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 .
Response to Arguments
Claim Interpretation Under 112(f):
Applicant's arguments filed 10/06/2025 have been fully considered.
Applicant argues that the “input device” and “rules engine” connote sufficient structure in the art and are not a generic placeholder requiring 112(f) interpretation.
In response to Applicant, “The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008)…. Note that there is no fixed list of generic placeholders that always result in 35 U.S.C. 112(f) interpretation.” See MPEP 2181(1)(A). Applicant’s recite “input device” and “rules engine” are interpreted to be analogous to placeholders similar to ones provided on this list, but as pointed out, this list is not exhaustive. For example, input device is analogous to the list’s recite “device” while rules engine is analogous to the list’s “machine”. Therefore, the claim interpretations are maintained.
Rejection Under 101:
Applicant's arguments filed 10/06/2025 have been fully considered.
Applicant argues that the claims are integrated into a practical application. The claims recite algorithmic techniques to analyze messages and generate follow-up questions; decision tree traversals, which cannot be performed in the mind at a scale and speed required for medical triage; and decision support interfaces. Due to these recitations, the claim is integrated into a practical application.
In response to Applicant’s argument, the argument appears to be directed toward the amendment and is therefore moot. However, the interface and processing parser are considered additional elements and amount to merely invoking the use of computer as tools to carry out the abstract idea. As for the decision tree traversal, a doctor would be able to determine the next question after a patient answered the last one (i.e., traversal path) since they routinely perform this task in their encounters with patients. Therefore, the claims do not recite a practical application.
The claims do not recite a judicial exception because the claimed features involve a technical processing in ways that cannot be performed in the human mind with the use of the processing parser and traversing the decision tree and implementing a rules engine.
In response to Applicant’s argument, the argument appears to be directed toward the amendment and is therefore moot. However, as discussed above, traversing a decision tree can be performed in the mind, for example a doctor does this when treating a patient. A doctor can also parse out the important information when reading messages. As well and determine the medical event from the messages and other information. The claims falls under the abstract idea of organizing human activity by following rules for providing patient information to practitioners that they can then use to later treat patients. See the updated rejection for further clarification.
The Office Action’s characterization oversimplifies the claims by focusing on high level functional descriptions while ignoring technical implementations that distinguish the claim from generic computer automation. Applicant’s claims recite specific technical implementations that improve computer processing capabilities that integrate the claim into a practical application consistent with Ex parte Desjardins.
In response to Applicant’s argument, claims where considered in whole, as demonstrated by the reproduction of the entire claim in the analysis. The entire claim was considered and as shown below in the analysis the abstract idea is underlined. Therefore the characterization is not oversimplified. The bolded elements of the claims do not amount to a practical application since it merely invokes the use of computers to carry out the abstract idea. See the updated rejection for further clarification.
The claims amount to significantly more than the abstract idea because of non-conventional and non-generic arrangement of technical features that provide specific improvements to computer functionality.
In response to Applicant’s argument, as discussed above the additional elements amount to nothing more than invoking the use of computers to carry out the abstract idea and therefore do not amount to significantly more than the abstract idea.
Rejection Under 102/103:
Applicant's arguments filed 10/06/2025 have been fully considered. Applicant argues that the claims do not recite all of the amended features of the claims and are therefore allowable.
In response to Applicant’s argument, the argument appears to be directed toward the amendment and is therefore moot. See the updated rejection below for further clarification in light of the new grounds of rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 (and similarly with claims 10, 16) recite “implement a natural language processing parser configured to automatically parse the received medical message, the medical message comprising unstructured natural language text, to extract one or more medical concepts and features.” After reviewing the specification, there does not appear to be sufficient support for this amendment. The specification at [0016] discusses a messaging system that uses algorithmic techniques to analyze text of messages but it is unclear if this technique is parsing text to extract medical concepts and features. Appropriate correction is required. The dependent claims are also rejected for inheriting the issues of the independent claims.
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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The dependent claims are rejected for inheriting the issues of the independent claims.
Claims 1, 10, and 16 recite the limitation "the traversal path." There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 5-6, 14, 20 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 5 recites substantially similar limitations to those recited in the amended claim 1 about the presented knowledge base editor interface and does not appear to further limit the independent claim. Claim 6 recites substantially similar limitations to those recited in the amended claim 1 about the presented rules engine editor interface and does not appear to further limit the independent claim. Claim 14 recites substantially similar limitations to those recited in the amended claim 10 about the presented knowledge base editor interface and does not appear to further limit the independent claim. Claim 20 recites substantially similar limitations to those recited in the amended claim 16 about the presented rules engine editor interface and does not appear to further limit the independent claim. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Regarding Claim 1 (and similarly for 10, 16) – The claim recites an input device configured to receive a medical message from a patient... See MPEP 2181. The claim limitation uses the term input device. The “input device” is modified by functional language “configured to receive a medical message….” The input device is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. See Spec. [0029] which describes the input device can be a touch screen. For examination purposes the input device is construed to be hardware such as a touch screen or a display.
Regarding Claim 1 (and similarly for 10, 16) – The claim recites a rules engine configured to determine the medical event... See MPEP 2181. The claim limitation uses the term rules engine. The “rules engine” is modified by functional language “configured to determine the medical event….” The rules engine is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. See Spec. [0012] describes using intelligent analysis for analyzing messages [0018] describes a computerized system using rules framework to analyze messages [0021] describes algorithmic techniques being used for the analysis. For examination purposes the rules engine is construed as using algorithmic techniques or intelligent analysis to analyze the messages.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-9 are drawn to a system, which is within the four statutory categories (i.e. apparatus). Claims 10-15 are drawn to a method, which is within the four statutory categories (i.e. process). Claims 16-20 are drawn to a non-transitory machine readable storage medium, which is within the four statutory categories (i.e. apparatus).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, claim 1 (and substantially similar with independent claim 10, 16) recites:
A system for increasing accuracy of medical information relayed to a medical practitioner, the system comprising:
a storage device, the storage device including a medical knowledge base;
an input device configured to receive a medical message from a patient regarding a medical event experienced by the patient; and
a processing circuitry configured to:
implement a natural language processing parser configured to automatically parse the received medical message, the medical message comprising unstructured natural language text, to extract one or more medical concepts and features;
identify, based on an analysis of the medical message, additional information needed by a medical practitioner to provide medical care for the medical event;
select, in real time and for each follow-up question, a next question to prompt the patient for additional information by deterministically traversing a stored decision tree within the medical knowledge base, wherein the traversal path is dynamically determined based on the immediately preceding answer from the patient;
generate one or more questions prompting the patient to provide the additional information; and
implement a rules engine configured to determine the medical event based on the medical message, the additional information, and the medical knowledge base;
provide an indication of the medical event to a medical practitioner;
present a knowledge base editor interface configured to receive a rule modification input from a clinician and, responsive to the rule modification input, generate an updated medical knowledge base: and
present a rules engine editor interface configured to receive a rule modification input from a clinician and, responsive to the rule modification input, generate an updated rules engine.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the system, storage device, input device, processing circuitry, processing parser, editor interface, the limitations in the context of this claim encompass an automation of organizing patient’s correspondences regarding their medical events and determine what information the doctor needs in order to provide care to the patient. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-9, 11-15, and 17-20 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, claim 1 (and substantially similar with independent claim 10, 16) recites:
A system for increasing accuracy of medical information relayed to a medical practitioner, the system comprising: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
a storage device, the storage device including a medical knowledge base; (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
an input device configured to (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) receive a medical message from a patient regarding a medical event experienced by the patient; and
a processing circuitry configured to: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
implement a natural language processing parser configured to (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) automatically parse the received medical message, the medical message comprising unstructured natural language text, to extract one or more medical concepts and features;
identify, based on an analysis of the medical message, additional information needed by a medical practitioner to provide medical care for the medical event;
select, in real time and for each follow-up question, a next question to prompt the patient for additional information by deterministically traversing a stored decision tree within the medical knowledge base, wherein the traversal path is dynamically determined based on the immediately preceding answer from the patient;
generate one or more questions prompting the patient to provide the additional information; and
implement a rules engine configured to (the computerized system per the claim interpretation - merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) determine the medical event based on the medical message, the additional information, and the medical knowledge base;
provide an indication of the medical event to a medical practitioner;
present a knowledge base editor interface configured to receive a rule modification input from a clinician and, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) responsive to the rule modification input, generate an updated medical knowledge base: and
present a rules engine editor interface configured to receive a rule modification input from a clinician and (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) responsive to the rule modification input, generate an updated rules engine.
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the system, storage device, input device, processing circuitry, processing parser, editor interface, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0012], [0018], [0021], [0029], [0031], see MPEP 2106.05(f))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claim 2, 11, 17 recites identifying urgency information and using the rules engine to predict severity of medical events, which amounts to furthering the abstract idea; claim 3, 12, 18 recites using the algorithmic techniques to identify the additional information, which amounts to furthering the abstract idea; claim 4, 13, 19 recites further defining which algorithmic techniques are used, which amounts to furthering the abstract idea; claim 5, 14 recites presenting a knowledge base editor interface, receiving a rule modification at the interface, and generating an update to the knowledge base based on the modification, which amounts to furthering the abstract idea and invoking computers as a tool to perform the abstract idea; claim 6, 20 recites presenting a rules engine editor interface, receiving a rule modification, and generating an update to the rules engine based on the modification input, which amounts to furthering the abstract idea and invoking computers as a tool to perform the abstract idea; claim 7, 15 recites further defining the additional information, which amounts to furthering the abstract idea; claim 8 recites further defining the additional information, which amounts to furthering the abstract idea; claim 9 recites further defining the additional information, which amounts to furthering the abstract idea; and claims 2-9, 11-15, and 17-20 additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
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 discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the system, storage device, input device, processing circuitry, processing parser, editor interface, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0012], [0018], [0021], [0029], [0031]); using the system, storage device, input device, processing circuitry, processing parser, editor interface, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
Dependent claims recite additional subject matter which beyond furthering the abstract idea, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea and are generally linking the abstract idea to a particular field of environment. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2022/0215957) in view of Sun et al. (US 2021/0027898) and Dew, Sr. et al. (US 2018/0301222).
Regarding claim 1, Chen discloses a system for increasing accuracy of medical information relayed to a medical practitioner, the system comprising:
a storage device, the storage device including a medical knowledge base; (Chen Figs. 1-2 and corresponding text; [0023] data store 111 coupled to a digital nursing server 101 [0034] Data store(s) 211 may be included in the one or more memories 203 of the computing device 200 or in another computing device and/or storage system distinct from but coupled to or accessible by the computing device 200. In some embodiments, the data store(s) 211 may store data in association with a database management system (DBMS) operable by the servers 101 and/or the client devices 103. For example, the DBMS could include a structured query language (SQL) DBMS, a NoSQL DMBS, etc. In some instances, the DBMS may store data in multi-dimensional tables comprised of rows and columns, and manipulate, e.g., insert, query, update and/or delete, rows of data using programmatic operations)
an input device configured to receive a medical message from a patient regarding a medical event experienced by the patient; and (Chen [0035] Input device(s) 207 may include any standard devices configured to receive a variety of control inputs (e.g., gestures, voice controls) from a user 125 or other devices. Non-limiting example input device 207 may include a touch screen (e.g., LED-based display) for inputting texting information, making a selection, and interacting with the user 125; motion-detecting input devices; audio input devices; other touch-based input devices; keyboards; pointer devices; indicators; and/or any other inputting components for facilitating communication and/or interaction with the user 125 or the other devices. [0088] in FIG. 5, in step 501, a patient reports a symptom. Based on the reported symptom and/or the patient medical information 503 (such as diagnosis, medication, treatment, etc.), a set of onset questions are then presented to the patient in step 505)
a processing circuitry configured to: (Chen [0021] FIG. 1 illustrates a block diagram of an example digital nursing system 100, according to embodiments of the disclosure. In implementations, a digital nursing system 100 may take the form of hardware and/or software components running on hardware. In some embodiments, a digital nursing system 100 may provide an environment for software components to execute, evaluate operational constraint sets, and utilize resources or facilities of the digital nursing system 100. For instance, software (e.g., applications or apps, operational instructions, modules, etc.) may be running on a processing device, such as a computer, mobile device (e.g., smartphone/phone, smartwatch, fitness tracker, tablet, laptop, personal digital assistant (PDA), patient monitoring device, etc.) and/or any other electronic device. In other instances, the components of a digital nursing system 100 disclosed herein may be distributed across and executable by multiple devices. For example, an input may be entered on a client device, and information may be processed or accessed from other devices (e.g., servers or other client devices, etc.) in a network)
identify, based on an analysis of the medical message, additional information needed by a medical practitioner to provide medical care for the medical event; (Chen [0040] Natural language processor 301 may be configured to parse user input (e.g., text, image, or voice input) to predict or identify user intent, according to embodiments of the disclosure… identify user intent, including identifying a to-be-reported symptom from the user input… may include certain syntactic analysis modules and/or lexical semantics modules for content parsing, sentence breaking, keyword identification, etc. These different modules or components collaboratively allow a prediction of the user intent (e.g., identify a to-be-reported symptom) based on various types of input received from a patient. [0041] Medical content associator 303 may be configured to determine suitable medical content associated with a reported symptom. For instance, based on the identified symptom reported by a patient, the medical content associator 303 may identify a list of diseases associated with the symptom, and even more specifically in which stage the symptom may occur in a disease. In some embodiments, the medical content associator 303 may further retrieve the user profile and medical information (e.g., medical history) of the patient in identifying a specific disease for the symptom reported by the patient. For instance, the reported symptom may match a disease previously diagnosed for the patient based on the medical record of the patient. In some embodiments, based on the identified disease, the medical content associator 303 may determine what supplemental information is necessary to assess the severity of the reported symptom)
generate one or more questions prompting the patient to provide the additional information; and (Chen [0042] In some embodiments, the medical content associator 303 may develop questions according to certain standards in healthcare practice, such as National Cancer Institute (NCI)'s patient-reported outcome (PRO)-common terminology criteria for adverse events (CTCAE) standard and oncology nurse triage protocols. Accordingly, two sets of questions may be developed by the medical content associator 303 according to some embodiments: onset question set and symptom assessment question set. The onset question set may include a set of questions that discover the date and time when a patient initially experienced a reported symptom, whether the symptom happened gradually or suddenly, etc. The symptom assessment questions may provide certain parameters for determining the severity of the reported symptom)
implement a rules engine configured to determine the medical event based on the medical message, the additional information, and the medical knowledge base; and (Chen Figs. 5-6 and corresponding text; [0088] Next, in step 507, a set of assessment questions for the reported symptom are then presented to the patient for assessment of the reported symptom and the associated severity. The assessment of the reported symptom and the associated severity may be a dynamic process, which is implemented after each response is received from the patient during the symptom assessment. [0089] In step 509, the parent symptom predictor 307 may predict parent symptom(s) and/or complication(s) associated with the reported symptom. The parent symptom predictor 307 may use the answers to the assessment questions as well as the medical information of the patient to predict the parent symptom(s) and/or complication(s) associated with the reported symptom. The parent symptom predictor 307 may use the trained denoising autoencoder 313 combined with the random forest classifier 315 to determine the potential parent symptom(s) and/or complication(s) [0140] In step 629, the digital nursing system 100 may assess the potential risk of the user based on the determined severity and the medical information of the user in view of future development of the disease(s) associated with the symptom(s), as described earlier)
provide an indication of the medical event to a medical practitioner. (Chen [0106] For instance, the assessment action module 311 may determine whether a notice should be generated and a healthcare provider should be notified for the identified severity and potential risk, whether an emergency alert should be generated to require an emergency dispatch, whether and/or when a follow-up symptom check should be scheduled, etc.)
Chen does not appear to disclose the following, however, Sun teaches it is old and well known in the art of healthcare data processing to:
implement a natural language processing parser configured to automatically parse the received medical message, the medical message comprising unstructured natural language text, to extract one or more medical concepts and features;(Sun [0073] In order for dynamic context-based collaborative medical concept interpreter 120 to dynamically and automatically generate and present summarized explanations of medical concepts, curation engine 122 initially identifies, from patient electronic medical records (EMRs) 130, previously recorded patient-provider communication texts, such as secure messages between patients and their provider, health-related Q&A collections from online communities associated with the patients, or the like. Curation engine 122 also identifies, from patient electronic medical records (EMRs) 130, previously recorded patient-provider speech communications, such as face-to-face or phone conversations between patients and their provider during clinical visits or phone consultations, or the like. From the communication texts, speech communication, or the like, curation engine 122 performs natural language processing to identify one or more medical concepts expressed explicitly by a patient as needing explanations. That is curation engine 122 identifies questions, the focus of the questions, or the like, through question analysis using rule-based sentence features identified using natural language processing)
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Chen to incorporate implement a natural language processing parser configured to automatically parse the received medical message, the medical message comprising unstructured natural language text, to extract one or more medical concepts and features, as taught by Sun, in order to have a summarized version of the text with the identified concepts so as to focus on what questions to ask the patient. See Sun [0073].
Chen-Sun does not appear to explicitly teach the following, however, Dew teaches it is old and well known in the art of healthcare data processing to:
select, in real time and for each follow-up question, a next question to prompt the patient for additional information by deterministically traversing a stored decision tree within the medical knowledge base, wherein the traversal path is dynamically determined based on the immediately preceding answer from the patient; (Dew [0022] The present invention comprises an interactive method, platform and system for directing, integrating, documenting, and tracking steps taken by a medical provider during the patient encounter. A medical professional's actions are directed or guided by prescriptive protocols, guidelines, payor requirements, etc. comprising prompts for information that together with the responses create a path through a decision tree or decision engine. [0025] The forms can be accessed and completed using conventional web browser software. During a structured work flow process as the user responds to each question or prompt, the knowledge-based platform provides the user with the next appropriate prompt or question [0293] FIG. 5 illustrates the steps required to generate the form as described herein. At a step 10 a document comprising relevant questions and prompts is generated by the subject matter expert,)
present a knowledge base editor interface configured to receive a rule modification input from a clinician and, responsive to the rule modification input, generate an updated medical knowledge base: and (Dew [0021] Form-based input of information is an enabling technology that permits widespread distribution of form-based applications across client platforms, such as a conventional content browser. In the context of Web-based forms, a markup language defined interface can form the principal conduit through which end users can interact with backend application logic. Often configured in the form of a Web page, the interface can be provided to the content browser by a content server, and can take the form either of a pre-defined static page, or a dynamically generated page. Form input fields can be positioned within the interface through which user input can be accepted and posted to the backend application logic for further processing [0075] guiding the physician's examination component [0076] Note that outcomes from each of the four listed components and any findings or data gathered during each of the four components, may govern the path that is traversed through the decision tree during the physical examination. Thus the physical examination proceeds as the user, in conjunction with data collected from the patient, essentially traverses through the decision tree during the examination, where the path through the tree is developed as new patient medical information or data is entered and the expert knowledge base is consulted under control of the processor. For instance, details from the clinical history may eliminate some questions in the other three components, shape the substance of other three components, and spawn certain questions unique to the clinical history as presented to this point. Note further that the embedded or implicit algorithm that governs the tree-traversal details for the examination component is in part dependent on these four areas, among other aspects of the patient encounter. {altering the questions to gather different data is construed as updating the medical knowledge base containing medical information})
present a rules engine editor interface configured to receive a rule modification input from a clinician and, responsive to the rule modification input, generate an updated rules engine. (Dew [0021] Form-based input of information is an enabling technology that permits widespread distribution of form-based applications across client platforms, such as a conventional content browser. In the context of Web-based forms, a markup language defined interface can form the principal conduit through which end users can interact with backend application logic. Often configured in the form of a Web page, the interface can be provided to the content browser by a content server, and can take the form either of a pre-defined static page, or a dynamically generated page. Form input fields can be positioned within the interface through which user input can be accepted and posted to the backend application logic for further processing. [0275] The web page is accessed and the form tested. Editors review and test the uploaded web form for correct logic and other errors, making corrections in the underlying code as necessary. [0075] guiding the physician's examination component [0076] details from the clinical history may eliminate some questions in the other three components, shape the substance of other three components, and spawn certain questions unique to the clinical history as presented to this point. Note further that the embedded or implicit algorithm that governs the tree-traversal details for the examination component is in part dependent on these four areas, among other aspects of the patient encounter)
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Chen-Sun, as modified above, to incorporate select, in real time and for each follow-up question, a next question to prompt the patient for additional information by deterministically traversing a stored decision tree within the medical knowledge base, wherein the traversal path is dynamically determined based on the immediately preceding answer from the patient; present a knowledge base editor interface configured to receive a rule modification input from a clinician and, responsive to the rule modification input, generate an updated medical knowledge base: and present a rules engine editor interface configured to receive a rule modification input from a clinician and, responsive to the rule modification input, generate an updated rules engine, as taught by Dew, in order to provide the most relevant questions and gather the necessary medical information for a patient encounter. See Dew [0076], [0293].
Regarding claim 2, Chen-Sun-Dew teaches the system of claim 1, wherein: the processing circuitry is further configured to identify additional urgency information needed to determine an urgency of the medical event and generate one or more questions prompting patient to provide the additional urgency information; the rules engine is further configured to determine the urgency of the medical event based on the medical message, the additional information, and the medical knowledge base, wherein the medical knowledge base includes medical information for predicting severity of medical events; the processing circuitry is further configured to provide an indication of the urgency to a medical practitioner. (Chen [0094] Referring back to FIG. 3, in some embodiments, the digital nursing application 107 further includes a medical severity classifier 309 for determining the severity of each reported or identified symptom and complication. [0095] The medical severity classifier 309 may be configured to determine the severity of the symptoms (e.g., reported symptom, parent symptom) and complications based on the associated parameters. The parameters provide an in-depth understanding of the symptoms and complications, and the severity describes how severe a symptom and complication is: non-urgent, urgent, or emergent. Based on the collected information for a given symptom, severity is determined. The logic for determining the severity is pre-defined according to certain standards (e.g., PRO CTCAE) and is built-in for each symptom. In one example, a scoring system may be applied to valuate patient responses to questions for a reported symptom. For each question answered by the patient, a score is assigned by the digital nursing system 100 [0106] For instance, the assessment action module 311 may determine whether a notice should be generated and a healthcare provider should be notified for the identified severity and potential risk, whether an emergency alert should be generated to require an emergency dispatch, whether and/or when a follow-up symptom check should be scheduled, etc.)
Regarding claim 3, Chen-Sun-Dew teaches the system of claim 1, wherein the processing circuitry uses algorithmic techniques to identify, based on an analysis of the medical message, the additional information. (Chen [0086] To predict the probability of the cause of a reported symptom, the random forest-based classification may be further applied to the features extracted by the denoising autoencoder 308. Random forest classifier 310 is an inherent multi-class classifier consisting of a large number of relatively uncorrelated decision trees that operate as an ensemble, which can be used to classify an object based on features).
Regarding claim 4, Chen-Sun-Dew teaches the system of claim 1, wherein the rules engine implements algorithmic techniques using decision trees. (Chen [0086] To predict the probability of the cause of a reported symptom, the random forest-based classification may be further applied to the features extracted by the denoising autoencoder 308. Random forest classifier 310 is an inherent multi-class classifier consisting of a large number of relatively uncorrelated decision trees that operate as an ensemble, which can be used to classify an object based on features).
Regarding claim 5, the claim recites substantially similar limitations as those already addressed in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 6, the claim recites substantially similar limitations as those already addressed in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 7, Chen-Sun-Dew teaches the system of claim 1, wherein identifying of the additional information further includes application of probabilistic techniques to analyze the medical message. (Chen [0086] To predict the probability of the cause of a reported symptom, the random forest-based classification may be further applied to the features extracted by the denoising autoencoder 308. Random forest classifier 310 is an inherent multi-class classifier consisting of a large number of relatively uncorrelated decision trees that operate as an ensemble, which can be used to classify an object based on features. Each individual tree in the random forest spits out a class prediction, where the class with the most votes becomes the model's prediction. For random forest classification, a sample of training set taken at random but with replacement is used to build a tree. When growing the tree, the best split is chosen among a random subset of the input features. As a result of this randomness, the model selects the classification/regression results that get the most votes from the trees in the forest, and thus help reduce the variance of the final model. Since a large number of relatively uncorrelated trees operating as a “committee” generally outperform any of the individual constituent models, a random forest classifier often demonstrates better performance than other classifiers. In addition, a random forest classifier generally is easy to tune and robust to overfitting, all of which makes the random forest classifier ideal to predict the parent symptom and/or complications for a reported symptom).
Regarding claim 8, Chen-Sun-Dew teaches the system of claim 1, wherein identifying of the additional information further includes application of heuristic techniques to analyze the medical message. (Chen [0086] To predict the probability of the cause of a reported symptom, the random forest-based classification may be further applied to the features extracted by the denoising autoencoder 308. Random forest classifier 310 is an inherent multi-class classifier consisting of a large number of relatively uncorrelated decision trees that operate as an ensemble, which can be used to classify an object based on features [0104] For instance, while the medical severity classifier 309 determines that the patient's symptom is not emergent at the moment of reporting nausea, the risk assessment module 311 may predict that the patient is at a high risk of intestinal obstruction, and thus still recommend an immediate care by a healthcare provider. In some embodiments, the risk assessment module 311 may assess the potential risk for the patient according to certain triage pathways. Additionally or alternatively, certain machine learning models may be used to predict the future disease state of a patient based on the patient's current medical condition and available history, and thus may be included in the risk assessment module 311).
Regarding claim 9, Chen-Sun-Dew teaches the system of claim 1, wherein identifying of the additional information further includes application of deterministic techniques to analyze the medical message. (Chen [0091] If a new symptom and/or complication is predicted by the parent symptom predictor 307 in step 509, the parent symptom predictor 307 may be back propagated to find associated symptoms in step 513. Typically, a parent symptom or a complication causes multiple symptoms. Other than the predicted parent symptom, there are associated symptoms. To find the most likely associated symptoms, back propagation of the prediction model (or the parent symptom predictor 307) may be applied. All symptoms found through the back propagation will be sorted using a sequential forward feature selection (SFFS). The first symptom accounts for the largest variance and is the most likely contributor, and the second symptom accounts for the second largest variance and is the second likely contributor, and so on. A threshold may be applied to find the most likely symptom(s), which are then considered as the associated symptom(s). The threshold may be empirically selected, to make sure that not too many or too few symptoms are selected).
Regarding claim 10, the claim recites substantially similar limitations as those already addressed in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 11, the claim recites substantially similar limitations as those already addressed in the rejection of claim 2, and, as such, is rejected for similar reasons as given above.
Regarding claim 12, the claim recites substantially similar limitations as those already addressed in the rejection of claim 3, and, as such, is rejected for similar reasons as given above.
Regarding claim 13, the claim recites substantially similar limitations as those already addressed in the rejection of claim 4, and, as such, is rejected for similar reasons as given above.
Regarding claim 14, the claim recites substantially similar limitations as those already addressed in the rejection of claim 5, and, as such, is rejected for similar reasons as given above.
Regarding claim 15, the claim recites substantially similar limitations as those already addressed in the rejection of claim 7, and, as such, is rejected for similar reasons as given above.
Regarding claim 16, the claim recites substantially similar limitations as those already addressed in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 17, the claim recites substantially similar limitations as those already addressed in the rejection of claim 2, and, as such, is rejected for similar reasons as given above.
Regarding claim 18, the claim recites substantially similar limitations as those already addressed in the rejection of claim 3, and, as such, is rejected for similar reasons as given above.
Regarding claim 19, the claim recites substantially similar limitations as those already addressed in the rejection of claim 4, and, as such, is rejected for similar reasons as given above.
Regarding claim 20, the claim recites substantially similar limitations as those already addressed in the rejection of claim 6, and, as such, is rejected for similar reasons as given above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. McNair et al. (US 11842816) is a decision support system that teaches presenting and editor interface to receive rule modifications and then updating medical knowledge bases and rule engines. McNair et al. (US 10854334) is an enhanced natural language processing system that teaches altering and updating knowledge bases.
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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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686