DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Regarding the arguments against the rejection of claims under 35 USC 101, the Examiner respectfully disagrees. Applicant argues that the claims do not recite a mental process, however this particular argument is rendered moot in view of the current amendment where the abstract idea is analyzed as “certain methods of organizing human activity” instead under Step 2A Prong One as presented below.
Applicant further argues that the claim features improve computer capabilities along with regarding individualized or specific patient characteristics without necessarily requiring such relatively impractical dedicated devices. Examiner asserts that as analyzed in the Step 2A Prong 2 below, there is no indication of a specific implementation of computing technology to carry out the abstract idea. Use of generic machine learning models that are trained does not recite a technical improvement, and improvements related to specific patient characteristics does not recite a technical field improvement, see MPEP 2106.05(a)II, particularly “Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”
Applicant further argues that at Step 2B, it cannot be said that the claims features represent the generic-ness as asserted in the previous Office Action. Examiner asserts that evidence has been presented to demonstrate the generic-ness of the additional elements of the claims, see MPEP 2106.05(d)(I.), more specifically “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d 1307, 1317; 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").” Again, the technical features of memory/processors, training and executing convolutional and generative NNs recite generic computer implementation as a tool for the abstract idea related to outputting estimated state information and a simulation image as further described in Step 2A Prong One of this Office Action.
Further, the system outputting concrete, real-world results recite the use of generic computing technology to output aspects of the abstract idea as noted in the rejection below. There is no indication of the “dual-model architecture” as being “significantly more” than generic computer implementation, as noted in the Step 2B section of the rejection and how the Specification does not demonstrate the technical improvement that these models provides as noted. In contrast to the decisions of Ex Parte Desjardins and Enfish, there is no indication of a technical improvement to machine learning in the instant application.
Regarding the arguments against the rejection of claims under 35 USC 102, the Examiner agrees and therefore this rejection is withdrawn.
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 1-11 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
It is appropriate for the Examiner to determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance to the Subject Matter Eligibility Test as recited in the following Steps: 1, 2A, and 2B, see MPEP 2106(III.).
Patent Subject Matter Eligibility Test: Step 1:
First, the Examiner is to establish whether the claim falls within any statutory category including a process, a machine, manufacture, or composition of matter, see MPEP 2106.03(II.) and MPEP 2106.03(I).
Claims 1-6 and 9 are related to a system, and claim 7, 10 is also related to a method (i.e., a process). Claim 8, 11 is related to a “non-transitory” processor readable media storing instructions. Accordingly, these claims are all within at least one of the four statutory categories.
Patent Subject Matter Eligibility Test: Step 2A- Prong One:
Step 2A of the Subject Matter Eligibility Test demonstrates whether a clam is directed to a judicial exception, see MPEP 2106.04(I.). Step 2A is a two-prong inquiry, where Prong One establishes the judicial exception. Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes, see MPEP 2106.04(II.)(A.)(1.) and 2106.04(a)(2).
Representative independent claim 1 includes limitations that recite at least one abstract idea as underlined in the following limitations. Specifically, independent claim 1 recites:
An information processing system comprising:
at least one memory storing instructions; and
at least one processor configured to execute the instructions to:
acquire, for each patient, medical record information;
determine, based on the medical record information, a target region of the patient;
acquire, for each patient, a plurality of patient-specific reference images of the target region at respective progress statuses including at least an admission status and a discharge-related status, together with state information labeling each reference image;
generate, for each patient:
a first physical condition estimation model implemented as a convolutional neural network trained using the plurality of patient-specific reference images and the state information to output estimated state information from an input photographed image of the target region, and
a second physical condition estimation model implemented as a generative decoder network or a generative adversarial network trained using the patient-specific reference images and the state information to output, from predetermined state information, a simulation image of the target region;
during any of monitoring after hospital discharge and examination, receive, from a patient terminal coupled to a camera, a photographed image of the target region of a target patient;
input the photographed image to the first physical condition estimation model to obtain the estimated state information of the target patient;
input the predetermined state information to the second physical condition estimation model to obtain the simulation image corresponding to a predetermined state;
compute, based on a similarity between the estimated state information and a stored status among any of the respective statuses, including the admission status and the discharge-related status, and a rate of change of a state level, a decision indicating whether the target patient needs to be examined by a doctor; and
control output to transmit to the patient terminal and to a hospital terminal at least (a) the decision, (b) the simulation image, and (c) a message prompting re-examination when the decision indicates that an examination is needed, while storing, specific to each patient, the first physical condition estimation model and the second physical condition estimation model in an estimation model database in association with a patient ID.
The Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity”, more specifically managing interactions between people as the following abstract limitations recite management of the care of a patient by generating information for the progress of the status of the patient:
“determine”, based on the medical record information, a target region of the patient, which recites an abstract limitation of analysis of gathered information related to a patient,
“output” estimated state information from an input photographed image of the target region, which recites an abstract limitation of analysis of the state information and an interaction of this information based on the analysis,
“output”, from predetermined state information, a simulation image of the target region, which recites an abstract limitation of a generation of an image for presentation, where under broadest reasonable interpretation the generated image is a representation of information that can be presented to another individual that does not necessarily need to be generated exclusively on a computer,
“obtain” the estimated state information of the target patient, which is an abstract limitation of an analysis of the inputted image to generate an estimate state of the target patient,
“obtain” the simulation image corresponding to a predetermined state, which recites abstract limitations for the generation of the simulation image based on the predetermined state,
“compute”, based on a similarity between the estimated state information and a stored status among any of the respective statuses, including the admission status and the discharge-related status, and a rate of change of a state level, a decision indicating whether the target patient needs to be examined by a doctor, which are abstract limitations of analysis for the management of the patient’s health status.
The claim limitations as a whole recite steps for the management of the care of a patient by generating information for the progress of the status of the patient, which recites social activity steps for the management of the health of the patient and therefore recite managing interactions between people and is a certain method of organizing human activity.
The abstract idea recited in claims 7 and 8 is the same as claim 1.
Any limitations not identified above as part of the abstract idea are deemed “additional elements” (i.e., processor) and will be discussed in further detail below.
Accordingly, the claim as a whole recites at least one abstract idea.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claim 3 recites further abstract limitations of case “determination” for the patient, further describing the abstract idea. Claim 4 recites further abstract limitations of “determining” the target region for each patient based on medical record information of the patient, further describing the abstract idea. Claim 5 recites further abstract limitation of “outputting” estimated state information of the patient and further “generating” information related to the physical condition of the target patient based on the estimated state information, further describing the abstract idea. Claim 6 recites further limitation describing the abstract idea of “outputting” a simulation image of the patient, which uses the predetermined state information, and where, under broadest reasonable interpretation, the simulated image can include information for the physical condition that can be generated mentally and uses a physical aid such as pen and paper. Claim 9 recites further abstract limitations of using the information related to the physical condition of the target patient is information for the target patient to make a decision about the physical condition, further describing the abstract idea. Claims 10 and 11 recite further abstract limitations describing the generated simulated image as being a good, bad, or normal based on the state of the patient, and where the simulation image is of the target patient, further describing the abstract idea.
Patent Subject Matter Eligibility Test: Step 2A- Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exceptions into a “practical application,” see MPEP 2106.04(II.)(A.)(2.) and 2106.04(d)(I.).
In the present case, the additional limitations beyond the above-noted at least one abstract idea of claim 1 are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
An information processing system comprising:
at least one memory storing instructions; and
at least one processor configured to execute the instructions to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
acquire, for each patient, medical record information (merely data gathering steps as noted below, see MPEP 2106.05(g) and Versata Dev. Group, Inc. v. SAP Am., Inc.);
determine, based on the medical record information, a target region of the patient;
acquire, for each patient, a plurality of patient-specific reference images of the target region at respective progress statuses including at least an admission status and a discharge-related status, together with state information labeling each reference image (merely data gathering steps as noted below, see MPEP 2106.05(g) and buySAFE, Inc. v. Google, Inc.);
generate, for each patient:
a first physical condition estimation model implemented as a convolutional neural network trained using the plurality of patient-specific reference images and the state information to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) output estimated state information from an input photographed image of the target region, and
a second physical condition estimation model implemented as a generative decoder network or a generative adversarial network trained using the patient-specific reference images and the state information to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) output, from predetermined state information, a simulation image of the target region;
during any of monitoring after hospital discharge and examination, receive, from a patient terminal coupled to a camera, a photographed image of the target region of a target patient (merely data gathering steps as noted below, see MPEP 2106.05(g) and Versata Dev. Group, Inc. v. SAP Am., Inc.);
input the photographed image to the first physical condition estimation model to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) obtain the estimated state information of the target patient;
input the predetermined state information to the second physical condition estimation model to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) obtain the simulation image corresponding to a predetermined state;
compute, based on a similarity between the estimated state information and a stored status among any of the respective statuses, including the admission status and the discharge-related status, and a rate of change of a state level, a decision indicating whether the target patient needs to be examined by a doctor; and
control output to transmit to the patient terminal and to a hospital terminal at least (a) the decision, (b) the simulation image, and (c) a message prompting re-examination when the decision indicates that an examination is needed, (merely post solution activity as noted below, see MPEP 2106.05(g) and Symantec) while storing, specific to each patient, the first physical condition estimation model and the second physical condition estimation model in an estimation model database in association with a patient ID (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)).
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of:
the overall information processing system comprising at least one memory storing instructions, at least one processor configured to execute instructions,
generate, for each patient: a first physical condition estimation model implemented as a convolutional neural network trained using the plurality of patient-specific reference images and the state information and a second physical condition estimation model implemented as a generative decoder network or a generative adversarial network trained using the patient-specific reference images and the state information,
input the photographed image to the first physical condition estimation model,
input the predetermined state information to the second physical condition estimation model, and
storing, specific to each patient, the first physical condition estimation model and the second physical condition estimation model in an estimation model database in association with a patient ID,
the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0082] of the Applicant’s Specification recites the use of the information processing system that is implemented by a generic personal computer. [0076, 0081] recites the use of generic processor and memory to carry out the steps. Regarding claim 8, this additionally recites the use of a non-transitory computer readable medium, which recites generic computer implementation as further shown in [0080]. [0042] recites the generation of the generic, computer-implemented physical condition estimation model as a machine learning model where inputs of data are used in a non-specific implementation as described in [0059]. [0037, 0042, 0051] recites the generation of the first physical condition estimation model, however this is merely a generic convolutional neural network that is generally trained using the reference images and the state information. [0022, 0066, 0051] recites the generation of the physical condition estimation model however this is recited as a generic adversarial network that is generally trained. [0059] recites the use of the photographed image as input to the first model, however this recites the use of a generic model to be used as a tool for the abstract idea. [0069] recites the input of the predetermined state info to the second model, however this recites the use of a generic model to be used as a tool for the abstract idea. [0043, 0085] recites the generic computer function of storing the models in a database in association with a patient ID, however the indication of the specific storing and use of patient ID does not recite a technical improvement to the functioning of the models nor the database system. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Claims 7 and 8 recite similar additional elements that are analyzed in a similar manner to claim 1.
Regarding the additional limitations of:
acquire, for each patient, medical record information,
acquire, for each patient, a plurality of patient-specific reference images of the target region at respective progress statuses including at least an admission status and a discharge-related status, together with state information labeling each reference image, and
during any of monitoring after hospital discharge and examination, receive, from a patient terminal coupled to a camera, a photographed image of the target region of a target patient,
these are merely pre-solution activities. The Examiner submits that these additional limitations merely add insignificant extra-solution activities of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [0031, 0032] of the Applicant’s Specification recites the acquiring of the reference images by using a camera. [0032] recites the process to acquire the medical record information. [0032] recites the acquiring of the images during the different statuses. [0046] recites the gathering of the state information with the image. [0029] further recites the transmission of the reference image for use in the system. [0033] recites the gathering of the image of the target region after discharge. The use of the camera to gather images at different time points and the gathering of relevant medical record and state information recites actions for data gathering for the abstract idea, and thus recites insignificant pre-solution activities.
Claims 7 and 8 recite similar additional elements that are analyzed in a similar manner to claim 1.
Regarding the additional limitation of control output to transmit to the patient terminal and to a hospital terminal at least (a) the decision, (b) the simulation image, and (c) a message prompting re-examination when the decision indicates that an examination is needed, these are merely post-solution activities. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of insignificant application to the at least one abstract idea in a manner that does not meaningfully limit the abstract idea (see MPEP § 2106.05(g)). [0036] of Applicant’s specification recites the use of the output control unit to merely output aspects of the abstract idea and how the output is for transmission to a terminal, and therefore recites impractical application.
Claims 7 and 8 recite similar additional elements that are analyzed in a similar manner to claim 1.
Taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to generate information related to the physical condition of a target patient, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Claim 2 recites further detail of the additional elements of the acquired state information containing information regarding a status suggesting a progress of a disease, however this detail merely further describes the gathered data and thus is still insignificant pre-solution activity. Claim 3 recites further detail of the additional elements of the acquired reference image including an image area of a target region, however this detail merely further describes the gathered data and thus is still insignificant pre-solution activity. Claim 5 recites further additional elements of a “first physical condition estimate model” that receives photograph image, which recites the use of a generic machine learning-implemented model and generically inputting received data such as the photographed image of the patient into the model, and amounts to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Claim 6 recites further additional elements of a “second physical condition estimate model” that receives the predetermined state information as input, which recites the use of a generic machine learning-implemented model and generically inputting received data into the model, and amounts to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Claim 9 recites additional elements of a machine learning model that is used to generated the first and physical condition estimation model, however this amounts to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components. Claims 10 and 11 recite further additional elements related to further describing the gathered predetermined information as information related to a physical condition of the target patient, however this still recites insignificant pre-solution activity.
Thus, taken alone and in ordered combination, the additional elements do not integrate the at least one abstract idea into a practical application.
Patent Subject Matter Eligibility Test: Step 2B:
Regarding Step 2B of the Subject Matter Eligibility Test, the independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application, see MPEP 2106.05(II.). Further, it may need to be established, when determining whether a claim recites significantly more than a judicial exception, that the additional elements recite well understood, routine, and conventional activities, see MPEP 2106.05(d).
Regarding claim 1:
Regarding the additional limitations of:
the overall information processing system comprising at least one memory storing instructions, at least one processor configured to execute instructions,
generate, for each patient: a first physical condition estimation model implemented as a convolutional neural network trained using the plurality of patient-specific reference images and the state information and a second physical condition estimation model implemented as a generative decoder network or a generative adversarial network trained using the patient-specific reference images and the state information,
input the photographed image to the first physical condition estimation model,
input the predetermined state information to the second physical condition estimation model, and
storing, specific to each patient, the first physical condition estimation model and the second physical condition estimation model in an estimation model database in association with a patient ID,
the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f) and MPEP § 2106.05(d)(II), specifically “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)”). [0082] of the Applicant’s Specification recites the use of the information processing system that is implemented by a generic personal computer. [0076, 0081] recites the use of generic processor and memory to carry out the steps. Regarding claim 8, this additionally recites the use of a non-transitory computer readable medium, which recites generic computer implementation as further shown in [0080]. [0042] recites the generation of the generic, computer-implemented physical condition estimation model as a machine learning model where inputs of data are used in a non-specific implementation as described in [0059]. [0037, 0042, 0051] recites the generation of the first physical condition estimation model, however this is merely a generic convolutional neural network that is generally trained using the reference images and the state information. [0022, 0066, 0051] recites the generation of the physical condition estimation model however this is recited as a generic adversarial network that is generally trained. [0059] recites the use of the photographed image as input to the first model, however this recites the use of a generic model to be used as a tool for the abstract idea. [0069] recites the input of the predetermined state info to the second model, however this recites the use of a generic model to be used as a tool for the abstract idea. [0043, 0085] recites the generic computer function of storing the models in a database in association with a patient ID, however the indication of the specific storing and use of patient ID does not recite a technical improvement to the functioning of the models nor the database system. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception. Additionally, the storing of data such as the models in the database recites well understood, routine, and conventional activity.
Claims 7 and 8 recite similar additional elements that are analyzed in a similar manner to claim 1.
Regarding the additional limitations of:
acquire, for each patient, medical record information,
acquire, for each patient, a plurality of patient-specific reference images of the target region at respective progress statuses including at least an admission status and a discharge-related status, together with state information labeling each reference image, and
during any of monitoring after hospital discharge and examination, receive, from a patient terminal coupled to a camera, a photographed image of the target region of a target patient,
these are merely pre-solution activities. The Examiner submits that these additional limitations merely add insignificant extra-solution activities of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)” and “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)”). [0031, 0032] of the Applicant’s Specification recites the acquiring of the reference images by using a camera. [0032] recites the process to acquire the medical record information. [0032] recites the acquiring of the images during the different statuses. [0046] recites the gathering of the state information with the image. [0029] further recites the transmission of the reference image for use in the system. [0033] recites the gathering of the image of the target region after discharge. The use of the camera to gather images at different time points and the gathering of relevant medical record and state information recites actions for data gathering for the abstract idea, and thus recites insignificant pre-solution activities and does not recite significantly more than the judicial exception. The recitation of transmitting the reference images from the camera and other patient information to the system using a network as described in [0032] recites well understood, routine, and conventional activity. The acquiring or retrieval of the medical record information from storage as described in [0032] from the terminal recites well understood, routine, and conventional activities.
Claims 7 and 8 recite similar additional elements that are analyzed in a similar manner to claim 1.
Regarding the additional limitation of control output to transmit to the patient terminal and to a hospital terminal at least (a) the decision, (b) the simulation image, and (c) a message prompting re-examination when the decision indicates that an examination is needed, these are merely post-solution activities. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of insignificant application to the at least one abstract idea in a manner that does not meaningfully limit the abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)”). [0036] of Applicant’s specification recites the use of the output control unit to merely output aspects of the abstract idea and how the output is for transmission to a terminal, and therefore recites impractical application and does not recite significantly more than the judicial exception. The transmission of the information to another device for the extra solution activity recites the use of a computing device to forward information, and is thus a well understood, routine, and conventional activity.
Claims 7 and 8 recite similar additional elements that are analyzed in a similar manner to claim 1.
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exceptions for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
For the reasons stated, the claims fail the Subject Matter Eligibility Test and therefore claims 1-11 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
The following previously cited references have been considered, however have not been used in the above rejections and do not teach the current invention:
US20210398676 A1 to Evans et al. teaches of using de-identified biological data, where once a model is generated, output medical condition state determination based on image input and encoded patient health data.
WO-2022035886-A1 to Maclellan et al. teaches of a system for gathering images of a user’s skin using a mobile application and then classifying skin color to determine the presence of a disease to further develop a treatment plan.
NPL “A method of skin disease detection using image processing and machine learning” to AlEnezi teaches of a system for image processing a picture of a patient’s skin to then put the data in a classifier to identify the skin condition and progression of the disease.
These references do not teach aspects of the current invention including but not limited to: “(i) a first physical condition estimation model implemented as a convolutional neural network trained using the plurality of patient-specific reference images and the state information to output estimated state information from an input photographed image of the target region, and(ii') a second physical condition estimation model implemented as a generative decoder network or a generative adversarial network trained using the patient-specific reference images and the state information to output, from predetermined state information, a simulation image of the target region”
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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/CONSTANTINE SIOZOPOULOS/
Examiner
Art Unit 3686