DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
2. Applicant's arguments received 02/24/2026 have been fully considered but are moot in view of the new ground(s) of rejection. Detailed response is given in sections 3-8 as set forth below in this Office Action.
Regarding the 101 rejection, Applicant argues (REMARKS, p.15-16):
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Examiner respectfully disagrees. Under the broadest reasonable interpretation (BRI), Examiner considers that the limitation (S2, see section 4 below) in question encompasses a process of gathering the data/information necessary for performing the abstract idea. In particular, the additional element “wherein the plurality of sensors include one or more of a camera, a positioning sensor, an accelerometer, and a gyroscope” amounts to mere tools for data gathering but does not impose meaningful limits on practicing the abstract idea. At most, the recited “the plurality of sensors” only generally link the judicial exception to a particular technological environment or field of use. See the USPTO’s July 2024 Subject Matter Eligibility Examples (e.g., Example 46, Claim 3, discussions of the limitations regarding a plurality of sensors that are each associated with a respective one of a plurality of entities …). Thus, Examiner asserts that the limitation S2 does not integrate the abstract idea into a practical application. Further, the limitation “a sensor system that includes a plurality of sensors … wherein the plurality of sensors include one or more of a camera, a positioning sensor, an accelerometer, and a gyroscope” is still recited at a high level of generality. It does not specify any particular structure/configuration of the sensor system to implement “one or more parameters related to current operating conditions associated with the plurality of sensors”. Therefore, claim 15 would monopolize the abstract idea across a wide range of applications. See MPEP 2106.05(g). Accordingly, Applicant’s arguments with respect to the claim eligibility are not persuasive.
Regarding the 102/103 rejection, Applicant argues (REMARKS, p.17-19):
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Examiner respectfully disagrees. With the BRI to the claim, Examiner maintains the position that Pu does disclose or teach the invention recited in claims 1, 15, 29 and 30 of the present application, including the limitations:
providing a pose tracking device (para. 0051: “two or more of a client system 130, a social-networking system 160, an assistant system 140, and a third-party system 170 may be connected to each other directly”; para. 0006: “The assistant system may additionally assist the user to manage different tasks such as keeping track of events”; para. 0161: “the one or more AR objects may comprise a user interface (UI) associated with an assistant system 140. The AR system 500 may intelligently determine how to represent the UI of the assistant system 140 in a 3D space”; para. 0174: “The AR system 500 may use sensor signals captured by sensors to detect transitions in user state. As an example and not by way of limitation, motion signals may help detect if the user transitioned from sitting to standing. As another example and not by way of limitation, audio signals may help detect if someone is talking to the use”);
selecting, by the pose tracking device, a pose tracking model (e.g., the models with cheapest modality (usually IMU and audio)) based on:
the set of sensor modalities (para. 0008: “To be power efficient, the AR system may use a cascaded classification approach, progressing from cheaper models to more expensive models, to determine the user's environment and state”; para. 0191: “The one or more sensor constraints may constrain usage of the one or more sensor signals by the cascaded inference process. Because of the sensor constraints (e.g., battery) on AR devices, when determining the contextual information, the AR system 500 may run models with cheapest modality (usually IMU and audio) first to make classifications and avoid using vision if possible since that's expensive”; para. 0107: “ … perform domain classification/selection 334 on user input … to classify the user input into predefined domains. … the meta-intent classifier 336a may be based on a machine-learning model”) and the one or more KPI requirements (e.g., “a network connectivity status for client system 130”, “available battery power (i.e., battery status) for the client system 130”, “privacy constraints”, etc.) related to the current context associated with the pose tracking configuration for the client application (see para. 0074),
wherein the selected pose tracking model is capable of performing context-adaptive pose tracking (para. 0006: “The assistant system may additionally assist the user to manage different tasks such as keeping track of events. … the assistant system may check privacy settings to ensure that accessing a user's profile or other user information and executing different tasks are permitted subject to the user's privacy settings”; para. 0008: “The AR system may further adapt the rendering of the AR content based on such environment and state information”; para. 0010: “determining the adaptation based on power states associated with the AR rendering device … determining the adaptation based on context information associated with the user, as the contextual information may indicate the user states and their environment to enable proactive experiences”; para. 0085: “the on-device dialog manager 216a may comprise a dialog state tracker 218a”; para. 0088: “the dialog state tracker 218a may track state changes over time as a user interacts with the world and the assistant system 140 interacts with the user. … the dialog state tracker 218a may track, for example, what the user is talking about, whom the user is with, where the user is, what tasks are currently in progress, and where the user's gaze is at subject to applicable privacy policies”; para. 0105: “an artificial/augmented reality (AR) system 500 may dynamically render AR content (e.g., the representation/user interface (UI) of the assistant system 140) for a user with spatial and contextual awareness”); and
optimizing, by the pose tracking device, the pose tracking model by performing at least one hardware or software optimization selected from: hardware reconfiguration (para. 0188: “the sensor availability may indicate that some of the sensors may be turned off in certain privacy modes”), quantization, or reconfiguration based on feedback associated with a model output (e.g., para. 0135: “based at least in part on a limited computing power of the client system 130, the assistant system 140 may optimize the personalized language model at runtime during the client-side process. … When a user input is associated with a request for assistance, the assistant system 140 may promptly switch between and locally optimize the pre-computed language models at runtime based on user activities”; para. 0188: “audio signals with small amounts of machine-learning processing may be relatively cheap”; para. 0191: “the AR system 500 may take into account the tradeoff between resource usage and accuracy or latency”).
Accordingly, Examiner asserts that Pu’s teaching of the “the models with cheapest modality (usually IMU and audio)”, which is selected based on the cascaded classification approach, does read on a pose tracking model as recited in instant claims 1, 15, 29 and 30.
The rest of the Applicant’s arguments are reliant upon the issues discussed above or have been fully addressed in the detailed response as set forth below in this Office Action. The rejections are therefore maintained.
Claim Rejections - 35 USC § 101
3. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 101 that form the basis for the rejections under this section made in this Office action:
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.
4. Claims 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG (now been incorporated into MPEP 2106), the revised procedure for determining whether a claim is "directed to" a judicial exception requires a two-prong inquiry into whether the claim recites: (1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human interactions such as a fundamental economic practice, or mental processes); and (2) additional elements that integrate the judicial exception into a practical application (see MPEP § 2106.05(a)-(c), (e)-(h)).
Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look to whether the claim: (3) adds a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional" in the field (see MPEP § 2106.0S(d)); or (4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Claims 1-30 are directed to an abstract idea of context-adaptive pose tracking.
Specifically, representative claim 15 recites:
A pose tracking device for power-efficient and performance-efficient context-adaptive pose tracking, comprising:
one or more memories; and
one or more processors, coupled to the one or more memories, configured to:
(S1) receive information that includes one or more key performance indicator (KPI) requirements related to a current context associated with a pose tracking configuration for a client application;
(S2) receive usability information from a sensor system that includes a plurality of sensors based on one or more parameters related to current operating conditions associated with the plurality of sensors, wherein the plurality of sensors include one or more of a camera, a positioning sensor, an accelerometer, and a gyroscope;
(S3) select a set of sensor modalities that includes one or more sensors from the plurality of sensors included in the sensor system based on the current context associated with the pose tracking configuration for the client application and the usability information related to the current operating conditions associated with the plurality of sensors;
(S4) select a pose tracking model based on the set of sensor modalities and the one or more KPI requirements related to the current context associated with the pose tracking configuration for the client application;
(S5) optimize the pose tracking model based on at least one hardware or software optimization selected from: hardware reconfiguration, neural network pruning, quantization, data compression, or reconfiguration based on feedback associated with a model output; and
(S6) estimate a pose associated with a tracked object using the pose tracking model based on sensor inputs associated with the set of sensor modalities.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
The highlighted portion of the claim constitutes an abstract idea under the 2019 Revised Patent Subject Matter Eligibility Guidance and the additional elements are NOT sufficient to amount to significantly more than the judicial exceptions, as analyzed below:
Step
Analysis
1. Statutory Category ?
Yes.
System/Apparatus
2A - Prong 1: Judicial Exception Recited?
Yes.
See the bolded portion as listed above.
Under its broadest reasonable interpretation (BRI), each and/or the combination of the limitations S3 and S4 recited in the bolded portion encompasses data analysis processes that can be performed by the human mind using mental steps/critical thinking.
Under its BRI, the limitation S5 covers concepts that can be performed in the human mind, including observation, evaluation, judgment and opinion (e.g., selecting a set of sensors that are relatively cheap for operating the pose tracking model).
Under its BRI, the limitation S6 encompasses mathematical concepts and/or calculations, namely a series of calculations leading to one or more numerical results or answers, which also encompasses mental processes, i.e. data manipulation, evaluation and judgment, that can be performed in the human mind or by a human using a pen and paper. The claim does not provide any details about how the pose tracking model itself operates to generate said estimate based on sensor inputs associated with the set of sensor modalities. In light of the USPTO’s July 2024 Subject Matter Eligibility Examples (e.g., Examples 47-49), merely using a machine learning model to perform calculations that are otherwise abstract does not take the claimed limitation(s) out of the categories of abstract idea.
Nothing in the bolded portion precludes the limitations S3, S4, S5 and S6 from practically being performed in the mind and/or with the aid of pen/paper.
Therefore, the bolded portion of instant claim 15, reciting a series of mathematical concepts and mental process, amounts to an abstract idea falling within a combination of the “Mental Process” and “Mathematical Concepts” groupings of Abstract Ideas defined by the 2019 PEG.
2A - Prong 2: Integrated into a Practical Application?
No.
Representative claim 15 recites “one or more memories; and
one or more processors, coupled to the one or more memories” at a high level of generality. Under the BRI, the combination of the processor and the memory reads on a generic processor performing a generic computer function of processing data. The generic processor limitation is no more than mere instructions to apply the abstract idea using a generic computer. It is held that performing an abstract idea using a general-purpose computer system would not amount to significantly more than the abstract algorithm itself. See, for example, Whitserve LLC v. Dropbox, Inc. and MPEP 2106.05(f).
Under its BRI, each of the limitation S1 and S2 encompasses a process of gathering the data/information necessary for performing the abstract idea. The limitation “wherein the plurality of sensors include one or more of a camera, a positioning sensor, an accelerometer, and a gyroscope”
amounts to mere tools for data gathering but does not impose any meaningful limits on practicing the abstract idea. At most, the recited “the plurality of sensors” only generally link the judicial exception to a particular technological environment or field of use. Thus, the limitations S1 and S2 do not integrate the abstract idea into a practical application. See MPEP 2106.05(g)(3): … that were described as mere data gathering in conjunction with a law of nature or abstract idea. See also Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 13863, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Further, the limitation of “a sensor system that includes a plurality of sensors …” is recited at a high level of generality. It does not specify any particular structure/configuration of the sensor system to implement “one or more parameters related to current operating conditions associated with the plurality of sensors”. As such, claim 15 would monopolize the abstract idea across a wide range of applications. See MPEP 2106.05(g).
The claim recites “one or more key performance indicator (KPI) requirements related to a current context associated with a pose tracking configuration for a client application”. Under its BRI, this limitation encompasses merely data characterization which can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of pose tracking. None of these additional elements is considered to be qualified for a significant or meaningful limitation because they do not impose any meaningful limits on practicing the abstract idea.
The claim as a whole does not meet any of the following criteria to integrate the abstract idea into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. However, in all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. At most, it only generally links the judicial exception to a particular technological environment or field of use. See MPEP 2106.04(d)(2).
2B: Claim provides an Inventive Concept?
No.
Focusing on what the inventors have invented exactly, it is deemed that the “heart” of the representative claim 15 is directed to an algorithm of context-adaptive pose tracking, which falls within a combination of the “Mental Process” and “Mathematical Concepts” groupings of abstract ideas.
As discussed with respect to Step 2A Prong Two above, each or the combination of the additional limitations in the claim amounts to no more than mere instructions to apply the exception using generic computer components and/or well-known/conventional techniques. The claim does not recite any limitation that can be treated as “significantly more” or an “inventive concept”. See MPEP 2106.05.
The claim is therefore ineligible under 35 USC 101.
The dependent claims 16-28 inherit attributes of the independent claim 15, but does not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrows) the abstract idea which does not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above.
In particular, claim 25 recites: “generate feedback that relates to performance of the pose tracking model in estimating the pose associated with the tracked object; and update one or more decision policies that are used to select at least one of the set of sensor modalities or the pose tracking model based on the feedback”. Under its BRI, “generate feedback that relates to performance of the pose tracking model in estimating the pose associated with the tracked object” encompasses mathematical concepts. The limitation of “update … the pose tracking model based on the feedback” reads on a process of re-training the pose tracking model based on the feedback (i.e., new training data) which can be implemented by optimizing the AI-based pose tracking model using a series of mathematical calculations to iteratively adjust the algorithms and/or parameter values of the AI models, therefore encompasses mathematical concepts. Similarly, the limitation of “generate one or more outputs to request one or more user interactions to calibrate one or more decision policies that are used to select at least one of the set of sensor modalities or the pose tracking model based on the feedback” recited in claim 26 encompasses a series of mathematical calculations to adjust the algorithms and/or parameter values of the AI-based pose tracking model.
Claim 27 recites: “generate feedback …, and share the feedback that relates to the performance of the pose tracking model with one or more external devices”. Under its BRI, this limitation encompasses merely insignificant extra-solution activities (i.e., generating and sharing the data/information output from the identified abstract idea) which are generally attached to the judicial exception, but does not amount to significantly more to integrated the abstract idea into a practical application.
Similar to claim 25, claim 28 also recites limitations that encompass a series of mathematical calculations to optimize the AI-based pose tracking model, which are treated as a part of the identified abstract idea.
As to the additional elements of “a context that includes one or more of a device type, a motion state, a current user activity state, a device placement state, or a device location state associated with the sensor” (claim 16), “one or more parameters related to a power consumption requirement for estimating the pose” (claim 18), “one or more parameters related to an accuracy requirement for estimating the pose” (claim 19)”, “a context that includes one or more of a device type, a motion state, a current user activity, a device placement state, or a device state location associated with the sensor” (claim 20), “feedback that relates to performance of the pose tracking model in estimating the pose associated with the tracked object” (claim 25), etc., under the BRI, they encompass merely data characterization which can be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of context-adaptive pose tracking but do not amount to be meaningful to integrate the judicial exception into a practical application.
Claims 1-14 and 29-30 are rejected under 35 U.S.C. § 101 for the same reason as for claims 15-28 set forth above.
Claim Rejections - 35 USC § 102
5. 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; or
(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.
6. Claims 1-9, 11-23 and 25-30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pu et al. (US 20220374130 A1).
Regarding claims 1, 15, 29 and 30, Pu discloses a method, a device and apparatus for practicing the method, including one or more memories, one or more processors coupled to the one or more memories for executing computer programs stored on the one or more memories (para. 0231-0233), for power-efficient and performance-efficient context-adaptive pose tracking (para. 0006: “e.g., user movements detected by the client device of the user”; para. 0008: “To be power efficient, the AR system may use a cascaded classification approach, progressing from cheaper models to more expensive models, to determine the user's environment and state”), comprising: receiving, by a pose tracking device (130 Fig. 1; see para. 0054), information that includes one or more key performance indicator (KPI) requirements (e.g., “a network connectivity status for client system 130”, “available battery power (i.e., battery status) for the client system 130”, “privacy constraints”, etc.) related to a current context associated with a pose tracking configuration for a client application (para. 0074); receiving, by the pose tracking device, usability information (para. 0177: under the BRI, broken sensors, system going to low-power mode, device going to privacy mode, etc. read on “usability information”, see also para. 0178: the particular sensor utility also read on “usability information”) from a sensor system (i.e., the onboard sensors of the AR-based client system/device 130, see para. 0158) that includes a plurality of sensors based on one or more parameters related to current operating conditions associated with the plurality of sensors (para. 0174, 0176-0178, 0188), wherein the plurality of sensors include one or more of a camera, a positioning sensor, an accelerometer, and a gyroscope (para. 0158); selecting, by the pose tracking device, a set of sensor modalities that includes one or more sensors from the plurality of sensors included in the sensor system based on the current context associated with the pose tracking configuration for the client application and the usability information related to the current operating conditions associated with the plurality of sensors (para. 0074: “selection of an operational mode may be based at least in part on a device state …”; para. 0174: “Using different types of sensor signals to determine the context may be an effective solution for addressing the technical challenge of accurately determining the context for adaptive rendering as these sensor signals may provide comprehensive information about a user's environment and state change for accurate context determination”; para. 0175: “in FIG. 15, the lowest level may be different sensor services 1510, which may be not part of the context engine 220 but dependent by different types of context engines 220”; para. 0176: “In the case where certain inference may benefit from using more than one type of sensor, a new microservice may be added, which directly subscribes to those sensors”; para. 0177-0178, 0181: “A set of machine-learning models may be used to generate such contexts from different combinations of sensor modalities”; see also para. 0183-0185, 0189, 0191: “The one or more sensor constraints may constrain usage of the one or more sensor signals by the cascaded inference process. Because of the sensor constraints (e.g., battery) on AR devices, when determining the contextual information, the AR system 500 may run models with cheapest modality (usually IMU and audio) first to make classifications and avoid using vision if possible since that's expensive”); selecting, by the pose tracking device, a pose tracking model (e.g., audio-based model) based on the set of sensor modalities and the one or more KPI requirements related to the current context associated with the pose tracking configuration for the client application (para. 0185, 0191: “Because of the sensor constraints (e.g., battery) on AR devices, when determining the contextual information, the AR system 500 may run models with cheapest modality (usually IMU and audio) first to make classifications and avoid using vision if possible since that's expensive”); optimizing, by the pose tracking device, the pose tracking model by performing at least one hardware or software optimization selected from: hardware reconfiguration (para. 0188: “the sensor availability may indicate that some of the sensors may be turned off in certain privacy modes”), quantization, or reconfiguration based on feedback associated with a model output (para. 0135: “In particular embodiments, based at least in part on a limited computing power of the client system 130, the assistant system 140 may optimize the personalized language model at runtime during the client-side process. … When a user input is associated with a request for assistance, the assistant system 140 may promptly switch between and locally optimize the pre-computed language models at runtime based on user activities”; para. 0188: “audio signals with small amounts of machine-learning processing may be relatively cheap”; see also para. 0192-0193); and estimating, by the pose tracking device, a pose associated with a tracked object (e.g., a user at the client system 130) using the pose tracking model based on sensor inputs associated with the set of sensor modalities (para. 0008, 0011, 0057, 0078, 0108, 0152, 0191, 0199).
Regarding claims 2 and 16, Pu discloses: wherein the usability information includes, for each sensor of the plurality of sensors included in the sensor system, a respective usability score (see discussions related to the “cascaded inference”) that is based on a context that includes one or more of a device type, a motion state, a current user activity state, a device placement state, or a device location state associated with the sensor (para. 0010: “The solution presented by the embodiments disclosed herein to address this challenge may be using a cascaded inference process where cheaper sensor signals are used first and more expensive sensor signals are used later, thereby gradually increasing the utilization of the limited computing power to make the best use of it”; para. 0188, 0190, 0191: “The cascaded inference process may be based on the one or more sensor signals. In particular embodiments, the cascaded inference process may be determined based on one or more sensor constraints associated with each of the one or more sensors … ”).
Regarding claims 3 and 17, Pu discloses: wherein the pose tracking model is selected based on a set of inputs that include one or more of selected sensor modalities, available hardware resources associated with the pose tracking device, an accuracy requirement for estimating the pose, the context, or the one or more KPI requirements (para. 0074, 0174, 0176, 0177-0178, 0181, 0183-0185, 0189, 0191).
Regarding claims 4 and 18, Pu discloses: wherein the one or more KPI requirements related to the current context associated with the pose tracking configuration include one or more parameters related to a power consumption requirement for estimating the pose (para. 0058, 0074, 0183).
Regarding claims 5 and 19, Pu discloses: wherein the one or more KPI requirements related to the current context associated with the pose tracking configuration include one or more parameters related to an accuracy requirement for estimating the pose (para. 0101, 0163, 0174: “Using different types of sensor signals to determine the context may be an effective solution for addressing the technical challenge of accurately determining the context for adaptive rendering as these sensor signals may provide comprehensive information about a user's environment and state change for accurate context determination”; para. 0185: “contexts may be determined through multiple different sensors and they may provide different levels of accuracy and cost …”; see also para. 0191).
Regarding claims 6 and 20, Pu discloses: wherein the one or more KPI requirements are based on a context that includes one or more of a device type, a motion state, a current user activity state, a device placement state, or a device location state associated with the sensor (para. 0074, 0112, 0152, 0175).
Regarding claims 7 and 21, Pu discloses: wherein the estimated pose relates to one or more of a position of the tracked object with respect to one or more axes or an orientation of the tracked object with respect to one or more axes (para. 0057: “the user may interact with the assistant system 140 by providing user input to the assistant application 136 via various modalities (e.g., audio, voice, text, vision, image, video, gesture, motion, activity, location, orientation)”; para. 0175: “adapting a pose of an AR object may comprise changing its orientation from vertical to horizontal”).
Regarding claims 8 and 22, Pu discloses: wherein the position of the tracked object includes an absolute position at a specific time instance (para. 0006: “The assistant system may enable the user to interact with the assistant system via user inputs of various modalities (e.g., audio, voice, text, image, video, gesture, motion, location, orientation) in stateful and multi-turn conversations to receive assistance from the assistant system. … the assistant system may proactively execute, without a user input, tasks that are relevant to user interests and preferences based on the user profile, at a time relevant for the user”; para. 0070: “Advertisement-pricing modules may combine social information, the current time, location information, or other suitable information to provide relevant advertisements, in the form of notifications, to a user”; see also para. 0103).
Regarding claims 9 and 23, Pu discloses: wherein the orientation of the tracked object includes an absolute orientation at a specific time instance (para. 0006, 0057, 0157, 0175).
Regarding claims 11 and 25, Pu discloses: generating feedback that relates to performance of the pose tracking model in estimating the pose associated with the tracked object (by inherency, training a machine learning model involves generating feedback and updating the model based on the feedback), and updating one or more decision policies that are used to select at least one of the set of sensor modalities or the pose tracking model based on the feedback (para. 0087, 0101: by inherency, the machine learning process used to train the machine-learning models involves: collecting and preparing data, choosing and training a model, and then evaluating and deploying it, wherein evaluating the trained model includes: testing the trained model on the unseen testing data to measure its accuracy and overall performance, and if the model is not performing as expected, updating one or more decision policies to adjust the algorithm, data, or features to improve its accuracy; para. 0181: “A set of machine-learning models may be used to generate such contexts from different combinations of sensor modalities”; see also para. 0158, 0199: “At step 2270, the AR system 500 may render, for the one or more displays of the AR display device, a second output image comprising the one or more AR objects, wherein one or more of the AR objects are adapted based on the detected change in the context of the first user, the detected change in the intent of the first user, and the power state, wherein adapting the one or more AR objects comprises …. Particular embodiments may repeat one or more steps of the method of FIG. 22, where appropriate”).
Regarding claims 12 and 26, Pu discloses: generating feedback that relates to performance of the pose tracking model in estimating the pose associated with the tracked object, and generating one or more outputs to request one or more user interactions to calibrate one or more decision policies that are used to select at least one of the set of sensor modalities or the pose tracking model based on the feedback (para. 0087, 0101: by inherency, the machine learning process used to train the machine-learning models involves: collecting and preparing data, choosing and training a model, and then evaluating and deploying it, wherein evaluating the trained models includes: testing the trained model on the unseen testing data to measure its accuracy and overall performance, and if the model is not performing as expected, updating one or more decision policies to adjust the algorithm, data, or features to improve its accuracy; see also para. 0158, 0181, 0199).
Regarding claims 13 and 27, Pu discloses: generating feedback that relates to performance of the pose tracking model in estimating the pose associated with the tracked object (by inherency, training a machine learning model involves generating feedback and updating the model based on the feedback), and sharing the feedback that relates to the performance of the pose tracking model with one or more external devices (para. 0087: “Federated parameters, by contrast, may be trained remotely on the server … the on-device dialog manager 216a may use an active federated learning model, which may transmit a global model trained on the remote server to client systems 130 and calculate gradients locally on the client systems 130. … client systems 130 may be selected in a semi-random manner based at least in part on a probability conditioned on the current model and the data on the client systems 130 in order to optimize efficiency for training the federated learning model”; see also para. 0106: “The CU objects may comprise dialog-session data and features associated with the user input, which may be shared with all the modules of the assistant system 140”).
Regarding claims 14 and 28, Pu discloses: generating feedback that relates to performance of the pose tracking model in estimating the pose associated with the tracked object (by inherency, training a machine learning model involves generating feedback and updating the model based on the feedback), and updating a context used to select at least one of the set of sensor modalities or the pose tracking model based on the feedback (para. 0158, 0199).
Claim Rejections - 35 USC § 103
7. 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 of this title, 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.
8. Claims 10 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Pu et al. in view of Ni et al. (US 20230199161 A1).
Regarding claims 10 and 24, Pu does not mention explicitly: estimating one or more velocities associated with the tracked object or one or more parameters to calibrate the one or more sensors using the pose tracking model and the sensor inputs associated with the set of sensor modalities.
Ni discloses a computer implemented method and apparatus for tracking the pose of an object (Abstract), comprising: providing a variety of pluggable sensor modalities, each of the sensor modalities including one or more sensors (para. 0063); tracking the pose of the object using a pose tracking model based on inputs from the sensor modalities (para. 0062-0065); and estimating one or more velocities associated with the tracked object or one or more parameters to calibrate the one or more sensors using the pose tracking model and the sensor inputs associated with the set of sensor modalities (para. 0073: “ … usually produce some detection errors, which may result in jittering problem especially with a large number of views. … The method for temporal smoothing 604 can also be based on a number of views and user motion patterns, e.g., velocity, periodic, etc.”).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ni’s teaching of sensor calibration technique into Pu to arrive the claimed invention. Doing so would allow for removing possible error in sensor measurements thus improving the detection results of the movement data (Ni, Abstract; para. 0073).
Conclusion
9. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
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/X.S/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
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