DETAILED ACTION
Applicant’s arguments, filed on 08/27/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 08/27/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1, 3, and 5-20 are the current claims hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Amendment Suggestions
The Examiner suggests the following claim amendments:
In claim 5, lines 2-3, “the external force indicating data” should read “the external force data” to use terminology consistent with independent claim 1
In claim 6, line 9, “the acquired external force data” should read “the obtained external force data” to use terminology consistent with independent claim 1
In claim 7, line 12, “the acquired external force data” should read “the obtained external force data” to use terminology consistent with independent claim 1
In claim 10, lines 3-4, “the acquired external force data” should read “the obtained external force data” to use terminology consistent with independent claim 1
In claim 14, lines 23-24, “the acquired external force data” should read “the obtained external force data” to use terminology consistent with independent claim 1
In claim 15, line 2, “the acquired external force data” should read “the obtained external force data” to use terminology consistent with independent claim 1.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 5, 8-9, 12-13, and 16-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 3, the claim recites the limitation “the medical image data” in line 2. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what this limitation is intending to reference, as there is no medical image data introduced. If it is meant to refer to the medical image of the subject from claim 1, it should read “the medical image”. If it is meant to refer to specific data from the medical image, the data needs to be introduced into the claims. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as the medical image or any possible data that could be extracted from the medical image.
Further regarding claim 3, the claim recites the limitation “the superimposed data” in line 3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what this limitation is intending to reference, as there is no superimposed data introduced. If it is meant to refer the overlay image where the reference information and medical image are superimposed, it should refer back to that limitation. If it is meant to refer to specific data from the overlay image, the data needs to be introduced in the claims. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as the overlay image or any possible data that could be extracted from the overlay image.
Regarding claim 5, the claim recites the limitation “the medical image data” in line 3. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what this limitation is intending to reference, as there is no medical image data introduced. If it is meant to refer to the medical image of the subject from claim 1, it should read “the medical image”. If it is meant to refer to specific data from the medical image, the data needs to be introduced into the claims. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as the medical image or any possible data that could be extracted from the medical image.
Regarding claim 8, the claim recites the limitation “the generated superimposed data” in line 4. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what this limitation is intending to reference, as there is no superimposed data introduced. If it is meant to refer the overlay image where the reference information and medical image are superimposed, it should refer back to that limitation. If it is meant to refer to specific data from the overlay image, the data needs to be introduced in the claims. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as the overlay image or any possible data that could be extracted from the overlay image. Claim 9 is also rejected due to its dependency on claim 8 and for is recitations of “the superimposed data” in line 2 and “the generated superimposed sate” in line 4.
Regarding claim 12, the claim recites the limitation “the generated superimposed data” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what this limitation is intending to reference, as there is no superimposed data introduced. If it is meant to refer the overlay image where the reference information and medical image are superimposed, it should refer back to that limitation. If it is meant to refer to specific data from the overlay image, the data needs to be introduced in the claims. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as the overlay image or any possible data that could be extracted from the overlay image. Claim 13 is also rejected due to its dependency on claim 12 and also for its recitations “the superimposed data” in line 2 and “the generated superimposed data” of lines 3-4.
Regarding claim 16, the claim recites the limitation “the generated superimposed data” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what this limitation is intending to reference, as there is no superimposed data introduced. If it is meant to refer the overlay image where the reference information and medical image are superimposed, it should refer back to that limitation. If it is meant to refer to specific data from the overlay image, the data needs to be introduced in the claims. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as the overlay image or any possible data that could be extracted from the overlay image. Claim 17 is also rejected due to its dependency on claim 16 and also for its recitations “the generated superimposed data” in line 2.
Regarding claim 18, the claim recites the limitation “the generated superimposed data” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear what this limitation is intending to reference, as there is no superimposed data introduced. If it is meant to refer the overlay image where the reference information and medical image are superimposed, it should refer back to that limitation. If it is meant to refer to specific data from the overlay image, the data needs to be introduced in the claims. The broad and indefinite scope of the limitation fails to inform a person of ordinary skill in the art with reasonable certainty of the metes and bounds of the claimed invention, therefore the claim is rendered indefinite. For purposes of examination, it is being interpreted as the overlay image or any possible data that could be extracted from the overlay image. Claim 19 is also rejected due to its dependency on claim 18 and for its recitation “the generated superimposed data” in line 2.
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, 3, 6-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ay (US 12274505) in further view of Kitagawa (JP 2002127857). Citations to JP 2002127857 will refer to the English Machine Translation that accompanies this Office Action.
Regarding independent claim 1, Ay teaches a diagnosis support apparatus (Column 1, lines 40-43: “this disclosure relates to user-specific devices for treating body conditions and methods of creating, using, monitoring and adapting user-specific devices for treating body conditions.”), comprising:
processing circuitry (Column 1, lines 47-49: “The method can include measuring, via a processor, physical parameters of the subject from the digital representation of the subject”) configured to acquire external force data regarding external force applied to a subject (Column 1, lines 47-49: “The method can include measuring, via a processor, physical parameters of the subject from the digital representation of the subject”; Column 35, lines 11-13: “the monitored data can include monitoring any parameter measurable, detectable, or observable by the sensors”; Column 17, lines 3-5: “The sensors 152 can include one or more force sensors 152. The one or more force sensors 152 can monitor the internal and external forces applied to the structure”).
However, Ay does not explicitly state that the data is related to high-energy trauma data regarding traffic accidents or fall accidents of a subject.
Kitagawa discloses an emergency alarm system for an automobile. Specifically, Kitagawa teaches the steps of acquire high-energy trauma data regarding traffic accidents or fall accidents of a subject ([0001]: “The present invention relates to an emergency notification system for automobiles that detects the occurrence of a vehicle collision accident and the accident situation, detects the load on the occupants' bodies, and notifies the detected information to rescue organizations outside the vehicle “; [0036]: “The accident detection means 101 detects the occurrence of an accident and the circumstances of the accident by using the on-board sensors 200 when the vehicle 1 collides with another vehicle or an obstacle such as a side wall or a utility pole. Information about the accident circumstances, such as the time and place of the accident and the other vehicle involved, is recorded in memory 300”; [0037]: “The behavior detection means 102 detects vehicle behavior, occupant behavior, and contact between the occupant's body and the interior of the vehicle. The behavior detection means 102 is divided into a vehicle behavior detection unit 102a that detects vehicle behavior, an occupant behavior detection unit 102b that detects occupant behavior, and a contact detection unit 102c that detects contact between the occupant's body and the interior of the vehicle, and each detection is performed using sensors 200 on board the vehicle”), and obtain external force data regarding an external force applied to the subject by analyzing the acquired high-energy trauma data ([0038]: “The load estimation means 103 estimates the load on the occupant's body by using a device 400 that performs calculations by referring to a database based on information obtained from the accident detection means 101 and the behavior detection means 102”). Ay and Kitagawa are analogous arts as they are both related to systems that analyze the body condition of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include trauma related to a traffic accident from Kitagawa as a body condition analyzed in Ay as it allows the device to analyze a user after an accident, which is important for determining the user’s condition and visualizing their possible injuries or health conditions.
The Ay/Kitagawa combination teaches the steps of acquire a medical image of the subject (Ay, Column 8, line 66 – Column 9, line 16: “The data can include data relating to the user's body and body condition, for example, (1) the anatomic indicators of the body condition, (2) the shape, size, topography, and dimensions of the body and body condition, (3) the shape, size and dimensions of one or multiple target body structures, (4) the shape, size and dimensions of one or multiple non-target body structures … The data (e.g., any combination of data (1)-(7)) can include images, image data, medical image data”);
generate an overlay image, as diagnosis support data for supporting diagnosis practice to the subject, in which both reference information based on the external force data and the medical image data are superimposed, and display the generated overlay image on a display (Ay, Column 35, lines 54-67: “The monitored data can be represented on a map of the body, for example, a map of the body can be electronically displayed on a display and the monitored data or a subset thereof can be displayed to the user or to someone who is not the user (e.g., the user's parents, a medical professional), or both. The map can be monitor map, for example, a body structure displacement map, a movement map, an anatomical change indicator map, a pressure map, a force map, a parameter map (e.g., showing any of the monitored parameters), or any combination thereof. The monitored data can be displayed in real-time and/or determined and stored when accessed by the data viewer (e.g., the user or someone who is not the user).”; Column 1, lines 49-59: “The method can include detecting, via an analysis of the physical parameters by the processor, a body condition of the subject. The method can include determining, via the processor, fit parameters of the body condition. The method can include determining, via the processor, a treatment parameter of the body condition, where the treatment parameter is a desired change in at least one of the fit parameters from a parameter first value to a parameter second value.” Figs. 3A-3D, 4B, 4D, 7A-7D, 8A-8F, 9A-9B, 10A-10C, and 11A-11C all show examples of the displays with the reference information based on force data and medical images superimposed.).
Regarding claim 3, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to input the medical image data of the subject to a trained model for generating the superimposed data of the subject (Ay, Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Ay, Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”).
Regarding claim 6, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, examination data relating to at least one of an examination necessity and an examination order based on the acquired external force data (Ay, Column 23, lines 39-56: “operation 202 can further involve assessing the diagnosed condition and making a treatment determination. The treatment determination can be a recommended treatment. The treatment determination can be multiple recommendations, ranked in order of, for example, expected efficacy of the treatment, projected treatment duration, estimated subject comfort during treatment, anticipated subject compliance during treatment, or any combination thereof. Such factors can also be provided where only one recommended treatment is provided by the method 200. The treatment determination can involve designing one or multiple user-specific medical or non-medical devices to treat the diagnosed condition (e.g., medical or non-medical condition). The treatment determination can be based on, for example, (1) the diagnosed condition and (2) the fit parameters of the condition, (3) the fit parameters of the subject, (4) the treatment parameters (e.g., desired physical changes of the subject), or any combination thereof.”).
Regarding claim 7, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 6, wherein the processing circuitry is further configured to input the acquired external force data of the subject to a trained model for generating the examination order of the subject among the examination data (Ay, Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Ay, Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”).
Regarding claim 8, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, examination data relating to at least one of an examination necessity and an examination order based on the generated superimposed data (Ay, Column 23, lines 39-56: “operation 202 can further involve assessing the diagnosed condition and making a treatment determination. The treatment determination can be a recommended treatment. The treatment determination can be multiple recommendations, ranked in order of, for example, expected efficacy of the treatment, projected treatment duration, estimated subject comfort during treatment, anticipated subject compliance during treatment, or any combination thereof. Such factors can also be provided where only one recommended treatment is provided by the method 200. The treatment determination can involve designing one or multiple user-specific medical or non-medical devices to treat the diagnosed condition (e.g., medical or non-medical condition). The treatment determination can be based on, for example, (1) the diagnosed condition and (2) the fit parameters of the condition, (3) the fit parameters of the subject, (4) the treatment parameters (e.g., desired physical changes of the subject), or any combination thereof.”).
Regarding claim 9, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 8, wherein the processing circuitry is further configured to input the superimposed data of the subject to a trained model for generating the examination order of the subject among the examination data based on the generated superimposed data (Ay, Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Ay, Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”).
Regarding claim 10, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, injured region data identifying an injured region of the subject based on the acquired external force data (Ay, Column 18, lines 7-18: “Operation 202 can involve creating one or multiple diagnostic tests to identify one or multiple body conditions (e.g., medical conditions, non-medical conditions), involving and/or including, for example, a user's finger, wrist, elbow, shoulder, arm, neck, spine (e.g., scoliosis), knee, hip, ankle, foot, toe, joint, conditions related to arthritis, fractures, degeneration, congenital, herniated disks, tumors, injury, infection, obesity, strain, sprain, osteoarthritis, spondylitis, compression fractures, deformed limbs, amputated limbs, avascular necrosis, pronation, supination, knee valgus, knee varus, tendonitis, torn ligaments, any body part or condition, or any combination thereof.”).
Regarding claim 11, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 10, wherein the processing circuitry is further configured to input the external force data of the subject to a trained model for generating the injured region data of the subject based on the acquired external force data (Ay, Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Ay, Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”).
Regarding claim 12, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, injured region data identifying an injured region of the subject based on the generated superimposed data (Ay, Column 18, lines 7-18: “Operation 202 can involve creating one or multiple diagnostic tests to identify one or multiple body conditions (e.g., medical conditions, non-medical conditions), involving and/or including, for example, a user's finger, wrist, elbow, shoulder, arm, neck, spine (e.g., scoliosis), knee, hip, ankle, foot, toe, joint, conditions related to arthritis, fractures, degeneration, congenital, herniated disks, tumors, injury, infection, obesity, strain, sprain, osteoarthritis, spondylitis, compression fractures, deformed limbs, amputated limbs, avascular necrosis, pronation, supination, knee valgus, knee varus, tendonitis, torn ligaments, any body part or condition, or any combination thereof.”).
Regarding claim 13, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 12, wherein the processing circuitry is further configured to input the superimposed data of the subject to a trained model for generating the injured region data of the subject based on the generated superimposed data (Ay, Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Ay, Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”).
Regarding claim 14, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, medical treatment data representing a medical treatment plan of the subject based on the acquired external force data (Ay, Column 18, lines 39-44: “Operation 202 can identify the subject's body condition, determine the extent (e.g., severity) of the body condition, determine the type treatment to treat the body condition (e.g., treatment with a user-specific device custom made for the user), and determine the fit requirements to effect the desired treatment”).
Regarding claim 15, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 14, wherein the processing circuitry is further configured to input the acquired external force data of the subject to a trained model for generating the medical treatment data of the subject (Ay, Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Ay, Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”).
Regarding claim 16, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 1, wherein the processing circuitry is further configured to generate, as the diagnosis support data, medical treatment data representing a medical treatment plan of the subject based on the generated superimposed data (Ay, Column 18, lines 39-44: “Operation 202 can identify the subject's body condition, determine the extent (e.g., severity) of the body condition, determine the type treatment to treat the body condition (e.g., treatment with a user-specific device custom made for the user), and determine the fit requirements to effect the desired treatment”).
Regarding claim 17, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 16, wherein the processing circuitry is further configured to input the generated superimposed data of the subject to a trained model for generating the medical treatment data of the subject (Ay, Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Ay, Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”).
Regarding independent claim 20, Ay teaches a method for supporting diagnosis (Abstract: Systems, devices and methods are disclosed for addressing body conditions, monitoring body conditions and adapting treatment of internal and/or external body conditions.”), comprising:
acquiring external force data regarding external force applied to a subject (Column 1, lines 47-49: “The method can include measuring, via a processor, physical parameters of the subject from the digital representation of the subject”; Column 35, lines 11-13: “the monitored data can include monitoring any parameter measurable, detectable, or observable by the sensors”; Column 17, lines 3-5: “The sensors 152 can include one or more force sensors 152. The one or more force sensors 152 can monitor the internal and external forces applied to the structure”).
However, Ay does not explicitly state that the data is related to high-energy trauma data regarding traffic accidents or fall accidents of a subject.
Kitagawa teaches the steps of acquiring high-energy trauma data regarding traffic accidents or fall accidents of a subject ([0001]: “The present invention relates to an emergency notification system for automobiles that detects the occurrence of a vehicle collision accident and the accident situation, detects the load on the occupants' bodies, and notifies the detected information to rescue organizations outside the vehicle “; [0036]: “The accident detection means 101 detects the occurrence of an accident and the circumstances of the accident by using the on-board sensors 200 when the vehicle 1 collides with another vehicle or an obstacle such as a side wall or a utility pole. Information about the accident circumstances, such as the time and place of the accident and the other vehicle involved, is recorded in memory 300”; [0037]: “The behavior detection means 102 detects vehicle behavior, occupant behavior, and contact between the occupant's body and the interior of the vehicle. The behavior detection means 102 is divided into a vehicle behavior detection unit 102a that detects vehicle behavior, an occupant behavior detection unit 102b that detects occupant behavior, and a contact detection unit 102c that detects contact between the occupant's body and the interior of the vehicle, and each detection is performed using sensors 200 on board the vehicle”), and obtaining external force data regarding an external force applied to the subject by analyzing the acquired high-energy trauma data ([0038]: “The load estimation means 103 estimates the load on the occupant's body by using a device 400 that performs calculations by referring to a database based on information obtained from the accident detection means 101 and the behavior detection means 102”).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include trauma related to a traffic accident from Kitagawa as a body condition analyzed in Ay as it allows the method to analyze a user after an accident, which is important for determining the user’s condition and visualizing their possible injuries or health conditions.
The Ay/Kitagawa combination teaches the steps of acquiring a medical image of the subject (Ay, Column 8, line 66 – Column 9, line 16: “The data can include data relating to the user's body and body condition, for example, (1) the anatomic indicators of the body condition, (2) the shape, size, topography, and dimensions of the body and body condition, (3) the shape, size and dimensions of one or multiple target body structures, (4) the shape, size and dimensions of one or multiple non-target body structures … The data (e.g., any combination of data (1)-(7)) can include images, image data, medical image data”);
generating an overlay image, as diagnosis support data for supporting diagnosis practice to the subject, in which both reference information based on the external force data and the medical image data are superimposed, and displaying the generated overlay image on a display (Ay, Column 35, lines 54-67: “The monitored data can be represented on a map of the body, for example, a map of the body can be electronically displayed on a display and the monitored data or a subset thereof can be displayed to the user or to someone who is not the user (e.g., the user's parents, a medical professional), or both. The map can be monitor map, for example, a body structure displacement map, a movement map, an anatomical change indicator map, a pressure map, a force map, a parameter map (e.g., showing any of the monitored parameters), or any combination thereof. The monitored data can be displayed in real-time and/or determined and stored when accessed by the data viewer (e.g., the user or someone who is not the user).”; Column 1, lines 49-59: “The method can include detecting, via an analysis of the physical parameters by the processor, a body condition of the subject. The method can include determining, via the processor, fit parameters of the body condition. The method can include determining, via the processor, a treatment parameter of the body condition, where the treatment parameter is a desired change in at least one of the fit parameters from a parameter first value to a parameter second value.” Figs. 3A-3D, 4B, 4D, 7A-7D, 8A-8F, 9A-9B, 10A-10C, and 11A-11C all show examples of the displays with the reference information based on force data and medical images superimposed.).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over the Ay/Kitagawa combination as applied to claim 1 above, and further in view of Bang (US 20240347548).
Regarding claim 5, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 5.
However, the Ay/Kitagawa combination does not disclose wherein the processing circuitry is configured not to display the external force indicating data in a non-display area set on an image of the medical image data, or in an area around a displayed mouse pointer.
Bang discloses a display device. Specifically, Bang teaches wherein the processing circuitry is further configured not to display the external force indicating data in a non-display area set on an image of the medical image data, or in an area around a displayed mouse pointer ([0068]: “The non-display area (NA) is an area where images are not displayed”). Ay and Bang are analogous arts as they both use displays to show the user important data and images.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the non-display area from Bang into the system from the Ay/Kitagawa combination as it allows the device to have more functions and display options, which allows the device to be more functional and have more features.
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over the Ay/Kitagawa combination as applied to claim 1 above, and further in view of Barhak (US 20210183523).
Regarding claim 18, the Ay/Kitagawa combination teaches the diagnosis support apparatus according to claim 2.
However, the Ay/Kitagawa combination does not disclose wherein the processing circuitry is configured to generate, as the diagnosis support data, cause-of death data representing a cause of death of the subject based on the generated superimposed data.
Barhak discloses a system and method for analyzing clinical data. Specifically, Barhak teaches wherein the processing circuitry is further configured to generate, as the diagnosis support data, cause-of death data representing a cause of death of the subject based on the generated superimposed data ([0106]: “the model results 810 can indicate a cause of death of virtual individuals in relation to one or more disease states related to the models 802 and/or with respect to other biological conditions that are not related to the models 802”). Ay and Barhak are analogous arts as they are both related to systems and methods for analyzing and displaying data from a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the cause of death analysis from Barhak into the system from the Ay/Kitagawa combination as it allows the system to analyze and determine more important factors of the data and provides the user with more important information.
Regarding claim 19, the Ay/Kitagawa/Barhak combination teaches the diagnosis support apparatus according to claim 18, wherein the processing circuitry is further configured to input the generated superimposed data of the subject to a trained model for generating the cause-of-death data of the subject (Ay, Column 18, lines 18-26: “The diagnosis can be performed by a computer (e.g., via the system 100) using the one or multiple diagnostic tests, by a person (e.g., a medical professional), by a person using a self-serve diagnostic test, by machine intelligence (e.g., a neural network), or any combination thereof. The method 200 can diagnose any body condition observable by a data acquisition device and can design a user-specific device to address the body condition. The above list is merely exemplary.”; Column 44, lines 2-17: “The method 200 can involve algorithm learning. The learning methods can include machine learning, online machine learning, online learning, or any combination thereof. For example, one or multiple operations in the method 200 (e.g., any one operation or subset of the operations) can use supervised and/or unsupervised online learning with machine learning techniques such as computer vision, statistical learning, deep learning, differential geometry, mathematical topology, natural language processing, including, regression, Markov models, support vector machines, Bayes Classifier, clustering, decision trees, neural networks, or any combination thereof. The system 100 can use such learning algorithms to iteratively improve the estimation and/or determination of the device adaptations”), the trained model being stored in a memory circuit (Ay, Column 13, lines 19-20: “The memory units 110 can store software, data, logs, or any combination thereof.”).
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant’s arguments with respect to the 102 and 103 rejections of claims 1, 3, and 5-20 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
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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/E.K.M./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791