DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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, 4-6, 9-10, and 12-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1
Claims 1-2, 4-6, 9-10, and 12-14 are within the four statutory categories. However, as will be shown below, claims 1-2, 4-6, 9-10, and 12-14 are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1 and 9, which are representative of the inventive concept recite:
Claim 1:
A computing system comprising a processor configured to:
obtain surgical data;
determine a first set of data and a second set of data, wherein the first set of data is determined based on a first processing task and a first privacy level associated with the first processing task, wherein the second set of data is determined based on a second processing task and a second privacy level associated with the second processing task, and wherein the first processing task is different from the second processing task;
generate, using a first machine learning model operating on a first network, a first output based on the first set of data and the first processing task, wherein the first network is included in a facility-edge network, wherein the facility-edge network is included in a protected health information (PHI)-compliant security boundary, and wherein the facility-edge network is configured to process data comprising at least patient-identifiable data;
generate, using a second machine learning model operating on a second network, a second output based on the second set of data and the second processing task, wherein the second network is excluded from the PHI-compliant security boundary, and wherein the second network is configured to receive a de-identified version of the second set of data;
determine, using a third machine learning model, a third set of data based on the first output and the second output;
determine a third output based on the third set of data and a third processing task associated with the third privacy level, and wherein the first output and the second output are further generated based on the third privacy level;
generate a data visualization based on the third output;
and generate a control signal configured to display the data visualization.
*Claim 9 recites similar limitations as claim 1
Step A2 Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
obtaining, determining, and generating) or using pen and paper. Other than reciting generic computer
components/functions such as “computing system”, “processor”, “machine learning model”, “edge network”, and “network”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the “computing system”, “processor”, “machine learning model”, “edge network”, and “network” language, the claim encompasses the user manually collecting data, creating a defined data processing structure by determining the requirements needed to process the data, and then process the data using those requirements.
If a claim limitation, under its broadest reasonable interpretation, cover performance of the
limitation in the mind but for the recitation of generic computer components/functions, then it falls
within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a
generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim
recites a mental process. The recitation of generic computer components/functions such as generating also covers behavioral or interactions between people (i.e. the computer and machine learning model), and/or managing personal behavior or relationships or interactions between people (i.e. social activities,
teaching, and following rules or instructions), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 2, 4-6, 10, and 12-14 recite additional subject matter which further narrows or
defines the abstract idea embodied in the claims.
Step 2A
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims
recite the following additional limitations:
Claim 1 recites “machine learning model”, “edge network”, “network”, and “generate a control signal configured to display the data visualization”, “and wherein the second network is configured to receive a de-identified version of the second set of data”
In particular, the additional elements do not integrate the abstract idea into a practical
application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
Amount to mere instructions to apply an exception. The limitations of obtaining and
determining data are recited as being performed by a computing system (or computer). A
computing system (or computer) is recited at a high level of generality and amounts to no more
than mere instructions to apply the exception using a generic computer. Similarly, determining
data and generating outputs recite “using” the machine learning model, edge network, and network, but provide nothing more than mere instructions to implement an abstract idea on a generic computer. The machine learning model is used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “generate a control signal configured to display the data visualization”, “and wherein the second network is configured to receive a de-identified version of the second set of data”.
Dependent claims 2 and 10 recite edge network storage and cloud network storage
In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
Amount to mere instructions to apply an exception. The limitations of obtaining and
determining data are recited as being performed by a computing system (or computer). A
computing system (or computer) is recited at a high level of generality and amounts to no more
than mere instructions to apply the exception using a generic computer. Similarly, determining
data and generating outputs recite using the edge network and cloud network, but provide nothing more than mere instructions to implement an abstract idea on a generic computer.
Dependent claims 4-6 and 12-14 do not include any additional elements beyond those already recited in independent claims 1 and 9, and dependent claims 2 and 10, hence also do not integrate the
aforementioned abstract idea into a practical application. 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 a computer or
machine learning model or improves any other technology. Their collective function merely provides
conventional computer implementation and do not impose a meaningful limit to integrate the abstract
idea into a practical application.
Step 2B
Claims 1 and 9 do not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to discussion of integration of the
abstract idea into a practical application, the additional elements: A machine learning model in claim 1;
amount to no more than mere instructions to apply and exception and add insignificant extra-solution
activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se,
amount to no more than limitations which amount to elements that have been recognized as well understood, routine, and conventional activity in particular fields as demonstrated by:
Recitation of display which is an electronic device for the visual presentation of data(Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344-45, 127 USPQ2d 1553, 1559-60 (Fed. Cir. 2018)) in a manner that would be well-understood, routine, and conventional.
Recitation of storage which is the process of recording, preserving, and retrieving digital information using various recording media and technologies in a manner that would be well-understood, routine, and conventional.
Para 0048, Seo(US-20250142289-A1) discloses: “The collection and processing system 210, which comprises conventional data storage and computer processing devices, is operative to receive a user's order placement, including signals and analytics.“
Col. 12, Line 23, Tilly(US 12165200 B1) discloses: “The exchange computer system can write the order status to local (i.e., storage that is part of the exchange computer system) or remote storage, to a local or remote database, or other data storage systems using conventional data storage techniques.”
Para 0053, Safary(US-20220318270-A1) discloses: “For example, conventional data storage arrangements that use a central data authority have a single point of failure (namely, the central storage location) which, if compromised by a malicious attacker, can lead to data tampering, unauthorized data disclosure, and exploitation and/or loss of operative control of the processes performed by the centralized system.”
Recitation of receiving data, which is the process of a device of computer accepting information sent to it from another source over a network, cable, or other communication medium (TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional.
Recitation of generating a signal, or transmitting data, which is the process of moving digital data from one device to another over a communication channel (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)) in a manner that would be well-understood, routine, and conventional.
Dependent claims 4-6 and 12-14 do not include any additional elements beyond those already recited in claims 1 and 9. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1 and 9 hence does not
amount to “significantly more” than the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective function merely provide conventional computer implementation.
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.
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 1-2, 4-5, 9-10, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Gaborit (US 20230027978 Al) in view of Gropper(US20070192140A1), and in further view of Schoenberg(US6463417B1).
Claim 1
Gaborit discloses:
A computing system comprising a processor configured to: obtain surgical data(Para 0057, Gaborit discloses: “…can include one or more databases that can contain patient information, medical information, patient image data, and parameters that define the surgical plans [SURGICAL DATA]. For example, medical images of the patient's diseased or damaged bone typically are generated preoperatively in preparation for an orthopedic surgical procedure [SURGICAL DATA]…also can include data… surgical guides selected for a particular patient, and details of the surgical procedure [SURGICAL DATA], such as entry points, cutting planes,…”);
determine a first set of data (Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset...”[FIRST SET OF DATA]) and a second set of data(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[SECOND SET OF DATA]), wherein the first set of data(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[FIRST SET OF DATA]) is determined based on a first processing task(Para 0011, Gaborit discloses “The machine-learned model may apply model parameters of the machine-learned model, where the model parameters are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model [FIRST PROCESSING TASK] ..”) and a associated with the first processing task, wherein the second set of data(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[SECOND SET OF DATA]) is determined based on a second processing task(Para 0011, Gaborit discloses “The machine-learned model may apply model parameters of the machine-learned model, where the model parameters are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model [SECOND PROCESSING TASK] ..”) , and a (Para 0011, Gaborit discloses “The machine-learned model may apply model parameters of the machine-learned model, where the model parameters are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model [FIRST PROCESSING TASK] ..”) is different from the second processing task(Para 0143, Gaborit discloses: “Thus, a plurality of machine-learned models (e.g., of same or different type[FIRST PROCESSING TASK IS DIFFERENT FROM SECOND]) can be trained based on training data.”);
generate, using a first machine learning model(Para 0143, Gaborit discloses: “Thus, a plurality [FIRST MACHINE MODEL] of machine-learned models (e.g., of same or different type) can be trained based on training data.”) operating on a first network(Para 0237, Gaborit discloses: “A Private VLAN may refer to a networking technique, which provides network segregation” [NETWORK SEGREGATION CAN INDICATE THE PRESENCE OF MULTIPLE NETWORKS, ONE OF WHICH CAN BE A FIRST NETWORK]), a first output(Para 0147, Gaborit discloses: “machine-learned model 902 can be trained or otherwise configured to receive the input data and, in response, provide the output data [FIRST OUTPUT]…”) based on the first set of data(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[FIRST SET OF DATA]) and the first processing task(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[FIRST OUTPUT]); wherein the first network(Para 0237, Gaborit discloses: “A Private VLAN may refer to a networking technique, which provides network segregation” [NETWORK SEGREGATION CAN INDICATE THE PRESENCE OF MULTIPLE NETWORKS, ONE OF WHICH CAN BE A FIRST NETWORK]) is included in a facility(Para 0057, Gaborit discloses: “ Storage system 206 can be a cloud-based storage system (as shown) or can be located at healthcare facility[FACILITY])-(Para 0057, Gaborit discloses: “ Storage system 206 can be a cloud-based storage system (as shown) or can be located at healthcare facility[FACILITY])-(Para 0057, Gaborit discloses: “ Storage system 206 can be a cloud-based storage system (as shown) or can be located at healthcare facility[FACILITY])- is configured to process data comprising at least
generate, using a second machine learning model(Para 0143, Gaborit discloses: “Thus, a plurality [SECOND MACHINE MODEL]of machine-learned models (e.g., of same or different type) can be trained based on training data.”) operating on a second network(Para 0237, Gaborit discloses: “A Private VLAN may refer to a networking technique, which provides network segregation” [NETWORK SEGREGATION CAN INDICATE THE PRESENCE OF MULTIPLE NETWORKS, ONE OF WHICH CAN BE A SECOND NETWORK]), a second output(Para 0147, Gaborit discloses: “machine-learned model 902 can be trained or otherwise configured to receive the input data and, in response, provide the output data [SECOND OUTPUT]…”) based on the second set of data(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[SECOND SET OF DATA]) and the second processing task(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[SECOND OUTPUT]); wherein the second network(Para 0237, Gaborit discloses: “A Private VLAN may refer to a networking technique, which provides network segregation” [NETWORK SEGREGATION CAN INDICATE THE PRESENCE OF MULTIPLE NETWORKS, ONE OF WHICH CAN BE A SECOND NETWORK]) is excluded from the (Para 0237, Gaborit discloses: “A Private VLAN may refer to a networking technique, which provides network segregation” [NETWORK SEGREGATION CAN INDICATE THE PRESENCE OF MULTIPLE NETWORKS, ONE OF WHICH CAN BE A SECOND NETWORK]) is configured to receive a (Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[SECOND SET OF DATA]); determine, using a third machine learning model(Para 0143, Gaborit discloses: “Thus, a plurality [THIRD MACHINE MODEL]of machine-learned models (e.g., of same or different type) can be trained based on training data.”), a third set of data(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[THIRD SET OF DATA]) based on the first output(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[FIRST OUTPUT]) and the second output(Para 0143 Gaborit discloses: “In addition, a combiner model can be trained to take the predictions from the other machine-learned models as inputs... [THIRD MACHINE MODEL USING OUTPUTS OF FIRST AND SECOND MACHINE MODELS AS INPUTS]”); and determine a third output(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[THIRD OUTPUT]) based on the third set of data(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[THIRD SET OF DATA]) and a third processing task(Para 0143 Gaborit discloses: “In addition, a combiner model can be trained to take the predictions from the other machine-learned models as inputs and, in response, produce a final inference or prediction [THIRD OUTPUT BASED ON THIRD SET OF DATA AND DATA PROCESSING]…”) associated with the (Para 0147, Gaborit discloses: “machine-learned model 902 can be trained or otherwise configured to receive the input data and, in response, provide the output data [FIRST OUTPUT]…”) and the second output(Para 0147, Gaborit discloses: “machine-learned model 902 can be trained or otherwise configured to receive the input data and, in response, provide the output data [SECOND OUTPUT]…”) are further generated based on the third privacy level; generate a data visualization based on the third output(Para 0234, Gaborit discloses:” Processing devices of server system 1104 may perform the operations defined by machine-learned model 902 … may output the information indicative of the operational duration [DATA BASED ON ML OUTPUT] of the implant back to client computing device… may then display information indicative of the operational duration of the implant or may further output the information indicative of the operational duration of the implant to visualization device…”); and generate a control signal configured to display the data visualization(Para 0232, Gaborit discloses: “ visualization device 213[VISUALIZATION DEVICE] may be configured to display the information indicative of the operational duration of the implant (e.g., likelihood and duration values, likelihood histograms [DATA VISUALIZATION], ranking system, etc.)”).
Gaborit does not explicitly disclose: first privacy level, second privacy level, edge network, PHI-compliant boundary, patient-identifiable data, de-identified, third privacy level
Gropper discloses:
edge network(Para 0315, Gropper discloses: “the edges of a communications network[EDGE NETWORK])
protected health information (PHI)-compliant security boundary(Para 0069, Gropper discloses: ““HIPAA-compliant DICOM router” includes a networking device which processes DICOM data using HIPAA based rules for the selection, assembly, transmission or display of logical entities of Protected Health Information.[HIPAA COMPLIANT ROUTER CAN CREATE A PHI COMPLIANT SECURITY BOUNDARY]”)
de-identified(Para 0337, Gropper discloses: “ranges from low for consent forms and de-identified (anonymous) records to high for genetic profile, infectious disease and neuropsychological information.”)
patient-identifiable data(Para 0153, Gropper discloses: “a legacy patient identifier (ID) is associated with a patient for handling patient healthcare information…”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the machine-learned models in support of surgical procedures, of Gaborit to add edge network included in a protected health information (PHI)-compliant security boundary and patient-identifiable data, as taught by Gropper. One of ordinary skill would have been so motivated to include an edge network on either side of a secure boundary to be able to process data that is both patient-identified and patient-identifiable, but in this case for a system which extends an information standard to compatible online access(Para 0011, Gropper discloses: “As the personal health record comes to include information with inestimable consequences such as the patient's genetic code and information that is critical in a hurricane or accident situation such as the patient's current medications list the functions of providing information privacy, security and accessibility services to the patient will become increasingly important and will shift to institutions specialize in securing and routing personal health information on the patient's behalf.”)
Gropper does not explicitly disclose: first privacy level, second privacy level, third privacy level
Schoenberg discloses:
first privacy level(Col 2, Line 66, Schoenberg discloses: “ …a plurality of security access codes, generating a plurality of hierarchical categories [CAN INCLUDE A FIRST PRIVACY LEVEL]…”)
second privacy level(Col 2, Line 66, Schoenberg discloses: “ …a plurality of security access codes, generating a plurality of hierarchical categories [CAN INCLUDE A SECOND PRIVACY LEVEL]…”)
third privacy level(Col 2, Line 66, Schoenberg discloses: “ …a plurality of security access codes, generating a plurality of hierarchical categories [CAN INCLUDE A THIRD PRIVACY LEVEL]…”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the computing system of Gaborit to add privacy levels, as taught by Schoenberg. One of ordinary skill would have been so motivated to provide a means to protect sensitive data in a way that maximizes confidentiality for patients in addition to complying with HIPAA regulations, but in this case for a system for distributing health information(Col. 2, Line 16, Schoenberg discloses: “It is an object of this invention to provide a method of and system for distributing medical information in which the medical care provider has quick access to a patient's medical record, but only to the information within the medical record that is necessary for the proper treatment of the patient at that time.”)
Claim 2
Gaborit discloses:
The computing system of claim 1, wherein the surgical data(Para 0057, Gaborit discloses: “…can include one or more databases that can contain patient information, medical information, patient image data, and parameters that define the surgical plans [SURGICAL DATA]. For example, medical images of the patient's diseased or damaged bone typically are generated preoperatively in preparation for an orthopedic surgical procedure [SURGICAL DATA]…also can include data… surgical guides selected for a particular patient, and details of the surgical procedure [SURGICAL DATA], such as entry points, cutting planes,…”); comprises surgical data(Para 0057, Gaborit discloses: “…can include one or more databases that can contain patient information, medical information, patient image data, and parameters that define the surgical plans [SURGICAL DATA]. For example, medical images of the patient's diseased or damaged bone typically are generated preoperatively in preparation for an orthopedic surgical procedure [SURGICAL DATA]…also can include data… surgical guides selected for a particular patient, and details of the surgical procedure [SURGICAL DATA], such as entry points, cutting planes,…”); from a facility storage(Para 0057, Gaborit discloses: “Storage system 206 can be a cloud-based storage system (as shown) or can be located at healthcare facility [FACILITY STORAGE]) , surgical data(Para 0057, Gaborit discloses: “…can include one or more databases that can contain patient information, medical information, patient image data, and parameters that define the surgical plans [SURGICAL DATA]. For example, medical images of the patient's diseased or damaged bone typically are generated preoperatively in preparation for an orthopedic surgical procedure [SURGICAL DATA]…also can include data… surgical guides selected for a particular patient, and details of the surgical procedure [SURGICAL DATA], such as entry points, cutting planes,…”); from an edge network storage and surgical data(Para 0057, Gaborit discloses: “…can include one or more databases that can contain patient information, medical information, patient image data, and parameters that define the surgical plans [SURGICAL DATA]. For example, medical images of the patient's diseased or damaged bone typically are generated preoperatively in preparation for an orthopedic surgical procedure [SURGICAL DATA]…also can include data… surgical guides selected for a particular patient, and details of the surgical procedure [SURGICAL DATA], such as entry points, cutting planes,…”); from a cloud network storage(Para 00148, Gaborit discloses: “One example way in which to receive the input data is through an application programming interface (API). As an example, the input data may be stored in a cloud [CLOUD NETWORK STORAGE] for one or more hospitals…”)
Gaborit does not explicitly disclose: edge network
Gropper discloses: edge network(Para 0315, Gropper discloses: “the edges of a communications network[EDGE NETWORK])
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the machine-learned models in support of surgical procedures, of Gaborit to add edge network, as taught by Gropper. One of ordinary skill would have been so motivated to include an edge network to better enable security of patient data, but in this case for a system which extends an information standard to compatible online access(Para 0011, Gropper discloses: “As the personal health record comes to include information with inestimable consequences such as the patient's genetic code and information that is critical in a hurricane or accident situation such as the patient's current medications list the functions of providing information privacy, security and accessibility services to the patient will become increasingly important and will shift to institutions specialize in securing and routing personal health information on the patient's behalf.”)
Claim 4
Gaborit discloses:
The computing system of claim 1, wherein the processor is further configured to: determine a first processing capability(Para 0011, Gaborit discloses “The machine-learned model may apply model parameters of the machine-learned model, where the model parameters[FIRST PROCESSING CAPABILITY] are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model..”) associated with the first processing task(Para 0011, Gaborit discloses “The machine-learned model may apply[FIRST PROCESSING TASK] model parameters of the machine-learned model, where the model parameters are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model..”), wherein the first data set (Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[FIRST DATA SET]) is further determined based on the first processing capability(Para 0011, Gaborit discloses “The machine-learned model may apply model parameters of the machine-learned model, where the model parameters[FIRST PROCESSING CAPABILITY] are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model..”); determine a second processing capability(Para 0011, Gaborit discloses “The machine-learned model may apply model parameters of the machine-learned model, where the model parameters[SECOND PROCESSING CAPABILITY] are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model..”) associated with the second processing task(Para 0011, Gaborit discloses “The machine-learned model may apply[SECOND PROCESSING TASK] model parameters of the machine-learned model, where the model parameters are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model..”), wherein the second data set(Para 0016, Gaborit discloses “wherein the model parameters of the machine-learned model are generated based on a machine learning dataset..”[SECOND DATA SET]) is further determined based on the second processing capability(Para 0011, Gaborit discloses “The machine-learned model may apply model parameters of the machine-learned model, where the model parameters[SECOND PROCESSING CAPABILITY] are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model..”); and determine a third processing capability(Para 0011, Gaborit discloses “The machine-learned model may apply model parameters of the machine-learned model, where the model parameters[THIRD PROCESSING CAPABILITY] are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model..”) associated with the third processing task(Para 0011, Gaborit discloses “The machine-learned model may apply[THIRD PROCESSING TASK] model parameters of the machine-learned model, where the model parameters are generated from a machine learning data set, and determine information indicative of the dimensions based on the applying of the model parameters of the machine-learned model..”), wherein the first output(Para 0147, Gaborit discloses: “machine-learned model 902 can be trained or otherwise configured to receive the input data and, in response, provide the output data [FIRST OUTPUT]…”) is further generated based on the third processing capability(Para 0143 Gaborit discloses: “In addition, a combiner model can be trained to take the predictions from the other machine-learned models as inputs... [FIRST OUTPUT GENERATED BYTHIRD MACHINE MODEL USING OUTPUT OF FIRST AND SECOND MACHINE MODELS AS INPUTS]”), and wherein the second output(Para 0147, Gaborit discloses: “machine-learned model 902 can be trained or otherwise configured to receive the input data and, in response, provide the output data [SECOND OUTPUT]…”) is further generated based on the third processing capability(Para 0143 Gaborit discloses: “In addition, a combiner model can be trained to take the predictions from the other machine-learned models as inputs... [THIRD MACHINE MODEL USING OUTPUTS OF FIRST AND SECOND MACHINE MODELS AS INPUTS]”).
Claim 5
Gaborit discloses:
The computing system of claim 1, wherein at least one of the first processing task or the second processing task is associated with data preparation(Para 0146, Gaborit discloses: “In some implementations, machine-learned model 902 can be used to preprocess the input data[PREPARE DATA] for subsequent input into another model…”).
Claim 9
Claim 9 has similar limitations as claim 1. See claim 1 analysis.
Claim 10
Claim 10 has similar limitations as claim 2. See claim 2 analysis.
Claim 12
Claim 12 has similar limitations as claim 4. See claim 4 analysis.
Claim 13
Claim 13 has similar limitations as claim 5. See claim 5 analysis.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gaborit(US 20230027978 A1) in view of Gropper(US20070192140A1), and in further view of Schoenberg(US6463417B1), in further view of Grantcharov et al(US20210076966)
Claim 6
Gaborit discloses: The computing system of claim 1, wherein the third processing task is associated with
determining one or more of surgical data classifications, surgical data trends, or surgical
recommendations(Para 0119, Gaborit discloses: “machine-learned model 902 can provide output data
in the form of one or more recommendations... [SURGICAL RECOMMENDATIONS]).
Gaborit, Gropper, and Schoenberg do not explicitly disclose: surgical data classifications, surgical data trends
Grantcharov discloses: surgical data classifications(Para 0395, Grantcharov discloses: “... may implement
process operations for formative feedback, self-assessment, learning and quality control, and to identify
patterns , correlations, dependencies and signatures [IDENTIFYING PATTERNS, CORRELATIONS,
DEPENDENCIES ARE CLASSIFICATION SCHEMES] from data collected...”), surgical data trends(Para 0395,
Grantcharov discloses: “... may implement process operations for formative feedback, self-assessment,
learning and quality control, and to identify patterns [SURGICAL TRENDS], correlations, dependencies
and signatures from data collected...)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the computing system of Gaborit to add surgical data classifications and surgical data trends, as taught by Grantcharov. One of ordinary skill would have been so motivated to delineate data outputs in a way that ensures that surgeons attain the insights required to make informed decisions, but in this case for a system for biometric data capture for event prediction(Para 0004, Grantcharov discloses: “The procedure of identifying threats, adverse events, or any other intraoperative segments of interests in the operating room is a technically challenging task with limitations that both restrict its effectiveness and potential for mitigating risk. These limitations significantly hinder the potential safety management systems from identifying threats, adverse events, and other intraoperative segments of interest in the pursuit of aiding surgical teams both in real time and post-operatively to improve performance, lower prevalence and mitigate severity of adverse events, and improve patients safety.”)
Claim 14
Claim 14 has similar limitations as claim 6. See claim 6 analysis.
Response to Arguments
Rejection under 35 U.S.C. 101
(Pages 8-9) Regarding the assertion that the amendments to claims 1 and 9 are considered improvements to the technical fields of privacy that is set forth in the specification.
Applicant's arguments filed have been fully considered but they are not persuasive. 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 a computer/machine learning model or improves any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
(Pages 9) Regarding the assertion that the claims do not merely “apply” an abstract idea on a generic computer and integrate the judicial exception into a practical application.
Applicant's arguments filed have been fully considered but they are not persuasive. The additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which amount to mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea.
Rejection under 35 U.S.C. 103
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Watanabe (US20240265536A1): An apparatus, method, a medical device, and storage medium storing a program capable of accurately classifying sequential data.
Ni et al (US20240346374A1): A method and apparatus to train machine models using medical images.
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.
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/S.G.P./Examiner, Art Unit 3685
/Bion A Shelden/Primary Examiner, Art Unit 3685