DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 10/16/2025. has been entered. Amendments to claims 1, 9, 12, and 14, addition of new claims 16-20, and cancellation of claims 2 and 15 is acknowledged. Claims 1, 3-14, and 15-20 remain pending in the application.
Claim Objections
Claims 14 and 20 are objected to because of the following informalities: “gate” should read “gait”. Appropriate correction is required.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention, considering all claim elements both individually and in combination as a whole, do not amount to significantly more than a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
Claim 1 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 1 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “analyzing, using a processor, the motion sensor data to infer a feature of a gait of the user”, “inferring, using the same processor or a different processor, a cognitive load of the user based on the feature of the gait” and “and adjusting notifications from background-running applications provided to the user via the head-mounted display based on the inference.”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “a motion sensor disposed adjacent a head of the user”, “head-mounted display”, and “wherein the motion sensor is integral with or installed in a head-mounted display worn by the user” in claim 1. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0002]: “A conventional head mounted display (HMD)” of Smith (US 20200233189 A1) and para [0034]: “of well-known motion sensors such as a gyroscope, an accelerometer, or a motion detecting camera” of Kang et al. (US 20130328662 A1), and para [0002]: “In the conventional art, a head tracking information is generated through an acceleration sensor, an angular velocity sensor, or a gyro sensor of a HMD (Head Mounted Display) device” of Oh et al. (US 20180239419 A1). Regarding the “processor” and “machine learning model”, generic computer structures are not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 9 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Dependent claim(s) 2-8 fail to cure the deficiencies of independent claim 9 by merely reciting additional abstract ideas, further limitations on abstract ideas already recited, and/or additional elements that are not significantly more. Thus, claim(s) 1-8 is/are rejected under 35 U.S.C. 101.
Claim 9 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 9 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “process a signal generated by the motion sensor using a first machine learning model to estimate an attribute of a gait performed by a user wearing the HMD, wherein the first machine learning model is trained by a classifier based on a correlation between the signal and a prior calibration signal from a separate sensor coupled to another body part of an individual;” and “determine an estimation of a cognitive load of the user based on the attribute of the gait.”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “a motion sensor to produce a signal indicative of captured motion”, “circuitry”, “first machine learning model” and “head-mounted display” in claim 9, and “mobile phone” in claim 11. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0002]: “A conventional head mounted display (HMD)” of Smith (US 20200233189 A1) and para [0034]: “of well-known motion sensors such as a gyroscope, an accelerometer, or a motion detecting camera” of Kang et al. (US 20130328662 A1). Regarding the “circuitry”, “first machine learning model”, and “mobile phone”, generic computer structures are not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 9 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Dependent claim(s) 10-13 fail to cure the deficiencies of independent claim 9 by merely reciting additional abstract ideas, further limitations on abstract ideas already recited, and/or additional elements that are not significantly more. Thus, claim(s) 9-13 is/are rejected under 35 U.S.C. 101.
Claim 14 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 14 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “extract a feature of the user's gait from the data indicative of motion of the head using a first machine learning model trained by a classifier based on a correlation between the data and a prior calibration signal from a separate sensor coupled to another body part of an individual”, “infer a cognitive load of the user based on the extracted feature using a second machine learning model alongside other non-gate-related inputs”, and “visually emphasize, on a display of the HMD, an object in the user's path based on the inferred cognitive load ” This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “a non-transitory computer-readable medium”, “a motion sensor disposed on or within the HMD”, “a processor”, “head-mounted display” in claim 14. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0002]: “A conventional head mounted display (HMD)” of Smith (US 20200233189 A1), para [0034]: “of well-known motion sensors such as a gyroscope, an accelerometer, or a motion detecting camera” of Kang et al. (US 20130328662 A1), and para [0002]: “In the conventional art, a head tracking information is generated through an acceleration sensor, an angular velocity sensor, or a gyro sensor of a HMD (Head Mounted Display) device” of Oh et al. (US 20180239419 A1).. Furthermore, a generic computer structure such as “a processor” and “first/second machine learning model” is not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 14 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Dependent claim(s) 15 fail to cure the deficiencies of independent claim 14 by merely reciting additional abstract ideas, further limitations on abstract ideas already recited, and/or additional elements that are not significantly more. Thus, claim(s) 14-15 is/are rejected under 35 U.S.C. 101.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3-13, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson et al. (US 20210124412), hereinafter Johnson, in view of Dasgupta (“‘You can tell by the way I use my walk.’” Predicting the presence of cognitive load with gait measurements” as cited by applicant’s IDS filed 05/05/2022).
Regarding claim 1, Johnson discloses generating, with a motion sensor disposed adjacent a head of the user, motion sensor data indicative of head movement of the user ([0033]: “variety of position sensors, such as an inertial measurement unit (IMU), an accelerometer, a gyroscope, a magnetometer (e.g., magnetic compass),”), wherein the motion sensor is integral with or installed in a head-mounted display worn by the user ([0025]: “a system 100 can include a plurality of sensors 104 a . . . n, processing circuitry 116, and one or more displays 172. The system 100 can be implemented using the HMD system 200”); analyzing, using a processor ([0026] : “processing circuitry”), the motion sensor data to infer a feature of a gait of the user; ([0056]: “the pose detector 148 can detect the pose of the user using data from various stages of processing by the system 100, including using accelerometer data (e.g., position, velocity, or acceleration data), gyroscope data (e.g., angular data, orientation data), or camera data (e.g., image data)”, wherein pose/orientation is an attribute of gait).
While Johnson discloses adjusting notifications from background-running applications provided to the user via the head-mounted display based on an inference, ( [0087]: “decrease the effect of velocity or acceleration data as the size metric decreases (e.g., in smaller spaces the user may be more aware of their surroundings) and increase the effect of velocity or acceleration data as the size metric increases (e.g., in larger spaces the user may be less aware of their surroundings and may make larger motions).”, wherein the velocity/acceleration is decreased based on an inference the user will be more/less aware of their surroundings, ie. carrying more/less of a cognitive load, [0102]: “can provide instructions to the image renderer 168 with the warning to cause the image renderer 168 to apply various display effects for displaying the warning at the periphery of the FOV to facilitate user awareness of the warning.”, wherein the weight is adjusted by the warning generator in the example provided in para [0087] of an awareness of the user), they fail to specifically disclose facilitating estimation of a cognitive load of the user based on the attribute of the gait.
Dasgupta discloses a method for estimation of a cognitive load of based on the attribute of the gait (pg. 2 para 4-5 "differentiating gait qualities due to cognitive load… predict … cognitive overload", pg. 12 para 3: “The results show that the LR, RF, and SVM algorithms recognized cognitive load accurately (with accuracy > 0.93) for both strides and windows.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the HMD disclosed by Johnson to further estimate a cognitive load as disclosed by Dasgupta in order to enable recognition of abnormal gait due to cognitive decline (Dasgupta pg. 2 para 2). Additionally, it would have been obvious to combine the method of determining gait features using motion sensor data disclosed by Johnson and the method of using gait features determined using motion sensor data to infer cognitive load disclosed by Dasgupta as each disclosed element performs the same function separately as they would in combination with the only difference being the lack of actual combination in a single prior art reference.
Regarding claim 4, Johnson discloses wherein the machine learning model is trained to map head movement to the feature of the gait ([0121]: “The method 300 can include detecting head position data regarding a head of a user (305)”).
Regarding claim 5, Johnson discloses wherein the machine learning model comprises a support vector machine, a random forest, a decision tree, or a neural network ([0067]: “e.g., support vector machines, regression models, supervised neural networks, decision trees)”).
Regarding claim 6, Johnson discloses wherein the feature of the gait comprises a walking speed of the user ([0005]: “the model can take into account any of a variety of motion data parameters, such as position, velocity, acceleration”) or a stride length of the user.
Regarding claim 7, Dasgupta further discloses wherein the inferring comprises applying the feature of the gait as one of a plurality of inputs (pg. 8 feature extraction “twelve features were extracted from each stride”) across a trained machine learning model to generate output indicative of the cognitive load of the user (pg. 10 machine learning results, pg. 12 para 4: “The results show that the LR, RF, and SVM algorithms recognized cognitive load accurately (with accuracy > 0.93) for both strides and windows”).
Regarding claim 8, Dasgupta further discloses altering a weight applied to another input of the plurality of inputs in response to a presence of the feature of the gait (pg. 8 feature selection: “in order to select which features to include in machine learning models, the following methods were performed: (1) for all four models, a correlation matrix between each feature was constructed; highly correlated feature pairs (r > 0.75) were found and within each pair, the feature with the highest mean absolute correlation was removed;”).
Regarding claim 9, Johnson discloses a head-mounted display ("HMD") (abstract) comprising: a motion sensor to produce a signal indicative of captured motion ([0033]: “variety of position sensors, such as an inertial measurement unit (IMU), an accelerometer, a gyroscope, a magnetometer (e.g., magnetic compass),”); and circuitry operably coupled with the motion sensor ([0056]: “The processing circuitry 116 can include a pose detector”), the circuitry to: process a signal generated by the motion sensor using a first machine learning model ([0022]: “use a machine learning model trained to generate a prediction of the user's modality or position.”) to estimate an attribute of a gait performed by a user wearing the HMD ([0056]: “the pose detector 148 can detect the pose of the user using data from various stages of processing by the system 100, including using accelerometer data (e.g., position, velocity, or acceleration data), gyroscope data (e.g., angular data, orientation data), or camera data (e.g., image data)”, wherein pose/orientation is an attribute of gait), wherein the first machine learning model is trained by a classifier based on a correlation between the signal and a prior calibration signal from a separate sensor coupled to another body part of an individual ([0022]: “The machine learning model can be trained using information from a skeletal inference model. The skeletal inference model can use position data from the headset and hand controllers to infer a skeletal pose of the user, and thus the corresponding modality. For example, the skeletal inference model can use statistical relationships regarding distances between the hands and head to infer the skeletal pose.”, [0071]: “is trained using motion capture data, such as training data samples generated from motion capture data. For example, the motion capture data may be generated based on image data regarding various users positioned in or moving through various poses, which may be labeled with the type of the pose”, wherein the model can be trained from sample sets);
Johnson fails to disclose facilitating estimation of a cognitive load of the user based on the attribute of the gait.
Dasgupta discloses a method for estimation of a cognitive load of based on the attribute of the gait (pg. 2 para 4-5 "differentiating gait qualities due to cognitive load… predict … cognitive overload", pg. 12 para 3: “The results show that the LR, RF, and SVM algorithms recognized cognitive load accurately (with accuracy > 0.93) for both strides and windows.”).
As Johnson discloses determining a user’s awareness ([0087]), it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the HMD disclosed by Johnson to further estimate a cognitive load as disclosed by Dasgupta in order to enable recognition of abnormal gait due to cognitive decline (Dasgupta pg. 2 para 2). Additionally, it would have been obvious to combine the method of determining gait features using motion sensor data disclosed by Johnson and the method of using gait features determined using motion sensor data to infer cognitive load disclosed by Dasgupta as each disclosed element performs the same function separately as they would in combination with the only difference being the lack of actual combination in a single prior art reference.
Regarding claim 10, Johnson further discloses to facilitate the estimation, the circuitry is to transmit data indicative of the attribute of the gait to a remote computing device ([0116]: “the HMD has portions of the processing circuitry 116 that work in cooperation with or in conjunction with any type of portable or mobile computing device or companion device that has the processing circuitry or portions thereof, such as in the form of a staging device, a mobile phone or wearable computing device”).
Regarding claim 11, Johnson further discloses wherein the remote computing device comprises a mobile phone ([0116]: “such as in the form of a staging device, a mobile phone or wearable computing device”), and the data indicative of the attribute is transmitted from the HMD to the mobile phone over a personal area network ([0115]: “The communications circuitry 204 can be used to transmit electronic communication signals to and receive electronic communication signals from at least one of a client device 208 or a server 212.”).
Regarding claim 12, Dasgupta further discloses wherein to determine the estimation, the circuitry is to analyze the attribute of the gait alongside other inputs to estimate the cognitive load (pg. 8 feature extraction : “twelve features were extracted from each stride”).
Regarding claim 13, Johnson further discloses the circuitry is to generate, for rendition on a display of the HMD, information about the estimated cognitive load of the user ([0018]: “The VR system can detect a user moving by walking in the real world and moving a hand, and can generate and update the display data based on detecting such information. The VR system can include the HMD (e.g., headset), which can be worn by the user to present the display data to the user, as well as one or more hand devices, such as hand-held controllers,”, as modified by Dasgupta to include cognitive load) .
Regarding claim 16, Johnson further discloses further comprising adjusting a visual output on a display of the head-mounted display based on the inference ([0076]: “the warning generator 160 can selectively generate icons, labels, or representations of the one or more obstacles to warn the user of the HMD of the potential collision. For example, the warning generator 160 can generate display data indicating gridded elements representing the one or more obstacles (e.g., gridded walls).”, as modified by Dasgupta to rely on cognitive load above).
Regarding claim 17, Dasgupta discloses wherein to determine the estimation of the cognitive load of the user based on the attribute of the gait includes to use a second machine learning model (methods para 1: “A binary classification was created by using logistic regression, support vector machine, random forest, and learning vector quantization to classify cognitive load vs. no cognitive load”, wherein the second machine learning model differs from the first disclosed by Johnson) alongside other inputs (pg. 8 feature extraction: “twelve features were extracted from each stride and each observation window in each of the six sensors”).
Regarding claim 18, Johnson further discloses comprising the circuitry to alter a weight applied to one of the other inputs in the second machine learning model in response to a presence of the attribute of the gait ([0082]: “a greater weight to motion data regarding the head of the user than motion data regarding the one or more hands of the user”), wherein the one of the other inputs is non-gait-related ([0082]: “regarding the one or more hands of the user”, wherein hand motion can be independent of gait).
Regarding claim 19, Johnson discloses further comprising a display, and wherein the circuitry is further to adjust a visual output on the display based on the estimation (Fig 1 element 168 to 172).
Claim(s) 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Wisbey et al. (US 20200401222 A1), hereinafter Wisbey.
Regarding claim 14, Johnson discloses a non-transitory computer-readable medium ([0142]: “Local storage 406 can include volatile storage media (e.g., conventional DRAM, SRAM, SDRAM, or the like) and/or non-volatile storage media (e.g., magnetic or optical disk, flash memory, or the like).”) comprising instructions that, in response to execution of the instructions by a processor of a head-mounted display ("HMD") ([0025]: “The system 100 can be implemented using the HMD system 200”, Fig 2 116: processing circuitry), cause the processor to: receive data indicative of motion of a head of user ([0017]: “can transmit and receive data with the client device 208 to leverage the client device 208's computing power and resources which may have higher specifications than those of the HMD.”), wherein the data is based on output of a motion sensor disposed on or within the HMD while the user gaits ([0018]: “The HMD and hand device may each include motion sensors that can generate motion data, such as velocity or acceleration data, regarding movement of the head and hands of the user.”); extract a feature of the user's gait from the data indicative of motion of the head of the user ([0020]: “Sensor data such as accelerometer data, gyroscope data, camera data, or any combination thereof, may be directly used to determine the type of the pose, or may be processed through various portions of a pose estimation”) using a first machine learning model trained by a classifier based on a correlation between the data and a prior calibration signal from a separate sensor coupled to another body part of an individual ([0022]: “The machine learning model can be trained using information from a skeletal inference model. The skeletal inference model can use position data from the headset and hand controllers to infer a skeletal pose of the user, and thus the corresponding modality. For example, the skeletal inference model can use statistical relationships regarding distances between the hands and head to infer the skeletal pose.”, [0071]: “is trained using motion capture data, such as training data samples generated from motion capture data. For example, the motion capture data may be generated based on image data regarding various users positioned in or moving through various poses, which may be labeled with the type of the pose”, wherein the model can be trained from sample sets).
While Johnson discloses inferring an awareness of the user based on the feature of ([0087]: “decrease the effect of velocity or acceleration data as the size metric decreases (e.g., in smaller spaces the user may be more aware of their surroundings) and increase the effect of velocity or acceleration data as the size metric increases (e.g., in larger spaces the user may be less aware of their surroundings and may make larger motions).”); and visually emphasize, on a display of the HMD, an object in the user’s path based on the inferred cognitive load. ( [0102]: “can provide instructions to the image renderer 168 with the warning to cause the image renderer 168 to apply various display effects for displaying the warning at the periphery of the FOV to facilitate user awareness of the warning.”, wherein the weight is adjusted by the warning generator in the example provided in para [0087]) and visually emphasizing, on a display of the HMD, an object in the user's path based on the inferred cognitive load ([0076]: “the warning generator 160 can selectively generate icons, labels, or representations of the one or more obstacles to warn the user of the HMD of the potential collision. For example, the warning generator 160 can generate display data indicating gridded elements representing the one or more obstacles (e.g., gridded walls).”), they fail to specifically disclose facilitating estimation of a cognitive load of the user based on the extracted feature using a second machine learning model alongside other non-gait-related inputs.
Wisbey discloses inferring a cognitive load of a user based on the extracted feature using a machine learning model alongside other non-gait-related inputs ([0137]: “EEG, PPG and motion sensors embedded in a gaming headset to capture EEG, HRV and movement data… variable data may then be run through a machine learning model such as an extreme gradient boosting (XGB) model or random decision forest (RDF) to determine the user's short term risk of an error, or mistake…”)
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the HMD disclosed by Johnson to further estimate a cognitive load as disclosed by Wisbey in order to provide users with insight into their level of cognitive fatigue (Wisbey [0006]). Additionally, it would have been obvious to combine the method of determining gait features using motion sensor data disclosed by Johnson and the method of using gait features determined using motion sensor data to infer cognitive load disclosed by Wisbey as each disclosed element performs the same function separately as they would in combination with the only difference being the lack of actual combination in a single prior art reference.
Regarding claim 20, Wisbey further discloses instructions that cause the processor to alter a weight applied to one of the other non-gate-related inputs in the second machine learning model based on the extracted feature ([0054]: “, a Focus measurement may be weighted to prioritize the most recent data captured in the five second window.”).
Response to Arguments
Applicant’s arguments, see Remarks pages 6-16, filed 10/16/2025, with respect to 35 U.S.C. § 101 have been fully considered and are not persuasive with respect to claims 9-20.
Regarding claim 1, “adjusting notifications from background-running applications provided to the user via the head-mounted display based on the inference,” does not effectively incorporate the judicial exception into a practical application. Though the modifying the operation of the head mounted display is modified as a result of the calculation, adjusting notifications constitutes a mental process can be performed mentally. Automation of a manual activity, adjusting notifications, does not improve an existing technology.
Applicant argues on page 11 that use of circuitry to facilitate calculation of a cognitive load precludes it from reciting a mental process. However, instructions to implement an abstract idea on a computer does not recite “significantly more” (see MPEP 2106.05 A). Additionally, HMDs are considered well-known/routine/conventional in the art (see rejection above). With respect to claim 14, visual emphasis of a path constitutes providing instructions to a user based on a calculation, in this case the location/presence of an object, which can be considered a mental process.
Applicant’s arguments, see Remarks pages 17-22, with respect to the rejection(s) of claims 1-15 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view 35 U.S.C. § 103 over Johnson in view of Dasgupta and Johnson in view of Wisbey (see rejection above).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KAVYA SHOBANA BALAJI/ Examiner, Art Unit 3791
/DANIEL L CERIONI/ Primary Examiner, Art Unit 3791