DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed May 28, 2026.
Claims 1-20 are pending.
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 Amendment(s)
Claim 17 has overcome the objection. Claims 1-20 are pending.
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 to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1, 11, and 18:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent Claim 1 falls within the statutory category of method
Independent Claim 11 falls within the statutory category of article of manufacture
Independent Claim 18 falls within the statutory category of machine
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 11, and 18 (Claim 1 being representative) claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in claims 1, 11, and 18 are (Claim 1 being representative):
A method for generating a care plan the method comprising:
based at least partially on unstructured data, generating, using…[…]…, a first data structure, a second data structure, or both comprising structured data via pattern matching, structural similarity or both, used to enable comparing the first data structure and the second data structure;
comparing the first data structure with the second data structure, wherein the first data structure comprises a set of health artifacts pertaining to a first condition of the patient, and the second data structure pertains to the patient and the first condition of the patient, and the second data structure comprises a subset of the set of the health artifacts, wherein the first data structure is a knowledge graph and the second data structure is a patient graph, and wherein the comparing comprises projecting the patient graph onto the knowledge graph to identify health-artifact nodes present in the knowledge graph and absent from the patient graph;
responsive to the comparing, generating the care plan comprising another subset of the set of health artifacts based on the identified absent nodes;
and modifying the another subset of the set of health artifacts in the care plan based on a detected tone of the patient, a detected emotion of the patient, a medical outcome desired by a physician, or some combination thereof.
This abstract idea is “managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” within certain methods of organizing human activity as it is generating a health care plan using data relationships. See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claim 1, 11, and 18 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional claim elements below:
electronically
one or more trained machine learning models
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
Within the noted above additional claim element, electronically, is generally linking the care plan to be implemented by computers in electronic format
The additional element, one or more trained machine learning models, is recited as “apply-it” as a tool or equivalent (e.g. using) to gather data
Independent claim 11 recites the additional claim elements below not already recited in claim 1:
A processing device with a tangible, non-transitory computer-readable medium storing instructions
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
Within the noted above additional claim element, A processing device with a tangible, non-transitory computer-readable medium storing instructions, is performing the abstract idea and is recited in the manner of merely invoking the element as a tool to “apply-it” or an equivalent and therefore is no more than using these generic elements as a tool to implement the abstract idea.
Independent claim 18 recites the additional claim elements below not already recited in claim 1:
A processing device with a memory storing instructions
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
Within the noted above additional claim element, A processing device with a memory storing instructions, is performing the abstract idea and is recited in the manner of merely invoking the element as a tool to “apply-it” or an equivalent and therefore is no more than using these generic elements as a tool to implement the abstract idea.
Accordingly, independent claims 1, 11, and 18 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements as analyzed above in step 2A prong 2, are merely generally linking and/or applying the abstract idea with general computer elements and therefore, do not amount to significantly more. The claims are patent ineligible.
Dependent Claims 2-10, 12-17, and 19-20:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 2-10 fall within the statutory category of method
The dependent claims 12-17 are presumed to fall within the statutory category of article of manufacture
The dependent claims 19-20 fall within the statutory category of machine
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-10, 12-17, and 19-20 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claims 1, 11, and 18 abstract idea by (1) further limiting the modification of health artifacts, (2) generating a net promoter score, (3) further limiting the data structures. Therefore, the dependent claims inherit the same abstract idea which is “managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” as it is generating a health care plan using data relationships. See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2-10, 12-17, and 19-20 this judicial exception is not integrated into a practical application.
The dependent claims recite the below additional claim elements not already recited in the independent claims:
a computing device
a machine learning model
an updated machine learning model
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a machine learning model , is merely generally linking the abstract idea to the technological environment of machine learning
The additional element, an updated machine learning model , is merely generally linking the abstract idea to the technological environment of machine learning
The additional element, a computing device, is recited in the manner of merely invoking the element as a tool to “apply-it” or an equivalent for data outputting and therefore is no more than using these generic elements as a tool to implement the abstract idea.
Accordingly, the dependent claims as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements as analyzed above in step 2A prong 2, are merely generally linking and/or applying the abstract idea with general computer elements and therefore, do not amount to significantly more. The claims are patent ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, 6, 8, 9, 11, 15, 16, and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cox (US10565309B2) in view of Giannulli et. al (hereinafter Giannulli) (US20230316095A1)
As per claim 1, Cox teaches:
A method for electronically generating a care plan, the method comprising: (Col. 5 lines 1-4 discloses, generating patient care plans based on the patient's medical condition)
…[…]…generating the care plan comprising another subset of the set of health artifacts; (Col. 38 lines 47-61 and Col. 39 lines 1-16 discloses, generating a care plan)
and modifying the another subset of the set of health artifacts in the care plan based on a detected tone of the patient, a detected emotion of the patient, a medical outcome desired by a physician, or some combination thereof. (Col. 39 lines 16-45, the rules for the care plan may be modified based on the medical outcome desired by the physician and exclude or include patients)
However, Cox does not explicitly teach:
based at least partially on unstructured data, generating, using one or more trained machine learning models, a first data structure
, a second data structure, or both
comprising structured data via pattern matching, structural similarity or both, used to enable comparing the first data structure and the second data structure;
comparing the first data structure with the second data structure, wherein the first data structure comprises a set of health artifacts pertaining to a first condition of the patient,
and the second data structure pertains to the patient and the first condition of the patient, and the second data structure comprises a subset of the set of the health artifacts,
wherein the first data structure is a knowledge graph
and the second data structure is a patient graph,
and wherein the comparing comprises projecting the patient graph onto the knowledge graph to identify health-artifact nodes present in the knowledge graph and absent from the patient graph; responsive to the comparing,…[…]…based on the identified absent nodes
However, Giannuli does teach:
based at least partially on unstructured data, generating, using one or more trained machine learning models, a first data structure (abstract discloses, “According to certain embodiments, the present disclosure includes a method for generating a knowledge graph of clinical information for use as a reference model to semantically represent relevant information from clinical encounters.” And see [0043] In certain instances, the problem-oriented clinical knowledgebase 110 is designed to serve as a source of content underlying a knowledge graph of clinical information 112, which can be used by and/or incorporated into virtual scribe technology. In some examples, this problem oriented clinical knowledgebase 110 contains codified clinical concepts and relationships that follow clinical evidence based best practices. For example, these codified clinical concepts and relationships are curated by clinical and informatics experts, based on published standards of care. As examples, relating concepts in this way furthers the virtual scribe technology as follows: “ and see [0044] discloses, “1. serves as a reference base of anticipated clinical concepts and their relationships to the encounter process and current topic of conversation (e.g., clinical complaint) to improve the performance of natural language understanding algorithms; and/or” and see [0045] discloses, “ 2. serves as machine learning training data, to include but not limited to:” and see [0046] discloses, “ a. feature data that consists of conversational text, which may include marked-up or tagged using natural language processing (NLP); and/or…[…]…” / examiner notes that the conversational text is unstructured data and the knowledge graph is first data structure)
, a second data structure, or both ([0068] discloses, “In certain embodiments, the nodes of the knowledge graph ( e.g., the knowledge graph 112) are organized by relationships, and a fundamental relationship that segregates the knowledge graph (e.g., the knowledge graph 112) is the clinical concern. As such, the knowledge graph (e.g., the knowledge graph 112) can be considered a problem-oriented knowledge graph with each problem consisting of a subgraph, called a module, such that each module is based upon a particular clinical problem or concern.” / subgraph called a module of patient particular problem is considered a second data structure patient graph)
comprising structured data via pattern matching, structural similarity or both, used to enable comparing the first data structure and the second data structure; ([0087] discloses, “As discussed above, in certain embodiments, the knowledge graph 112 and/or the portion of the knowledge graph 300 represents a reference set of clinically relevant concepts and relationships to a given concern. As such, according to certain embodiments, the method 200 includes mapping raw data of a clinical encounter to the knowledge graph 112 and/or 300 to semantically represent the contents of the clinical encounter (block 214). For example, for a clinical encounter, the knowledge graph 112 and/or 300 is used by an automated virtual scribe system to compare sequential output from a NLP pipeline of the automated virtual scribe system and organize these concepts in their natural relationship to the module 302 pertaining to a particular clinical concern, the section 304A-304H and/or position in the encounter flow which they are recorded for a clinical encounter, as well as their identification with respect to clinical ontologies and/or taxonomies. Stated another way, in some embodiments, a neural network is used to map the raw data from a clinical encounter to one or more of the knowledge graphs 112 and/or 300. According to certain embodiments, the neural network ( e.g., a recurrent neural network (RNN)) uses sequence data from the clinical encounter in order to map the clinical encounter to the knowledge graph 300. For example, NLP software may receive and process correspondence between a provider and a patient where the patient is complaining about right knee pain. In response to the right knee pain, the provider may ask what part of the patient's right knee hurts. In one example, the patient may state the medial aspect of the patient's knee hurts. In another example, the patient may state the lateral aspect of patient's knee hurts. In practice, these different examples may result in different mappings of the clinical encounter to the knowledge graph 300.” / examiner notes the edges and node relationships between 300 knowledge graph for ontology and taxonomy are structural similarity of the data structures for mapping)
comparing the first data structure with the second data structure, wherein the first data structure comprises a set of health artifacts pertaining to a first condition of the patient, ([0070] discloses, “According to certain embodiments, the portion of the knowledge graph 300A and other portions of the knowledge graph 300B-300F collectively form the knowledge graph 300 for a particular clinical concern, e.g., knee pain. And, the knowledge graphs 300 associated with each clinical concern collectively form the knowledge graph 112, in certain embodiments. Other examples of clinical concerns besides knee pain include, but are not limited to, hip pain, back pain, chest pain, skin rash, and/or fainting.”)
and the second data structure pertains to the patient and the first condition of the patient, and the second data structure comprises a subset of the set of the health artifacts, ([0074] discloses, “In the example illustrated in FIG. 3, the clinical concern and/or chief complaint represented by the module 302 is knee pain. In certain embodiments, the next set of relationships are hierarchal and are shown as section nodes 304A-304H of the encounter flow. As such, in certain embodiments, the module 302 is associated with at least one section node 304A-304H. The sections nodes 304A-304H include, for example, Diagnoses 304A, Care Plans 304B, Demographic information 304C, Examination 304D, Vitals 304E, Review of Systems (RoS) 304F, Patient History (Medical, Social, Surgical, Family, etc.) (PMSFH) 304G, and/or History of Present Illness (HPI) 304H.” / examiner notes the knowledge graph has a module 302 as previously cited module is a subgraph for the health artifact of knee pain and the subset of artifacts is e.g. patient history)
wherein the first data structure is a knowledge graph (abstract discloses, “According to certain embodiments, the present disclosure includes a method for generating a knowledge graph of clinical information for use as a reference model to semantically represent relevant information from clinical encounters.” And see [0043] In certain instances, the problem-oriented clinical knowledgebase 110 is designed to serve as a source of content underlying a knowledge graph of clinical information 112, which can be used by and/or incorporated into virtual scribe technology.”)
and the second data structure is a patient graph, ([0068] discloses, “In certain embodiments, the nodes of the knowledge graph ( e.g., the knowledge graph 112) are organized by relationships, and a fundamental relationship that segregates the knowledge graph (e.g., the knowledge graph 112) is the clinical concern. As such, the knowledge graph (e.g., the knowledge graph 112) can be considered a problem-oriented knowledge graph with each problem consisting of a subgraph, called a module, such that each module is based upon a particular clinical problem or concern.” / subgraph called a module of patient particular problem is considered a second data structure patient graph)
and wherein the comparing comprises projecting the patient graph onto the knowledge graph to identify health-artifact nodes present in the knowledge graph and absent from the patient graph; responsive to the comparing,…[…]…based on the identified absent nodes ([0043] discloses, “In certain instances, the problem-oriented clinical knowledgebase 110 is designed to serve as a source of content underlying a knowledge graph of clinical information 112, which can be used by and/or incorporated into virtual scribe technology. In some examples, this problem oriented clinical knowledgebase 110 contains codified clinical concepts and relationships that follow clinical evidence based best practices. For example, these codified clinical concepts and relationships are curated by clinical and informatics experts, based on published standards of care. As examples, relating concepts in this way furthers the virtual scribe technology as follows:” and see [0049] discloses, “3. establishes an initial model of clinical understanding that can be expanded upon and/or tuned via real world use and serves as a basis for continuous learning and performance tuning; according to certain embodiments, the expansion and/or tuning” and see [0051] discloses, “b. historical use across a broader set of users to identify gaps in the graph that may serve to improve overall robustness for all users;” and see [0056] discloses, “In certain embodiments, the knowledge graph 112 includes a set of nodes and relationships that each have specific attributes that form a clinical reference for a given clinical concern within the context of a clinical encounter. As an example, the nodes of the knowledge graph 112 are organized around a given clinical concern and/or encode the expected clinical concepts that are expressed within an encounter, their relevance, related ontology and/or codification hierarchy, sequence and/or related expression with respect to the encounter document.” / examiner notes someone of ordinary skill would understand that the broader knowledge graph based on clinical information, standards etc. executing codification and ontologies purpose is to define a focused schema for an area such as knee pain, not to include every possible entity from an unfiltered knowledge base)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cox’s teachings of generating a care plan with Giannuli’s teachings of knowledge and patient graphs utilizing machine learning, the motivation being Cox teaches challenges with EHR records and monitoring patients but introduces utilizing NLP (e.g. Cols 1 & 2) therefore it would improve the accuracy and management of patients health information to give proper care plans in an efficient and timely manner through knowledge and patient graphs while reducing utilization of resources with no unexpected results as the NLP and machine learning can be implemented in the environment of Cox without rendering it inoperable.
As per claim 5, Cox further teaches:
The method of claim 1, further comprising causing the care plan including modifications to the another subset of the set of the health artifacts to be presented on a computing device. (Fig. 7, 790 and Col. 33 lines 24-29)
As per claim 6, Cox further teaches:
The method of claim 1, further comprising modifying the another subset of the set of the health artifacts in the care plan based on the medical outcome desired by the physician by receiving instructions from a computing device of a physician to select a health artifact that corresponds to the medical outcome and to include the health artifact in the another subset of the set of the health artifacts. (Col. 26 lines 10-15 discloses, a request to generate a personalized care plan from a physician's computing system and Col. 42 lines 35-55 discloses, the original personalized care plan and system work in conjunction with the cohort database and cohort rules to identify cohorts of patients / as previously cited the another subset of the set of health artifacts is the cohort of patients)
As per claim 8, Cox further teaches:
The method of claim 1, further comprising including, in the care plan, action instructions pertaining to the another subset of the set of the health artifacts, wherein the action instructions are directed toward a medical personnel, the patient, or both. (Col. 30 lines 3-21 discloses, e.g. reminder to schedule an appointment)
As per claim 9, Cox further teaches:
The method of claim 1, further comprising: receiving input from a computing device, wherein the input specifies a number and a type of health artifacts in the set of the health artifacts the patient selects to manage; (Col. 32 lines 16-32 discloses, receiving health information from a monitoring device and the patient may input the specific health artifacts into a computer of interest to monitor)
and selecting, based on the comparing, the another subset of the set of the health artifacts in the first data structure by selecting the another subset based on the number and the type of health artifacts specified by the patient. (Col. 46 lines 49-64 and Col. 47 lines 1-37 discloses, when comparing patient cohorts that are successful and not successful choosing based on the compliance with a number of health artifacts that were previously cited as possibly coming from patient input)
As per claims 11, 15, and 16, they are article of manufacture claims which repeats the same limitations of claims 1, 5, and 6 the corresponding method claims, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Cox and Giannulli as well as motivations to combine disclose the underlying process steps that constitute the method of claims 1, 5, and 6 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claims 11, 15, and 16 are rejected for the same reasons given above for claims 1, 5, and 6.
As per claim 18, it is a system claim which repeat the same limitations of claim 1 the corresponding method claim, as a collection of elements as opposed to a series of process steps. Since the teachings of Cox and Giannulli as well as motivations to combine disclose the underlying process steps that constitute the methods of claim 1 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claim 18 is rejected for the same reasons given above for claims 1.
Claims 2 and 12 are rejected to under 35 U.S.C. 103 as being unpatentable over Cox (US10565309B2) in view of Giannulli et. al (hereinafter Giannulli) (US20230316095A1) and in view of Mander et. al (hereinafter Mander) (US2018/0181716A1)
As per claim 2, Cox and Giannulli do not teach:
The method of claim 1, further comprising modifying the another subset of the set of the health artifacts in real-time or near real-time.
However, Mander does teach:
The method of claim 1, further comprising modifying the another subset of the set of the health artifacts in real-time or near real-time. ([0065] discloses, A recommendation modifies a user ' s screen in real time to impact his or her workflow in the moment / where the user is making decisions related to the recommendation)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cox’s teachings of generating a care plan with Mander’s teachings of showing health information in real time for the purpose of interacting with health data in a timely manner (see e.g. Mander [0004] and [0065]) with the motivation of improving the accuracy and management of patients health information to give proper care plans in an efficient and timely manner.
As per claim 12, it is an article of manufacture claims which repeats the same limitations of claim 2, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings and motivations of Cox, Giannulli, and Mander as well as motivations to combine disclose the underlying process steps that constitute the methods of claim 2 it is respectfully submitted that they disclose the executable instructions that perform the steps as well. As such, the limitations of claim 12 is rejected for the same reasons given above for claim 2.
Claims 3, 4, 13, 14 and 20 are rejected to under 35 U.S.C. 103 as being unpatentable over Cox (US10565309B2) in view of Giannulli et. al (hereinafter Giannulli) (US20230316095A1) and in view of RIISTAMA et. al (hereinafter RIISTAMA)(US2017/0344713A1)
As per claim 3, Cox and Giannulli do not teach:
The method of claim 1, further comprising detecting the detected tone of the patient based on words spoken by the patient, text entered by the patient, or some combination thereof.
However, RIISTAMA does teach:
The method of claim 1, further comprising detecting the detected tone of the patient based on words spoken by the patient, text entered by the patient, or some combination thereof. ([ 0149 ] discloses, Whether the patient is sad depressed or angry can be derived from and [ 0150 ] discloses, tone of speaking and [ 0151 ] discloses, muscle tension in facial muscles and [ 0152 ] discloses, face recognition of emotions.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cox’s teachings of generating a care plan and modifying it with RIISTAMA’s teachings of detecting the emotion or tone of a patient to make collaborative positive decisions in the care of a patient (see e.g. RIISTAMA [0003] and [0149]-[0152]) with the motivation of improving the accuracy and management of patients health information to give proper care plans in an efficient and timely manner that the patient is more likely to respond to and follow.
As per claim 4, Cox and Giannulli do not teach:
The method of claim 1, further comprising detecting the detected emotion of the patient based on words spoken by the patient, text entered by the patient, a detected facial expression of the patient, or some combination thereof.
However, RIISTAMA does teach:
The method of claim 1, further comprising detecting the detected emotion of the patient based on words spoken by the patient, text entered by the patient, a detected facial expression of the patient, or some combination thereof. ([ 0149 ] discloses, Whether the patient is sad depressed or angry can be derived from and [ 0150 ] discloses, tone of speaking and [ 0151 ] discloses, muscle tension in facial muscles and [ 0152 ] discloses, face recognition of emotions.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cox’s teachings of generating a care plan and modifying it with RIISTAMA’s teachings of detecting the emotion or tone of a patient to make collaborative positive decisions in the care of a patient (see e.g. RIISTAMA [0003] and [0149]-[0152]) for the same reasons given above for claim 3.
As per claims 13 and 14, they are article of manufacture claims which repeats the same limitations of claims 3 and 4, the corresponding method claims, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of. Since the teachings and motivations of Cox, Giannulli, and RIISTAMA disclose the underlying process steps that constitute the methods of claim 3 and 4 it is respectfully submitted that they disclose the executable instructions that perform the steps as well. As such, the limitations of claim 13 and 14 are rejected for the same reasons given above for claims 3 and 4.
As per claim 20, it is a system claim which repeat the same limitations of claims 3, 4, and 6 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Cox, Giannulli, and RIISTAMA and the motivations to combine disclose the underlying process steps that constitute the methods of claim 3, 4, and 6 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claim 20 is rejected for the same reasons given above for claims 3,4, and 6.
Claims 7, 17, and 19 are rejected to under 35 U.S.C. 103 as being unpatentable over Cox (US10565309B2) in view of Giannulli et. al (hereinafter Giannulli) (US20230316095A1) in view of Barnard et. al (hereinafter Barnard) (US2018/0025126A1) and in further view of (Appelbaum et. al (hereinafter Appelbaum)(US2022/0051773A1)
As per claim 7, Cox and Giannulli do not teach:
The method of claim 1, further comprising: generating a net promoter score based on the detected tone of the patient, the detected emotion of the patient, or both in response to the patient interacting with the care plan;
and updating a machine learning model based on the net promoter score being below a threshold value to obtain an updated machine learning model that outputs different health artifacts for subsequent patients having the condition.
However, Barnard does teach:
The method of claim 1, further comprising: generating a net promoter score based on the detected tone of the patient, the detected emotion of the patient, or both in response to the patient interacting with the care plan; ([0015] discloses, In one embodiment , the Life Context Graph may be generally schema - less , being represented in any machine state by a “ property dictionary ” of keys and values …[…]…As a specific example , a text string may be “ [ Subject ] is feeling [ emotion ] , ” wherein the subject and emotion are values . In instances where the subject is “ John Doe ” and the emotion used to describe John Doe ' s emotional state is “ happy , " this key and value combination may be published to the Life Context Graph for John Doe as a contextual “ State .and [ 0071 ] discloses, In step 402 , the system derives population - or peer - group - level statistics from the aggregated Life Context Graph information of the selected subjects . Exemplary statistics include…[…]…and Net Promoter Score.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cox’s teachings of generating a care plan with Barnard’s teachings of a net promoter score in relation to a patients emotion to take into account mental health of a patient for data analysis (see e.g. Barnard [0005] and [0015]) with the motivation of improving the accuracy and management of patients health information to give proper care plans that can identify emotional triggers that can influence the care plan.
However, Barnard also does not teach:
and updating a machine learning model based on the net promoter score being below a threshold value to obtain an updated machine learning model that outputs different health artifacts for subsequent patients having the condition.
However, Appelbaum does teach:
and updating a machine learning model based on the net promoter score being below a threshold value to obtain an updated machine learning model that outputs different health artifacts for subsequent patients having the condition. ([0064] discloses ,The CDS software in the exemplary system 100 may be instructed to deliver an alert to a provider device 113 when a patient's blood sugar is likely to drop below a predetermined threshold , and thus predict that the patient's medication should be decreased. And [0065]-[0068] discloses, various information about a patient is used with machine learning to identify a patient cohorts likelihood of completing a treatment and these algorithms are updated based on the real time data and [ 0177 ] All behavioral app participants are asked to complete a net promoter score ( NPS ) at Day 75 of their program . The NPS is a standardized tool for measuring a participant's overall satisfaction with the program . Participants are asked how likely they are to recommend the app to a friend with a relevant condition and are asked to enter a number from 1 to 10)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cox’s teachings of generating a care plan with Barnard’s teachings of a net promoter score in relation to a patients emotion to take into account mental health of a patient for data analysis (see e.g. Barnard [0005] and [0015]) with Appelbaum’s teaching of updating a machine learning model based on the net promoter score, the motivation of improving the accuracy and management of patients health information to drive higher compliance and satisfaction with patients who are most unsatisfied with the care plan.
As per claim 17 it is an article of manufacture claim which repeats the same limitations of claim 7, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Cox, Giannulli, Barnard, and Appelbaum as well as the motivations to combine disclose the underlying process steps that constitute the method of claim 7 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claim 17 are rejected for the same reasons given above for claim 7.
As per claim 19, it is a system claim which repeat the same limitations of claims 7, the corresponding method claim, as a collection of elements as opposed to a series of process steps. Since the teachings and motivations to combine of Cox, Giannulli, Barnard, and Appelbaum disclose the underlying process steps that constitute the methods of claim 7, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claim 19 is rejected for the same reasons given above for claim 7.
Claim 10 is rejected to under 35 U.S.C. 103 as being unpatentable over Cox (US10565309B2) in view of Giannulli et. al (hereinafter Giannulli) (US20230316095A1)
As per claim 10, Cox further teaches:
The method of claim 1, wherein: the subset of the set of the health artifacts correspond with actions already performed by the patient, (Col. 27 lines 1-7, discloses, the patient having a sedentary job for example and as previously cited the subset of the set can be lifestyle specific information such as sedentary lifestyle) and the another subset of the set of the health artifacts correspond with actions that have not yet been performed by the patient; (Col. 13 lines 4-22 and Col. 14 lines 3-10 discloses, determining future success of patient cohorts by reviewing similar or historical cohorts to make actions the current patient cohort will more likely be able to adhere too.)
However, Cox does not teach:
the comparing further comprises projecting the second data structure onto the first data structure; and the first data structure is a knowledge graph, and the second data structure is a patient graph.
However, Giannulli does teach:
the comparing further comprises projecting the second data structure onto the first data structure; and the first data structure is a knowledge graph, and the second data structure is a patient graph. (abstract discloses, “According to certain embodiments, the present disclosure includes a method for generating a knowledge graph of clinical information for use as a reference model to semantically represent relevant information from clinical encounters.” And see [0043] In certain instances, the problem-oriented clinical knowledgebase 110 is designed to serve as a source of content underlying a knowledge graph of clinical information 112, which can be used by and/or incorporated into virtual scribe technology.” ([0068] discloses, “In certain embodiments, the nodes of the knowledge graph ( e.g., the knowledge graph 112) are organized by relationships, and a fundamental relationship that segregates the knowledge graph (e.g., the knowledge graph 112) is the clinical concern. As such, the knowledge graph (e.g., the knowledge graph 112) can be considered a problem-oriented knowledge graph with each problem consisting of a subgraph, called a module, such that each module is based upon a particular clinical problem or concern.” ([0043] discloses, “In certain instances, the problem-oriented clinical knowledgebase 110 is designed to serve as a source of content underlying a knowledge graph of clinical information 112, which can be used by and/or incorporated into virtual scribe technology. In some examples, this problem oriented clinical knowledgebase 110 contains codified clinical concepts and relationships that follow clinical evidence based best practices. For example, these codified clinical concepts and relationships are curated by clinical and informatics experts, based on published standards of care. As examples, relating concepts in this way furthers the virtual scribe technology as follows:” and see [0049] discloses, “3. establishes an initial model of clinical understanding that can be expanded upon and/or tuned via real world use and serves as a basis for continuous learning and performance tuning; according to certain embodiments, the expansion and/or tuning” and see [0051] discloses, “b. historical use across a broader set of users to identify gaps in the graph that may serve to improve overall robustness for all users;” and see [0056] discloses, “In certain embodiments, the knowledge graph 112 includes a set of nodes and relationships that each have specific attributes that form a clinical reference for a given clinical concern within the context of a clinical encounter. As an example, the nodes of the knowledge graph 112 are organized around a given clinical concern and/or encode the expected clinical concepts that are expressed within an encounter, their relevance, related ontology and/or codification hierarchy, sequence and/or related expression with respect to the encounter document.” / examiner notes someone of ordinary skill would understand that the broader knowledge graph based on clinical information, standards etc. executing codification and ontologies purpose is to define a focused schema for an area such as knee pain, not to include every possible entity from an unfiltered knowledge base)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cox’s teachings of generating a care plan with Giannulli’s teachings for the same reasons given in claim 1.
Response to Arguments Regarding 35 U.S.C § 101 Rejection
The applicant argues on pages 1-4 of the submitted remarks that the claims under 35 U.S.C § 101 are eligible for the following:
Claims 1-20 stand rejected under 35 U.S.C. § 101 as allegedly directed to an abstract idea. Applicant respectfully traverses. Amended independent claim 1 recites, inter alia:
based at least partially on unstructured data, generating, using one or more trained machine learning models, a first data structure, a second data structure, or both comprising structured data via pattern matching, structural similarity or both, used to enable comparing the first data structure and the second data structure; comparing the first data structure with the second data structure, wherein the first data structure comprises a set of health artifacts pertaining to a first
condition of the patient, and the second data structure pertains to the patient and the first condition of the patient, and the second data structure comprises a subset of the set of the health artifacts, wherein the first data structure is a knowledge graph and the second data structure is a patient graph, and wherein the comparing comprises projecting the patient graph onto the knowledge graph to identify health-artifact nodes present in the knowledge graph and absent
from the patient graph; responsive to the comparing, generating the care plan comprising another subset of the set of health artifacts based on the identified absent nodes; and modifying the another subset of the set of health artifacts in the care plan based on a detected tone of the patient, a detected emotion of the patient, a medical outcome desired by a physician, or some combination thereof. (Emphasis added.)
First, the rejection characterizes representative claim 1 as "managing personal behavior or relationships or interactions between people" because the claim allegedly recites generating a health care plan using data. (See Office Action, Eligibility Step 2A-1.) That characterization is inconsistent with the claims as amended and improperly abstracts away the claim's actual focus. Amended claim 1 does not merely require generating a care plan; it recites generating, using one or more trained machine learning models and based at least partially on unstructured data, a first data structure, a second data structure, or both comprising structured data via pattern matching, structural similarity, or both, and then comparing those structured data objects by projecting a patient graph onto a knowledge graph to identify health-artifact nodes present in the knowledge graph and absent from the patient graph. Independent claims 11 and 18 recite corresponding computer-readable medium and system implementations. These limitations define a computer-implemented machine learning (ML)-and-graph transformation pipeline, not a human activity of following rules or managing personal behavior. Under Alice/Mayo Step 2A, Prong 1, the amended claims are not directed to a mental process or method of organizing human activity.
The claims require trained machine learning models to generate, based at least on unstructured data, data structures via pattern matching and/or structural similarity, and require a graph-projection comparison between a patient graph and a knowledge graph to identify absent health-artifact nodes. These specialized computer operations are rooted in computer technologies and improve the computer technology personalized care plan generation via specific technical and practical applications. That is, these are specific data- processing operations performed to generate structured graph representations from unstructured data and performed on the structured graph representations. The Office Action's analysis does not address these operations as part of the claim's focus, but instead reduces the claims to the result of "generating a health care plan using data." That reduction is legally insufficient because eligibility must be assessed based on the claims as a whole, including the particular computerized mechanisms by which the claimed care-plan artifact is generated and modified.
At a minimum, under Step 2A, Prong 2, any alleged abstract idea is integrated into a
practical application. MPEP § 2106.04(d)(1) recognizes that a claim integrates an exception into a practical application when the claim applies the exception in a meaningful way, and MPEP § 2106.05(a) recognizes improvements to computer functionality or another technology as a relevant consideration. Here, the amended claims recite a concrete technological implementation: trained machine learning models generate structured data from unstructured data using pattern matching and structural similarity, a patient graph is projected onto a knowledge graph to identify health-artifact nodes present in the knowledge graph and absent from the patient graph, and the identified absent nodes drive generation of the care-plan subset. The specification describes this same technical pipeline. For example, unstructured data may be cognified into cognified data using a knowledge graph and logical structure, with patterns detected by structural similarities between tags and logical structure. Spec. 0085. The trained machine learning models may transform unstructured patient notes into cognified data using knowledge graphs, logical structures, structural similarity comparison mechanisms, and pattern recognition. Spec. 0126. The specification also explains that a patient graph may be projected onto a knowledge graph to generate a care plan. Spec. 0111. In the illustrated graph embodiment, the platform projects the patient graph onto the knowledge graph, identifies overlapping nodes and nodes present in the knowledge graph but not in the patient graph, and selects the missing nodes for inclusion in the care plan. Spec. 0461-0462. Thus, the graph and machine learning limitations are not mere field-of-use or "apply it" language; they are the mechanism that determines the computer-generated output.
The specification further confirms the practical technological benefit of the claimed
approach. Conventional EMR review may require a physician to access numerous screens and perform multiple database queries, wasting client computing resources, server processing resources, and network resources. Spec. 0080. The disclosed system instead presents cognified data generated via machine learning models and projected graphs on a computing device so the physician need not perform numerous EMR searches or access numerous EMR screens. Spec. 0086. The amended claims align with that improvement by transforming unstructured inputs into structured graph data and using graph projection to identify missing health-artifact nodes that drive care-plan generation and modification. Like the computer-functionality improvement recognized in Enfish, the claims are directed to a specific improvement in the way computerized information is generated via trained ML models, structured, compared, and output-not to merely automating a longstanding human practice on a generic computer.
The claims also recite significantly more under Step 2B when considered as an ordered
combination. The rejection treats the computer and machine-learning elements as generic tools, but the amended ordered combination requires ML-based structuring of unstructured data, knowledge- graph/patient-graph representations, projection of the patient graph onto the knowledge graph, node- presence/node-absence identification, generation of a care-plan subset based on the absent nodes, and modification of that subset based on detected tone, detected emotion, physician-desired medical outcome, or a combination thereof.
This combination meaningfully limits the claims to a particular computer-centric workflow for transforming data and generating an electronic care-plan artifact, which includes a technical practical application. Accordingly, Applicants respectfully request withdrawal of the @ 101 rejection of these claims and, further, allow the same.
Examiner appreciates applicant’s arguments but does not find them persuasive. The claims are directed to the abstract idea within certain methods of organizing human activity as it is generating a health care plan using data relationships (MPEP § 2106.04(a)(2), subsection II).
The MPEP states that the recitation of a computer to execute the claims more quickly does not disprove the claims from being directed to the enumerated subgroupings of abstract ideas and mere automation with a computer is not enough when the computer and additional elements are merely “apply-it”. Examiner did in fact analyze the claim as a whole by analyzing each and every limitation and element as either abstract idea or additional element. The claim as a whole is generating care plans using data relationships made through graphical processing and review. The additional elements such as machine learning and the computer are recited at a high level of generality as no more than “apply-it” level to execute the abstract idea more quickly. It is not would a human do it but could a human do it and a human has and could make relationships of health artifacts through comparison to generate care plans. Care plan generation is not understood to be a computer rooted technological problem as characterized by applicant rather it is reasonably understood to be a problem arising in physician skill diagnostics.
The claimed invention is using a computer as a tool and any improvement present is an improvement to the abstract idea. The abstract idea cannot bring forth a practical application. Thus the claims have no nexus with Enfish as no computer confined additional element is bringing forth a practical application thus the claims do not bring forth significantly more. Were applicant’s line of reasoning correct Alice corp. would have been deemed eligible as It is in improvement in settlement risk mitigation. Further examiner notes the claims do not recite a technical problem confined to the computer with converting unstructured to structured data but rather utilize the computer to manipulate data through abstract graphical representations claimed at a high level of generality.
Examiner maintains the 35 U.S.C 101 rejection.
Response to Arguments Regarding 35 U.S.C § 102/103 Rejection
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Prior Art Made of Record but Not Cited
US20170249434A1 – Brunner et. al
Real - time and individualized disease monitoring is central to rapidly evolving medical sciences and technologies , but for the vast majority of patients , disease progression and treat ment are monitored only in an irregular and discontinuous fashion . Consequently , disease progression and relapse are often allowed to proceed too far before they are detected , compromising the possibility of any effective treatment . For one patient , this can mean becoming refractory to the few early drug treatments that are available ; for another , missing early detection may be deadly . This invention provides a method for the detection of early signals of disease and recovery thereof comprising a universal yet personalized health - monitoring solution using cell phones or other wear able smart device data that generate extensive real - time data . The invention further provides a system and method to provide answers to a variety of questions related to the patient health status and health trajectory . Its flexibility and generality is designed for a preferred application to rare disorders and rare questions for which other analytical system are lacking .
Barnard et. al (hereinafter Barnard) (US2018/0025126A1)
Giannulli et. al (hereinafter Giannulli ) (US11636350Bl)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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