DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
After further search and consideration the claims in the instant application are unpatentable as indicated in the rejections below. In the claims filed May 4, 2026, Claims 1, 3, 6-8, 10, 11, 13, 16, 17, 20 and 22 have been amended. Claims 2 and 12 has been previously canceled. Claims 1, 3-11, and 13-22 are pending and rejected hereinbelow.
Claim Objections
The objection to the title is hereby withdrawn pursuant to the amendment made to the title on August 19, 2026.
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, 3-11, and 13-22 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication Number 2019/0205441, Shelton, et al., hereinafter Shelton, in view of United States Patent Application Publication Number 2021/0407632, Carlson, et al., hereinafter Carlson.
Regarding claim 1, Shelton discloses a medical data processing system, comprising:
a processor configured to:
obtain a dataset comprising medical data, (para. 271, the data collection and aggregation module 7022 could be used to generate self-describing data (e.g., metadata) including identification of notable features or configuration (e.g., trends), management of redundant data sets, and storage of the data in paired data sets which can be grouped by surgery but not necessarily keyed to actual surgical dates and surgeons. In particular, pair data sets generated from operations of surgical instruments 7012 can comprise applying a binary classification, e.g., a bleeding or a non-bleeding event. More generally, the binary classification may be characterized as either a desirable event (e.g., a successful surgical procedure) or an undesirable event (e.g., a misfired or misused surgical instrument 7012). The aggregated self-describing data may correspond to individual data received from various groups or subgroups of surgical hubs 7006. Accordingly, the data collection and aggregation module 7022 can generate aggregated metadata or other organized data based on raw data received from the surgical hubs 7006. To this end, the processors 7008 can be operationally coupled to the hub applications 7014 and aggregated medical data databases 7011 for executing the data analytics modules 7034. The data collection and aggregation module 7022 may store the aggregated organized data into the aggregated medical data databases 2212.);
input the medical data to a machine learning (ML) model to generate an ML output that indicates a number of interactions associated with the medical data in the dataset, (para. 284, the situational awareness system includes a pattern recognition system, or machine learning system (e.g., an artificial neural network), that has been trained on training data to correlate various inputs (e.g., data from databases 5122, patient monitoring devices 5124, and/or modular devices 5102) to corresponding contextual information regarding a surgical procedure. In other words, a machine learning system can be trained to accurately derive contextual information regarding a surgical procedure from the provided inputs);
obtain an indication of a control program, (para. 274, control program updating module 7026 could be configured to implement various surgical instrument 7012 recommendations when corresponding control programs are updated);
determine the operational parameter based on contributing the data to the control program, wherein the operational parameter limits one or more of over-compression, collateral damage, or tissue tension, (para. 273, The patient outcome analysis module 7028 can analyze surgical outcomes associated with currently used operational parameters of surgical instruments 7012. The patient outcome analysis module 7028 may also analyze and assess other potential operational parameters. In this connection, the recommendations module 7030 could recommend using these other potential operational parameters based on yielding better surgical outcomes, such as better sealing or less bleeding, and paragraphs 412-415, whether the patient is a smoker results in thicker, stiffer lung tissue that would suggest load clamping rates, lower pre-compression levels, and slower firing actuation of the knife/I-beam to prevent adjacent collateral damage around the perimeter of the anvil and cartridge due to excessive pressure differential during treatment); and
send, via a surgical hub, control instructions that cause the medical device to alter an operation according to the operational parameter that limits the one or more of the over-compression, the collateral damage, or the tissue tension, (para. 353, in connection with one or more medical hubs, may be configured to adjust connected devices based on one or more sets of contextual data and paragraphs 373-379, firing speed can have implications on local tissue compression. In the case of I-beam coupled surgical stapling and cutting instruments, the advancement of the firing member causes local tissue compression around the I-beam location, in addition to cutting tissue and deploying the staples against the anvil pockets. This local running compression wave moves proximal to distal with the I-beam location. The viscoelastic aspect of the tissue causes the rate of this advancement to be directly related to the local compression force applied, as well as the size of the rolling compression wave. This local rolling compression is capable of causing local tissue tension damage within the treatment area, as well as collateral damage because the rate of compression is likely more than the rate of pre-compression).
Shelton does not explicitly disclose determine, for the control program, a utilization score of the dataset based on the ML output;
determine data to contribute to the control program based on the utilization score of the dataset, wherein the utilization score indicates a valuableness of the dataset in generating one or more of a pattern, a conclusion, or an outcome that are used to influence an operational parameter of a medical device.
However, Carlson teaches determine, for the control program, a utilization score of the dataset based on the ML output, (para. 6, para. 28, para. 40, the system may log the information or record sources accessed by one or more users and identify which of the logged sources are utilized most frequently. This could be based on a threshold, a ranking, and/or a machine learning mechanism, and 41-42, the system modifies the patient cohort and/or the identified one or more types of information or records associated with the patient cohort using additional information. For example, one or more patients in the cohort and/or one or more identified records can be ranked, filtered, added, removed, or otherwise modified using clinical databases or other sources of relevant information. As an example, clinical concepts linked to the cohort for diagnostic or therapy decisions can be identified using the additional information and can thus be preferentially reported. Among the many sources of additional information are databases such as Medscape, PubMed, Wikipedia, medical journals, other knowledge-based databases, clinician-curated data, and many more sources);
determine data to contribute to the control program based on the utilization score of the dataset, wherein the utilization score indicates a valuableness of the dataset in generating one or more of a pattern, a conclusion, or an outcome that are used to influence an operational parameter of a medical device, (para. 45, once a new patient is identified in a patient cohort, the patient's notes, social media, activity patterns, and/or other data sources can be analyzed, such as using semantic or natural-language processing. This information may modify or further refine to which patient cohort the patient belongs, or may modify or further refine which records associated with the identified patient cohort are provided or preferred, and para. 46, starting with the ranked list of ontological concepts described above with respect to step 136, use NLP to extract any ontological concepts from this patient's records and then compare to the ranked list of concepts for the cohort to determine which documents include concepts that match the list. The system may then select a number of documents (e.g., a preconfigured number or a number that may be displayed on the UI according to the current display configuration and the size of snippets to be displayed as explained below) to be displayed. For example, the system may select the documents matching the highest ranked concepts in the ranked list.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelton to include to determine, for the control program, a utilization score of the dataset based on the ML output; and determine data to contribute to the control program based on the utilization score of the dataset, wherein the utilization score indicates a valuableness of the dataset in generating one or more of a pattern, a conclusion, or an outcome that are used to influence an operational parameter of a medical device, as taught by Carlson, in order to provide a system for analyzing electronic medical records and facilitate the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. (Carlson, para. 6 and para. 19).
Regarding claim 3, Shelton discloses the system of claim 1 as described above. Shelton does not explicitly disclose wherein the processor is further configured to:
predict the valuableness of the dataset to the control program based on the ML output, wherein the ML output further indicates one or more of an association, a pattern, or a trend associated with the medical data in the dataset, wherein the utilization score is determined based on the predicted valuableness of the dataset to the control program.
However, Carlson teaches predict the valuableness of the dataset to the control program based on the ML output, wherein the ML output further indicates one or more of an association, a pattern, or a trend associated with the medical data in the dataset, wherein the utilization score is determined based on the predicted valuableness of the dataset to the control program, (para. 45, once a new patient is identified in a patient cohort, the patient's notes, social media, activity patterns, and/or other data sources can be analyzed, such as using semantic or natural-language processing. This information may modify or further refine to which patient cohort the patient belongs, or may modify or further refine which records associated with the identified patient cohort are provided or preferred, and para. 46, starting with the ranked list of ontological concepts described above with respect to step 136, use NLP to extract any ontological concepts from this patient's records and then compare to the ranked list of concepts for the cohort to determine which documents include concepts that match the list. The system may then select a number of documents (e.g., a preconfigured number or a number that may be displayed on the UI according to the current display configuration and the size of snippets to be displayed as explained below) to be displayed. For example, the system may select the documents matching the highest ranked concepts in the ranked list.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelton to include to predict the valuableness of the dataset to the control program based on the ML output, wherein the ML output further indicates one or more of an association, a pattern, or a trend associated with the medical data in the dataset, wherein the utilization score is determined based on the predicted valuableness of the dataset to the control program, as taught by Carlson, in order to provide a system for analyzing electronic medical records and facilitate the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. (Carlson, para. 6 and para. 19).
Regarding claim 4, Shelton discloses the system of claim 1 as described above. Shelton does not explicitly disclose wherein the processor is further configured to:
determine a type of the control program based on the indication of the control program, wherein the utilization score of the dataset is determined further based on the type of the control program, and wherein the type of the control program comprises a program type configured to control the operational parameter of the medical device.
However, Carlson teaches determine a type of the control program based on the indication of the control program, wherein the utilization score of the dataset is determined further based on the type of the control program, and wherein the type of the control program comprises a program type configured to control the operational parameter of the medical device, (para. 6, para. 19, any interconnection of two or more devices (including controllers or processors) that facilitates the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network, para. 28, para. 40, the system may log the information or record sources accessed by one or more users and identify which of the logged sources are utilized most frequently. This could be based on a threshold, a ranking, and/or a machine learning mechanism, and 41-42, the system modifies the patient cohort and/or the identified one or more types of information or records associated with the patient cohort using additional information. For example, one or more patients in the cohort and/or one or more identified records can be ranked, filtered, added, removed, or otherwise modified using clinical databases or other sources of relevant information. As an example, clinical concepts linked to the cohort for diagnostic or therapy decisions can be identified using the additional information and can thus be preferentially reported. Among the many sources of additional information are databases such as Medscape, PubMed, Wikipedia, medical journals, other knowledge-based databases, clinician-curated data, and many more sources).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelton to include to determine a type of the control program based on the indication of the control program, wherein the utilization score of the dataset is determined further based on the type of the control program, and wherein the type of the control program comprises a program type configured to control the operational parameter of the medical device, as taught by Carlson, in order to provide a system for analyzing electronic medical records and facilitate the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. (Carlson, para. 6 and para. 19).
Regarding claim 5, Shelton discloses the system of claim 1 as described above. Shelton further discloses wherein the processor is further configured to:
determine metadata associated with the dataset comprising the medical data, (para. 271, the data collection and aggregation module 7022 can generate aggregated metadata or other organized data based on raw data received from the surgical hubs); and
retrieve the metadata associated with the dataset from a metadata repository, wherein the metadata repository comprises one or more of structural metadata, administrative metadata, or external metadata, (para. 271, the data collection and aggregation module 7022 can generate aggregated metadata or other organized data based on raw data received from the surgical hubs, and para. 318, additional analysis can be performed on the composite data, which can then be utilized for providing business metadata (via the metadata layer 204020), reporting, and analysis (via the reporting and analysis layer).
Shelton does not explicitly disclose wherein the utilization score of the dataset is determined further based on the metadata associated with the dataset.
However, Carlson teaches wherein the utilization score of the dataset is determined further based on the metadata associated with the dataset, (para. 9, the patient cohort generator is further configured to refine the associated identified one or more types of information using data from an additional medical information source, and para. 46, Paragraph [0046] discusses the system identifies, based on the patient cohort with which the patient is associated, one or more records of the patient that include information matching the one or more identified types of information associated with the patient cohort, for example, the system may select the documents matching the highest ranked concepts in the ranked list).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelton to include wherein the utilization score of the dataset is determined further based on the metadata associated with the dataset, as taught by Carlson, in order to provide a system for analyzing electronic medical records and facilitate the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. (Carlson, para. 6 and para. 19).
Regarding claim 6, Shelton discloses the system of claim 1 as described above. Shelton further discloses wherein the processor is further configured to:
compartmentalize the dataset into a first subset of medical data and a second subset of medical data, wherein the first subset of medical data is associated with the first field, and the second subset of medical data is associated with the second field, (para. 234, multiple imaging devices are placed at different positions in the surgical field to provide multiple views, and para. 270, modules for resource optimization 7020, data collection and aggregation 7022, authorization and security 7024, control program updating 7026, patient outcome analysis 7028, recommendations 7030, and data sorting and prioritization).
Shelton does not explicitly disclose determine that a first field in the dataset contributes to the utilization score to a greater extent than a second field in the dataset.
However, Carlson teaches determine that a first field in the dataset contributes to the utilization score to a greater extent than a second field in the dataset, (para. 6, para. 19, any interconnection of two or more devices (including controllers or processors) that facilitates the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network, para. 28, para. 40, the system may log the information or record sources accessed by one or more users and identify which of the logged sources are utilized most frequently. This could be based on a threshold, a ranking, and/or a machine learning mechanism, and 41-42, the system modifies the patient cohort and/or the identified one or more types of information or records associated with the patient cohort using additional information. For example, one or more patients in the cohort and/or one or more identified records can be ranked, filtered, added, removed, or otherwise modified using clinical databases or other sources of relevant information. As an example, clinical concepts linked to the cohort for diagnostic or therapy decisions can be identified using the additional information and can thus be preferentially reported. Among the many sources of additional information are databases such as Medscape, PubMed, Wikipedia, medical journals, other knowledge-based databases, clinician-curated data, and many more sources).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelton to include determine that a first field in the dataset contributes to the utilization score to a greater extent than a second field in the dataset, as taught by Carlson, in order to provide a system for analyzing electronic medical records and facilitate the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. (Carlson, para. 6 and para. 19).
Regarding claim 7, Shelton discloses the system of claims 1 and 6 as described above. Shelton further discloses wherein the processor is further configured to:
continue collecting the medical data associated with the first field, (para. 297, The surgical hub 106, 206 can receive this data from the paired modular devices and other data sources and continually derive inferences (i.e., contextual information) about the ongoing procedure as new data is received, such as which step of the procedure is being performed at any given time. The situational awareness system of the surgical hub 106, 206 is able to, for example, record data pertaining to the procedure for generating reports, verify the steps being taken by the medical personnel, provide data or prompts (e.g., via a display screen) that may be pertinent for the particular procedural step, adjust modular devices based on the context (e.g., activate monitors, adjust the field of view (FOV) of the medical imaging device, or change the energy level of an ultrasonic surgical instrument or RF electrosurgical instrument), and take any other such action described above); and
add the collected medical data to the first subset of medical data, wherein a collection of additional medical data associated with the second field is discontinued, (para. 263, receives a clock input 314 and is coupled to a suspend/resume logic and frame timer 316 circuit and a hub repeater circuit 318 to control communication between the upstream USB transceiver port 302 and the downstream USB transceiver ports and para. 334, additional data that may be gathered from other data sets).
Regarding claim 8, Shelton discloses the system of claim 1 as described above. Shelton discloses wherein the dataset is a first dataset, (para. 271, the data collection and aggregation module 7022 could be used to generate self-describing data (e.g., metadata) including identification of notable features or configuration (e.g., trends), management of redundant data sets, and storage of the data in paired data sets which can be grouped by surgery but not necessarily keyed to actual surgical dates and surgeons), wherein the first dataset is obtained from a database, (para. 271, the data collection and aggregation module 7022 could be used to generate self-describing data (e.g., metadata) including identification of notable features or configuration (e.g., trends), management of redundant data sets, and storage of the data in paired data sets which can be grouped by surgery but not necessarily keyed to actual surgical dates and surgeons), and wherein the processor is further configured to:
obtain a second dataset from the database, (para. 268, can aggregate data from specific data generated by various surgical instruments 7012 and their corresponding hubs 7006. Such aggregated data may be stored within the aggregated medical databases 7011 of the cloud 7004. In particular, the cloud 7004 may advantageously perform data analysis and operations on the aggregated data to yield insights and/or perform functions that individual hubs 7006 could not achieve on their own, and para. 271, the data collection and aggregation module 7022 could be used to generate self-describing data (e.g., metadata) including identification of notable features or configuration (e.g., trends), management of redundant data sets, and storage of the data in paired data sets which can be grouped by surgery but not necessarily keyed to actual surgical dates and surgeons. In particular, pair data sets generated from operations of surgical instruments 7012 can comprise applying a binary classification, e.g., a bleeding or a non-bleeding event. More generally, the binary classification may be characterized as either a desirable event (e.g., a successful surgical procedure) or an undesirable event (e.g., a misfired or misused surgical instrument 7012). The aggregated self-describing data may correspond to individual data received from various groups or subgroups of surgical hubs 7006. Accordingly, the data collection and aggregation module 7022 can generate aggregated metadata or other organized data based on raw data received from the surgical hubs 7006. To this end, the processors 7008 can be operationally coupled to the hub applications 7014 and aggregated medical data databases 7011 for executing the data analytics modules 7034. The data collection and aggregation module 7022 may store the aggregated organized data into the aggregated medical data databases 2212.);
Shelton does not explicitly teach the following:
The utilization score is a first utilization score;
determine a second utilization score for the second dataset; and
rank the first dataset and the second dataset based on the first utilization score and the second utilization score.
However, Carlson teaches the utilization score is a first utilization score, (para. 6, para. 28, para. 40, the system may log the information or record sources accessed by one or more users and identify which of the logged sources are utilized most frequently. This could be based on a threshold, a ranking, and/or a machine learning mechanism, and 41-42, the system modifies the patient cohort and/or the identified one or more types of information or records associated with the patient cohort using additional information. For example, one or more patients in the cohort and/or one or more identified records can be ranked, filtered, added, removed, or otherwise modified using clinical databases or other sources of relevant information. As an example, clinical concepts linked to the cohort for diagnostic or therapy decisions can be identified using the additional information and can thus be preferentially reported. Among the many sources of additional information are databases such as Medscape, PubMed, Wikipedia, medical journals, other knowledge-based databases, clinician-curated data, and many more sources):
determine a second utilization score for the second dataset, (para. 22, snippets from the highest ranking entries can be displayed on the home screen for that patient's EMR. Upon clicking the snippet, the user is presented the full entry from which the snippet is taken, and para. 46, to avoid cumulative information, the system may select only one document for each concept in the ranked list. In such embodiments, for example, even if three documents include concept #1 in the ranked list, the system may select only one (e.g., based on the most recent, the additional concepts included in the document, at random, or based on other selection criteria), and move on to select a document that includes concept #2); and
rank the first dataset and the second dataset based on the first utilization score and the second utilization score, (para. 22, snippets from the highest ranking entries can be displayed on the home screen for that patient's EMR. Upon clicking the snippet, the user is presented the full entry from which the snippet is taken, and para. 46, to avoid cumulative information, the system may select only one document for each concept in the ranked list. In such embodiments, for example, even if three documents include concept #1 in the ranked list, the system may select only one (e.g., based on the most recent, the additional concepts included in the document, at random, or based on other selection criteria), and move on to select a document that includes concept #2).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelton to include determine a second utilization score for the second dataset; and rank the first dataset and the second dataset based on the first utilization score and the second utilization score, as taught by Carlson, in order to provide a system for analyzing electronic medical records and facilitate the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. (Carlson, para. 6 and para. 19).
Regarding claim 9, Shelton discloses the system of claims 1 and 8 as described above. Shelton further discloses wherein the processor is further configured to:
determine that the first dataset is ranked higher than the second dataset, (para. 297, The surgical hub 106, 206 can receive this data from the paired modular devices and other data sources and continually derive inferences (i.e., contextual information) about the ongoing procedure as new data is received, such as which step of the procedure is being performed at any given time. The situational awareness system of the surgical hub 106, 206 is able to, for example, record data pertaining to the procedure for generating reports, verify the steps being taken by the medical personnel, provide data or prompts (e.g., via a display screen) that may be pertinent for the particular procedural step, adjust modular devices based on the context (e.g., activate monitors, adjust the field of view (FOV) of the medical imaging device, or change the energy level of an ultrasonic surgical instrument or RF electrosurgical instrument), and take any other such action described above and para. 352, the stratification of context includes a hierarchy of influencing factors, where some can be more important or functionally interrelated at a higher priority than other interrelations.); and
discontinue a collection of additional medical data associated with the second dataset based on the determination that the first dataset is ranked higher than the second dataset, (para. 263, receives a clock input 314 and is coupled to a suspend/resume logic and frame timer 316 circuit and a hub repeater circuit 318 to control communication between the upstream USB transceiver port 302 and the downstream USB transceiver ports and para. 334, additional data that may be gathered from other data sets).
Regarding claim 10, Shelton discloses the system of claim 1 as described above. Shelton does not explicitly disclose wherein the medical data is collected from a controller of the medical device when the medical device is in a control loop operation, and wherein the utilization score is determined based on the medical data collected and additional medical data.
However, Carlson teaches wherein the medical data is collected from a controller of the medical device when the medical device is in a control loop operation, and wherein the utilization score is determined based on the medical data collected and additional medical data, (para. 19, any interconnection of two or more devices (including controllers or processors) that facilitates the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network, and para. 46, to avoid cumulative information, the system may select only one document for each concept in the ranked list. In such embodiments, for example, even if three documents include concept #1 in the ranked list, the system may select only one (e.g., based on the most recent, the additional concepts included in the document, at random, or based on other selection criteria), and move on to select a document that includes concept #2).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelton to include wherein the medical data is collected from a controller of the medical device when the medical device is in a control loop operation, and wherein the utilization score is determined based on the medical data collected and additional medical data, as taught by Carlson, in order to provide a system for analyzing electronic medical records and facilitate the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. (Carlson, para. 6 and para. 19).
Regarding claims 11 and 13-20, these claims are rejected for the same reasons as set forth above with regard to claims 1 and 3-10.
Regarding claim 21, Shelton discloses the system of claim 1 as described above. Shelton further discloses further comprising the surgical hub and the medical device coupled to the surgical hub, wherein the surgical hub comprises the processor, (para. 210, The hub 106 includes a hub display 135, an imaging module 138, a generator module 140 (which can include a monopolar generator 142, a bipolar generator 144, and/or an ultrasonic generator 143), a communication module 130, a processor module 132, and a storage array 134. In certain aspects, as illustrated in FIG. 3, the hub 106 further includes a smoke evacuation module 126, a suction/irrigation module 128, and/or an OR mapping module 133).
Regarding claim 22, Shelton discloses the system of claim 1 as described above. Shelton does not explicitly disclose wherein the processor is further configured to determine a data collection frequency associated with the dataset based on the utilization score of the dataset, and wherein the control instructions indicate the data collection frequency.
However, Carlson teaches wherein the processor is further configured to determine a data collection frequency associated with the dataset based on the utilization score of the dataset, and wherein the control instructions indicate the data collection frequency, (para. 40, the system may log the information or record sources accessed by one or more users and identify which of the logged sources are utilized most frequently. This could be based on a threshold, a ranking, and/or a machine learning mechanism. Different patient cohorts may have the same commonly accessed record types, some overlapping commonly accessed record types, or non-overlapping commonly accessed record types. In some embodiments the device may utilize the access history of only the expert users (across all cohorts or for this particular cohort) to identify which types of information the expert users most frequently access for patients of this cohort. The method may, at this step, apply natural language processing to extract concepts identified by a clinical ontology from documents accessed by experts for patients in this cohort, and then rank the concepts in terms of frequency of access).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Shelton to include wherein the processor is further configured to determine a data collection frequency associated with the dataset based on the utilization score of the dataset, and wherein the control instructions indicate the data collection frequency, as taught by Carlson, in order to provide a system for analyzing electronic medical records and facilitate the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. (Carlson, para. 6 and para. 19).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patient compliance monitor (US 6514200 B1) teaches sensors physically carried by a medical appliance such that as the medical appliance is properly fitted to and worn by the patient, the sensors provide an electrical signal confirming that which may be then timed to provide data which confirms a patient's compliance with a recommended protocol. By combining and correlating the sensor data with the clock or timer provided as part of the controller, a time chart of data may be created indicating when and for how long the patient actually wears the device. Additionally, other sensors may be used to collect data relating to various operational parameters of the device including the amount of negative pressure presented under a vacuum dome or other parameters related to the operation of the medical appliance itself, as desired.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amber Misiaszek whose telephone number is 571-270-1362. The examiner can normally be reached M-F 8:00-5:30, First Friday Off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached on 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682
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