DETAILED ACTION
This action is filed in response to the application filed on 5/17/2024.
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 .
Information Disclosure Statement
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 5/17/2024. This IDS has been considered.
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 13-15 are rejected under 35 USC § 101 because they are directed to non-statutory subject matter.
Regarding Claim 13, Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 13 is not considered to be in a statutory category.
Claim 13 teaches “A computer program product.” The descriptions or expressions of programs are not physical “things.” They are neither computer components nor statutory processes, as they are not “acts” being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer, which permit the computer program’s functionality to be realized. In contrast, a claimed a non-transitory computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer which permit the computer program’s functionality to be realized, and is thus statutory. Accordingly, it is important to distinguish claims that define descriptive material per se from claims that define statutory inventions.
In order to overcome this rejection, the following language is suggested:“***. (Currently amended) A non-transitory computer readable medium comprising instructions …”
Claims 14-15 are dependent claims and therefore inherit the deficiencies of Claim 13 and are likewise rejected.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 7, 9, and 11- 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Buras (WO2018140415 A1).
Regarding Claim 1, Buras teaches a computer implemented method for optimizing a medical training procedure, in particular a virtual reality and/or augmented reality procedure (e.g. see [0004] “In some embodiments, systems of the present disclosure provide real-time guidance to a medical equipment user. In some embodiments, the systems disclosed herein provide three-dimensional (3D) augmented-reality (AR) guidance to a medical device user. In some embodiments, systems of the present disclosure provide machine learning guidance to a medical device user. Guidance systems disclosed herein may provide improved diagnostic or treatment results for novice users of medical devices. Use of systems of the present invention may assist novice users to achieve results comparable to those obtained by expert or credentialed medical caregivers for a particular medical device or technology”),
carried out by a user on a virtual patient in a virtual or real operating room (e.g. see [0029] “The ARUI 300 may comprise a visor having a viewing element (e.g., a viewscreen, viewing shield or viewing glasses) that is partially transparent to allow a medical equipment user to visualize a workspace (e.g., an examination room, table or portion thereof)”),
wherein the training procedure includes a sequence of consecutive expected actions defining an expected pathway stored in a database and the user performs a sequence of consecutive virtual actions corresponding to each of the expected actions during said training procedure (e.g. see [0066] “one embodiment, the library 500 may obtain and display via the ARUI 300 an electronic medical procedure 530, which may include displaying step-by-step written, visual, audio, and/or tactile instructions for performing the procedure”), the method comprising: assigning an initial specific competence level to the user among a plurality of competence levels by a competence module before starting the training procedure (e.g. see [0078] “In an alternative embodiment, multiple nomenclature options may be provided to users, and different users may receive instructions that vary based on the level of skill and background of the user”);
acquiring action data indicative of the virtual actions performed by the user during the training procedure (e.g. see [0029] “in one embodiment, the ARUI 300 includes a screen upon which virtual objects or information can be displayed to aid a medical equipment user in real-time (i.e., with minimal delay between the action of a novice user and the AR feedback to the action,” and [0033] “Accordingly, in some embodiments, alternative or additional 3D guidance systems for determining the position of the patient, tracking the user's actions, or tracking one or more portions of the medical equipment system 200 (e.g., an ultrasound probe) may be used instead of a magnetic GPS system”);
determining the presence of a deviated action by a deviation module as a consequence of a deviation of at least one of the virtual actions performed by the user during the training procedure from the corresponding expected action (e.g. see [0042] “Based on the comparison of the movements of the novice user and the reference performance, the MLM 600 may then determine discrepancies or variances of the performance of the novice user and the reference performance”);
determining a final performance index of the user based on at least the number of deviated actions during the training procedure by a performance module (e.g. see [0071] “Based on the comparison of the novice user's movements to those of the expert user, the MLM 600 may determine in real time whether the novice user 50 is acceptably performing the task or procedure (i.e., within a desired margin of error to that of an expert user),” and [0073] “Regardless of the type of equipment used, outcome-based feedback is generated by the MLM 600 based on data generated by the equipment that indicates whether or not the novice user successfully performed a desired task or procedure,”);
modifying or confirming the expected pathway of the training procedure based on the initial competence level of the user and the final performance index, and updating the expected pathway stored in the database if the expected pathway is modified (e.g. see [0075] “The novice user's performance may be tracked over time to determine areas in which the novice user repeatedly fails to implement previously provided feedback. In such cases, training exercises may be generated for the novice user focusing on the specific motions or portions of the medical procedure that the novice user has failed to correct, to assist the novice user to achieve improved results,” and [0077] “MLM 600 may further augment previously-provided instructions as the user repeats a medical procedure or portion thereof and improves in performance”).
Regarding Claim 2, Buras teaches comparing action data indicative of the virtual actions with information data of the corresponding expected actions, a deviated action being determined if at least one of the following event occurs:
a. one or more medical instruments used in the virtual action are different from those in the corresponding expected action; and/or
b. the sequence of virtual actions is different from the sequence of expected actions; and/or
c. the time duration of at least one virtual action is longer or shorter than a reference time duration for the corresponding expected action ; and/or
d. the number of virtual actions is different from the number of expected actions; and/or
e. one or more medical instruments or objects in the virtual or real operating room are positioned in a different place from an expected positioning place (e.g. see [0042] “In one embodiment, MLM 600 includes a first module for receiving and processing real-time user positioning data, a second module for comparing the real-time user positioning data (obtained by the 3DGS 400) to corresponding stored reference positioning data in patient library 500 of the motion and position/orientation obtained during a reference performance of the same medical procedure or task. Based on the comparison of the movements of the novice user and the reference performance, the MLM 600 may then determine discrepancies or variances of the performance of the novice user and the reference performance”); and/or
f. the virtual patient is positioned by the user in a different position from an expected patient position; and/or
g. the difference between the action data indicative of a virtual action and the information data of the corresponding expected action is outside a tolerance range (e.g. see [0071] “Based on the comparison of the novice user's movements to those of the expert user, the MLM 600 may determine in real time whether the novice user 50 is acceptably performing the task or procedure (i.e., within a desired margin of error to that of an expert user),”).
Regarding Claim 7, Buras teaches the limitations of Claim 1. Buras further discloses generating a final alert signal by an alert module if the final performance index is below a final performance threshold (e.g. see [0044] “The MLM 600 may also indicate to the novice user whether an acceptable and/or optimal outcome in the use of the device has been achieved,” and [0045] “Thus, MLM 600 may in some instances provide both "coarse" and "fine" feedback to the novice user to help achieve a procedural outcome similar to that of a reference outcome (e.g., obtained from an expert user)” Examiner notes the threshold is the reference outcome of the expert user to which the user’s data is compared).
Regarding Claim 9, Buras teaches the limitations of claim 1. Buras further discloses further comprising, determining at least an intermediate performance index of the user based at least on the number of deviated actions occurred during the training procedure up to an intermediate evaluation point of the expected pathway (e.g. see [0075] “the novice user's performance may be tracked over time to determine areas in which the novice user repeatedly fails to implement previously provided feedback”); and
assigning a different specific competence level or confirming the competence level to the user for the remaining duration of the training procedure based on a comparison between said intermediate performance index and an intermediate performance threshold (e.g. see [0077]).
Regarding Claim 11, Buras teaches the limitations of Claim 1. Buras further discloses providing the user with an alert message each time a deviated action is determined during the training procedure (e.g. see [0038] “an idealized reference path or position/orientation (e.g., as taken during the same examination performed by an expert), may be used to provide real-time 3D AR feedback to the novice user via the ARUI 300. This feedback enables the novice user to correct mistakes or incorrect usage of the medical equipment and achieve an outcome similar to that of the expert user. The real-time 3D AR feedback may include visual information (e.g., a visual display of a desired path for the novice user to take with the probe, a change in the position or orientation of the probe, etc.), tactile information (e.g., vibrations or pulses when the novice user is in the correct or incorrect position), or sound (e.g., beeping when the novice user is in the correct or incorrect position)”).
Regarding Claim 12, Buras teaches the limitations of Claim 1. Buras further discloses wherein the expected pathway of the training procedure has at least a complexity degree based on the initial specific competence level assigned to the user (e.g. see [0078] “To provide usable real time 3D AR feedback-based guidance to a medical device user, the MLM 600 may include a standardized nomenclature module (not shown) to provide consistent real-time feedback instructions to the user. In an alternative embodiment, multiple nomenclature options may be provided to users, and different users may receive instructions that vary based on the level of skill and background of the user”).
Regarding Claim 13, Buras teaches the limitations of Claim 1. Buras further discloses a computer program product comprising computer readable instructions which, when implemented on a computer or a control unit, causes the computer to carry out a method (e.g. see [0027-0028]).
Regarding Claim 14, Buras teaches the limitations of Claim 13. Buras further discloses a storage medium comprising the computer program (e.g. see [0066] “Information on a variety of procedures that may be performed by novice user 50 may be provided by Library 500, which in some embodiments may be stored on a cloud-based server as shown in Fig. 2. In other embodiments, the information may be stored in a conventional memory storage unit. In one embodiment, the library 500 may obtain and display via the ARUI 300 an electronic medical procedure 530, which may include displaying step-by-step written, visual, audio, and/or tactile instructions for performing the procedure”).
Regarding Claim 15, Buras teaches the limitations of Claim 13. Buras further discloses a workstation for carrying out a medical training procedure, comprising: a computer system implementing computer readable instructions of the computer program product (e.g. see [0066] “Information on a variety of procedures that may be performed by novice user 50 may be provided by Library 500, which in some embodiments may be stored on a cloud-based server as shown in Fig. 2. In other embodiments, the information may be stored in a conventional memory storage unit. In one embodiment, the library 500 may obtain and display via the ARUI 300 an electronic medical procedure 530, which may include displaying step-by-step written, visual, audio, and/or tactile instructions for performing the procedure”);
a head mounted device, in particular a virtual reality headset (e.g., see [0064] “A wide variety of available ARUI units 300, many comprising a Head-Mounted Display (HMD), may be used in systems of the present invention. These may include the Microsoft HoloLens, the Vuzix Wrap 920AR and Star 1200, Sony HMZ-T1, Google Glass, Oculus Rift DK1 and DK2, Samsung GearVR, and many others”); and
a haptic device (e.g. see [0072] “In some types of equipment, for example, feedback generated by MLM solely based on the novice user's manipulation of a portion of the equipment (i.e., movements of a probe, joystick, lever, rod, etc.)”).
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 should not be negated by the manner in which the invention was made.
Claims 3-6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Buras (WO2018140415 A1) in view of Gates (US20210043011 A1).
Regarding Claim 3, Buras teaches the limitations of Claim 1. Buras does not explicitly disclose a. assigning a relevance weight to each deviated action before determining the final performance index; and/or b. assigning a relevance weight to each deviated action based on the competence level of the user.
In the same field of endeavor, Gates teaches a. assigning a relevance weight to each deviated action before determining the final performance index; and/or
b. assigning a relevance weight to each deviated action based on the competence level of the user (e.g. see [0033-0034] “The inventor has determined that this tool that assesses cognitive load and cognitive performance (resulting with a neural efficiency score) can empower instructors to make more informed decisions over key problem areas faster, at a lower cost, and with greater accuracy. As described herein, by comparing an individual trainee's real-time cognitive load to their performance and their expected performance throughout a particular 3D scenario, the AI engine 150 can derive a neural efficiency score… The inventor has also determined that these neural efficiency scores can also be applied to everyday training to assess a trainee's neural performance relative to expected neural efficiency scores”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the deviated actions of Buras with the weighted scores of Gates for the purpose of assessing the skills of a user with the advantage of additional data to strengthen the accuracy of the determination.
Regarding Claim 4, Buras teaches the limitations of Claim 1. Buras does not explicitly disclose modifying or confirming the competence level of the user based on the initial competence level and the final performance index.
In the same field of endeavor, Gates teaches modifying or confirming the competence level of the user based on the initial competence level and the final performance index (e.g. see [0065] “At block 708, AI engine 150 may determine whether the computed neural efficiency score for the trainee user is greater than a threshold (e.g. an expected neural efficiency score for the particular 3D scenario). At block 714, if the computed neural efficiency score for the trainee user is determined to be greater than a threshold, then AI engine 150 may accelerate the training process for a particular trainee user via the dynamic decision matrix (e.g. FIG. 3) and/or provide a recommendation to the training administrator (e.g. via administrator interface 160) that the training process for a particular trainee user be accelerated”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the competency levels of Buras with the modifications of Gates for the purpose of assessing the skills of a user with the advantage of ensuring each user takes training that most fits their competency level in order to yield the most accurate testing result.
Regarding Claim 5, Buras and Gates teach the limitations of Claim 4. Buras does not explicitly disclose a. modifying the consecutive expected actions if the final performance index is above a final performance threshold and the competence level of the user has been changed compared to the initial competence level; and/or b. modifying the consecutive expected actions if the final performance index is above a final performance threshold, the competence level of the user has been confirmed compared to the initial competence level, and the training procedure has been repeated by the user more than one time.
In the same field of endeavor, Gates teaches a. modifying the consecutive expected actions if the final performance index is above a final performance threshold (e.g. see [0065] “i At block 714, if the computed neural efficiency score for the trainee user is determined to be greater than a threshold, then AI engine 150 may accelerate the training process for a particular trainee user via the dynamic decision matrix (e.g. FIG. 3) and/or provide a recommendation to the training administrator (e.g. via administrator interface 160) that the training process for a particular trainee user be accelerated”) and the competence level of the user has been changed compared to the initial competence level (e.g. see [0038] “the dynamic decision matrix for a given user may be subject to change based on the individual and collective analytics gathered over a period of time and/or based on the neural efficiency score computed for the user”); and/or
b. modifying the consecutive expected actions if the final performance index is above a final performance threshold, the competence level of the user has been confirmed
compared to the initial competence level, and the training procedure has been repeated by the user more than one time.
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the competency levels of Buras with the modifications of Gates for the purpose of assessing the skills of a user with the advantage of ensuring each user takes training that most fits their competency level in order to yield the most accurate testing result.
Regarding Claim 6, Buras and Gates teach the limitations of Claim 4. Buras does not explicitly disclose saving a user history of the virtual actions if the competence level of the user has been changed compared to the initial competence level, wherein the expected pathway of the training procedure is confirmed or modified based also on the user history.
In the same field of endeavor, Gates teaches saving a user history of the virtual actions if the competence level of the user has been changed compared to the initial competence level, wherein the expected pathway of the training procedure is confirmed or modified based also on the user history (e.g. see [0038] “Database 120 may be further configured to store the received measured user response to one or more dynamic 3D assets of the selected one 3D rendering and/or during the corresponding time period of the 3D scenario. The non-transitory computer-readable storage medium of the AI engine 150 may be further encoded with program code executable by the AI engine 150 for generating a respective personal performance profile for each of a plurality of users, including the user. Each personal performance profile may be generated and updated based on stored measured user responses for the respective user and for the period of time within the particular 3D scenario. The AI engine 150 may further comprise a respective dynamic decision matrix for each of the plurality of users based on the respective corresponding personal performance profile. The dynamic decision matrix may be interconnected with environmental, physiological, cognitive, and performance analytics (e.g. with a neural efficiency score). The dynamic decision matrix for a given user may be subject to change based on the individual and collective analytics gathered over a period of time and/or based on the neural efficiency score computed for the user.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the competence levels of Buras with the user history of Gates for the purpose of assessing the skills of a user with the advantage of ensuring each user takes a training that most fits their competency level in order to yield the most accurate testing result.
Regarding Claim 8, Buras teaches the limitations of claim 1. Buras does not explicitly disclose based on the initial competence level of the user: a. modifying the training procedure by adding the deviated action in the expected pathway of the training procedure as alternative to the corresponding expected action; and/or b. modifying the training procedure by replacing an expected action with a corresponding deviated action in the expected pathway of the training procedure.
In the same field of endeavor, Gates teaches based on the initial competence level of the user: a. modifying the training procedure by adding the deviated action in the expected pathway of the training procedure as alternative to the corresponding expected action; and/or b. modifying the training procedure by replacing an expected action with a corresponding deviated action in the expected pathway of the training procedure (e.g. see [0065] “At block 714, if the computed neural efficiency score for the trainee user is determined to be greater than a threshold, then AI engine 150 may accelerate the training process for a particular trainee user via the dynamic decision matrix (e.g. FIG. 3) and/or provide a recommendation to the training administrator (e.g. via administrator interface 160) that the training process for a particular trainee user be accelerated” Examiner notes claim 2 of the instant application lists the time duration being shorter as a deviated action. Therefore, the embodiment of the prior art of accelerating the training based on the users accelerated pace fulfills the limitations of this claim).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the competence levels of Buras with the training modifications of Gates for the purpose of assessing the skills of a user with the advantage of ensuring each user takes a training that most fits their competency level in order to yield the most accurate testing result.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Buras (WO2018140415 A1) in view of Gates (US20210043011 A1) and in further view of Jesneck (US 11145407 B1).
Regarding Claim 10, Buras teaches the limitations of Claim 1. Buras does not explicitly disclose wherein the plurality of competence levels comprise at least a first level, a second level, and a third level based on at least the experience of the user in carrying out a real medical procedure and on the number of repeated virtual reality medical training procedures.
In the same field of endeavor, Gates teaches wherein the plurality of competence levels comprise at least a first level, a second level, (e.g. see [0072] “dynamically adjusts one or more dynamic 3D assets 610 in the 3D rendering 600 to ensure the difficulty and complexity of the simulation results in trainees performing their tasks at a peak performance level”) based on at least the experience of the user in carrying out a real medical procedure and on the number of repeated virtual reality medical training procedures (e.g. see [0005] “some embodiments, the predetermined response values are adjustable based on an experience or training level of the user. In some embodiments, the immersive ecosystem further comprises a web application configured to execute on a computing device and further configured to enable the user to select an experience level, training level, or one of the plurality of 3D renderings”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the competency levels of Buras with those of Gates for the purpose of accurately determining the level of training to provide to a user with the advantage of assigning that level based on past performance.
Examiner notes while the prior art teaches competency levels and changing the level based on performance, the prior art does not explicitly disclose wherein the plurality of competence levels comprise at least a first level, a second level.
In the same field of endeavor, Jesneck teaches wherein the plurality of competence levels comprise at least a first level, a second level, and a third level (e.g. see [Col 11 lines 31-40] “We have therefore developed a Bayesian learning curve model (a model based on the probability of an event occurring, based on prior knowledge of conditions related to the event) that incorporates surgical case history along with Likert-scale and Zwisch-scale [not the only evaluation scale] evaluation data to infer and quantify resident operative autonomy,” and “This model has been refined over the past several years, and now consists of four levels named “Show & Tell,” “Active Help,” “Passive Help,” and “Supervision Only.” Each level describes the amount of guidance provided by faculty to residents. The Zwisch Scale, as summarized in Table 1, describes the amount of guidance provided by faculty to residents”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the competency levels of Buras as modified by Gates with the multiple levels of Jesneck for the purpose of accurately determining the level of training to provide to a user with the advantage of a plurality of levels in order to independently assess each user.
Conclusion
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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