DETAILED ACTION
Claims 1-20 are currently presented for examination
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 .
Priority
Applicant's claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has complied with the conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e).
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 1, 2023, and December 26, 2023, have been considered by the Examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters 1116 and 1114 are both referred to as lighting sensors in the specification (see [0144]), but figure 11 labels 1116 as friends/family and 1114 as lighting sensors.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: reference numbers 1010, 1012, 1014 in figure 10, reference number 1110 in figure 11, and reference number 1212 in figure 12.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: [0004] states “…whether the a space is…”. It should be “…whether a space is…”. Appropriate correction is required.
Claim Objections
Claim 4 objected to because of the following informality: it states “…whether the a space is…”. It should be “…whether a space is…”. Appropriate correction is required.
Claim 7 objected to because of the following informality: it states “…to the determining the…”. It should be “…to determining the…”. Appropriate correction is required
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 14-15, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yang Peng et al., “Digital Twin Hospital Buildings: An Exemplary Case Study through Continuous Lifecycle Integration,” Hindawi Advances in Civil Engineering (2020) (henceforth referred to as Peng).
Regarding claim 1, Peng anticipates a building management system (BMS) of a building for controlling at least building equipment of a healthcare facility (Abstract, Sections 2.2, 2.3, figure 1: “Figure 1 shows the main steps to establish a DT for the hospital building, starting from a real-world hospital and end with a complete DT. The first step was to digitize the hospital through geometry modeling. Geometry modeling was mainly conducted with CAD drawings from the design phase, while the 3D point cloud method by laser scanning or photogrammetry [8] was tested upon complex mechanical rooms. After that, the modeling process of BIM was one of the main integration steps.” Building management of a hospital is done using BIM and a digital twin.);
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the BMS comprising one or more processing circuits comprising one or more non-transitory memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to (Sections 3.0, Figure 1: “A unified DT system across desktop client, smartphone app, and tablet app was developed based on the continuous integration method.” Examiner notes that anyone skilled in the art would recognize that any computer device listed uses memory and a processor to execute instructions.);
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initialize a remote digital twin (Section 2.3, Figure 1: “Figure 1 shows the main steps to establish a DT for the hospital building, starting from a real-world hospital and end with a complete DT.” A digital twin is initialized and run by the processors.);
input a remote request into the remote digital twin (Sections 1.0, 4.2.5, Figure 2: “The system also collected requests from automatic fault diagnosis modules.” The digital twin collects remote requests from building modules. Examiner interprets operation commands and real time prompts to be remote requests.);
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conduct a remote digital twin compliance check based on the remote request using the remote digital twin (Abstract, Section 3.0: “Finally, some targeted commands or suggestions were given towards the digital twin hospital building (Step 6). If any diagnosis suggestion was confirmed as incorrect, it was sent back to the engine as negative training sets. The AI algorithms should make corresponding adjustments (Step 7).” The digital twin can make facility diagnoses and operation and optimization suggestions based on AI trained policy. Examiner interprets targeted commands and suggestions and training of AI algorithms using negative feedback as a compliance check.);
and provide a remote digital twin result (Abstract, Section 3.0, Figure 1: “A unified DT system across desktop client, smartphone app, and tablet app was developed based on the continuous integration method.” The digital twin result is received by management.).
Regarding claim 2, Peng anticipates the limitations of claim 1. Peng also anticipates wherein the remote digital twin compliance check is further based on a digital twin policy (Abstract, Section 3.0: “Finally, some targeted commands or suggestions were given towards the digital twin hospital building (Step 6). If any diagnosis suggestion was confirmed as incorrect, it was sent back to the engine as negative training sets. The AI algorithms should make corresponding adjustments (Step 7).” The digital twin can make facility diagnoses and operation and optimization suggestions based on AI trained policy. Examiner interprets targeted commands and suggestions as a compliance check and training of AI algorithms using negative feedback as a digital twin policy.);
and wherein the instructions further cause the one or more processors to train the digital twin policy using historical information (Sections 3.0, 4.3.2, Figure 2: “First, raw data from a real hospital and digital shadow were processed, some through Apache Kafka engine as high-density stream data (Step 1 and 2). Then, two kinds of big data methods, the Apache Flink engine and scheduled Extract-Transform-Load (ETL), both continuously transformed analysis data of building status into a predefined data warehouse—a tidy format suitable for AI training (Step 3). The data warehouse first provided proper training data to AI models.” The digital twin receives training data from the raw hospital data on spatial information, past facility failures, repairs, etc. Examiner interprets raw hospital data as historical information.).
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Regarding claim 3, Peng anticipates the limitations of claim 1. Peng also anticipates wherein the instructions further cause the one or more processors to provide the remote digital twin with real world test information (Sections 3.2, 4.3.2, Figure 2: “In East Hospital, the hardware networks of BA consisted of more than 1900 intelligent sensors from 13 subsystems including the elevator system, meanwhile about 230 digital meters from two energy monitoring systems (electricity and water supply), and 100 sensors from three medical gas systems. Because positions and serial codes of all sensors were already ensured consistent by early involvement of the general contractor, dynamic data could be integrated automatically into DT ruled by correct correspondence.” The digital twin is constantly receiving real world information about the facility.);
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and wherein the remote digital twin compliance check is further based on real world test information (Sections 3.0, 3.1, Figure 2: “Finally, some targeted commands or suggestions were given towards the digital twin hospital building (Step 6). If any diagnosis suggestion was confirmed as incorrect, it was sent back to the engine as negative training sets. The AI algorithms should make corresponding adjustments (Step 7).” The digital twin can make facility diagnoses and operation and optimization suggestions based on AI trained policy. The DT uses the data received to train its AI algorithms. Examiner interprets targeted commands and suggestions as a compliance check and training of AI algorithms using negative feedback as a digital twin policy.).
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Regarding claim 14, it is the machine embodiment of claims 2, and 3 with similar limitations to claims 2, and 3, and is therefore rejected with the same reasoning as claims 2, and 3.
Regarding claim 15, it is the machine embodiment of claim 2 with similar limitations to claim 2, and is therefore rejected with the same reasoning as claim 2.
Regarding claim 20, Peng anticipates the limitations of claim 14. Peng also anticipates identifying system relationships using the digital twin policy (Figures 5-10: The digital twin can monitor relationships within the facility.);
build a knowledge graph of the system relationships (Figures 5-10: The digital twin can create many different graphs and charts and visualizations of data and relationships within the facility. Examiner interprets the charts and graphs shown as knowledge graphs.);
and provide the knowledge graph to a user via a graphical user interface (Figures 5-10: The digital twin can create many different graphs and charts and visualizations of data and relationships within the facility which are presented to users.).
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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 4-6, 8,-11, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Peng in view of Bechtel et al., US 2012/0323591 A1 (henceforth referred to as Bechtel).
Regarding claim 4, Peng teaches the limitations of claim 1. Peng also teaches the remote digital twin compliance check and the remote digital twin compliance check determines whether a space is suitable [for reconfiguration]. (Abstract, Section 3.0-3.2, Figure 2: “Finally, some targeted commands or suggestions were given towards the digital twin hospital building (Step 6). If any diagnosis suggestion was confirmed as incorrect, it was sent back to the engine as negative training sets. The AI algorithms should make corresponding adjustments (Step 7).” The digital twin can make facility diagnoses and operation and optimization suggestions based on AI training. This can be checked against historic data for a space and a suggestion given based on the result. Examiner interprets targeted commands and suggestions as a compliance check for suitability and training of AI algorithms using negative feedback as a digital twin policy.).
Peng does not explicitly teach wherein the remote request is a reconfiguration request.
Bechtel teaches wherein the remote request is a reconfiguration request ([0087], [0088], Figures 4A-C: “The Zones and components discussed above with respect to FIGS. 2 and 3 are used to transition a smart clinical care room (i.e., the Smart clinical care room 200 of FIG. 2) from a first scene to a second scene. The transition from a first scene to a second scene can be in response to some triggering event.” A room can be requested to transition from one scene to another by a remote request such as automatic detection or by a computing device, which is interpreted by the Examiner as a reconfiguration request.).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to virtualize transitioning a room between states by using a digital twin. Bechtel would modify Peng by taking a reconfiguration request specifically and checking it against the AI model training. The benefit of doing so is that the completion of real-world activities is optimized to meet the needs of clinicians, patients and family members. (Bechtel, Abstract)
Regarding claim 5, Peng teaches the limitations of claim 1. Peng does not explicitly teach wherein the instructions further cause the one or more processors to control the building equipment based on the remote digital twin result.
Bechtel teaches wherein the instructions further cause the one or more processors to control the building equipment [based on the remote digital twin result] ([0063], [0064], [0094], [0087], [0088], Figures 4A-C: “In one aspect, the environmental module automatically adjusts the lighting settings and/or temperature settings in the room in response to a triggering event. For example, upon initiation of a code blue event, the lights may be brought to full illumination above the patient's bed.” Equipment in the facility can be controlled such as lights, bed adjustment, airflow, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to allow the digital twin to control the facility based on its results. Bechtel would modify Peng by adding the function of controlling equipment. The benefit of doing so is that the completion of real-world activities is optimized to meet the needs of clinicians, patients and family members. (Bechtel, Abstract)
Regarding claim 6, Peng teaches the limitations of claim 1. Peng also teaches initialize a digital twin policy [using the action prompt] (Abstract, Sections 3.0-3.2, Figure 1: “Finally, some targeted commands or suggestions were given towards the digital twin hospital building (Step 6). If any diagnosis suggestion was confirmed as incorrect, it was sent back to the engine as negative training sets. The AI algorithms should make corresponding adjustments (Step 7).” The digital twin can make facility diagnoses and operation and optimization suggestions based on AI trained policy. Examiner interprets training of AI algorithms for a digital twin using negative feedback as initializing a digital twin policy.);
generate a digital twin result using the digital twin policy (Sections 2.3, 3.0-3.2, Figure 1: “Figure 1 shows the main steps to establish a DT for the hospital building, starting from a real-world hospital and end with a complete DT… By monitoring processes through business systems and sensor devices, a dynamic BIM or digital shadow emerged. Analysis engines with predefined knowledge and AI model should be added right after monitor data coming.” A digital twin is initialized and run by the processors and trained on AI trained policy. Examiner interprets training of AI algorithms for a digital twin using negative feedback as a digital twin policy.);
and train the digital twin policy based on the digital twin result, [the physical action, and the environmental result] (Abstract, Sections 3.0-3.2, Figures 1, 2: “Finally, some targeted commands or suggestions were given towards the digital twin hospital building (Step 6). If any diagnosis suggestion was confirmed as incorrect, it was sent back to the engine as negative training sets. The AI algorithms should make corresponding adjustments (Step 7).” A digital twin is initialized and run by the processors and trained on AI trained policy and real-world data. The digital twin can make facility diagnoses and operation and optimization suggestions based on AI trained policy. Examiner interprets targeted commands and suggestions as a compliance check and training of AI algorithms using negative feedback as a digital twin policy.);
Peng does not explicitly teach recognize an action prompt, recognize a physical action, and determine an environmental result in response to the physical action.
Bechtel teaches recognize an action prompt ([0067], [0087], [0088], Figures 4A-C: “The transition from a first scene to a second scene can be in response to Some triggering event. The triggering event may be a manual trigger by a clinician, a patient…” The system can detect manual and automatic triggers, which can be things such as an interactive station or movements. Examiner interprets this as an action prompt);
recognize a physical action ([0067], [0087], [0088], Figures 4A-C: “The transition from a first scene to a second scene can be in response to Some triggering event. The triggering event may be a manual trigger by a clinician, a patient…” The system can detect manual and automatic triggers, which can be things such as an interactive station or movements. Examiner interprets this as a physical action.);
determine an environmental result in response to the physical action ([0087], [0088], Figures 4A-C: “service 302 referenced in FIG. 3) to request a reading scene. Upon receiving the request for the reading scene, the Smart clinical care room is automatically and appropriately illuminated, acoustic outputs are automatically minimized, and the patient bed is automatically adjusted to a position that facilitates reading.” A reconfiguration request can be made physically, which will trigger a scene transition, which is interpreted by the Examiner as an environmental result.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to virtualize transitioning a room between states by using a digital twin, recognize physical actions and prompts, and determine environmental responses. Bechtel would modify Peng by allowing the digital twin to recognize physical actions and determine environmental results which can be used train the policy since the physical action would be now part of the real-world data that is used and yield expected results. The benefit of doing so is that the completion of real-world activities is optimized to meet the needs of clinicians, patients and family members. (Bechtel, Abstract)
Regarding claim 8, Peng teaches the limitations of claim 1. Peng also teaches train the remote digital twin using historical information (Sections 3.0, 4.3.2, Figure 2: “First, raw data from a real hospital and digital shadow were processed, some through Apache Kafka engine as high-density stream data (Step 1 and 2). Then, two kinds of big data methods, the Apache Flink engine and scheduled Extract-Transform-Load (ETL), both continuously transformed analysis data of building status into a predefined data warehouse—a tidy format suitable for AI training (Step 3). The data warehouse first provided proper training data to AI models.” The digital twin receives training data from the raw hospital data on spatial information, past facility failures, repairs, etc. Examiner interprets raw hospital data as historical information.);
return a digital twin result based on the [environmental] request (Sections 2.3, Figures 1, 2: “Figure 1 shows the main steps to establish a DT for the hospital building, starting from a real-world hospital and end with a complete DT.” A digital twin is initialized and run by the processors based on commands, which is interpreted by Examiner as requests.);
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wherein the digital twin result provides an improved efficiency environmental request (Abstract, Sections 1.0, 4.3.1, Figures 2, 7: “Managers have the ability to grasp the detailed status of the whole hospital by visual management and receive timely facility diagnosis and operation suggestions that are automatically sent back from the digital building to reality. the case has been steadily running for more than a year in the hospital and achieved desired performance by reducing energy consumption.” The digital twin can give optimized energy management operation suggestions. Examiner interprets these operation suggestions as including improved efficiency environmental requests.);
Peng does not explicitly teach query the remote digital twin using an environmental request and control operation of building systems to implement the digital twin result.
Bechtel teaches query [the remote digital twin using] an environmental request ([0067], [0087], [0088], Figures 4A-C: “The transition from a first scene to a second scene can be in response to Some triggering event. The triggering event may be a manual trigger by a clinician, a patient…” Examiner interprets requesting a scene transition via a trigger as an environmental request.).
and control operation of building systems [to implement the digital twin result] ([0063], [0064], [0094], [0087], [0088], Figures 4A-C: “In one aspect, the environmental module automatically adjusts the lighting settings and/or temperature settings in the room in response to a triggering event. For example, upon initiation of a code blue event, the lights may be brought to full illumination above the patient's bed.” Equipment in the facility can be controlled such as lights, bed adjustment, airflow, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to change the environment based on input using the digital twin in ways previously shown used by the digital twin, for the changes to be optimized for efficiency, and for the twin to make an environmental request and make the necessary changes. Bechtel would modify Peng by adding the ability to request different environments from the digital twin and then control the equipment. The benefit of doing so is that it can adaptively and automatically transform hospital rooms from one scene to another scene in order to meet the needs of clinicians, patients and family members. (Bechtel, [0004])
Regarding claim 9, Peng teaches the limitations of claim 1. Peng also teaches predict an undesirable condition using the remote digital twin and based on received information (Sections 4.2.1, 4.2.4, 4.3.2: “The right part listed the most critical information that should be checked by on-site managers, such as alarming sensors, predicted machine fault risks, unhandled repair requests, the trend of passenger flow, and monitoring video at the clinical areas, etc.” The digital twin can predict failures, like with air handling, machine faults, repairs, etc. based on received information.);
and determine a pre-emptive corrective action using the remote digital twin and based on the undesirable condition (Sections 4.2.1-4.2.4: “The right part listed the most critical information that should be checked by on-site managers, such as alarming sensors, predicted machine fault risks, unhandled repair requests, the trend of passenger flow, and monitoring video at the clinical areas, etc.” The digital twin can predict failures and give diagnosis and operation suggestions.).
Peng does not explicitly teach and implement the pre-emptive corrective action by controlling equipment of the healthcare facility condition.
Bechtel teaches and implement the [pre-emptive] corrective action by controlling equipment of the healthcare facility condition ([0063], [0064], [0094]: “In one aspect, the environmental module automatically adjusts the lighting settings and/or temperature settings in the room in response to a triggering event. For example, upon initiation of a code blue event, the lights may be brought to full illumination above the patient's bed.” Equipment in the facility can be controlled such as lights, bed adjustment, airflow, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to allow the digital twin to control the facility based on its results. Bechtel would modify Peng by adding the function of controlling equipment. The benefit of doing so is that the completion of real-world activities is optimized to meet the needs of clinicians, patients and family members. (Bechtel, Abstract)
Regarding claim 10, it is the method embodiment of claims 1, 4, and 5 with similar limitations to claims 1, 4, and 5, and is therefore rejected with the same reasoning as claims 1, 4, and 5.
Regarding claim 11, the combination of Peng and Bechtel teach the limitations of claim 10. Peng teaches determining that the space is suitable [for reconfiguration] using the digital twin comprises determining, using the digital twin, that the building systems are capable of ensuring compliance with a policy for the space [associated with the desired purpose for the space] (Abstract, Section 3.0: “Finally, some targeted commands or suggestions were given towards the digital twin hospital building (Step 6). If any diagnosis suggestion was confirmed as incorrect, it was sent back to the engine as negative training sets. The AI algorithms should make corresponding adjustments (Step 7).” The digital twin can make facility diagnoses and operation and optimization suggestions based on AI trained policy. Examiner interprets targeted commands and suggestions as a compliance check and training of AI algorithms using negative feedback as a digital twin policy.).
Peng does not explicitly teach the reconfiguration request for the space indicates a desired purpose for the space.
Bechtel teaches the reconfiguration request for the space indicates a desired purpose for the space ([0087], [0088], Figures 4A-C: “In another aspect, the patient interacts with Some other component of a Smart room service (i.e., the Smart room service 302 referenced in FIG. 3) to request a reading scene.” A reconfiguration request can be made to transition a room from one scene to another specified scene, which the Examiner interprets as a desired purpose.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to use the digital twin policy to determine if the desired purpose for the space fits the policy of the space. Bechtel would modify Peng by including a desired purpose for the space in the request, which can be checked against the AI model training. The benefit of doing so is that it can adaptively and automatically transform hospital rooms from one scene to another scene in order to meet the needs of clinicians, patients and family members. (Bechtel, [0004])
Regarding claim 16, Peng teaches the limitations of claim 14. Peng also teaches wherein determining the compliance results using the digital twin policy and based on the received real world test information comprises checking compliance of a space with a policy [associated with a purpose of the space] (Abstract, Section 3.0: “Finally, some targeted commands or suggestions were given towards the digital twin hospital building (Step 6). If any diagnosis suggestion was confirmed as incorrect, it was sent back to the engine as negative training sets. The AI algorithms should make corresponding adjustments (Step 7).” The digital twin can make facility diagnoses and operation and optimization suggestions based on AI trained policy. Examiner interprets targeted commands and suggestions as a compliance check and training of AI algorithms using negative feedback as a digital twin policy.).
Peng does not explicitly teach [wherein determining the compliance results using the digital twin policy and based on the received real world test information comprises checking compliance of a space with] a policy associated with a purpose of the space.
Bechtel teaches [wherein determining the compliance results using the digital twin policy and based on the received real world test information comprises checking compliance of a space with] a policy associated with a purpose of the space ([0087], [0088], [0109], Figures 4A-C, Figure 9: “In another aspect, the patient interacts with Some other component of a Smart room service (i.e., the Smart room service 302 referenced in FIG. 3) to request a reading scene.” A reconfiguration request can be made which specifies the purpose for a space and will be optimized to fit the space. Examiner interprets this as a policy for the purpose of a space.).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to check the compliance of a space with the policy for the purpose of the space. Bechtel would modify Peng by including a desired purpose for the space, which can be checked by the AI algorithms. The benefit of doing so is that it can adaptively and automatically transform hospital rooms from one scene to another scene in order to meet the needs of clinicians, patients and family members. (Bechtel, [0004])
Regarding claim 17, 1, Peng and Bechtel teach the limitations of claim 16. Peng teaches wherein determining the compliance results using the digital twin policy and based on the real world test information comprises predicting compliance of the space (Abstract, Section 3.2, 4.3.2, 4.2.1, 4.2.4: Abstract, Section 1.0, 3.0: “DT improves common functions during the lifecycle service of buildings, such as real-time monitoring, energy consumption forecast, failure prediction, operation guide, etc. [19].” The digital twin can make facility diagnoses and operation and optimization suggestions based on AI trained policy. Examiner interprets targeted commands and suggestions as a compliance check, training of AI algorithms using negative feedback as a digital twin policy, and forecasting and prediction of failures as predicting compliance.).
Peng does not teach with an additional policy associated with a reconfigured purpose for the space.
Bechtel teaches with an additional policy associated with a reconfigured purpose for the space ([0087], [0088], [0109], Figures 4A-C, Figure 9: “In another aspect, the patient interacts with Some other component of a Smart room service (i.e., the Smart room service 302 referenced in FIG. 3) to request a reading scene.” A reconfiguration request can be made which specifies the purpose for a space and will be optimized to fit the space. Examiner interprets this as a policy for the purpose of a space.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to check the digital twin policy for a reconfigured purpose of the rooms. Bechtel would modify Peng by adding the ability to have a reconfiguration of a space. The benefit of doing so is that it can adaptively and automatically transform hospital rooms from one scene to another scene in order to meet the needs of clinicians, patients and family members. (Bechtel, [0004])
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Peng in view of Bechtel et al. and in further view of Yoon et al., US 20160217672 A1 (henceforth referred to as Yoon).
Regarding claim 7, Peng-Bechtel teaches the limitations of claim 1. Peng teaches determine a digital twin result in response to determining the [sleep] event (Sections, 2.3, 4.2.4: “After setting the threshold value of the temperature and humidity of the incoming air, CO2, and CO concentration monitoring value, if the real-time data is detected to exceed the threshold value, warnings or alarms were prompted in different colors.” The digital twin can determine an event, such as real time data exceeding a threshold, or a person entering the building.);
Peng does not explicitly teach determine a sleep event based on received information, adapt a healthcare schedule based on the digital twin result, and control smart room features based on the digital twin result.
Bechtel teaches control smart room features based on the digital twin result ([0063], [0064], [0094], [0087], [0088], Figures 4A-C: “In one aspect, the environmental module automatically adjusts the lighting settings and/or temperature settings in the room in response to a triggering event. For example, upon initiation of a code blue event, the lights may be brought to full illumination above the patient's bed.” Equipment in the facility can be controlled such as lights, bed adjustment, airflow, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to control the equipment in the facility. Bechtel would modify Peng by adding the function of controlling equipment. The benefit of doing so is that the completion of real-world activities is optimized to meet the needs of clinicians, patients and family members. (Bechtel, Abstract)
Peng-Bechtel does not explicitly teach determine a sleep event based on received information, and adapt a healthcare schedule based on the digital twin result.
Yoon teaches determine a sleep event based on received information ( [0020]: “According to an aspect of one or more exemplary embodiments, there is provided an apparatus configured to improve a sleep of an object who is sleeping, the apparatus including: a communicator configured to receive bio-information of the object that is measured by a sensor; a controller configured to determine a sleep state of the object based on the bio-information…” An apparatus can receive bio-information to determine the sleep state of a person.);
adapt a healthcare schedule based on the [digital twin] result ([0020]: “…determine a first wake-up time of the object based on schedule information corresponding to a schedule of the object, and change the first wake-up time to a second wake-up time based on the sleep state of the object; and an output device configured to output awake-up alarm signal at the second wake-up time.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng-Bechtel with the teachings of Yoon in order to determine the action of sleeping as well as modifying the environment based on time observations using the digital twins. Yoon would modify Peng-Bechtel by adding detection of sleep parameters to the events it can detect and the functionality to create a schedule based on that. The benefit of doing so is that it can reduce physical and psychological stresses on human health. (Yoon, [0005])
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Peng in view of Bechtel, in further view of Liu et al., A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin (IEEE, 2019) (henceforth referred to as Liu), and in further view of Agronin, WO 2009137563A1 (henceforth referred to as Agronin).
Regarding claim 12, the combination of Peng and Bechtel teaches the limitations of claim 10. Peng-Bechtel does not teach determining a fall parameter indicative of a patient's likelihood of falling based on data relating to the space, comparing the fall parameter to a threshold, and automatically turning on lights in the space in response to the fall parameter becoming greater than or equal to the threshold.
Liu teaches determining a fall parameter indicative of a patient's likelihood of falling based on data relating to the space (Section V: “Furthermore, dangerous events such as falling can be predicted through iteration of the virtual DTH model using machine learning algorithms, and these dangerous events signals will be input into the crisis early warning system.” Events such as falling can be predicted by a model using machine learning and other data.);
comparing the fall parameter to a threshold (Section V: “Furthermore, dangerous events such as falling can be predicted through iteration of the virtual DTH model using machine learning algorithms, and these dangerous events signals will be input into the crisis early warning system.” The fall event is determined by an algorithm. Examiner interprets using an algorithm as comparing to a threshold.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng-Bechtel with the teachings of Liu in order to determine the action of falling and compare likelihood of falling using algorithms. Liu would modify Peng-Bechtel to add detection of falling as a parameter to measure by the digital twin. The benefit of doing so is providing more efficient, accurate, and faster services for elderly healthcare. (Liu, Abstract)
Peng-Bechtel-Liu fails to teach automatically turning on lights in the space in response to the fall parameter becoming greater than or equal to the threshold.
Agronin teaches automatically turning on lights in the space in response to the fall parameter becoming greater than or equal to the threshold ([0089]: “In this regard, the automatic switch control 10 can turn lights on in the room 22 when motion is detected and keep the lights on for the time period set by the second input mechanism 88.” Lights turn on automatically in response to a trigger. Examiner interprets the detection of motion as equivalent to determining a threshold parameter.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to further modify Peng-Bechtel-Liu with the teachings of Agronin in order to turn on the lights automatically when the likelihood of falling is determined by the algorithm to be higher than a threshold. Agronin would modify Peng-Bechtel-Liu to pair the functionality of controlling the lights with the detection of falling. The benefit of doing so is that the lighting can be controlled without physically contacting the device. (Agronin, [0003])
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Peng in view of Bechtel and in further view of Luo et al., CN 107280637 A (henceforth referred to as Luo).
Regarding claim 13, the combination of Peng and Bechtel teaches the limitations of claim 10. Peng also teaches initializing a digital twin policy (Abstract, Section 3.0: “Finally, some targeted commands or suggestions were given towards the digital twin hospital building (Step 6). If any diagnosis suggestion was confirmed as incorrect, it was sent back to the engine as negative training sets. The AI algorithms should make corresponding adjustments (Step 7).” The digital twin can make facility diagnoses and operation and optimization suggestions based on AI trained policy. Examiner interprets targeted commands and suggestions as a compliance check and training of AI algorithms using negative feedback as a digital twin policy.);
monitoring environmental parameters of the healthcare facility (Sections 3.2, 4.2.4: “In East Hospital, the hardware networks of BA consisted of more than 1900 intelligent sensors from 13 subsystems including the elevator system, meanwhile about 230 digital meters from two energy monitoring systems (electricity and water supply), and 100 sensors from three medical gas systems.” The digital twin has many monitors in the facility to monitor the environment.);
training the digital twin policy using the associated [health] outcomes (Sections 3.0, 4.3.2, Figure 2: “First, raw data from a real hospital and digital shadow were processed, some through Apache Kafka engine as high-density stream data (Step 1 and 2). Then, two kinds of big data methods, the Apache Flink engine and scheduled Extract-Transform-Load (ETL), both continuously transformed analysis data of building status into a predefined data warehouse—a tidy format suitable for AI training (Step 3). The data warehouse first provided proper training data to AI models.” The digital twin receives training data from the hospital data on spatial information, past facility failures, repairs, etc. Examiner interprets hospital data as historical information and associated outcomes.);
Peng does not teach adjusting the operation of the building systems based on the trained digital twin policy.
Bechtel teaches adjusting the operation of the building systems [based on the trained digital twin policy] ([0063], [0064], [0094], [0087], [0088], Figures 4A-C: “In one aspect, the environmental module automatically adjusts the lighting settings and/or temperature settings in the room in response to a triggering event. For example, upon initiation of a code blue event, the lights may be brought to full illumination above the patient's bed.” Equipment in the facility can be controlled such as lights, bed adjustment, airflow, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Bechtel in order to allow the digital twin to control the facility based on its results. Bechtel would modify Peng by adding the function of controlling equipment. The benefit of doing so is that the completion of real-world activities is optimized to meet the needs of clinicians, patients and family members. (Bechtel, Abstract)
Peng-Bechtel does not teach associating health outcomes to the environmental parameters using the digital twin policy.
Luo teaches associating health outcomes to the environmental parameters [using the digital twin policy] (Abstract: “A personal health status prediction and evaluation of the invention claims a method, device, system, storage medium, and computer equipment, through the pre-receiving input comprising the personal physical condition of the user, data of state of health and medical records and user surrounding environment; and the detecting unit at the same time measurement around the user environmental parameter level data…” The personal health of patients is predicted, evaluated, and associated with the patient environment parameters.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng-Bechtel with the teachings of Luo in order to monitor patient data in combination with environmental data. Luo would modify Peng-Bechtel by associating the environmental parameters already taken by the digital twin with the health outcomes of patients. The benefit of doing so is that it can automatically and promptly provide accurate predictions and evaluations of the personal health status of the users. (Luo, Abstract)
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Peng in view of Schmidt, Prediction of the Influenza Virus Propagation by using different Epidemiological and Machine Learning Models (Heilbronn University of Applied Sciences, 2019) (henceforth referred to as Schmidt).
Regarding claim 18, Peng teaches the limitations of claim 14. Peng does not teach initializing a contamination threat engine using historical information, receiving contamination threat information, determining a contamination threat using the contamination threat engine and based on the contamination threat information, tracking the contamination threat using the contamination threat engine, associating a health outcome to the contamination threat using the contamination threat engine, training the contamination threat engine using the associated health outcome, identifying the contamination threat as a critical threat using the contamination threat engine, and sending a notification for corrective action using the contamination threat engine in response to determining the critical threat.
Schmidt teaches initializing a contamination threat engine using historical information (2.4.4: “The CDC regression model uses historical ILI data to predict future trends which in the absence of a new mutation impacting the case, yields good prediction.” A model is initialized with historic data of diseases. Examiner interprets the model as a contamination threat engine.);
receiving contamination threat information (2.2: “The models track the number of people in some or all of the following categories: Susceptible (S), Exposed (E), Infectious (I) and Recovered (R).” Examiner interprets tracking the number of people who are affected by a disease as receiving contamination threat information.);
determining a contamination threat using the contamination threat engine and based on the contamination threat information (2.2: “The models track the number of people in some or all of the following categories: Susceptible (S), Exposed (E), Infectious (I) and Recovered (R).” Examiner interprets tracking the number of people who are affected by a disease as determining a contamination threat.);
tracking the contamination threat using the contamination threat engine (2.2: “The models track the number of people in some or all of the following categories: Susceptible (S), Exposed (E), Infectious (I) and Recovered (R).” Examiner interprets tracking peoples condition as tracking contamination threat.);
associating a health outcome to the contamination threat using the contamination threat engine (2.2: “The models track the number of people in some or all of the following categories: Susceptible (S), Exposed (E), Infectious (I) and Recovered (R).” Examiner interprets determining the health outcome of people as associating health outcomes.);
training the contamination threat engine using the associated health outcome (2.3.1, 2.2.4: “In the SIRS model, the individual is susceptible, then infectious, then it has a temporary immunization and finally becomes susceptible again. In this case the immunisation is valid for a short period during the epidemic period.” Examiner interprets the tracking categories as associated health outcomes.);
identifying the contamination threat as a critical threat using the contamination threat engine (2.2: “The models track the number of people in some or all of the following categories: Susceptible (S), Exposed (E), Infectious (I) and Recovered (R).” Examiner interprets determining the health outcome of people as determining a critical threat, which in light of the specifications is an increased risk of a negative health outcome.);
and sending a notification for corrective action using the contamination threat engine in response to determining the critical threat (1.3.1: “They can inform messaging to health care providers regarding influenza vaccination and antiviral treatment for patients.” Examiner interprets messaging providers as sending a notification for corrective action.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Schmidt in order to track the contamination status of people within the facility using a model and to use the data to train it and send notifications about treatment. Schmidt would modify Peng by introducing the ability to track users based on their contamination status and use the data as parameters in the digital twin. The benefit of doing so is that outbreaks and propagation of diseases can be predicted better. (Schmidt, 5.1)
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Peng in view of Quigley et al., Impact of Patient-Engaged Video Surveillance on Nursing Workforce Safety (J Nurse Care Qual, Vol. 35, No. 3, 2019) (henceforth referred to as Quigley) and in further view of Lenaghan et al., Preventing Emergency Department Violence through Design (Journal of Emergency Nursing, 2018) (henceforth referred to as Lenaghan).
Regarding claim 19, Peng teaches the limitations of claim 14. Peng does not teach determining a first hostile action based on camera and/or microphone information, implementing a first response to the first hostile action, wherein the first response is a de-escalating response, determining a second hostile action based on the camera and/or microphone information, and implementing a second response to the second hostile action, wherein the second response is an isolating response.
Quigley teaches determining a first hostile action based on camera and/or microphone information (Page 215: “An audio-video feed is transmitted across the hospital’s secured wireless network to a workstation where a trained monitoring staff member can interact with up to 16 patients at once.” An audio-video feed is transmitted and can be used to determine verbal or physical abuse.);
implementing a first response to the first hostile action, wherein the first response is a de-escalating response (Page 215: “During surveillance, the monitoring staff observe the patient’s agitating behaviors and verbally engage the patient to redirect and/or prevent escalation.”);
determining a second hostile action based on the camera and/or microphone information (Page 215: “In the case a patient does not respond, and there is an urgent or emergent observed behavior, a PEVS alarm is triggered.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng with the teachings of Quigley in order to use audio and visual information to identify certain threatening activity and control the building operations to de-escalate a hostile. Quigley would modify Peng by using audio-video devices to identify a threat and allow the staff to respond properly. The benefit of doing so is that patient aggression toward nursing staff can be tracked more effectively. (Quigley, page 218)
Peng-Quigley fails to teach implementing a second response to the second hostile action, wherein the second response is an isolating response.
Lenaghan teaches implementing a [second] response to the [second] hostile action, wherein the [second] response is an isolating response (Page 10: “In the case of intrusion, hospitals should have the procedures and infrastructure to lock down the emergency department, while allowing those in the waiting area safe egress to an adjacent location.” Examiner interprets a lockdown as an isolating response.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify Peng-Quigley with the teachings of Lenaghan in order to isolate a hostile as a secondary action. Leneghan would modify Peng-Quigley by making the second response to the hostile action an isolating one. The benefit of doing so is that patient and nursing workforce safety and decreasing violence in the health care setting. (Lenaghan, page 11)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Karakra, “HospiT’Win : designing a discrete event simulation-based digital twin for real-time monitoring and near-future prediction of patient pathways in the hospital” HAL Open Science (2021).
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/A.J.D./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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