different set of identifiers altogether that are not claimed. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites “the first plurality of expert-system rules” in the last paragraph of claim 17. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites “the second plurality of expert-system rules” in the last paragraph of claim 17. There is insufficient antecedent basis for this limitation in the claim.
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 17-24are 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 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 for display thereby and not transmitting the ranked subset of the set identifiers that are under the threshold; wherein the first plurality of expert-system rules and the 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.
Response to Arguments
The arguments filed 10/15/2025 have been fully considered.
The arguments pertaining to the 101 rejection regarding claims 1-4, 14-16 and 25-42 are persuasive. Applicant argues that the AI techniques are structurally and functionally integrated into the system to create privacy-preserving architecture that avoids identifying patients which is a practical application that overcomes the abstract idea. However, the specification is silent as to how the AI techniques are structurally and functionally integrated into the system, resulting in a 112 rejection. The processor appears to be a generic computing component that uses the AI techniques as a calculation in the specification. The claim limitations of “the processor is physically configure to analyze the health record tokens for information about a patient to be used to find relevant information without analyzing the identity of the patient” and “the processor is physically configure to analyze the health record tokens for information about a patient to be used to find relevant information without analyzing the identity of the patient” are not supported in the specification. The database extracts data from multiple formats and uses a generic processor to filter the data (paragraphs 27 and 77). The specification is silent on the security, privacy, or anonymity of the patient data creating a privacy-preserving architecture as claimed. Specifically, the specification is silent on AI techniques on a processor being physically configured in a specific, non-generic way, to create privacy-preserving architecture. The 101 rejection is tentatively withdrawn for claims 1-4, 4-16, and 25-42, however a 112 rejection remains in view of the arguments presented in how the processor architecture is working.
Regarding the 101 rejection pertaining to claims 17-24, this 101 rejection remains. 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. Applicant argues that the claimed invention improves how medical literature is accessed and interpreted, integrating the abstract idea into a practical application and that the system is improved through efficiency of the AI techniques integrated into the system. Examiner respectfully disagrees. The AI techniques as argued are not present in the claimed invention of claim 17. Claim 17 recites a rule-based expert system that uses artificial intelligence, however the system recited in paragraph 62 appears to be a generic computing system using logic programming language to formulate search queries. The medical literature is accessed and interpreted using a generic computing system. 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Fueyo (US 20100010831 A1) teaches using patient data securely in a database query system, however it is silent on using specific machine learning models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY A SASS whose telephone number is (571)272-4774. The examiner can normally be reached 7AM-5PM (EST).
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/KIMBERLY A. SASS/ Examiner, Art Unit 3686
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