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 .
DETAILED ACTION
Status of the Application
Claims 1-2, 4-11, and 13-22 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Amendment to the Claims and Remarks filed on 02/17/2026.
Claims 1, 10, 17, and 19 are currently amended.
Claims 3 and 12 are canceled and not considered at this time.
Claims 21-22 are newly added.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/16/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-11, and 13-22 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-2, 4-9, and 21-22 fall within the statutory category of a process. Claims 10-11 and 13-18 fall within the statutory category of an apparatus or system. Claims 19-20 fall within the statutory category of an article of manufacture as a computer-readable medium.
Step 2A, Prong One
As per Claims 1, 10, and 19, the limitations of generating a healthcare protocol associated with diagnosis or treatment of a user or preventing health issues of the user accessing the medical station; receiving the healthcare protocol; determining mapping between segments in the healthcare protocol and the functionalities indicated by the stored information, each of the segments including one or more nodes of the healthcare protocol; and generating logic corresponding to the sequence of the segments and the mapping, the logic indicating a series of the functionalities to be invoked to execute the healthcare protocol, under its broadest reasonable interpretation, covers managing personal behavior and personal interactions including following rules and instructions. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or personal interactions, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – a computing device comprising processors and memory (Claim 10) as well as a non-transitory storage medium storing instructions (Claim 19). The processors, memory, and non-transitory storage medium in these steps are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recite the additional element of storing information on tools, sensors, and plug-in components of the medical station, the information indicating functionalities enabled by the tools, sensors, and plug-in components which amounts to mere instructions to apply the exception because storing information is using a general purpose computer for its ordinary purpose, as per MPEP 2106.05(f)(2). The type of information stored is described as indicating functionalities enabled by the tools, sensors, and plug-in components, but this merely describes the information and does not add any functionality to the claim. The claims also recites the additional elements of sending instructions to a language model which amounts to mere instructions to apply the exception. The use of a computer in its ordinary capacity for tasks such as sending and receiving data is found to be mere instructions to apply the exception as per MPEP 2106.05(f)(2). The language model is described in the specification as AI language models or large language models ([0027]), which is a known mathematical algorithm. The use of a mathematical algorithm applied on a general purpose computer has been found by the courts to be mere instructions to apply the exception, as per MPEP 2106.05(f)(2). The claims also recite the additional element generating an application incorporating the logic for execution by the medical station which amounts to mere instructions to apply the exception because this recites only the idea of a solution of outcome without reciting details of how a solution is accomplished. As per MPEP 2106.05(f)(1), this amounts to mere instructions to apply the exception. The claim recites generating an application for execution by the medical station, which attempts to cover any solution with no identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which as per MPEP 2106.05(F)(1) does not integrate a judicial exception into a practical application or provide significantly more because this has been found by the courts to be equivalent to the words “apply it”. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional elements of a computing device comprising processors and memory (Claim 10) as well as a non-transitory storage medium storing instructions (Claim 19) to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The system including the processors, memory, and non-transitory computer-readable medium are recited at a high level of generality and are recited as generic computer components by reciting a processor as a general-purpose or embedded processor (specification [0035]), a memory taking the form of any type of memory structure such as dynamic random access memory, etc. (specification [0037]), and non-transitory computer-readable medium as memory storing software modules (specification [0037]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. The claims also include the additional elements of storing information on tools, sensors, and plug-in components of the medical station and sending instructions to a language model and generating an application incorporating the logic for execution by the medical station which amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 2-9, 11-18, and 20 add further limitations which are also directed to an abstract idea. For example, Claims 2, 4, 11, 13, and 20 include limitations which further specify or limit the elements of the independent claims, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 10, and 19. Claims 5 and 14 include collecting materials on healthcare protocols from a plurality of sources; processing the collected materials, and feeding the processed materials to the language model to fine-tune which is activity that is managing personal interactions and personal behavior and falls into the abstract grouping of certain methods of organizing human activity. Claims 6 and 15 includes processing and filtering the collected material according to a criteria which is activity that is managing personal interactions and personal behavior and falls into the abstract grouping of certain methods of organizing human activity. Claims 7 and 16 include generating content which falls into certain methods of organizing human activity. The claims also include using a graphics engine, speech synthesizer, or generative artificial intelligence to carry out the abstract idea. This amounts to mere instructions to apply the exception because the use of a known algorithm such as artificial intelligence, which is recited at a very high-level of generality, to carry out the abstract idea is found to be mere instructions to apply the exception, as per MPEP 2106.05(f)(2). Claims 8 and 17 include compiling the logic into code executable by the medical station, linking the content to the executable code; and packaging the executable code and content into the application which is activity involved in generating instructions such as software, which describes managing personal behavior and therefore falls into the abstract grouping of certain methods of organizing human activity. Claims 9 and 18 include sending the application via a network for execution which is an additional element that amounts to mere instructions to apply the exception because sending data over a network is use of a computer for its ordinary purpose of transmitting data, as per MPEP 2106.05(f)(2). Claim 21 includes compiling different versions of the logic for candidate medical stations having different configurations or capabilities; and selecting one of the code versions based on configuration information of the medical station, which describes customizing logic for different medical stations which involves organizing information, which describes managing personal behavior and following instructions and therefore falls into the abstract grouping of certain methods of organizing human activity. Claim 22 further specifies the limitations of Claim 5 by assigning weight values to the collected materials for fine-tuning or training the language model. Assigning weight values is part of the abstract idea, which can also be considered to be a mental process. The fine-tuning or training of the model is not actively recited and results in an intended use, but because it is recited at such a high-level of generality, the Examiner is required to analyze the training step given the broadest reasonable interpretation. The training of the model is considered to be part of the abstract idea because they fall under data manipulations that human performs and thus are part of the rules or instructions. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Gnanasambandam et al. (US 2022/0384052 A1), hereinafter Gnanasambandam, in view of Aoun et al. (US 2022/0101989 A1), hereinafter Aoun.
As per Claims 1, 10, and 19, Gnanasambandam discloses a computing device for generating an application for execution by a medical station, comprising:
one or more processors (see Fig. 14, [0155]);
a memory storing instructions (see Fig. 14, [0155]);
non-transitory storage medium storing instructions thereon, the instructions when executed by one or more processors cause the one or more processors (see Fig. 14, [0155]) to:
store information on tools, sensors and plug-in components on the medical station ([0636-0637] intervention to perform can include messaging services, i.e. plug-in, utilization unit identifies the intervention, [0653] processing device stores the determined utilization unit in a memory device, Examiner interprets information on tools, sensors, and plug-in components to be any information related to these which includes the use of them such as the utilization unit being a service performed), the information indicating functionalities enabled by the tools, sensors and plug-in components ([0651-652] determine a utilization unit based on the mappings identified, [0638] where the mappings are the knowledge graph compared to the patient graph/patient data, see also [0341] data repository storing services, devices);
generate a healthcare protocol ([0262] cognitive agent recommends devices or gadgets, health tips, reminders; [0263] plot a next best action sequence including health care plan template, [0312] defining an action plan);
receive the healthcare protocol from the language model responsive to sending the instructions to the language model ([0262-0263] language model analyzes conversation and recommends topics/items based on a set of allowed actions and desired outcomes, i.e. protocols, action can be determined using the linguistics and natural language understanding system to relate conversation material to actions, i.e. sending the conversation material to the natural language system and receiving the actions/protocol in return);
determine mapping between segments in the healthcare protocol and functionalities indicated by the stored information ([0262] determining a recommendation of topics and items using the cognitive agent including devices or gadgets to be used, [0263] the set of allowed actions is related to the outcome desired), each of the segments including one or more nodes of the healthcare protocol ([0117-0118] the knowledge graph includes nodes including the guidelines, see Fig. 57A where the nodes indicate actions to be taken, i.e. protocol);
generate logic corresponding to sequence of the segments and the mapping ([0117] generate a knowledge graph represents disease information and follows a logical structure to connect the various individual elements, [0119] the tags of the data specifies the logic or relationships in the knowledge graph); and
generate an application incorporating the logic for execution by the medical station (see Fig. 58 generate a care plan to be executed for the patient based on the comparison of the first and second data structure, and present the care plan on a computing device, [0575] the data structures are those of the knowledge graph, i.e. logic/protocol for that disease, and patient graph, these operations are generated as instructions to be executed by the processor, [0578] based on the comparison of patient data to protocol, select health artifacts which are not included in the patient graph and need to be performed by the patient based on the knowledge graph, [0581] generate natural language of the artifacts to be presented on the computing device).
However, Gnanasambandam may not explicitly disclose the following which is taught by Aoun: send instructions to a language model to generate a healthcare protocol ([0057] rendering engine can implement a neural network /machine learning model to perform NLP, recommender systems, [0051] rendering engine integrated into a computing device, [0055] process data to generate suggestions, [0116] described methods implemented by computer-executable instructions stored on computer-readable media, Examiner notes that it would be obvious in order to execute the language model instructions would be sent from the storage media), the healthcare protocol associated with diagnosis or treatment of a user or preventing health issues of the user accessing the medical station ([0082-0084], Fig. 4 providing a customized medical treatment by utilizing the data from the medical care site to determine whether/what treatment is necessary for the given condition of the patient, [0061]/[0063] based on received and analyzed patient information, dynamically provide suggestions including suggestions of actions to take, treatments, examinations, [0087] determine if patient has a condition and appropriate treatment to recommend for the condition); and
the logic indicating a series of the functionalities to be invoked to execute the healthcare protocol ([0111] storage device can store hardware module for performing a particular function including the software component to carry out the functionality).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of sending instructions to a language model to generate the healthcare protocol associated with treatment of a user from Aoun with the known system of generating a healthcare protocol from Gnanasambandam in order to provide accurate and reliable data to provide treatments and medical device as needed for a patient (Aoun [0003-0004]).
As per Claims 2, 11, and 20, Gnanasambandam and Aoun discloses the limitations of Claims 1, 10, and 19. Gnanasambandam also teaches the healthcare protocol is represented as a decision tree or a Boolean logic including the one or more nodes ([0263] the actions and include a sequence of actions and subsequent actions to be performed in a logical manner).
As per Claims 4 and 13, Gnanasambandam and Aoun discloses the limitations of Claims 1 and 10. Gnanasambandam also teaches the healthcare protocol indicates one or more actions to be taken by at least one of the user or the medical station according to determination at the segments ([0146] compare the patient graph and knowledge graph based on condition and generate a care plan which includes an action instructions for the user device for the patient, [0149] patient graph includes actions to be taken by user for condition).
As per Claims 5 and 14, Gnanasambandam and Aoun discloses the limitations of Claims 1 and 10. Gnanasambandam also teaches collect materials on the healthcare protocols from a plurality of sources ([0157], [0171] collect inputs from several sources and entities for the knowledge cloud, [0173] collect information from other source as the facility/hospital/medical center/pharmacy/etc., [0176] collect information from sources including other eco-system participants and integrates into the knowledge);
process the collected materials ([0176] analyze and process the information collected from other sources to consolidate and integrate the information in the knowledge); and
feed the processed materials to the language model to fine-tune or perform training of the language model ([0161] train model using clinical based evidence, clinical trials, physician data and various knowledge pertaining to the medical condition, [0463] train the artificial intelligence engine on cognified data, evidence-based guidelines including known information for the particular disease/condition; [0602] training data for model includes care plans for conditions and feedback, [1022] train model using feedback from medical personnel).
As per Claims 6 and 15, Gnanasambandam and Aoun discloses the limitations of Claims 5 and 14. Gnanasambandam also teaches the collected material is processed and filtered according to one or more criteria ([0161] the data collected such as evidence, trials, facts, properties, etc. are grouped/associated/filtered based on the medical condition).
As per Claims 7 and 16, Gnanasambandam and Aoun discloses the limitations of Claims 1 and 10. Aoun also teaches generating content using at least one of a graphics engine, a speech synthesizer and a generative artificial intelligence, wherein the application further incorporates the generated content ([0046] use of artificial intelligence to generate speech audio to output audio instructions to the user of the suggestions/messages).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of using artificial intelligence to generate content from Aoun with the known system of generating a healthcare protocol from Gnanasambandam in order to provide accurate and reliable data to provide treatments and medical device as needed for a patient (Aoun [0003-0004]).
As per Claims 8 and 17, Gnanasambandam and Aoun discloses the limitations of Claims 7 and 16. Aoun also teaches compiling the logic into code executable by the medical station ([0107] method is implemented as code including an application);
linking the content to the executable code ([0107] method is implemented as code including an application); and
packaging the executable code and the content into the application ([0107] method is implemented as code including an application).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of generating executable code into an application from Aoun with the known system of generating a healthcare protocol from Gnanasambandam in order to provide accurate and reliable data to provide treatments and medical device as needed for a patient (Aoun [0003-0004]).
As per Claims 9 and 18, Gnanasambandam and Aoun discloses the limitations of Claims 1 and 10. Aoun also teaches sending the application via a network for execution by the medical station (see Fig. 1 network communication between the medical system and the medical care site or offsite, [0107] method is implemented as code including an application).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of sending the application to the medical station from Aoun with the known system of generating a healthcare protocol from Gnanasambandam in order to provide accurate and reliable data to provide treatments and medical device as needed for a patient (Aoun [0003-0004]).
As per Claim 22, Gnanasambandam and Aoun discloses the limitations of Claim 1. Gnanasambandam also teaches assigning weight values to the collected materials based on one or more criteria, wherein the weight values are used for fine-tuning or training the language model ([0657] applying a weight value to each indicators to determine a confidence level of the utilization units, [0637] the artificial intelligence engine outputs the utilization unit, Examiner interprets the use of weights for determining the confidence of the output of the engine reads on fine tuning the model).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Gnanasambandam (US 2022/0384052 A1), in view of Aoun (US 2022/0101989 A1), in view of Yoo (US 2011/0015504 A1), hereinafter Yoo.
As per Claim 21, Gnanasambandam and Aoun discloses the limitations of Claim 1. Gnanasambandam and Aoun may not explicitly disclose the following which is taught by Yoo: compiling different versions of the logic for candidate medical stations having different configurations or capabilities ([0088-0089] software which includes a protocol corresponding to each of the different medical examination devices, where the protocol instructs the user on how to use the medical examination device); and
selecting one of the code versions based on configuration information of the medical station ([0088-0089] GUI includes each of the configuration of medical examination devices and the examination includes execution of commands in sequential order for the order of use of medical examination devices, see Fig. 4-9 which show icons for selection of medical examination device available to the user to be used in examination, [0127] GUI buttons for selecting one of the medical devices).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of compiling logic for the capabilities of the medical station and selection code based on the configuration from Yoo with the known system of generating a healthcare protocol for a medical station from Gnanasambandam and Aoun in order to provide a medical examination apparatus with a simple user interface capable of easily being used by a patient (Yoo [0006]).
Response to Arguments
Applicant’s arguments, see Pages 8-14, “Response to Rejections under 35 U.S.C. §101”, filed 02/17/2026 with respect to claims 1-2, 4-11, and 13-20 have been fully considered but they are not persuasive.
Applicant argues that the claims of the present application are not directed to an abstract idea because the claims do not recite managing personal behavior or relationships or interactions between people. Applicant argues that the claims are a technical operation for configuring a particular class of medical hardware systems, not management of human interpersonal behavior. Examiner respectfully disagrees. The steps of configuring a system including generating a healthcare protocol associated with diagnosis or treatment of a user or preventing health issues of the user, determining mapping between segments in the healthcare protocol and the functionalities indicated by the stored information, generating logic corresponding to sequence of the segments and the mapping, and generating an application incorporating the logic describe generation of software for diagnosing a user by determining logic to be executed by the computer. The diagnosis of a user is activity performed by a doctor in the treatment of a patient and generating the logic and application for generating the diagnosis is following rules or instructions to customize the logic for the medical station. Therefore, the claims are directed to an abstract idea of certain methods of organizing human activity.
Applicant argues that the claims as a whole integrate the abstract idea into a practical application of automated generation of a medical-station-specific application from a healthcare protocol, which results in automatic deployment of station-executable applications tailored to different configurations of modular medical stations. Examiner respectfully disagrees. The generation of an application that is tailored to a particular configuration of a medical station is the generation of instructions for a particular set of information, which is directed to the abstract idea and falls into the grouping of certain methods of organizing human activity. Applicant further argues that the claims provide an improvement to medical-station configuration and software deployment by mapping protocol nodes to station functionalities and compiling the logic into code which improves the technology of software deployment by transforming static protocol documents into executable, station-specific applications. Examiner respectfully disagrees. The claims do not show any improvement to software deployment itself. The generation of a healthcare protocol by mapping protocol segments to the medical station functionalities is merely matching information and configuring a station set-up, which falls into the abstract grouping of certain methods of organizing human activity. Generating the logic corresponding to the mapping is not specified in the claims and is therefore directed to the abstract idea. Generating an application incorporating the logic is recited at a high level of generality such that it is directed to the abstract idea and does not provide improvement to software development. Therefore, these elements do not integrate the abstract idea into a practical application.
Applicant argues that the claimed method uses a particular machine which integrates the abstract idea into a practical application. Examiner respectfully disagrees. The medical station is not positively recited in the claims. The claims store information related to the medical station, and the information is then used to generate the application for execution on the medical station. The medical station does not provide any functionality in the claims. Therefore, there is no particular machine recited in the claims to be considered in the 101 analysis. The application is generated so that it can be executed by the medical station, but the execution is merely the intended use of the application.
Applicant argues the claims recite significantly more than the abstract idea including the additional elements of storing information on tools, sensors, plug-in components of the medical station, determining mapping between segments in the healthcare protocol and the functionalities indicated by the stored information , each of the segments including nodes of the healthcare protocol, and generating logic corresponding to sequence of the segments and the mapping. Applicant further argues that the combination of these elements creates an automated protocol-to-application generation pipeline that transforms high-level healthcare protocols into concrete, executable sequences of medical-station functionalities tailored to specific station configuration. Examiner respectfully disagrees. Storing information is an additional element, however, storing information is a routine computer function and therefore amounts to mere instructions to apply the exception. The generation of tailored protocols for a medical station is the abstract idea itself as it describes tailoring instructions for a medical station based on the configuration of the medical station and the corresponding protocols. Using software to apply these protocols does not provide a technical improvement, because requiring the use of software to tailor information is found by the courts to be mere instructions to apply the exception, as per MPEP 2106.05(f)(2). Applicant further argues that these elements provide an improvement to a technology or technical field, apply the judicial exception with a particular machine, and are not well-understood, routine, and conventional. Examiner respectfully disagrees. Examiner has addressed above the argument with regard to the claims providing an improvement to a technical field and also with regard to the use of a particular machine. The claims do not provide significantly more than the abstract idea for the same reasoning as provided above. With regard to the assertion that the claims provide elements that are not well-understood, routine, and conventional, Examiner respectfully disagrees. The steps of determining mapping between segments and functionalities of the medical station, and generating logic corresponding to the sequence of the segments and the mapping are directed to the abstract idea itself and therefore cannot provide significantly more than the abstract idea. These elements are not analyzed for well-understood, routine, and conventional under Step 2B. The additional element of storing information on tools, sensors, and plug-in components of the medical station amounts to mere instructions to apply the exception. Additionally, the storing limitation is recited at an extremely high level of generality such that is well-understood, routine, and conventional similar to storing and retrieving information in memory, as per MPEP 2106.05(d)(II). Therefore, the claims do not recite elements which provide significantly more than the abstract idea.
Applicant’s arguments, see Pages 14-16, “Response to Rejections under 35 U.S.C. §103”, filed 02/17/2026 with respect to claims 1-2, 4-11, and 13-20 have been fully considered but they are not persuasive.
Applicant argues that Gnasambandam does not teach receiving the healthcare protocol from the language model in response to sending the instructions to the language model; determining mapping between segments in the healthcare protocol and the functionalities indicated by the stored information, each of the segments including one or more nodes of the healthcare protocol. Examiner respectfully disagrees. Specifically, Applicant argues that Gnasambandam does not disclose explicitly healthcare protocol artifact generated by a language model and then returned as such to another component. Examiner notes that the claims do not explicitly recite a healthcare protocol artifact or returning a generated artifact to another component. The claim, under broadest reasonable interpretation, recites receiving a healthcare protocol from the language model in response to sending the instructions to the language model. The instructions are not clearly defined in the claims. The healthcare protocol is merely recited as associated with diagnosis or treatment of a user. In Gnasambandam, the instructions which are sent to the language model are taught as the conversation material which is analyzed by the cognitive intelligence platform (which is a linguistics and natural language understanding system, i.e. language model) and outputs actions including the next best healthcare action. Gnasambandam teaches sending the conversation material to the natural language system and receiving the actions/protocol in return in [0262-0263]. This reads on the BRI of the claim limitation. Applicant also argues that Gnasambandam does not teach or disclose determining mapping between segments in the healthcare protocol and the functionalities indicated by the stored information because Gnasambandam does not teach mapping between protocol segments and physical station functionalities. Examiner respectfully disagrees. The claim language, as recited, includes determining a mapping between parts of a healthcare protocol and the functionalities indicated by the stored information. Under BRI, this is any connection made between a protocol segment or part of actions to be taken and the functionalities indicated by the stored information, which can be any action taken by the tools, sensor, or plug-in components. The functionalities are merely indicated by the stored information and are not the stored information themselves. Gnasambandam teaches relating the actions, sequences of actions and plot the next best action sequence based on the allowed actions in [0262-0263]. Therefore, Gnasambandam reads on the BRI of the claim limitations as currently recited.
Applicant additionally argues that Aoun fails to teach or disclose storing a capability model of a specific modular medical station that enumerates its tools, sensors, and plug-in components and indicates the functionalities enabled by each component. Examiner notes that the arguments are directed to more specific limitations than what is recited in the claims. The claims do not recite a capability model or a specific modular medical station. Therefore, the arguments are moot. Additionally, the claims merely recite storing information on tools, sensors, and plug-in components. The information indicates the functionalities but that does not recite the functionalities themselves.
Therefore, the rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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