DETAILED ACTION
The following is a Final Office Action in response to the Amendment received on 28 July 2025. Claims 1-6, 8-10, 12, 17 and 20 have been amended. Claims 1-21 remain pending in this application.
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 .
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.
Response to Arguments
Applicant's arguments filed 28 July 2025 have been fully considered but they are not persuasive.
In response to applicants’ argument that MAY et al. does not appear to teach, suggest, or disclose identify a code blue event from a text string included in nurse call information using a machine learning model as recited in Claim 1 or "determining, by a machine learning model, that a text string included in nurse call information is indicating that a code blue event is occurring in a healthcare facility" as recited in Claim 17; Examiner disagrees. The MAY et al. reference discloses (see [0036]), [t]he output of the machine learning algorithms may be related to detecting an adverse event and raising an alarm such as code blue or sepsis or the general deterioration of a patient’s conditions. In some embodiments, the patient may be prompted by for example a text message on the monitoring device or the smart device to contact the emergency services. (See [0044].) Critical alarms may be predictive measures that raise the awareness of the healthcare providers and patients prior to an adverse event. Critical alarms may also be interpretable in that the message indicating the critical alarm may also provide some information as for the reason of the alarm so that the health care providers may act more efficiently by knowing how to respond the alarm. Depending on the condition of the patient and the mode of operation of the devices and systems described herein, critical alarms may indicate different conditions. For example, in an intensive care unit (ICU) setting in a hospital the critical alarm may be indicative of code blue, sepsis or flat line. (See [0052].) It may be crucial to select the suitable data based on patient’s condition and the operation setting such as critical hospital setting or ambulatory setting. The selected data may then be monitored and used by the artificial intelligence algorithms to be able to detect warning signs or the critical alarms with high accuracy. (See [0053].) Examiner maintains the rejection under 35 USC 103.
Claim Objections
The amendment to the claim was received on 28 July 2025. This correction is acceptable.
Claim Rejections - 35 USC § 103
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) 1-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub. No. 2012/0323591 Al to BECHTEL et al. in view of WO 2021127566 Al to MAY et al.
As per claim 1, the BECHTEL et al. reference discloses a building management system (BMS) of a building for controlling a healthcare facility, the BMS comprising: one or more processors (see [0041], “processor”); and one or more memory devices (“memory”) configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: identify a code blue event (see [0063], “triggering event”) from a text string included in nurse call information using a machine learning mode; adjust at least one of a temperature (“temperature settings”), a pressure, a humidity level, a lighting system (“adjusts the lighting settings”), or an air composition in the room automatically (“automatically”) in response to the code blue event (see [0063], “triggering event”).
The BECHTEL et al. reference does not disclose the limitations taught by the MAY et al. reference, namely: identify a code blue (see [0036], “code blue”) event (“adverse event”) from a text string (see [0044], “text message”) included in nurse call information (see [0037], “computer-based patient monitoring system”) using a machine learning mode (see [0053], “artificial intelligence algorithms”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the smart clinical care room taught by the BECHTEL et al. with the machine learning/artificial intelligence algorithms taught by the MAY et al. reference.
One of ordinary skill in the art would have been motivated to modify the smart clinical care room with the machine learning/artificial intelligence algorithms for adaptively utilizing a clinical care room to facilitate caring for a patient.
As per claim 2, the BECHTEL et al. reference discloses the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: adjust at least one of an entertainment system (see [0108], “entertainment”) or a shade system in response to the nurse call information (see MAY [0037], “computer-based patient monitoring system”).
As per claim 3, the BECHTEL et al. reference discloses the one or more processing circuits (“processor”) include one or more processing circuits (“processor”) located on-premises and comprising one or more memory devices (“memory”), and wherein the one or more memory devices (“memory”) of the one or more processing circuits (“processor”) located on-premises (see [0044], “in the room”) are configured to store instructions (“instructions”) thereon that, when executed by one or more processors (“processor”), cause the one or more processors (“processor”) to: receive nurse call information (see [0085], “present information to and receive information from a nurse (or other healthcare professional)”) including the text string (see MAY [0044], “text message”) from a nurse call system (“nurse call system”).
As per claim 4, the BECHTEL et al. reference disclose the one or more processing circuits (“processor”) include one or more processing circuits (“processor”) located on-premises (see [0044], “in the room”) and comprising one or more memory (“memory”) devices, and wherein the one or more memory devices (“memory”) of the one or more processing circuits (“processor”) located on-premises (“in the room”) are configured to store instructions (“instructions”) thereon that, when executed by one or more processors (“processor”), cause the one or more processors (“processor”) to: communicate (“connectivity engine 310”) with an internet-of-things device interface (see [0046], “smart room service 302, connectivity engine 310, clinician dashboard component 312, patient interactive station component 314’) to receive information from associated internet-of-things connected devices (“bed interface 316, interactive room link component 318, medical device interface 320, sensor interface 322, communication component 324”); and control (“clinician dashboard component 312, patient interactive station component 314”) one or more of the associated internet-of- things connected devices (“bed interface 316, interactive room link component 318, medical device interface 320, sensor interface 322, communication component 324”) via the internet-of-things device interface (“smart room service 302, connectivity engine 310, clinician dashboard component 312, patient interactive station component 314”) to adjust (“adjusts”) at least one of the temperature (“temperature settings”), the pressure, the humidity level, the lighting system (“lighting settings”), or the air composition in the room (“in the room”) automatically (“automatically”) in response to the nurse call information (see MAY [0037], “computer-based patient monitoring system”).
As per claim 5, the BECHTEL et al. reference discloses the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: communicate with an enterprise management system (see [0042], “enterprise-wide computer networks”) to provide coordinated control of building systems (“computing system environment 300”) and reporting of actions taken (“electronic medical record (EMR) 304”) in response to the nurse call information (see MAY [0037], “computer-based patient monitoring system”).
As per claim 6, the BECHTEL et al. reference discloses the one or more processing circuits (“processor”) include one or more processing circuits (“processor”) located on-premises (see [0044], “in the room”) and comprising one or more memory devices (“memory”), and wherein the one or more memory devices (“memory”) of the one or more processing circuits (“processor”) located on-premises (“in the room”) are configured to store instructions (“instructions”) thereon that, when executed by one or more processors (“processor”), cause the one or more processors (“processor”) to: define a set of code blue actions (“code blue event”) that includes adjusting (“adjusts”) at least one of the temperature (“temperature settings”), the pressure, the humidity level, the lighting system (“lighting settings”), the air composition, an entertainment system (see [0108], “entertainment”), or a shade system in the room (“in the room”) automatically in response to the nurse call information (see MAY [0037], “computer-based patient monitoring system”); and implement the code blue actions (“code blue event”) automatically (“automatically”) in response to the receiving the signal (“triggering event”) directly without control of any off-premises processing circuits.
As per claim 7, the BECHTEL et al. reference discloses the set of code blue actions (“code blue event”) includes defining inputs (see [0093], “voice, touch or gesture, triggering event”) received by the one or more processing circuits (“processor”) located on-premises (“in the room”), processing steps performed by the one or more processing circuits (“processor”) located on-premises (“in the room”), and outputs (see [0093], “notification sent to one or more mobile devices” and [0094], “dome light outside the room entrance is illuminated and the room signage displays a message, acoustic outputs are minimized”) provided by the one or more processing circuits (“processor”) located on-premises (“in the room”) to other on-premises devices (“bed interface 316, interactive room link component 318, medical device interface 320, sensor interface 322, communication component 324”) structured to adjust (“adjusts”) at least one of the temperature (“temperature settings”), the pressure, the humidity level, the lighting system (“lighting settings”), the air composition, an entertainment system (“entertainment”), or the shade system.
As per claim 8, the BECHTEL et al. reference discloses the nurse call information (see MAY, “computer-based patient monitoring system”) includes the text string (see MAY, “text message”); and wherein the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: parse the text string (see MAY, “text message”) and extract a code blue identifier (“code blue alert”) and a room identifier (see [0093], “name and location in the healthcare facility”); determine an active code blue event (“code blue alert”) is occurring based on the code blue identifier (“code blue alert”); determine a room location (“name and location in the healthcare facility”) where the code blue event (“code blue alert”) is occurring based on the room identifier (“name and location in the healthcare facility”); and adjust (“adjusts”) at least one of the temperature (“temperature settings”), the pressure, the humidity level, the lighting system (“lighting settings”), the air composition, an entertainment system (“entertainment”), or a shade system in the determined room location (“location in the healthcare facility”) automatically (“automatically”) in response to determine that the active code blue event (see [0093], “full code blue alert”) is occurring.
As per claim 9, the BECHTEL et al. reference discloses the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: automatically (“automatically”) communicate with a code blue team (see [0093, “code blue team 601”) in response to the nurse call information (see MAY [0037], “computer-based patient monitoring system”); or automatically provide wayfinding directions (see [0083], “turn- by-turn directions”) to the code blue team member (“code blue team 601”) via a companion system (see [0082], “mobile computing device”).
As per claim 10, the BECHTEL et al. reference discloses an integration engine of a building management system, the integration engine comprising: one or more processing circuits (see [0041], “processor”) located on-premises (“in the room”) and comprising one or more memory devices (“memory”) coupled to one or more processors (“processor”), the one or more memory devices (“memory”) configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: receive nurse call information (see [0085], “present information to and receive information from a nurse (or other healthcare professional)”) from a nurse call system (“nurse call system”); determine, by a machine learning model, a code blue identifier (see [0063], “code blue event”) based on a text string included in the nurse call information (“present information to and receive information from a nurse (or other healthcare professional)”); determine, by a machine learning model, a room identifier (see [0093], “name and location in the healthcare facility”) based on the text string included in the nurse call information (“present information to and receive information from a nurse (or other healthcare professional)”); determine, by a machine learning model, that an active code blue event (see [0093], “full code blue alert”) is occurring based on the code blue identifier (“code blue event”); determine, by a machine learning model, a room location (“name and location in the healthcare facility”) where the active code blue event (“full code blue alert”) is occurring based on the room identifier (“name and location in the healthcare facility”); initiate a code blue event response (see [0093], “clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6) is automatically notified”); and automatically (“automatically”) implement a set of code blue actions (see [0079], “signals”) in response to initiating the code blue event response (“clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6) directly without control of any off- premises processing circuits, the set of code blue actions (“signals”) including controlling at least one of a room heating ventilation (“temperature settings”), and air conditioning (HVAC) system (“temperature settings”) or a lighting system (“lighting settings”).
The BECHTEL et al. reference does not disclose the limitations taught by the MAY et al. reference, namely: determining, by a machine learning model (see [0053], “artificial intelligence algorithms”), a code blue identifier (see [0052], “indicative of code blue, sepsis or flat line”) based on a text string (see [0044], “text message”) included in nurse call information (see [0037], “computer-based patient monitoring system”) using a machine learning mode (see [0053], “artificial intelligence algorithms”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the smart clinical care room taught by the BECHTEL et al. with the machine learning/artificial intelligence algorithms taught by the MAY et al. reference.
One of ordinary skill in the art would have been motivated to modify the smart clinical care room with the machine learning/artificial intelligence algorithms for adaptively utilizing a clinical care room to facilitate caring for a patient.
As per claim 11, the BECHTEL et al. reference discloses the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: automatically control the HVAC system (see [0110], “one or more environmental conditions”) to lower a temperature of the room location (“clinical care room”) in response to initiating the code blue event response (“clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6).
As per claim 12, the BECHTEL et al. reference discloses the nurse call information (“present information to and receive information from a nurse (or other healthcare professional)”) includes the text string (see MAY, “text message”); and wherein the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: parse the text string (see MAY, “text message”) and determine the code blue identifier (“code blue event”) and the room identifier (see [0093], “name and location in the healthcare facility”) based on the text string (see MAY, “text message”).
As per claim 13, the BECHTEL et al. reference discloses the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: automatically (“automatically”) communicate with a code blue team (see [0093], “code blue team 601”) in response to initiating the code blue event response (“clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6).
As per claim 14, the BECHTEL et al. reference discloses the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: automatically provide location information (see [0093], “name and location in the healthcare facility”) including a location of a crash cart (see [0094], “crash cart may be associated with the patient”) to a code blue team (see [0093], “code blue team 601”) in response to initiating the code blue event response (“clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6).
As per claim 15, the BECHTEL et al. reference discloses the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: automatically provide wayfinding information (see [0083], “turn-by-turn directions”) to a code blue team (see [0093], “code blue team 601”) in response to initiating the code blue event response (“clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6).
As per claim 16, the BECHTEL et al. reference discloses the one or more memory devices (“memory”) are further configured to store instructions (“instructions”) thereon that, when executed by the one or more processors (“processor”), cause the one or more processors (“processor”) to: automatically implement the set of code blue actions (“signals”) in response to initiating the code blue event response (“clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6), the set of code blue actions (“signals”) including controlling at least one of a patient engagement system (see [0056], “patient interactive station”) or an AV distribution system (see [0107], “television, sound system”).
As per claim 17, the BECHTEL et al. reference discloses a method comprising: determining, by a machine learning model, that a text string (see MAY [0044], “text message”) included in nurse call information (see [0085], “present information to and receive information from a nurse (or other healthcare professional)”) is indicating that a code blue event (see [0063], “code blue event”) is occurring in a healthcare facility (see [0093], “healthcare facility”); and adjusting (“adjusts”) at least one of a temperature (“temperature settings”), pressure, or humidity level in the healthcare facility (“healthcare facility”) automatically in response to the nurse call information (“present information to and receive information from a nurse (or other healthcare professional)”).
The BECHTEL et al. reference does not disclose the limitations taught by the MAY et al. reference, namely: determining, by a machine learning model (see [0036], “machine learning algorithms”), that nurse call information (“the parameters of interest to be monitored or the available data wherein features can be extracted and monitored”) is indicating that a code blue (“code blue”) event (“adverse event”) is occurring in a healthcare facility (see [0008], “patient care provider facility”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the smart clinical care room taught by the BECHTEL et al. with the machine learning algorithms taught by the MAY et al. reference.
One of ordinary skill in the art would have been motivated to modify the smart clinical care room with the machine learning algorithms for adaptively utilizing a clinical care room to facilitate caring for a patient.
As per claim 18, the BECHTEL et al. reference discloses receiving the nurse call information (“present information to and receive information from a nurse (or other healthcare professional)”) from a nurse call system (“nurse call system”); determining a code blue identifier (see [0063], “code blue event”) based on the nurse call information (“present information to and receive information from a nurse (or other healthcare professional)”); determining a room identifier (see [0093], “name and location in the healthcare facility”) based on the nurse call information (“present information to and receive information from a nurse (or other healthcare professional)”); determining that the code blue event (“code blue event”) is occurring based on the code blue identifier (“code blue event”); determining a room location (“name and location in the healthcare facility”) where the active code blue event (see [0093], “full code blue alert”) is occurring based on the room identifier (“name and location in the healthcare facility”); initiating a code blue event response (see [0093], “clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6) is automatically notified”); and automatically implementing a set of code blue actions (see [0079], “signals”) in response to initiating the code blue event response (“clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6) is automatically notified”), the set of code blue actions (“signals”) including controlling at least one of a room heating ventilation (“temperature settings”), and air conditioning (HVAC) system (“temperature settings”) or a lighting system (“lighting settings”).
As per claim 19, the BECHTEL et al. reference discloses automatically implementing the set of code blue actions (“signals”) in response to initiating the code blue event response (“clinician responsible for the patient is notified, code blue team (referenced by the numeral 601 in FIG. 6) is automatically notified”), the set of code blue actions (“signals”) including controlling at least one of a patient engagement system (see [0056], “patient interactive station”) or an AV distribution system (see [0107], “television, sound system”).
As per claim 20, the BECHTEL et al. reference discloses the nurse call information (“present information to and receive information from a nurse (or other healthcare professional)”) includes the text string (see MAY, “text message”); and further comprising: parsing the text string (see MAY, “text message”) and determining the code blue identifier (see [0063], “code blue event”) and the room identifier (see [0093], “name and location in the healthcare facility”) based on the text string (see MAY, “text message”).
As per claim 21, the BECHTEL et al. reference discloses automatically communicating with a code blue team member (see [0093], “code blue team 601”) via a companion system (see [0082], “mobile computing device”); and automatically providing wayfinding directions (see [0083], “turn-by-turn directions”) to a code blue team member (see [0093], “code blue team 601”) via the companion system (“mobile computing device”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references are cited to further show the state of the art with respect to building management in healthcare industry:
US 12,327,629 B2 to Sanford
US 10,157,536 B2 to Zuckerman et al.
US 9,411,934 B2 to Robinson et al.
US 8,180,457 B2 to Matos
US 8,050,939 B2 to Graves et al.
US 2015/0025329 A1 to Amarasingham et al.
US 2010/0305966 A1 to Coulter et al.
EP 3975209 A1 to RECEVEUR et al.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Crystal J Barnes-Bullock whose telephone number is (571)272-3679. The examiner can normally be reached Monday - Friday 8 am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CRYSTAL J BARNES-BULLOCK/Primary Examiner, Art Unit 2117 10 October 2025
