DETAILED ACTION
Notice to Applicant
This communication is in response to the application submitted August 27, 2025. This application is a continuation of United States Patent Application Serial No. 17/316,571 (now U.S. Patent Number 12,419,585) titled "Patient Data Management Systems and Conversational Interaction Methods" which is a continuation of United States Patent Application Serial No. 15/933,266 (now U.S. Patent Number 11,000,236) titled " Patient Data Management Systems and Conversational Interaction Methods". 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 .
Claim Objections
Claims 5 and 14 are objected to because of the following informalities: The limitation “at least one directed graph data structure includes edges between nodes indicative a causal relationship” omitted the word “of”. The limitation should read “…..indicative of a causal relationship”. Appropriate correction is required.
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., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One
Claims 1 – 20 are drawn to a method and system, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1, 10, and 19 recite receiving a prospective therapy modification for a patient, wherein the prospective therapy modification comprises a modification to a therapy regimen including conversational input; determining a therapy recommendation that, when incorporated into the therapy regimen, is most likely to yield a better outcome with respect to a physiological condition of the patient, wherein the therapy recommendation comprises a recommendation regarding the therapy delivered; and applying the therapy recommendation.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, by recommending therapy plans for a patient based on historical data and similar patients. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “a need to facilitate improved glucose control that accounts for the numerous different variables in a personalized manner.” (paragraph 4 of the published specification). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claim 1: “input query”, “medical device”, “automatically”, “controlling the medical device”, “dosage command”
Claim 2: “sensor”
Claim 3: “querying a database”
Claim 4: “encoded by directed graph data structures within different logical layers of a database”
Claim 5: “at least one directed graph data structure includes edges between nodes indicative a causal relationship”
Claim 6: “parsing the conversational input based on the current operational context of the medical device”
Claim 7: “parsing the conversational input based on the current operational context of the medical device comprises determining an intent of the conversational input, and wherein the method further comprises determining a logical layer of a database to query based on the intent of the conversational input to determine the therapy recommendation”
Claim 8: “querying the logical layer of the database and receiving results based on the query; and querying a second logical layer of the database based on the received results”
Claim 9: “medical device”
Claim 10: “system”, “one or more processors”, “one or more processor-readable media storing instructions”, “input query”, “medical device”, “automatically”, “controlling the medical device”, “dosage command”
Claim 11: “system”, “sensor”
Claim 12: “system”, “instructions”, “querying a database”
Claim 13: “system”, “encoded by directed graph data structures within different logical layers of a database”
Claim 14: “system”, “at least one directed graph data structure includes edges between nodes indicative a causal relationship”
Claim 15: “system”, “instructions”, “parsing the conversational input based on the current operational context of the medical device”
Claim 16: “system”, “parsing the conversational input based on the current operational context of the medical device comprises determining an intent of the conversational input, and wherein the method further comprises determining a logical layer of a database to query based on the intent of the conversational input to determine the therapy recommendation”
Claim 17: “system”, “querying the logical layer of the database and receiving results based on the query; and querying a second logical layer of the database based on the received results”
Claim 18: “system”, “medical device”
Claim 19: “input query”, ““medical device”, “intent of the input query by parsing the conversational input”, “querying a database …medical device”, “conversational output”
Claim 20: “a logical layer of the database to query based on the intent of the input query”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The published specification supports this conclusion as follows:
[0042] In exemplary embodiments, the electronic devices 106 include one or more medical devices, such as, for example, an infusion device, a sensing device, a monitoring device, and/or the like. Additionally, the electronic devices 106 may include any number of non-medical client electronic devices, such as, for example, a mobile phone, a smartphone, a tablet computer, a smart watch, or other similar mobile electronic device, or any sort of electronic device capable of communicating with the computing device 102 via the network 108, such as a laptop or notebook computer, a desktop computer, or the like. One or more of the electronic devices 106 may include or be coupled to a display device, such as a monitor, screen, or another conventional electronic display, capable of graphically presenting data and/or information pertaining to the physiological condition of a patient. Additionally, one or more of the Electronic devices 106 also includes or is otherwise associated with a user input device, such as a keyboard, a mouse, a touchscreen, a microphone, or the like, capable of receiving input data and/or other information from a user of the electronic device 106.
[0044] The computing device 102 generally represents a server or other remote device configured to receive data or other information from the electronic devices 106, store or otherwise manage data in the database 104, and analyze or otherwise monitor data received from the electronic devices 106 and/or stored in the database 104, as described in greater detail below. In practice, the computing device 102 may reside at a location that is physically distinct and/or separate from the electronic devices 106, such as, for example, at a facility that is owned and/or operated by or otherwise affiliated with a manufacturer of one or more medical devices utilized in connection with the patient data management system 100. For purposes of explanation, but without limitation, the computing device 102 may alternatively be referred to herein as a server, a remote server, or variants thereof. The server 102 generally includes a processing system and a data storage element (or memory) capable of storing programming instructions for execution by the processing system, that, when read and executed, cause processing system to create, generate, or otherwise facilitate the applications or software modules configured to perform or otherwise support the processes, tasks, operations, and/or functions described herein. Depending on the embodiment, the processing system may be implemented using any suitable processing system and/or device, such as, for example, one or more processors, central processing units (CPUs), controllers, microprocessors, microcontrollers, processing cores and/or other hardware computing resources configured to support the operation of the processing system described herein. Similarly, the data storage element or memory may be realized as a random access memory (RAM), read only memory (ROM), flash memory, magnetic or optical mass storage, or any other suitable non-transitory short or long term data storage or other computer-readable media, and/or any suitable combination thereof.
[0082] Depending on the embodiment, the motor control module 612 may be implemented or realized with a general purpose processor, a microprocessor, a controller, a microcontroller, a state machine, a content addressable memory, an application specific integrated circuit, a field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. In exemplary embodiments, the motor control module 612 includes or otherwise accesses a data storage element or memory, including any sort of random access memory (RAM), read only memory (ROM), flash memory, registers, hard disks, removable disks, magnetic or optical mass storage, or any other short or long term storage media or other non-transitory computer-readable medium, which is capable of storing programming instructions for execution by the motor control module 612. The computer-executable programming instructions, when read and executed by the motor control module 612, cause the motor control module 612 to perform or otherwise support the tasks, operations, functions, and processes described herein.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2 – 9, 11 – 18, and 20 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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.
Claim(s) 1 – 3, 6 – 7, 9 – 12, 15 – 16, and 18 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al., herein after Brown (U.S. Publication Number 2014/0074454 A1) in view of Hoglund et al., herein after Hoglund (U.S. Publication Number 2017/0091419 A1).
Claim 1. Brown teaches a method comprising:
receiving an input query pertaining to a prospective therapy modification for a patient, wherein the prospective therapy modification comprises a modification to a therapy regimen including therapy delivered using a medical device and wherein the input query comprises conversational input (paragraph 28 discloses smart devices to apply reasoning and logic to interpret what a user is saying; paragraph 31 discloses a series of dialog representations exhibiting input from the patient and responses from the virtual assistant; paragraph 40 discloses after mapping the patient’s input into one of multiple intents based on both the identified concepts and the context associated with the input, the technique may map the intent to one of multiple different responses associated with the intent (e.g. recommended nutritional information)).
Brown fails to explicitly teach the following limitations met by Hoglund as cited:
determining, based on the input query and information on a current operational context of the medical device, a therapy recommendation that, when incorporated into the therapy regimen, is most likely to yield a better outcome with respect to a physiological condition of the patient, wherein the therapy recommendation comprises a recommendation regarding the therapy delivered using the medical device (paragraph 21 discloses recommended dosages for managing and treating the individual's medical problem may be determined based on the determined trend and the individual's lifestyle information; paragraph 64 discloses the recommendation may include a recommendation associated with a dosage of treatment associated with managing the medical problem (e.g., dosage of insulin, food, beverages, supplements, and/or other medications to manage diabetes); paragraph 71 discloses the analytics system may identify a current state and one or more historical states ( e.g., historical instances of similar states, states that match the current state within a threshold, or the like), identify historical recommendations, historical predictions and outcomes associated with the one or more historical states, and generate the recommendation based on the historical recommendations and outcomes (e.g. such that recommendations and/or predictions that resulted in positive outcomes may be repeated)); and
applying the therapy recommendation through controlling the medical device to deliver a therapeutic dose of medication, wherein controlling the medical device comprises automatically generating a dosage command for the medical device, or generating a dosage command based on user input in accordance with the therapy recommendation (paragraph 82 discloses the recommendation regarding dosage may be automatically provided to an insulin pump, and the individual may accept, modify, or cancel the recommendation via a user interface of the insulin pump. In some implementations, recommendation regarding dosage may include a basil rate, bolus, and/or other information that an insulin pump may use to adjust the dosage of insulin being provided to the individual).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Brown to further include a monitoring and treatment dosage prediction system where the information associated with the recommendation or the prediction may be provided to identify a dosage associated with treating or managing the medical condition, where the dosage may be identified based on the recommendation or the prediction as disclosed by Hoglund.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Brown in this way to provide a computer-implemented monitoring and treatment dosage prediction system that is capable of determining lifestyle information associated with an individual (Hoglund: paragraph 13).
Claim 2. Brown and Hoglund teach the method of claim 1.
Brown fails to explicitly teach the following limitations met by Hoglund as cited:
wherein determining the therapy recommendation is further based on a current measurement of a physiological condition of the patient obtained from a sensor (paragraph 18 discloses a threshold value may be used to determine a recommendation for treating or managing the individual's medical condition (e.g., a recommended dosage of insulin or glucose consumption). In some implementations, the glucose levels prior to the current time Tc may be based on actual glucose levels (e.g., as measured by a CGM), and the glucose levels after the current time Tc may be forecasted levels that are determined based on the lifestyle timeline; paragraph 50 discloses lifestyle information may include biological information associated with the individual, such as a weight, a height, an age, a gender, a blood pressure, a resting heart rate, a body temperature, a body fat measurement, a blood ketone measurement, a blood glucose measurement, a cholesterol measurement, or the like).
The motivation to combine the teachings of Brown and Hoglund are discussed in the rejection of claim 1, and incorporated herein.
System claim 11 repeats the subject matter of claim 2. As the underlying processes of claim 11 has been shown to be fully disclosed by the teachings of Brown and Hoglund in the above rejections of claim 2; as such, these limitations (claim 11) are rejected for the same reasons given above for claim 2 and incorporated herein.
Claim 3. Brown and Hoglund teach the method of claim 1. Brown teaches a method further comprising querying a database to obtain historical data associated with a subset of similar patients to the patient, wherein determining the therapy recommendation is further based on the historical data (paragraph 42 discloses connect the patient with other patients having similar health issues or interests as part of a virtual community. The virtual assistant may offer to present the patient's queries to the virtual community to see if another patient has had a similar system situation or has an answer).
System claim 12 repeats the subject matter of claim 3. As the underlying processes of claim 12 has been shown to be fully disclosed by the teachings of Brown and Hoglund in the above rejections of claim 3; as such, these limitations (claim 12) are rejected for the same reasons given above for claim 3 and incorporated herein.
Claim 6. Brown and Hoglund teach the method of claim 1. Brown teaches a method further comprising parsing the conversational input based on the current operational context of the medical device (Figure 10 and paragraph 25 discloses mapping the input to a particular intent with reference to both concepts expressed in the input and a context associated with the input; paragraph 34 discloses the assumptions may include parameters used by speech recognition engines to parse the input from the patient, various language models and logic used by NLPs to interpret the patient input, and external factors such as user profiles, learned behavior, and context indicia).
System claim 15 repeats the subject matter of claim 6. As the underlying processes of claim 15 has been shown to be fully disclosed by the teachings of Brown and Hoglund in the above rejections of claim 6; as such, these limitations (claim 15) are rejected for the same reasons given above for claim 6 and incorporated herein.
Claim 7. Brown and Hoglund teach the method of claim 6. Brown teaches a method wherein parsing the conversational input based on the current operational context of the medical device comprises determining an intent of the conversational input (Figure 10 and paragraph 25 discloses mapping the input to a particular intent with reference to both concepts expressed in the input and a context associated with the input; paragraph 34 discloses the assumptions may include parameters used by speech recognition engines to parse the input from the patient, various language models and logic used by NLPs to interpret the patient input, and external factors such as user profiles, learned behavior, and context indicia), and wherein the method further comprises determining a logical layer of a database to query based on the intent of the conversational input to determine the therapy recommendation (paragraph 109 discloses keywords from query used to determine concepts for search).
System claim 16 repeats the subject matter of claim 7. As the underlying processes of claim 16 has been shown to be fully disclosed by the teachings of Brown and Hoglund in the above rejections of claim 7; as such, these limitations (claim 16) are rejected for the same reasons given above for claim 7 and incorporated herein.
Claim 9. Brown and Hoglund teach the method of claim 1.
Brown fails to explicitly teach the following limitations met by Hoglund as cited:
wherein the medical device is an insulin infusion pump (paragraph 24 discloses a wearable communication device, including an insulin pump), and wherein the current operational context of the medical device comprises at least one of:
a recent insulin dosage delivered to the patient (paragraph 62 discloses historical lifestyle information including current value associated with a health metric related to the medical problem; paragraph 72 discloses analytics system may receive historical lifestyle information associated with the individual and/or one or more other individuals, and may create a prediction model associated with predicting and/or forecasting the recommended dosage of the treatment and/or a prediction associated with the medial problem (e.g., a predicted trend of blood glucose levels), indicating a recent insulin dosage delivered to the patient); or
whether insulin delivery by the insulin infusion pump is currently suspended.
The motivation to combine the teachings of Brown and Hoglund are discussed in the rejection of claim 1, and incorporated herein.
System claim 18 repeats the subject matter of claim 9. As the underlying processes of claim 18 has been shown to be fully disclosed by the teachings of Brown and Hoglund in the above rejections of claim 9; as such, these limitations (claim 18) are rejected for the same reasons given above for claim 9 and incorporated herein.
Claim 10. Brown teaches a system comprising:
one or more processors (paragraph 52 discloses one or more processors); and
one or more processor-readable media storing instructions which, when executed by one or more processors (paragraph 55 discloses various memories, and store modules and data, and may include volatile and/or nonvolatile memory, removable and/or non-removable media, and the like, which may be implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules) cause performance of:
receiving an input query pertaining to a prospective therapy modification for a patient, wherein the prospective therapy modification comprises a modification to a therapy regimen including therapy delivered using a medical device and wherein the input query comprises conversational input (paragraph 28 discloses smart devices to apply reasoning and logic to interpret what a user is saying; paragraph 31 discloses a series of dialog representations exhibiting input from the patient and responses from the virtual assistant; paragraph 40 discloses after mapping the patient’s input into one of multiple intents based on both the identified concepts and the context associated with the input, the technique may map the intent to one of multiple different responses associated with the intent (e.g. recommended nutritional information)).
Brown fails to explicitly teach the following limitations met by Hoglund as cited:
determining, based on the input query and information on a current operational context of the medical device, a therapy recommendation that, when incorporated into the therapy regimen, is most likely to yield a better outcome with respect to a physiological condition of the patient, wherein the therapy recommendation comprises a recommendation regarding the therapy delivered using the medical device (paragraph 21 discloses recommended dosages for managing and treating the individual's medical problem may be determined based on the determined trend and the individual's lifestyle information; paragraph 64 discloses the recommendation may include a recommendation associated with a dosage of treatment associated with managing the medical problem (e.g., dosage of insulin, food, beverages, supplements, and/or other medications to manage diabetes); paragraph 71 discloses the analytics system may identify a current state and one or more historical states ( e.g., historical instances of similar states, states that match the current state within a threshold, or the like), identify historical recommendations, historical predictions and outcomes associated with the one or more historical states, and generate the recommendation based on the historical recommendations and outcomes (e.g. such that recommendations and/or predictions that resulted in positive outcomes may be repeated)); and
applying the therapy recommendation through controlling the medical device to deliver a therapeutic dose of medication, wherein controlling the medical device comprises automatically generating a dosage command for the medical device, or generating a dosage command based on user input in accordance with the therapy recommendation (paragraph 82 discloses the recommendation regarding dosage may be automatically provided to an insulin pump, and the individual may accept, modify, or cancel the recommendation via a user interface of the insulin pump. In some implementations, recommendation regarding dosage may include a basil rate, bolus, and/or other information that an insulin pump may use to adjust the dosage of insulin being provided to the individual).
The motivation to combine the teachings of Brown and Hoglund are discussed in the rejection of claim 1, and incorporated herein.
Claim 19. Brown teaches a method comprising:
receiving an input query pertaining to a prospective therapy modification for a patient, wherein the prospective therapy modification comprises a modification to a therapy regimen including therapy delivered using a medical device and wherein the input query comprises conversational input (paragraph 28 discloses smart devices to apply reasoning and logic to interpret what a user is saying; paragraph 31 discloses a series of dialog representations exhibiting input from the patient and responses from the virtual assistant; paragraph 40 discloses after mapping the patient’s input into one of multiple intents based on both the identified concepts and the context associated with the input, the technique may map the intent to one of multiple different responses associated with the intent (e.g. recommended nutritional information));
determining an intent of the input query by parsing the conversational input (Figure 10 and paragraph 25 discloses mapping the input to a particular intent with reference to both concepts expressed in the input and a context associated with the input; paragraph 34 discloses the assumptions may include parameters used by speech recognition engines to parse the input from the patient, various language models and logic used by NLPs to interpret the patient input, and external factors such as user profiles, learned behavior, and context indicia).
Brown fails to explicitly teach the following limitations met by Hoglund as cited:
querying a database based on the intent of the input query and a current operational context of the medical device to determine a therapy recommendation that, when incorporated into the therapy regimen, is most likely to yield a better outcome with respect to a physiological condition of the patient, wherein the therapy recommendation comprises a recommendation regarding the therapy delivered using the medical device (paragraph 21 discloses recommended dosages for managing and treating the individual's medical problem may be determined based on the determined trend and the individual's lifestyle information; paragraph 64 discloses the recommendation may include a recommendation associated with a dosage of treatment associated with managing the medical problem (e.g., dosage of insulin, food, beverages, supplements, and/or other medications to manage diabetes); paragraph 71 discloses the analytics system may identify a current state and one or more historical states ( e.g., historical instances of similar states, states that match the current state within a threshold, or the like), identify historical recommendations, historical predictions and outcomes associated with the one or more historical states, and generate the recommendation based on the historical recommendations and outcomes (e.g. such that recommendations and/or predictions that resulted in positive outcomes may be repeated)); and
providing the therapy recommendation as conversational output (paragraph 82 discloses the recommendation regarding dosage may be automatically provided to an insulin pump, and the individual may accept, modify, or cancel the recommendation via a user interface of the insulin pump. In some implementations, recommendation regarding dosage may include a basil rate, bolus, and/or other information that an insulin pump may use to adjust the dosage of insulin being provided to the individual).
The motivation to combine the teachings of Brown and Hoglund are discussed in the rejection of claim 1, and incorporated herein.
Claim 20. Brown and Hoglund teach the method of claim 19. Brown teaches a method further comprising determining a logical layer of the database to query based on the intent of the input query (paragraph 109 discloses keywords from query used to determine concepts for search).
Claim(s) 4 – 5, 8, 13 – 14, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al., herein after Brown (U.S. Publication Number 2014/0074454 A1) in view of Hoglund et al., herein after Hoglund (U.S. Publication Number 2017/0091419 A1) further in view of Vesto et al., herein after Vesto (U.S. Publication Number 2016/0063191 A1).
Claim 4. Brown and Hoglund teach the method of claim 3.
Brown and Hoglund fail to explicitly teach the following limitations met by Vesto as cited:
wherein similarities between the subset of similar patients are encoded by directed graph data structures within different logical layers of a database (Figure 4; paragraph 94 discloses metadata about connections in the healthcare ecosystem is stored and tracked in a graph database; paragraph 138 discloses a graph database of care connections is used to shore relationships within the healthcare ecosystem; paragraph 139 discloses graph databases apply graph theory to the storage of information about the relationships between entries; paragraph 139 discloses vertices or nodes and lines called edges that connect them, a node/vertex represents a patient, provider, resource, etc., and an edge shows a connection between two or more nodes; paragraph 140 discloses the graph database is dynamically updated to track relationships and connections within the healthcare ecosystem).
It would have been obvious to one of ordinary skill at the time of the invention to expand the method of Brown and Hoglund to further include a system, method and apparatus to improve connections within a healthcare ecosystem as disclosed by Vesto.
One of ordinary skill in the art at the time of the invention would have been motivated to expand the method of Brown and Hoglund in this way to continuously improve connectivity within a healthcare ecosystem/environment by optimizing and improving on cost, time to implementation, and patient outcome (Vesto: paragraph 32).
System claim 13 repeats the subject matter of claim 4. As the underlying processes of claim 13 has been shown to be fully disclosed by the teachings of Brown, Hoglund, and Vesto in the above rejections of claim 4; as such, these limitations (claim 13) are rejected for the same reasons given above for claim 4 and incorporated herein.
Claim 5. Brown, Hoglund, and Vesto teach the method of claim 4.
Brown and Hoglund fail to explicitly teach the following limitations met by Vesto as cited:
wherein at least one directed graph data structure includes edges between nodes indicative a causal relationship (paragraph 39 discloses health information relates to information associated with the health of one or more patients, information may include patient specific data and/or comparative data; paragraph 139 discloses vertices or nodes and lines called edges that connect them, a node/vertex represents a patient, provider, resource, etc., and an edge shows a connection between two or more nodes; paragraph 139 discloses vertices or nodes and lines called edges that connect them, a node/vertex represents a patient, provider, resource, etc., and an edge shows a connection between two or more nodes; paragraph 143 discloses a connection optimizer is a decision support system component can determine optimum or improved routing of information for connections to be established within the ecosystem).
The motivation to combine the teachings of Brown, Hoglund, and Vesto are discussed in the rejection of claim 4, and incorporated herein.
System claim 14 repeats the subject matter of claim 5. As the underlying processes of claim 14 has been shown to be fully disclosed by the teachings of Brown, Hoglund, and Vesto in the above rejections of claim 5; as such, these limitations (claim 14) are rejected for the same reasons given above for claim 5 and incorporated herein.
Claim 8. Brown and Hoglund teach the method of claim 7.
Brown and Hoglund fail to explicitly teach the following limitations met by Vesto as cited:
further comprising: querying the logical layer of the database and receiving results based on the query (paragraph 52 discloses a processor processes data received at the input and generates a result that can be provided to one or more of the output, memory, and communication interface; paragraph 94 discloses metadata about connections in the healthcare ecosystem is stored and tracked in a graph database; paragraph 140 discloses the graph database is dynamically updated to track relationships and connections within the healthcare ecosystem); and
querying a second logical layer of the database based on the received results (paragraph 81 discloses one entity of a healthcare enterprise may define a clinical workflow for a certain event in a first manner, a second entity of the healthcare enterprise may define a clinical workflow of that event in a second, different manner).
The motivation to combine the teachings of Brown, Hoglund, and Vesto are discussed in the rejection of claim 4, and incorporated herein.
System claim 17 repeats the subject matter of claim 8. As the underlying processes of claim 17 has been shown to be fully disclosed by the teachings of Brown, Hoglund, and Vesto in the above rejections of claim 8; as such, these limitations (claim 17) are rejected for the same reasons given above for claim 8 and incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Davis et al. (U.S. Publication Number 2017/0220751 A1) discloses a system and method for decision support using lifestyle factors related to decision making for the management of diabetes.
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KRISTINE K. RAPILLO
Examiner
Art Unit 3626
/KRISTINE K RAPILLO/Examiner, Art Unit 3682
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