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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 5 is objected to because of the following informalities: at line 2, “parameters based” should apparently read –parameters is based--. Appropriate correction is required.
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 1-17 and 19-27 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.
Claim 1 at line 11 recites “determining an updated predictive model based…on…the outcome function”. It is unclear if this step of “determining” implies determining which model to use or if the determining implies configuring or application of the model, for instance.
At line 14 of claim 1, it is unclear if “values” are the same as or different than “a value” recited at line 9; “the calculated value” recited at line 12; or “an estimated value” recited at lines 12-13.
Claim 17 at line 2 recites the limitation "the acquisition function". There is insufficient antecedent basis for this limitation in the claim.
Claim 19 at line 18 recites “determining an updated predictive model based…on…the outcome function”. It is unclear if this step of “determining” implies determining which model to use or if the determining implies configuring or application of the model, for instance.
Claim 19 at line 22 recites “iteratively repeating the method…”. It is unclear if “the method” referred to in line 22 is referring to the method described at lines 4-8 or the method described at lines 11-21 or both.
Claim 21 at line 2 recites the limitation "the user device". There is insufficient antecedent basis for this limitation in the claim.
Claim 22 at line 2 recites the limitation "the user device". There is insufficient antecedent basis for this limitation in the claim.
Claim 24 at line 2 recites the limitation "the user device". There is insufficient antecedent basis for this limitation in the claim.
Claim 27 at line 15 recites “determining an updated predictive model based…on…the outcome function”. It is unclear if this step of “determining” implies determining which model to use or if the determining implies configuring or application of the model, for instance.
At line 18 of claim 27, it is unclear if “values” are the same as or different than “a value” recited at line 13; “the calculated value” recited at line 16; or “an estimated value” recited at lines 16-17.
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 1-17 and 19-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 19 and 27 recite a method/system for a mental process of creating a code, receiving information regarding a measurement, analyzing/calculating an outcome based on the measurement, determining a model to apply, calculating more values mentally, repeating those steps until a set of criteria is satisfied and then outputting more parameters based on the criteria being satisfied, which can be considered as a mental process and/or implemented with pen/paper.
The step of “rendering a visual neuromodulatory code” could be interpreted as providing/creating instructions based on a set of parameters which could amount to creating instructions based on data, which can be implemented via pen/paper on how to apply the code.
The step of “receiving output of one or more sensors” could be interpreted as an individual/practitioner being handed sensor data on printed matter.
The step of “calculating a value of an outcome function based on said…physiological responses” could be interpreted as mentally calculating/determining a value of an outcome based on observing the sensor data.
The step of “determining an updated predictive model based …on a current…model and the calculated value” could amount to making a mental determination/judgement of a model to ultimately apply based on the current model being used and the calculated value.
The step of “calculating values for a set of adapted…parameters” could be interpreted as mentally calculating/determining values for another set of parameters.
The step iteratively repeating the method using the set of adapted parameters amounts to repeating the aforementioned steps until a set of criteria are determined mentally to be satisfied.
The step of “outputting…the adapted…code based on the set of adapted…parameters” is non-specific with respect to the “outputting” in this clause, and therefore could amount to providing/creating instructions based on the adapted parameters which could amount to creating new instructions/code based on the adapted parameters, which can be implemented via pen/paper on how to apply the code.
The step of “retrieving one or more adapted…codes” (claim 19) could be receiving the codes/instructions as data/instructions on printed matter, which could constitute data/instructions on how to apply the code.
This judicial exception is not integrated into a practical application because the claims, as written, can be performed in a practitioner’s mind or implemented with pen/paper and further do not positively recite any application of these steps in claims 1-17 and 19-27. While claims 1 and 19 do recite the additional element of “outputting the code to be displayed on electronic screens”, the additional element of a plurality of electronic screens is not sufficient to amount to significantly more than the judicial exception because it merely recites a step which could be implemented on a generic computer element (screen/display) and thus does not add a meaningful limitation to the abstract idea since it simply amounts to implementing the abstract on a generic computer component.
While claim 27 recites the additional elements of a processor and computer storage/memory, these additional elements are not sufficient to amount to significantly more than the judicial exception because the mental steps as outlined above are merely being implemented on generic computer elements and thus do not add a meaningful limitation to the abstract idea since they simply amount to implementing the abstract idea on a generic computer and computer component (memory containing an application which is essentially a set of instructions).
Dependent claims 2-3 merely further limit the generic steps, further describing only what the “outcome function” is indicative of.
While claim 4 appears to teaches projecting a latent representation of the code onto a parameter space of a rendering engine, this amounts to the generic step of applying the code using the additional element of a VR display/headset for alleviating stress (therapeutic reasons), for instance. However, such a device is well-understood and conventional in the art as suggested by U.S. Pub. No. 2020/0302825 in paragraphs ([0180] and [0155]-[0157]).
Dependent claims 5-6 merely further limit the generic steps of calculating values which can be performed mentally as noted above.
Dependent claim 7 merely further limit the generic steps with “characterizing a…code” by analyzing the code, which can be performed mentally, modeling performance of each of the spaces, which can be implemented via pen/paper without further specificity; and “selecting one of said…spaces based…on said modeling” which can also be performed mentally.
Dependent claim 8 recites “modeling of the performance…using a Bayesian optimization algorithm”, which simply amounts to an added generic step of “apply it” (or equivalent thereof) by simply reciting using a Bayesian optimization algorithm” which could be implemented using a generic computer application. However, such an optimization algorithm for modeling performance and predictions is well-understood and conventional in the art as suggested by U.S. Pub. No. 2020/0381098 at [0020]; and U.S. Pub. No. 2014/0057232 at [0158].
Dependent claims 9-11 merely further limit the generic steps by describing the types of data of the descriptive spaces.
Dependent claims 12 and 13 merely further limit the type of sensor data received.
Dependent claim 14 merely further limit the generic step of repeating the method to produce more (adapted) codes and forming a dynamic code based on the adapted codes, which does not further limit the generic steps referenced above in claim 1.
Dependent claims 15 and 16 merely further limits the forming of the dynamic codes, by “processing” and “combining”, neither of which defines over capabilities of a practitioner with pen/paper.
Dependent claim 17 merely limits the type of “criteria” the stopping criteria of claim 1 constitutes.
While claim 20 recites the additional elements of a network and computer storage/memory, these additional elements are not sufficient to amount to significantly more than the judicial exception because the mental steps as outlined above are merely being implemented on generic computer elements and thus do not add a meaningful limitation to the abstract idea since they simply amount to implementing the abstract idea on a generic computer and computer component (memory containing an application which is essentially a set of instructions) via a network.
While claims 21-23 appear to teach displaying codes for a determined amount of time, in addition to displaying codes with other displayed content, via a user interface, this amounts to the generic step of applying/displaying the code(s) using the additional element of a VR display/headset (user interface) for alleviating stress (therapeutic reasons), for instance. However, such a device is well-understood and conventional in the art as suggested by U.S. Pub. No. 2020/0302825 in paragraphs ([0180] and [0155]-[0157]).
Claim 24 merely recites a generic step of “obtaining user feedback indicative of responses to the user during said outputting to an electronic display” which could amount to a user simply providing verbal feedback during viewing of the display.
Claim 25 merely recites a generic step of “obtaining user feedback indicative of responses of the user comprises using components of the user device…receiving input to displayed prompts” which could amount to a user providing feedback via a generic component of a computer (keyboard) during viewing of the display.
While claim 26 recites the additional element of obtaining user feedback via data from a wearable neurological sensor, this additional element is not sufficient to amount to significantly more than the judicial exception. Such a device is well-understood and conventional in the art as suggested by U.S. Pub. No. 2020/0302825 in paragraphs [0128]-[0130], [0122] and [0125].
Claim Rejections - 35 USC § 102
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 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)(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 1-4, 12-17 and 19-27 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jackson et al. (U.S. Pub. No. 2021/0383912). Regarding claim 1, Jackson et al. (hereinafter Jackson) teaches a method to generate non-figurative visual neuromodulatory codes adapted to produce physiological responses having therapeutic or performance-enhancing effects ([0046] and [0050]: improve user anxiety/fear/mental health [0050]), the method comprising:
rendering a visual neuromodulatory code based on a set of rendering parameters (baseline/bootstrapping in order to provide imagery to the user that the system would determine to be preferred [0051]-[0053], wherein the imagery/object may define additional data such as texture, color or other visual aspects of the object [0071]);
outputting the visual neuromodulatory code [0051] to be displayed on a plurality of electronic screens to be viewed simultaneously by a plurality of subjects ([0026], [0037]; [0016]: “plurality of audio/video display devices”; [0059]: “multi-user”);
receiving output of one or more sensors 219 that measure, during said outputting the visual neuromodulatory code, one or more physiological responses of each of the plurality of subjects ([0036], [0038], [0042], [0046], [0060]-[0061]);
calculating a value of an outcome function based on said one or more physiological responses of each of the plurality of subjects (positive/negative effect [0063]);
determining an updated predictive model based at least in part on a current predictive model and the calculated value of the outcome function, the predictive model providing an estimated value of the outcome function for a given set of rendering parameters (may user pre-existing virtual models [0071]; mood learning module and visual replacement module determine where to place an object in the XR environment with the user and then weight the complexity of the artificially generated object [0065]; the models also determine how the inserted object will modify the behavior/mood of the user [0064]; “The mood reflects in part user anxiety and the estimation by the mood learning module 236 of the mood of the user” [0068]);
calculating values for a set of adapted rendering parameters (reweighing of the probabilities of possibly generated objects [0068]);
iteratively repeating the method using the set of adapted rendering parameters, to produce an adapted visual neuromodulatory code ([0068] “continuous updates of information about use activity and biometric values. This permits the mood learning module 236 to determine the effect of objects in the immersive environment on the mood of the user”), until a defined set of stopping criteria are satisfied ([0043]: “The AR engine 221 creates the XR environment, receives from the user 214 and the XR environment information about the performance of the user 214 in the environment as feedback, and in turn, modifies the XR environment to accomplish the desired end, such as training or behavioral modification for the user 214”); and
outputting, upon satisfying the defined set of stopping criteria, the adapted visual neuromodulatory code based on the set of adapted rendering parameters ([0030], [0073], [0034] and [0130]).
Regarding claim 2, the outcome function is indicative of a therapeutic effectiveness of the visual neuromodulatory code (positive/negative effect on user mood - [0063]).
Regarding claim 3, the outcome function is indicative of a degree of generalizability, among the plurality of subjects, of the therapeutic effectiveness of the visual neuromodulatory code ([0012]: “Decisions derived from machine learning or artificial intelligence may be based on data from the user, or on data from similar users, or on aggregate data from many users, for example”; [0053]: “the engagement and interactions of multiple users could be pooled with others or aggregated to define behavioral preferences”; [0054]: “the historical experience profile database 230 may store information about learning tasks involving multiple users or aggregated information about how multiple users reacted to the training data”).
Regarding claim 4, said rendering the visual neuromodulatory code based on the set of rendering parameters comprises projecting a latent representation of the visual neuromodulatory code onto a parameter space of a rendering engine ([0031], [0056]; virtual objects/elements presented to user which may elicit a reaction).
Regarding claim 12, said one or more sensors are adapted to measure at least one of the following: neurological responses, physiological responses, and behavioral responses ([0038], [0046], [0060]-[0061]).
Regarding claim 13, said one or more sensors comprise one or more of the following: electroencephalogram (EEG), quantitative EEG, magnetoencephalography (MEG), single-photon emission computed tomography (SPECT), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), EMG, electrocardiogram (ECG), pulse rate, blood pressure, and galvanic skin response (GSR) ([0038], [0046], [0060]-[0061]).
Regarding claim 14, the method further comprises: repeating the method to produce a plurality of adapted visual neuromodulatory codes; and forming a dynamic adapted visual neuromodulatory code based at least in part on said plurality of adapted visual neuromodulatory codes ([0073]-[0075], [0034] and [0058]; determined to also be “dynamic” given its rendering in a XR environment [0015], [0026]-[0027]).
Regarding claim 15, said forming a dynamic adapted visual neuromodulatory code comprises combining said plurality of adapted visual neuromodulatory codes to form a sequence of adapted visual neuromodulatory codes (progressive exposure - [0061]).
Regarding claim 16, said forming a dynamic adapted visual neuromodulatory code further comprises processing said plurality of adapted visual neuromodulatory codes to form intermediate images in the sequence of adapted visual neuromodulatory codes (intermediate images occur in progressive exposure with spiders - [0061]).
Regarding claim 17 and in view of its indefinite nature, the stopping criteria are based on at least one of: a predefined number of iterations, characteristics of the acquisition function, and a determination that convergence of the outcome function with target criteria will not occur within a defined number of iterations (based on characteristics of the outcome function (positive/negative effect on mood) [0063].
Regarding claim 19, Jackson teaches a method to provide non-figurative visual neuromodulatory codes adapted to produce physiological responses having therapeutic or performance-enhancing effects ([0046] and [0050]: improve user anxiety/fear/mental health [0050]), the method comprising:
retrieving one or more adapted visual neuromodulatory codes, said one or more adapted visual neuromodulatory codes being adapted to produce physiological responses having therapeutic or performance-enhancing effects ([0030]: “The AR system 202 retrieves information about the user from the historical experience profile 204. The AR system 202 further retrieves information about the user's preferences from the user preference profile 206. The AR system 202 may thus determine that the user has a fear of heights that may be triggered by the steep drop-off at the particular location where the user is currently hiking. Based on information retrieved from the user preference profile 206, the AR system 202 may determine that the user would prefer a view of a forest with trees and other forest wildlife”; and
outputting to an electronic display of a device viewable by a user said one or more adapted visual neuromodulatory codes([0030]: “The AR system 202, in conjunction with the AR smart glasses or other immersive technology employed by the user, to substitute in the user's field of view a forested scene as shown in image 210 in place of the desert drop-off scene 208. The user may be reassured by the view of the forested scene. Further, by eliminating or substituting for the view of the desert drop-off, the user's fear of heights may be overcome or become manageable”),
wherein said one or more adapted visual neuromodulatory codes are generated by performing
rendering a visual neuromodulatory code based on a set of rendering parameters (baseline/bootstrapping in order to provide imagery to the user that the system would determine to be preferred [0051]-[0053], wherein the imagery/object may define additional data such as texture, color or other visual aspects of the object [0071]);
outputting the visual neuromodulatory code [0051] to be displayed on a plurality of electronic screens to be viewed simultaneously by a plurality of subjects ([0026], [0037]; [0016]: “plurality of audio/video display devices”; [0059]: “multi-user”);
receiving output of one or more sensors 219 that measure, during said outputting the visual neuromodulatory code, one or more physiological responses of each of the plurality of subjects ([0036], [0038], [0042], [0046], [0060]-[0061]);
calculating a value of an outcome function based on said one or more physiological responses of each of the plurality of subjects (positive/negative effect [0063]);
determining an updated predictive model based at least in part on a current predictive model and the calculated value of the outcome function, the predictive model providing an estimated value of the outcome function for a given set of rendering parameters (may user pre-existing virtual models [0071]; mood learning module and visual replacement module determine where to place an object in the XR environment with the user and then weight the complexity of the artificially generated object [0065]; the models also determine how the inserted object will modify the behavior/mood of the user [0064]; “The mood reflects in part user anxiety and the estimation by the mood learning module 236 of the mood of the user” [0068]);
calculating values for a set of adapted rendering parameters (reweighing of the probabilities of possibly generated objects [0068]);
iteratively repeating the method using the set of adapted rendering parameters, to produce an adapted visual neuromodulatory code ([0068] “continuous updates of information about use activity and biometric values. This permits the mood learning module 236 to determine the effect of objects in the immersive environment on the mood of the user”), until a defined set of stopping criteria are satisfied ([0043]: “The AR engine 221 creates the XR environment, receives from the user 214 and the XR environment information about the performance of the user 214 in the environment as feedback, and in turn, modifies the XR environment to accomplish the desired end, such as training or behavioral modification for the user 214”); and
outputting, upon satisfying the defined set of stopping criteria, the adapted visual neuromodulatory code based on the set of adapted rendering parameters ([0030]; [0073], [0034] and [0130]).
Regarding claim 20, said retrieving said one or more adapted visual neuromodulatory codes comprises receiving said one or more adapted visual neuromodulatory codes via a network or retrieving said one or more adapted visual neuromodulatory codes from a memory of the user device ([0030], [0029], [0054], [0065], [0076]).
Regarding claim 21, said outputting to the electronic display of the user device said one or more adapted visual neuromodulatory codes, each of said one or more adapted visual neuromodulatory codes is displayed for a determined time period, the determined time period being adapted based on user feedback data indicative of responses of the user ([0056]: “this process may continue throughout operation of the method 224 as objects change or move or as the mood of the user 214 changes. The scene understanding and positioning module 232 may establish and maintain a map of the physical environment and the virtual environment that together form the XR environment, and update the map during progress of the method”; [0060]: “This continues as the AR engine 221 modifies the XR environment by changing visual, audible and other aspects of the XR environment”; [0061]: “if the user is being conditioned to overcome a fear of a particular object, such as a spider, the AR server 220 may progressively expose the user 214 to images and experiences of spiders in the XR environment experienced by the user 214. The attitude or fear level or comfort level (generally, the mood) of the user 214 may vary as the experience of the user 214 with the object is varied by the AR engine”; and [0056] and [0073]: all paragraphs of which disclose presentation of a scene/resolution/visual effect until user feedback indicates changing/adjustment of the scene/resolution/visual effect or satisfaction of mood/condition achievement [0043] and [0080]).
Regarding claim 22, said outputting to the electronic display of the user device said one or more adapted visual neuromodulatory codes comprises combining said one or more adapted visual neuromodulatory codes with displayed content (virtual objects presented/inserted in immersive environment ([0064], [0066] and [0082]).
Regarding claim 23, the displayed content comprises at least one of: displayed output of an app, displayed output of a browser, and a user interface of the user device [0026].
Regarding claim 24, the method further comprises obtaining user feedback data indicative of responses of the user during said outputting to an electronic display of the user device said one or more adapted visual neuromodulatory codes ([0031]-[0032]).
Regarding claim 25, said obtaining user feedback data indicative of responses of the user comprises using components of the user device to perform at least one of: measuring voice stress levels, detecting movement, tracking eye movement, and receiving input to displayed prompts ([0061]: detecting movement such as gestures).
Regarding claim 26, said obtaining user feedback data indicative of responses of the user comprises receiving data from a wearable neurological sensor ([0038]: “The sensors 219 may further gather information about the user 214. Such information may include biometric information, such as pulse rate or respiratory rate, skin conductivity, pupil dilation, haptic information about one or more touches of the user 214, and so forth. Thus, the sensors may include or be part of a wearable device such as a watch, belt or harness”; pulse rate/respiratory rate/skin conduction/pupil dilation are all affected by neurological responses such as fear).
Regarding claim 27, Jackson teaches a system to generate non-figurative visual neuromodulatory codes adapted to produce physiological responses having therapeutic or performance-enhancing effects ([0046] and [0050]: improve user anxiety/fear/mental health [0050]), the system comprising:
at least one processor; and at least one non-transitory processor-readable medium that stores processor-executable instructions which, when executed by the at least one processor ([0036]-[0037] and Fig. 2B), cause the at least one processor to perform:
rendering a visual neuromodulatory code based on a set of rendering parameters (baseline/bootstrapping in order to provide imagery to the user that the system would determine to be preferred [0051]-[0053], wherein the imagery/object may define additional data such as texture, color or other visual aspects of the object [0071]);
outputting the visual neuromodulatory code [0051] to be displayed on a plurality of electronic screens to be viewed simultaneously by a plurality of subjects ([0026], [0037]; [0016]: “plurality of audio/video display devices”; [0059]: “multi-user”);
receiving output of one or more sensors 219 that measure, during said outputting the visual neuromodulatory code, one or more physiological responses of each of the plurality of subjects ([0036], [0038], [0042], [0046], [0060]-[0061]);
calculating a value of an outcome function based on said one or more physiological responses of each of the plurality of subjects (positive/negative effect [0063]);
determining an updated predictive model based at least in part on a current predictive model and the calculated value of the outcome function, the predictive model providing an estimated value of the outcome function for a given set of rendering parameters (may user pre-existing virtual models [0071]; mood learning module and visual replacement module determine where to place an object in the XR environment with the user and then weight the complexity of the artificially generated object [0065]; the models also determine how the inserted object will modify the behavior/mood of the user [0064]; “The mood reflects in part user anxiety and the estimation by the mood learning module 236 of the mood of the user” [0068]);
calculating values for a set of adapted rendering parameters (reweighing of the probabilities of possibly generated objects [0068]);
iteratively repeating the method using the set of adapted rendering parameters, to produce an adapted visual neuromodulatory code ([0068] “continuous updates of information about use activity and biometric values. This permits the mood learning module 236 to determine the effect of objects in the immersive environment on the mood of the user”), until a defined set of stopping criteria are satisfied ([0043]: “The AR engine 221 creates the XR environment, receives from the user 214 and the XR environment information about the performance of the user 214 in the environment as feedback, and in turn, modifies the XR environment to accomplish the desired end, such as training or behavioral modification for the user 214”); and
outputting, upon satisfying the defined set of stopping criteria, the adapted visual neuromodulatory code based on the set of adapted rendering parameters ([0030], [0073], [0034] and [0130]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE HOPKINS MATTHEWS whose telephone number is (571)272-9058. The examiner can normally be reached Monday - Friday, 7:30 am - 4:00 pm.
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/CHRISTINE H MATTHEWS/Primary Examiner, Art Unit 3791
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