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 . 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 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.
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
Claims 4 and 16 are objected to because of the following informalities:
Regarding claim 4, the recitation of “running on UE” should instead read –running on the UE--.
Regarding claim 16, the recitation of “wherein the instructions, the instructions” should instead read –wherein the instructions--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 6 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 6, there is no support for determining light intensity data from inertial sensor data. The inertial sensors do not provide light intensity data.
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 4, 6, and 16 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 pre-AIA the applicant regards as the invention.
The terms “good,” “bad,” “worse,” and “in-call” in claim 4 are relative and/or subjective terms which render the claim indefinite. The terms are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For example, it is unclear what criteria make something “good,” even with the presence of Fig. 6. What actually are the weights? Do they apply only to listed body parts, and if so, are there default values for other parts? Additionally, the term “worse” is different than “worst” in Fig. 6, and “in-call” is different than “call.”
Regarding claim 6, there is insufficient antecedent basis for the recitation of “the at least one exercise for the eyes.”
Regarding claim 16, the recitation of “cause the system to” is unclear because the claim is introduced as a UE claim, not a system claim.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 9-16 are directed to a “system” and a “user equipment (UE),” which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claims 1-8 are directed to a “method,” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 1, 9, and 16 recite (“set forth” or “describe”) the abstract idea of a mental process and a mathematical concept, substantially as follows:
determining an application type running on the UE; predicting, by a neural network, a holding orientation of the UE based on the inertial sensor data, the application type, and the touch screen data, wherein the holding orientation indicates whether a user is holding and currently operating the UE; determining, by the neural network, a body posture of the user and at least one impacted body part based on the inertial sensor data, based on the holding orientation; determining, by the neural network, an impact level of the at least one impacted body part based on the body posture, the holding orientation and the inertial sensor data, or
predict, by a neural network, a holding orientation of the UE based on the inertial sensor data and the touch screen data; determine, by the neural network, a body posture of a user and a first body part and a second body part impacted by the body posture, based on the holding orientation; determine, by the neural network, a first impact level of the first body part and a second impact level of the second body part based on the body posture; and recommend a body posture correction and at least one exercise for at least one of the first body part or the second body part, based on the first impact level and the second impact level.
The predicting and determining steps can be practically performed in the human mind, with the aid of a pen and paper, but for performance on a generic computer, in a computer environment, or merely using the computer as a tool to perform the steps. If a person were to see a printout of e.g. the inertial sensor data and the touch screen data, they would be able to make the associated determinations and predictions based on e.g. morphological features in the data. There is nothing to suggest an undue level of complexity in the predictions or determinations. Therefore, a person would be able to perform the steps mentally or with pen and paper.
The steps also involve the mathematical concepts of calculation/prediction/determination, including based on sequential steps, data synthesis, comparison with thresholds, etc. These steps correspond to “[w]ords used in a claim operating on data to solve a problem [that] can serve the same purpose as a formula.” See MPEP 2106.04(a)(2)(I).
Prong Two: Claims 1, 9, and 16 do not include additional elements that integrate the mental process or mathematical concept into a practical application. Therefore, the claims are “directed to” the mental process and mathematical concept. The additional elements merely:
recite the words “apply it” (or an equivalent) with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g. a neural network for performing processing/classification functions, a processor and a memory with instructions, etc.), and
add insignificant extra-solution activity (the pre-solution activity of: receiving inertial sensor data and touch screen data, using generic data-gathering components (e.g. an inertial sensor and a touch screen); and the post-solution activity of: recommending a correction and an exercise).
As a whole, the additional elements merely serve to gather and feed information to the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way (e.g., nobody needs to see or act on the recommendation). No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1, 9, and 16 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
Dependent Claims
The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
further describe the abstract idea (e.g. classifying and comparing body posture (claims 4 and 12), determining an angle, impact score, and impact level (claims 5 and 13), determining light intensity and impact level (claims 6 and 14), modifying the blood pressure signal based on behavior pattern information (claim 10), etc.),
further describe the extra-solution activity (or the structure used for such activity) (e.g. displaying information (claims 2 and 10), receiving feedback and updating the recommendation (claims 3 and 11), providing another recommendation (claims 6 and 14), specifying the inertial sensor (claims 7 and 15), specifying the data received (claim 8), etc.),
describe field-of-use context (e.g. the device being a smart phone having a fingerprint ID button, a flash lamp, and a camera (claims 4 and 5), etc.).
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way (e.g. nobody needs to see or act on the recommendations). They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves another technology or technical field, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 2021/0005070 (“Pellegrini”) in view of US Patent Application Publication 2020/0133450 (“Lu”), US Patent Application Publication 2022/0385327 (“lliffe-Moon”), and US Patent 11,302,448 (“Jain”).
Regarding claim 1, Pellegrini teaches [a] method for determining impact on at least one body part while using a user equipment (UE) and recommending at least one exercise (Abstract generally – also see ¶ 0163, corrective exercise program), the method comprising: receiving inertial sensor data (¶ 0083, sensor 202 of device 200 – also see ¶ 0090) and touch screen data of the user equipment (UE) (via the application as shown in e.g. Fig. 37); …; predicting … a holding orientation of the UE based on the inertial sensor data …, wherein the holding orientation indicates whether a user is holding and currently operating the UE (¶ 0071, improving phone viewing angle requires determining holding orientation); determining … a body posture of the user and at least one impacted body part based on the inertial sensor data, based on the holding orientation (¶¶s 0071, 0096, 0115, etc. – adjusting phone viewing angle to reduce neck impact); determining … an impact … of the at least one impacted body part based on the body posture, the holding orientation and the inertial sensor data (¶¶s 0069, 0071, etc., determining how poor/impactful the current angle is (on e.g. neck disc tears) based on the holding/viewing angle); and recommending a body posture correction and the at least one exercise for the at least one impacted body part based on the impact level (¶ 0071, using e.g. the LED flashes to recommend the better holding/viewing angle; ¶¶s 0163-0168, recommending a corrective exercise program based on impact/severity).
Pellegrini does not explicitly describe quantifying impact (i.e., an impact level) to the body part (although this is suggested).
Lu teaches using an ergonomic tracker to determine whether a strain on a user in performing a task exceeds a threshold (¶ 0036), and teaches evaluating strain for different body parts (¶ 0040).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to quantify impact level via e.g. a threshold, as in Lu, for the purpose of being able to address the impact based on clear criteria (Lu: ¶ 0036; Pellegrini: ¶ 0071).
Pellegrini-Lu does not appear to explicitly teach determining an application type running on the UE, and predicting a holding orientation based on the application type and the touch screen data.
lliffe-Moon teaches determining an application type and predicting a desired orientation based on the application type and touch screen data (¶ 0082, changing orientation based on the content type of the application, and based on the selection of the application via the interface). Thus, the interrelationship between application type, touch screen data, and device orientation is known.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine application type, and use application type together with touch screen data to predict a holding orientation, as in lliffe-Moon, for the purpose of making the prediction of holding orientation more accurate via more inputs, and for properly accounting for holding orientation when using the device (lliffe-Moon: ¶ 0082).
Pellegrini-Lu-lliffe-Moon does not appear to explicitly teach use of a neural network for processing (although Pellegrini does describe use of machine learning modules to run applications in ¶ 0177 and Fig. 45).
Jain teaches using machine learning models, including neural networks, as predictive models/processing elements (col. 35, lines 7-33).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a neural network for the predictions and determinations of the combination, as in Jain, for the purpose of implementing a known prediction/classification framework (Jain: col. 35, lines 7-33), and as a simple substitution with predictable results (making decisions based on data).
Regarding claim 9, Pellegrini teaches [a] system for determining impact on at least one body part while using a user equipment (UE) and recommending at least one exercise (Abstract generally – also see ¶ 0163, corrective exercise program), the system comprising: memory storing instructions (¶¶s 0177, 0178, 0180, etc.); and at least one processor configured to execute the instructions, wherein the instructions, when executed by the at least one processor (¶¶s 0177, 0178, 0180, etc.), cause the system to: receive inertial sensor data (¶ 0083, sensor 202 of device 200 – also see ¶ 0090) and touch screen data of the user equipment (UE) (via the application as shown in e.g. Fig. 37); …; predict … a holding orientation of the UE based on the inertial sensor data …, wherein the holding orientation of the UE indicates whether a user is holding and currently operating the UE (¶ 0071, improving phone viewing angle requires determining holding orientation); determine … a body posture of the user and at least one impacted body part based on the inertial sensor data, based on the holding orientation (¶¶s 0071, 0096, 0115, etc. – adjusting phone viewing angle to reduce neck impact); determine … an impact … of the at least one impacted body part based on the body posture, the holding orientation and the inertial sensor data (¶¶s 0069, 0071, etc., determining how poor/impactful the current angle is (on e.g. neck disc tears) based on the holding/viewing angle); and recommend a body posture correction and the at least one exercise for the at least one impacted body part based on the impact level (¶ 0071, using e.g. the LED flashes to recommend the better holding/viewing angle; ¶¶s 0163-0168, recommending a corrective exercise program based on impact/severity).
Pellegrini does not explicitly describe quantifying impact (i.e., an impact level) to the body part (although this is suggested).
Lu teaches using an ergonomic tracker to determine whether a strain on a user in performing a task exceeds a threshold (¶ 0036), and teaches evaluating strain for different body parts (¶ 0040).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to quantify impact level via e.g. a threshold, as in Lu, for the purpose of being able to address the impact based on clear criteria (Lu: ¶ 0036; Pellegrini: ¶ 0071).
Pellegrini-Lu does not appear to explicitly teach determining an application type running on the UE, and predicting a holding orientation based on the application type and the touch screen data.
lliffe-Moon teaches determining an application type and predicting a desired orientation based on the application type and touch screen data (¶ 0082, changing orientation based on the content type of the application, and based on the selection of the application via the interface). Thus, the interrelationship between application type, touch screen data, and device orientation is known.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine application type, and use application type together with touch screen data to predict a holding orientation, as in lliffe-Moon, for the purpose of making the prediction of holding orientation more accurate via more inputs, and for properly accounting for holding orientation when using the device (lliffe-Moon: ¶ 0082).
Pellegrini-Lu-lliffe-Moon does not appear to explicitly teach use of a neural network for processing (although Pellegrini does describe use of machine learning modules to run applications in ¶ 0177 and Fig. 45).
Jain teaches using machine learning models, including neural networks, as predictive models/processing elements (col. 35, lines 7-33).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a neural network for the predictions and determinations of the combination, as in Jain, for the purpose of implementing a known prediction/classification framework (Jain: col. 35, lines 7-33), and as a simple substitution with predictable results (making decisions based on data).
Regarding claim 16, Pellegrini teaches [a] user equipment (UE) comprising: an inertial sensor (¶ 0083); a touch screen (as shown in Fig. 37); memory storing instructions (¶¶s 0177, 0178, 0180, etc.); and at least one processor configured to execute the instructions, wherein the instructions, the instructions, when executed by the at least one processor (¶¶s 0177, 0178, 0180, etc.), cause the system to: receive inertial sensor data from the inertial sensor (¶ 0083, sensor 202 of device 200 – also see ¶ 0090) and touch screen data from the touch screen (via the application as shown in e.g. Fig. 37); …; predict … a holding orientation of the UE based on the inertial sensor data … (¶ 0071, improving phone viewing angle requires determining holding orientation); determine … a body posture of a user and a first body part and a second body part impacted by the body posture, based on the holding orientation (¶¶s 0071, 0096, 0115, etc. – adjusting phone viewing angle to reduce neck impact – also see ¶ 0169, correlating to the anatomical position of the neck and spine); determine … a first impact … of the first body part and a second impact … of the second body part based on the body posture (¶¶s 0069, 0071, etc., determining how poor/impactful the current angle is (on e.g. neck disc tears) based on the holding/viewing angle – also see ¶ 0169); and recommend a body posture correction and at least one exercise for at least one of the first body part or the second body part, based on the first impact level and the second impact level (¶ 0071, using e.g. the LED flashes to recommend the better holding/viewing angle; ¶¶s 0163-0169, recommending a corrective exercise program based on impact/severity).
Pellegrini does not explicitly describe quantifying impact (i.e., an impact level) to the body part (although this is suggested).
Lu teaches using an ergonomic tracker to determine whether a strain on a user in performing a task exceeds a threshold (¶ 0036), and teaches evaluating strain for different body parts (¶ 0040, neck, eye, arm, finger, etc.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to quantify impact level via e.g. a threshold, as in Lu, and to quantify it for different body parts (as already contemplated: Pellegrini – neck and spine), for the purpose of being able to address the impact based on clear criteria (Lu: ¶¶s 0036, 0040, etc.; Pellegrini: ¶ 0071).
Pellegrini-Lu does not appear to explicitly teach predicting a holding orientation based on the touch screen data.
lliffe-Moon teaches determining an application type and predicting a desired orientation based on an application type and touch screen data (¶ 0082, changing orientation based on the content type of the application, and based on the selection of the application via the interface). Thus, the interrelationship between application type, touch screen data, and device orientation is known.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine application type, and use application type as touch screen data to predict a holding orientation, as in lliffe-Moon, for the purpose of making the prediction of holding orientation more accurate via more inputs, and for properly accounting for holding orientation when using the device (lliffe-Moon: ¶ 0082).
Pellegrini-Lu-lliffe-Moon does not appear to explicitly teach use of a neural network for processing (although Pellegrini does describe use of machine learning modules to run applications in ¶ 0177 and Fig. 45).
Jain teaches using machine learning models, including neural networks, as predictive models/processing elements (col. 35, lines 7-33).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a neural network for the predictions and determinations of the combination, as in Jain, for the purpose of implementing a known prediction/classification framework (Jain: col. 35, lines 7-33), and as a simple substitution with predictable results (making decisions based on data).
Regarding claims 2 and 10, Pellegrini-Lu-lliffe-Moon-Jain teaches all the features with respect to the corresponding claims 1 and 9, as outlined above. Regarding claim 2, Pellegrini-Lu-lliffe-Moon-Jain further teaches wherein the recommending the body posture correction and the at least one exercise comprises: displaying an exercise repetition distribution indicating a frequency and a type of the at least one exercise to be performed by the user (Pellegrini: ¶¶s 0163-0169 (tracking training progress, a quick start program that has default settings, choosing from corrective exercises, etc.), ¶ 0071, reoccurring intervals); and displaying a video for the at least one exercise based on the at least one impacted body part and the impact level (Pellegrini: ¶ 0169).
Claim 10 is rejected in like manner.
Regarding claims 3 and 11, Pellegrini-Lu-lliffe-Moon-Jain teaches all the features with respect to the corresponding claims 2 and 10, as outlined above. Regarding claim 3, Pellegrini-Lu-lliffe-Moon-Jain further teaches receiving, by a touch screen of the UE, feedback from the user while the user performs the at least one exercise for the at least one impacted body part (Pellegrini: ¶ 0168 teaches tailoring the training format based on user input, and it would have been obvious to make this input (e.g. a survey) providable during a training session, as described in ¶ 0163, for the purpose of being able to address pain level and areas of pain as they come up, instead of only after the fact); and updating the exercise repetition distribution based on the feedback (Pellegrini: ¶ 0168, tailoring the training format based on user input).
Claim 11 is rejected in like manner.
Regarding claims 4 and 12, Pellegrini-Lu-lliffe-Moon-Jain teaches all the features with respect to the corresponding claims 1 and 9, as outlined above. Regarding claim 4, Pellegrini-Lu-lliffe-Moon-Jain further teaches wherein the determining the body posture of the user and the at least one impacted body part comprises: classifying the body posture of the user as one of good, bad, worse, or in-call based on the inertial sensor data and the application type running on UE while the user is holding and currently operating the UE (Pellegrini: ¶ 0071, describing use of an optimal angle, an angle range, or multiple angles based on severity. The angles are known results-effective variables because they can be changed as desired to tailor different degrees of severity for different users. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to classify the different types/degrees of posture based on different angles, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges through routine experimentation is not inventive. In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955)); and comparing the classified body posture with a predefined table to determine the at least one impacted body part, wherein the predefined table indicates the at least one body part corresponding to the classifying of the body posture (Pellegrini: ¶ 0071, etc.; Lu: ¶ 0040, impacting the neck and/or spine, or other body parts, based on thresholds as described).
Claim 12 is rejected in like manner.
Regarding claims 5, 6, 13, and 14, Pellegrini-Lu-lliffe-Moon-Jain teaches all the features with respect to the corresponding claims 1 and 9, as outlined above. Regarding claims 5 and 6, Pellegrini-Lu-lliffe-Moon-Jain further teaches wherein the determining the impact level of the at least one impacted body part based on the body posture comprises: determining an angle of usage of the UE (Pellegrini: ¶ 0007, orientation including angle of inclination), a duration of usage of the UE (Pellegrini: ¶ 0169, how long the user is in various positions), a proximity of the UE to a face of the user (Lu: ¶¶s 0012 and 0013, distance between the user and the device. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to evaluate distance in Pellegrini as in Lu, for the purpose of being able to determine and correct eye strain (Lu: ¶ 0013)), while the user is holding and currently operating the UE based on the inertial sensor data; computing an impact score for each of the at least one impacted body part based on the angle of usage, the duration of usage and the proximity to the face of the user (Pellegrini: ¶ 0168, based on an assessment of severity. It would have been obvious to use these additional factors (e.g. duration and proximity) to determine impact level for the purpose of more comprehensively evaluating the impact on the body); and determining the impact level of each of the at least one impacted body part based on the impact score, wherein the impact level is indicated as one of a high level, a medium level, and a low level (Pellegrini: ¶ 0071, describing use of an optimal angle, an angle range, or multiple angles based on severity/impact. The angles are known results-effective variables because they can be changed as desired to tailor different degrees of severity/impact for different users. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to classify different impact levels based on the different angles (and the other impact factors), since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges through routine experimentation is not inventive); determining, from the inertial sensor data, light intensity data of the UE and the proximity upon predicting the holding orientation (Lu: ¶ 0013, noting the effects of brightness (or lack thereof) on a user’s posture. Thus, it would have been obvious to track brightness/light intensity for the purpose of accounting for its effects on posture – also see ¶¶s 0012 and 0013, describing determination of proximity); determining, by the neural network, the impact level on eyes of the user based on the light intensity data and the proximity (Lu: ¶ 0040, thresholds related to eye strain and squinting); and recommending, by the neural network, the at least one exercise for the eyes of the user based on the impact level (as above, addressing the impact based on clear criteria (Lu: ¶ 0036; Pellegrini: ¶ 0071)).
Claims 13 and 14 are rejected in like manner.
Regarding claims 7 and 15, Pellegrini-Lu-lliffe-Moon-Jain teaches all the features with respect to the corresponding claims 1 and 9, as outlined above. Regarding claim 7, Pellegrini-Lu-lliffe-Moon-Jain further teaches wherein the inertial sensor data comprises data from at least one of an accelerometer and a gyroscope (Pellegrini: ¶ 0083).
Claim 15 is rejected in like manner.
Regarding claim 8, Pellegrini-Lu-lliffe-Moon-Jain teaches all the features with respect to claim 1, as outlined above. Pellegrini-Lu-lliffe-Moon-Jain further teaches wherein the touch screen data comprises at least one of touch coordinates, a hover distribution, and a duration of touch on a touch screen of the UE (lliffe-Moon: selecting an application requires mapping it to a particular location/coordinates. Also see Lu: ¶¶s 0013, 0020, etc., suggesting the tracking of duration of actions).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREY SHOSTAK whose telephone number is (408) 918-7617. The examiner can normally be reached Monday-Friday, 7am-3pm PT.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson, can be reached at telephone number (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREY SHOSTAK/Primary Examiner, Art Unit 3791
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