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 on 01/22/2026 has been entered. Claims 1-3 remain pending, and claims 4-20 have been newly added.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract ideas without significantly more.
Under Step 1 of the analysis of subject matter eligibility under 35 U.S.C. 101 as listed in MPEP § 2106, it is determined that claims 1 and 11 recite matter that falls within a statutory category of processes and a machine respectively, and are thus eligible under this step.
Next, under Step 2A Prong One of the analysis of subject matter eligibility, it is determined that the independent claims 1 and 11 recite the judicial exception of mental processes. The following processes of claims 1 and 11 are considered mental processes:
“determining a pedestrian class based on the motion data”, which encompasses a person looking at motion data and mentally classifying the data as indicative of motion or not;
“determining a device placement based on the motion data”, which encompasses a person looking at motion data and mentally determining where the device is based on the motion data as motion data has different properties based on the placement of the device;
“determining a device speed based on the motion data, the pedestrian class, and the device placement”, which encompasses a person being given motion data, a determination of whether the device is moving or not, a determination of where the device is places, and then applying a calculation with paper and pencil to determine the speed of the device;
“determining a direction of travel based on the motion data and the device placement”, which encompasses a person being given motion data and a placement of a device and computing a direction of travel using a pen and paper based on the data provided;
“determining a first velocity based on the pedestrian class, the estimate of direction of travel and the device speed”, which encompasses a person being given a pedestrian class, a direction of travel, and a speed, and computing a velocity using pen and paper;
“determining a second velocity based on a kinematic model and the motion data”, which encompasses a person using a kinematic model and pen and paper to determine a velocity when given motion data;
“selecting the first velocity or the second velocity based on selection logic”, which encompasses a person being given two velocities and a metric for choosing one or the other, and the person subsequently selecting one of the velocities; and
“determining a relative position of the device based on the selected estimated velocity”, which encompasses a person being given a previous device position and a velocity, the person using pen and paper to determine a new device position.
It is noted that some of these claimed processes are disclosed to be performed by “the at least one processor”. However, performing a process on a generic computer processor does prevent such processes from being considered mental processes. See MPEP § 2104.04(a)(2), subsection III.C.
Additionally, some of these processes are disclosed to be performed “with a neural network”. Per a recently issued MEMO by Charles Kim, Deputy Commissioner for Patents, entitled “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101”, “Claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping.” However, in the current claim language, the extent in which the neural network is described or used is only to perform the mental processes described. There is no sufficient process performed on or by this neural network, such as a sufficiently described training step, that would readily remove the claimed processes from being able to be “practically performed in the human mind”. Therefore, as the use of “a neural network” amounts to merely using the words “apply it” in regards to the mental processes, these claimed processes remain considered as mental processes.
Next, under Step 2A Prong Two of the analysis of subject matter eligibility, it is determined that these judicial exceptions are not integrated into a practical application.
The additionally claimed components of the independent claims, such as “the at least one processor”, “a neural network, and “a motion sensor of a mobile device”, are merely for gathering input data and/or performing the mental processes. The generic way in which these components are described fails to add significant extra-solution activity to the judicial exception. The additionally claimed process of “obtaining motion data from a motion sensor” amounts to pre-solution activity in the form of generic data gathering as necessary to perform the claimed mental processes, and also fails to integrate these processes into a practical application. There exists no post-solution activity in the claims to sufficiently integrate the judicial exceptions into a practical application.
Finally, under Step 2A Prong Two of the analysis of subject matter eligibility, it is determined that the independent claims 1 and 11 additionally do not include additional elements that are sufficient to amount to significantly more than the judicial exceptions.
In this regard, the generality of how the claims are defined fails to demonstrate an inventive concept beyond what is considered ordinary to those in the art. Applicant states in their specification that various PDR operations are well known in the art ([0003]). These PDR operations “include estimates of speed and direction of travel (DoT)” ([0003]), just as is included as part of the present claims. As a result of the nonspecific language of the claimed invention, the recognized pre-solution activity is a generic method for obtaining the device data that is used in PDR estimation, with a variety of methods to obtain said measurements being well known to those in the art.
Claims 2-4, 6, 9-10, 12-14, 16, and 19-20 fail to add limitations that remove the recognized mental processes above from being able to be performed practically in the human mind, and fail to integrate these mental processes into a practical application.
Claims 5, 7-8, 15, and 17-18 now additionally recite the judicial exception of a mathematical concept as these claims define how mathematical calculations are performed on inputted variables in order to produce a direction of travel (DOT). Additionally, the other processes as part of the independent claims still remain drawn to the remaining processes in the independent claims, which are still recognized as mental processes that remain unintegrated into a practical application.
Therefore, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2 and 11-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 11 recite that the relative position of the device is determined “based on the selected estimated velocity”. There is insufficient antecedent basis for this limitation in the claims as no velocities are being estimated in the claims. Applicant is recommended to delete the term “estimated”.
Claims 2 and 12 recite “wherein the second velocity is determined at a start of each epoch and runs for a specified period of time”. An epoch is recognized by the examiner as the start of a period of time. However, there’s no explicit period of time in which the epoch is in reference to. Additionally, it is unclear what is being claimed to “run for a specified period of time”. Therefore, it is indefinite what the epoch is referring to in the claim, and what is being claimed to run for a period of time.
Claim Interpretation
With regards to the 112(b) rejections given to claims 2 and 12 above, for the purpose of conducting prior art searching for the limitations added by these claims, examiner is interpreting these claims to specify that the second velocity is determined at an epoch of a period of time, and is repeatedly determined to allow the first velocity to stabilize.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4, 6, 10-12, 14, 16, and 20 are rejected under 35 U.S.C. 103 as being obvious over Bellusci et al. (US 20160123738 A1) in view of Shin et al. (NPL 'Motion Recognition-Based 3D Pedestrian Navigation System Using Smartphone').
Regarding claim 1, Bellusci teaches a method comprising:
obtaining, with at least one processor, motion data from a motion sensor of a mobile device ([0007]);
determining, with the at least one processor, pedestrian class based on the motion data ([0070]: the step frequency is calculated and certain equations are applied if the step frequency is nonzero, i.e. the class is pedestrian, as opposed to the step frequency being estimated to be zero, i.e. the class is non-pedestrian);
determining a device speed based on the motion data and the pedestrian class ([0033], [0049], and [0101]: step frequency, i.e. pedestrian class, is determined from motion data, and step frequency and motion data are used to determine the speed of the device);
determining, with the at least one processor, a direction of travel based on the motion data ([0054]: the walking direction, i.e. direction of travel, is estimated);
determining, with the at least one processor, a first velocity based on the pedestrian class, the estimate of direction of travel and the device speed ([0067-0068] and [0070]: the corrected velocity estimation, i.e. a first velocity, is estimated using an equation involving the estimated speed and walking direction applied with the estimated step frequency is determined to be non-zero, i.e. when the class is pedestrian);
determining, with the at least one processor, a second velocity based on a kinematic model and the motion data ([0065]: the new sensing unit velocity, i.e. a second velocity, is determined as a result of motion data inputted to a velocity mechanization equation, i.e. kinematic model);
and determining, with the at least one processor, a relative position of the device based on the estimated velocity ([0098]).
The preferred embodiment of Bellusci teaches that position of the device is determined based on a first velocity that is the result of the correction of the second velocity. It does not explicitly teach an embodiment where the estimated velocity used to determine a relative position is chosen by selecting, with the at least one processor, the first velocity or the second velocity based on selection logic.
However, Bellusci that the velocity estimation produced by the velocity mechanization equation, i.e. the second velocity, maintains its accuracy for a certain period of time ([0026] and [0048]). Bellusci further teaches that the frequency of analysis can be modified by to be shorter or longer, with a lower frequency resulting in reduced processing and memory requirements, and a higher frequency resulting in increased fidelity and responsiveness ([0066]). Additionally, Bellusci teaches that the equation used to produce the corrected velocity estimation i.e. the first velocity, may selectively not be performed in certain scenarios, such as when a stillness or no-step condition is determined, and a well-known zero velocity update (ZUPT) can be performed instead ([0070-0071]).
As such, a skilled artisan would have been able and motivated to include a step of selecting, with the at least one processor, the first velocity or the second velocity based on selection logic. This would be advantageous so the equation for estimating the first velocity is selectively not performed in scenarios where the second velocity is deemed to be accurate, such as when the frequency of analysis is sufficiently low, or the device is in a state of stillness.
Thus, it would have been obvious to one of ordinary skill in the art at the effective date of filing to combine the teachings of Bellusci by selectively using a first velocity or second velocity estimation based on a reasonable expectation of success and motivation to increase the processing speed and efficiency of the method by avoiding the need to perform the computational step of estimating the first velocity when the device is in situations where the accuracy of the second velocity estimation remains high.
Although Bellusci teaches determining a device speed based on step frequency and first direction of travel, and additionally teaches that models can be used to convert step frequency and step length into speed ([0049]), it does not specify what model is used and does not incorporate any factors of the placement of the device. Bellusci does not teach that the method includes a step of determining, with a neural network, a device placement based on the motion data, using a neural network to determine a device speed based on the device placement, and using the device placement to determine the direction of travel.
In the same field of endeavor, Shin teaches a PDR method comprising:
determining, with a neural network, a device placement based on the motion data (Page 6979, Col. 1, Paras. 2-3 and Col. 2, Para. 1: neural networks are used to recognize and classify human motion, with the classifications of motion indicating that the device’s placement as in a pocket, in a swinging hand, in a hand for a phone call etc., as noted in Table II),
using a neural network to determine a step frequency and step length based on the device placement (Page 6982, Col. 1, Para. 2: step frequency is detected “according to each motion” as determined by a neural network, as seen in Table III; Page 6983, Col. 2, Para. 2: “different methods of the step length estimation should be applied according to the recognized motion” as determined by a neural network, as seen in Table IV);
and using the device placement to determine the direction of travel (Page 6984, Col. 1, Para. 1: the motion classifier recognizes the motion, then a heading classifier is chosen to determine the heading, i.e. direction of travel, based on the determined type of motion).
One of ordinary skill in the art would have been able to modify Bellusci with the teachings of Shin for the use of a neural network in determining a step frequency, step length, and heading determination. Bellusci uses the step frequency and step length to determine the device speed ([0049] and [0101]), meaning that the resulting combination would determine a device speed with a neural network. It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the process of Bellusci in this way based on a reasonable expectation of success and motivation to achieve the result of an increase accuracy of the resulting PDR methods as achieved by the inventions of Shin (see section VII, where the performance of the disclosed teachings of Shin is analyzed and found to achieve accurate results. “These positioning results verify the feasibility of the advanced PDR and the applicability of the system to indoor navigation for pedestrians.”
Regarding claim 2, Bellusci teaches:
wherein the second velocity is determined at a start of each epoch and runs for a specified period of time to allow the first velocity to stabilize ([0062] and [0065-0066] and Eq. (1): the second velocity is calculated at the start of an epoch, i.e. new sensing unit velocity at a new instant, with respect to the velocity at a previous instant, and is repeatedly determined “for time intervals up to several seconds” to allow for a removal of measurement outliers, thus resulting in a stabilized, more accurate velocity to be calculated).
Regarding claim 4, Bellusci teaches:
wherein the device placement is one of on-body, in-hand, on- wrist, on-leg or unknown ([0029]: operations performed when phone is in pocket of a user, in a user’s hand, etc.).
Regarding claim 6, Bellusci teaches:
wherein the device placement is in-hand, the motion data includes roll, pitch and yaw angles in a device body reference frame, and the direction of travel (DoT) is determined from the roll, pitch and yaw angles ([0054]: “signals from a 3D accelerometer and a 3D gyroscope” are the angles / angular accelerations in three-dimensions, i.e. x, y, and z direction, which is recognized as the roll, pitch, and yaw; [0055-0057] and [0062]: when the phone is in-hand, these signals are filtered and used to determine the walking direction).
Regarding claim 10, while Bellusci does teach the determining of a pedestrian class ([0041] and [0070]: the step frequency is calculated and certain equations are applied if the step frequency is nonzero, i.e. the class is pedestrian, as opposed to the step frequency being estimated to be zero, i.e. the class is non-pedestrian), it does not teach that this is determined by a neural network.
Shin teaches that such a determination is performed by a neural network (Page 6979, Col. 1, Paras. 2-3 and Col. 2, Para. 1: neural networks are used to recognize and classify human motion, with the classifications of motion indicating that user is walking/running, i.e. a pedestrian, or standing, i.e. non-pedestrian).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Bellusci to use a neural network in the determination of pedestrian class based on a reasonable expectation of success and motivation, as taught by Shin, of the ease of the process and higher level of performance provided by the incorporation of a neural network compared to alternative means for classifying user motion (Page 6979, Col. 1 Para. 3 and Col. 2 Para. 1).
Regarding claim 11, Bellusci teaches a system comprising:
motion sensors configured to provide motion data ([0024]);
at least one processor ([0024]);
and memory storing instruction that when executed by the at least one processor causes the at least one processor to perform operations ([0025]) comprising:
obtaining motion data from a motion sensor of a mobile device ([0007]);
determining a pedestrian class based on the motion data ([0070]: the step frequency is calculated and certain equations are applied if the step frequency is nonzero, i.e. the class is pedestrian, as opposed to the step frequency being estimated to be zero, i.e. the class is non-pedestrian);
determining a device speed based on the motion data and the pedestrian class ([0033], [0049], and [0101]: step frequency, i.e. pedestrian class, is determined from motion data, and step frequency and motion data are used to determine the speed of the device);
determining a direction of travel based on the motion data ([0054]: the walking direction, i.e. direction of travel, is estimated);
determining a first velocity based on the pedestrian class, the estimate of direction of travel and the device speed ([0067-0068] and [0070]: the corrected velocity estimation, i.e. a first velocity, is estimated using an equation involving the estimated speed and walking direction applied with the estimated step frequency is determined to be non-zero, i.e. when the class is pedestrian);
determining a second velocity based on a kinematic model and the motion data ([0065]: the new sensing unit velocity, i.e. a second velocity, is determined as a result of motion data inputted to a velocity mechanization equation, i.e. kinematic model);
and determining a relative position of the device based on the estimated velocity ([0098]).
The preferred embodiment of Bellusci teaches that position of the device is determined based on a first velocity that is the result of the correction of the second velocity. It does not explicitly teach an embodiment where the estimated velocity used to determine a relative position is chosen by selecting the first velocity or the second velocity based on selection logic.
However, Bellusci that the velocity estimation produced by the velocity mechanization equation, i.e. the second velocity, maintains its accuracy for a certain period of time ([0026] and [0048]). Bellusci further teaches that the frequency of analysis can be modified by to be shorter or longer, with a lower frequency resulting in reduced processing and memory requirements, and a higher frequency resulting in increased fidelity and responsiveness ([0066]). Additionally, Bellusci teaches that the equation used to produce the corrected velocity estimation i.e. the first velocity, may selectively not be performed in certain scenarios, such as when a stillness or no-step condition is determined, and a well-known zero velocity update (ZUPT) can be performed instead ([0070-0071]).
As such, a skilled artisan would have been able and motivated to include a step of selecting the first velocity or the second velocity based on selection logic. This would be advantageous so the equation for estimating the first velocity is selectively not performed in scenarios where the second velocity is deemed to be accurate, such as when the frequency of analysis is sufficiently low, or the device is in a state of stillness.
Thus, it would have been obvious to one of ordinary skill in the art at the effective date of filing to combine the teachings of Bellusci by selectively using a first velocity or second velocity estimation based on a reasonable expectation of success and motivation to increase the processing speed and efficiency of the method by avoiding the need to perform the computational step of estimating the first velocity when the device is in situations where the accuracy of the second velocity estimation remains high.
Although Bellusci teaches determining a device speed based on step frequency and first direction of travel, and additionally teaches that models can be used to convert step frequency and step length into speed ([0049]), it does not specify what model is used and does not incorporate any factors of the placement of the device. Bellusci does not teach that the method includes a step of determining a device placement based on the motion data, using a neural network to determine a device speed based on the device placement, and using the device placement to determine the direction of travel.
In the same field of endeavor, Shin teaches a PDR method comprising:
determining a device placement based on the motion data (Page 6979, Col. 1, Paras. 2-3 and Col. 2, Para. 1: neural networks are used to recognize and classify human motion, with the classifications of motion indicating that the device’s placement as in a pocket, in a swinging hand, in a hand for a phone call etc., as noted in Table II),
using a neural network to determine a step frequency and step length based on the device placement (Page 6982, Col. 1, Para. 2: step frequency is detected “according to each motion” as determined by a neural network, as seen in Table III; Page 6983, Col. 2, Para. 2: “different methods of the step length estimation should be applied according to the recognized motion” as determined by a neural network, as seen in Table IV);
and using the device placement to determine the direction of travel (Page 6984, Col. 1, Para. 1: the motion classifier recognizes the motion, then a heading classifier is chosen to determine the heading, i.e. direction of travel, based on the determined type of motion).
One of ordinary skill in the art would have been able to modify Bellusci with the teachings of Shin for the use of a neural network in determining a step frequency, step length, and heading determination. Bellusci uses the step frequency and step length to determine the device speed ([0049] and [0101]), meaning that the resulting combination would determine a device speed with a neural network. It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the process of Bellusci in this way based on a reasonable expectation of success and motivation to achieve the result of an increase accuracy of the resulting PDR methods as achieved by the inventions of Shin (see section VII, where the performance of the disclosed teachings of Shin is analyzed and found to achieve accurate results. “These positioning results verify the feasibility of the advanced PDR and the applicability of the system to indoor navigation for pedestrians.”
Regarding claim 12, Bellusci teaches:
wherein the second velocity is determined at a start of each epoch and runs for a specified period of time to allow the first velocity to stabilize ([0062] and [0065-0066] and Eq. (1): the second velocity is calculated at the start of an epoch, i.e. new sensing unit velocity at a new instant, with respect to the velocity at a previous instant, and is repeatedly determined “for time intervals up to several seconds” to allow for a removal of measurement outliers, thus resulting in a stabilized, more accurate velocity to be calculated).
Regarding claim 14, Bellusci teaches:
wherein the device placement is one of on-body, in-hand, on- wrist, on-leg or unknown ([0029]: operations performed when phone is in pocket of a user, in a user’s hand, etc.).
Regarding claim 16, Bellusci teaches:
wherein the device placement is in-hand, the motion data includes roll, pitch and yaw angles in a device body reference frame, and the direction of travel (DoT) is determined from the roll, pitch and yaw angles ([0054]: “signals from a 3D accelerometer and a 3D gyroscope” are the angles / angular accelerations in three-dimensions, i.e. x, y, and z direction, which is recognized as the roll, pitch, and yaw; [0055-0057] and [0062]: when the phone is in-hand, these signals are filtered and used to determine the walking direction).
Regarding claim 20, while Bellusci does teach the determining of a pedestrian class ([0041] and [0070]: the step frequency is calculated and certain equations are applied if the step frequency is nonzero, i.e. the class is pedestrian, as opposed to the step frequency being estimated to be zero, i.e. the class is non-pedestrian), it does not teach that this is determined by a neural network.
Shin teaches that such a determination is performed by a neural network (Page 6979, Col. 1, Paras. 2-3 and Col. 2, Para. 1: neural networks are used to recognize and classify human motion, with the classifications of motion indicating that user is walking/running, i.e. a pedestrian, or standing, i.e. non-pedestrian).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Bellusci to use a neural network in the determination of pedestrian class based on a reasonable expectation of success and motivation, as taught by Shin, of the ease of the process and higher level of performance provided by the incorporation of a neural network compared to alternative means for classifying user motion (Page 6979, Col. 1 Para. 3 and Col. 2 Para. 1).
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being obvious over Bellusci in view of Shin as applied to claims 1 and 11 above, and further in view of Yang et al. (CN 112734938 A).
Regarding claim 3, the prior combination does not teach the limitations of the claim.
In the same field of endeavor, Yang teaches a PDR method, including:
wherein a maximum bound is placed on the device speed ([0106]: walking speed is bounded by a threshold that is “the upper limit of pedestrian walking speed”).
It would have been obvious to one of ordinary sill in the art at the effective date of filing to modify the prior combination by bounding the device speed based on a reasonable expectation of success and motivation, as taught by Yang, of "avoid[ing] speed prediction errors that could lead to large deviations in subsequent pedestrian position predictions" ([0106]).
Regarding claim 13, the prior combination does not teach the limitations of the claim.
In the same field of endeavor, Yang teaches a PDR method, including:
wherein a maximum bound is placed on the device speed ([0106]: walking speed is bounded by a threshold that is “the upper limit of pedestrian walking speed”).
It would have been obvious to one of ordinary sill in the art at the effective date of filing to modify the prior combination by bounding the device speed based on a reasonable expectation of success and motivation, as taught by Yang, of "avoid[ing] speed prediction errors that could lead to large deviations in subsequent pedestrian position predictions" ([0106]).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being obvious over Bellusci in view of Shin as applied to claims 4 and 14 above, and further in view of Hare et al. (US 20150022447 A1) and Jansen et al. (NPL ‘How gravity and muscle action control mediolateral center of mass excursion during slow walking’).
Regarding claim 5, Bellusci teaches:
wherein the device placement is on-body ([0029]: the device is on the body of the user, including in their pocket), and the motion data includes acceleration data ([0033]: “3D accelerometer samples”).
Bellusci teaches representing the acceleration data ([0033]), but it is not privy to the mechanical aspects of what this data represents, and does not teach that it is indicative of a cyclical vertical oscillation of a center of mass of a user of the mobile device accompanied by a lateral left and right sway of the center of mass when the user is stepping. However, one of ordinary skill in the art would have recognized that these limitations are an invariable property of how the center of mass of a person accelerates as they walk. Therefore, it is implicit that the acceleration data is indicative of a cyclical vertical oscillation of a center of mass of a user of the mobile device accompanied by a lateral left and right sway of the center of mass when the user is stepping as these are intrinsic properties of human motion. This is further evidenced by Jansen, which analyzed the acceleration of center mass of user’s while stepping and showed that the center of mass of humans undergoes a cyclical, vertical acceleration in the vertical direction, and is accompanied by sway of the center of mass in the medial and lateral direction (see Fig. 1 of Jansen: the center of mass (COM) experiences cyclical vertical oscillation, and the COM in the mediolateral direction experiences sway, depending on the % gait cycle).
Bellusci teaches representing the acceleration data in a plane normal to the gravity vector ([0052]), and does not teach that representing the acceleration data by a space curve in a three-dimensional (3D) acceleration space, computing a tangent-normal-binormal (TNB) reference frame from the acceleration data, where the TNB reference frame describes instantaneous geometric properties of the acceleration space curve over time; and computing the DoT of the user based on an orientation of a B unit vector of the TNB reference frame.
Analyzing the motion of an object in a TNB reference frame, i.e. a Frenet-Serret frame, is a well-known mathematical practice. In the field of the localization and the tracking of a user’s motion, Hare teaches:
representing the data of an object by a space curve in a three-dimensional (3D) space ([0068-0069] and [0081]: object is represented in a 3D space as shown in Fig. 3B);
computing a tangent-normal-binormal (TNB) reference frame from the data, where the TNB reference frame describes instantaneous geometric properties of the space curve over time ([0068-0069]: the tangent, normal, and binormal vectors are calculated to represent the data overtime, as seen in Fig. 3B. TNB reference frames, i.e. Frenet-Serret frames, “describe the kinematic properties of a particle moving along a continuous, differentiable curve in 3D space”); and
computing the DoT of the user based on an orientation of a B unit vector of the TNB reference frame ([0069]: “direction of movement” is calculated).
A skilled artisan would have been able to apply this TNB frame analysis to the acceleration data of Bellusci, modifying the representation of acceleration in Bellusci to occur in a TNB reference frame as opposed to a frame normal to gravity. The principles of TNB acceleration vectors are well known, with the motion of the object being represented in the osculating plane of the T and N unit vectors, which is based on the orientation of the B unit vector as the B unit vector is perpendicular to the osculating plane.
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Bellusci to represent and analyze the acceleration data in a TNB reference frame based on a reasonable expectation of success and motivation, as taught by Hare, of improved motion tracking of an object in a Frenet-Serret coordinate system as opposed to a traditional Cartesian coordinate system, which Hare teaches are ”suboptimal for filtering of motion to smooth out noise in 3D space” ([0032-0033]).
Regarding claim 15, Bellusci teaches:
wherein the device placement is on-body ([0029]: the device is on the body of the user, including in their pocket), and the motion data includes acceleration data ([0033]: “3D accelerometer samples”).
Bellusci teaches representing the acceleration data ([0033]), but it is not privy to the mechanical aspects of what this data represents, and does not teach that it is indicative of a cyclical vertical oscillation of a center of mass of a user of the mobile device accompanied by a lateral left and right sway of the center of mass when the user is stepping. However, one of ordinary skill in the art would have recognized that these limitations are an invariable property of how the center of mass of a person accelerates as they walk. Therefore, it is implicit that the acceleration data is indicative of a cyclical vertical oscillation of a center of mass of a user of the mobile device accompanied by a lateral left and right sway of the center of mass when the user is stepping as these are intrinsic properties of human motion. This is implicit principle further evidenced by Jansen, which analyzed the acceleration of center mass of user’s while stepping and showed that the center of mass of humans undergoes a cyclical, vertical acceleration in the vertical direction, and is accompanied by sway of the center of mass in the medial and lateral direction (see Fig. 1 of Jansen: the center of mass (COM) experiences cyclical vertical oscillation, and the COM in the mediolateral direction experiences sway, depending on the % gait cycle).
Bellusci teaches representing the acceleration data in a plane normal to the gravity vector ([0052]), and does not teach that representing the acceleration data by a space curve in a three-dimensional (3D) acceleration space, computing a tangent-normal-binormal (TNB) reference frame from the acceleration data, where the TNB reference frame describes instantaneous geometric properties of the acceleration space curve over time; and computing the DoT of the user based on an orientation of a B unit vector of the TNB reference frame.
Analyzing the motion of an object in a TNB reference frame, i.e. a Frenet-Serret frame, is a well-known mathematical practice. In the field of the localization and the tracking of a user’s motion, Hare teaches:
representing the data of an object by a space curve in a three-dimensional (3D) space ([0068-0069] and [0081]: object is represented in a 3D space as shown in Fig. 3B);
computing a tangent-normal-binormal (TNB) reference frame from the data, where the TNB reference frame describes instantaneous geometric properties of the space curve over time ([0068-0069]: the tangent, normal, and binormal vectors are calculated to represent the data overtime, as seen in Fig. 3B. TNB reference frames, i.e. Frenet-Serret frames, “describe the kinematic properties of a particle moving along a continuous, differentiable curve in 3D space”); and
computing the DoT of the user based on an orientation of a B unit vector of the TNB reference frame ([0069]: “direction of movement” is calculated).
A skilled artisan would have been able to apply this TNB frame analysis to the acceleration data of Bellusci, modifying the representation of acceleration in Bellusci to occur in a TNB reference frame as opposed to a frame normal to gravity. The principles of TNB acceleration vectors are well known, with the motion of the object being represented in the osculating plane of the T and N unit vectors, which is based on the orientation of the B unit vector as the B unit vector is perpendicular to the osculating plane.
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Bellusci to represent and analyze the acceleration data in a TNB reference frame based on a reasonable expectation of success and motivation, as taught by Hare, of improved motion tracking of an object in a Frenet-Serret coordinate system as opposed to a traditional Cartesian coordinate system, which Hare teaches are ”suboptimal for filtering of motion to smooth out noise in 3D space” ([0032-0033]).
Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being obvious over Bellusci in view of Shin as applied to claims 4 and 14 above, and further in view of LeMarchand et al. (US 20160313126 A1).
Regarding claim 7, the prior combination does not consider when the device placement is on-wrist or on-leg, and does not teach the rest of the claim.
In the same field of endeavor, LeMarchand teaches a system for analyzing human motion, wherein the device placement is on-wrist or on-leg ([0118] and [0184]: device can be worn on the wrist) and determining the direction of travel (DoT) includes:
determining a maximum arm rotation magnitude from an arm-swing trajectory based on the motion data ([0085]: determines “rotation transformation” using “components of the acceleration signal”; [0069] and [0071]: determines when the stride “essentially exhibits power”, i.e. when the signals due to rotational acceleration reach a maximum magnitude, as seen in the peaks of the acceleration graphs of Fig. 12);
determining a principal axis of rotation based on the maximum arm rotation magnitude ([0078-0080]: the medio-lateral axis, i.e. principal axis of rotation, is computed by analyzing which axis “exhibits a power spike [i.e. maximum arm rotation magnitude] at the stride rate”); and
determining the DoT to be 90 degrees offset from the principal axis of rotation ([0078-0079] and [0139]: the determined medio-lateral axis is “perpendicular and horizontal to the principal axis of the trajectory”, i.e. direction of travel, or the antero-posterior direction).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to perform the operations of LeMarchand when the device is on the wrist of a human based on a reasonable expectation of success and motivation to accuracy determine the direction of travel when the device is on the wrist of the user and for the motivation, as taught by LeMarchand, of improving the accuracy of the determination of direction of travel by accounting for accelerations perpendicular to the direction of advance that could obfuscate any estimated direction of travel ([0017]).
Regarding claim 8, LeMarchand teaches:
wherein arm-swing direction is disambiguated using inertial vertical acceleration at inflection points on the arm-swing trajectory ([0081]: vertical acceleration is used to differentiate between forward/backward trajectory and lateral trajectory of the pedestrian as the forward backward trajectory acceleration has the principle of being 90 degrees out of phase with the vertical acceleration as seen in Fig. 12).
Regarding claim 17, the prior combination does not consider when the device placement is on-wrist or on-leg, and does not teach the rest of the claim.
In the same field of endeavor, LeMarchand teaches a system for analyzing human motion, wherein the device placement is on-wrist or on-leg ([0118] and [0184]: device can be worn on the wrist) and determining the direction of travel (DoT) includes:
determining a maximum arm rotation magnitude from an arm-swing trajectory based on the motion data ([0085]: determines “rotation transformation” using “components of the acceleration signal”; [0069] and [0071]: determines when the stride “essentially exhibits power”, i.e. when the signals due to rotational acceleration reach a maximum magnitude, as seen in the peaks of the acceleration graphs of Fig. 12);
determining a principal axis of rotation based on the maximum arm rotation magnitude ([0078-0080]: the medio-lateral axis, i.e. principal axis of rotation, is computed by analyzing which axis “exhibits a power spike [i.e. maximum arm rotation magnitude] at the stride rate”); and
determining the DoT to be 90 degrees offset from the principal axis of rotation ([0078-0079] and [0139]: the determined medio-lateral axis is “perpendicular and horizontal to the principal axis of the trajectory”, i.e. direction of travel, or the antero-posterior direction).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to perform the operations of LeMarchand when the device is on the wrist of a human based on a reasonable expectation of success and motivation to accuracy determine the direction of travel when the device is on the wrist of the user and for the motivation, as taught by LeMarchand, of improving the accuracy of the determination of direction of travel by accounting for accelerations perpendicular to the direction of advance that could obfuscate any estimated direction of travel ([0017]).
Regarding claim 18, LeMarchand teaches:
wherein arm-swing direction is disambiguated using inertial vertical acceleration at inflection points on the arm-swing trajectory ([0081]: vertical acceleration is used to differentiate between forward/backward trajectory and lateral trajectory of the pedestrian as the forward backward trajectory acceleration has the principle of being 90 degrees out of phase with the vertical acceleration as seen in Fig. 12).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being obvious over Bellusci in view of Shin as applied to claims 1 and 11 above, and further in view of Janardhanan et al. (US 20120136573 A1).
Regarding claim 9, the prior combination doesn’t teach that the device speed is fused from multiple device speeds.
In the same field of endeavor, Janardhanan using sensor fusion for pedestrian navigation estimation, wherein the device speed is fused from multiple device speeds ([0165] and [0167]).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to fuse a device speed from multiple device speeds based on a reasonable expectation of success and motivation to improve the accuracy of the device speed by relying on additional sources to correlate the speed estimation, thereby being less susceptible to outliers.
Regarding claim 19, the prior combination doesn’t teach that the device speed is fused from multiple device speeds.
In the same field of endeavor, Janardhanan using sensor fusion for pedestrian navigation estimation, wherein the device speed is fused from multiple device speeds ([0165] and [0167]).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to fuse a device speed from multiple device speeds based on a reasonable expectation of success and motivation to improve the accuracy of the device speed by relying on additional sources to correlate the speed estimation, thereby being less susceptible to outliers.
Response to Arguments
Applicant's arguments filed 01/22/2026 have been fully considered.
Regarding the 35 U.S.C. 101 rejection given, applicant argues over the analysis in Step 2A, Prong One, contending that the amendments to the independent claim 1 that add that a neural network is used in some of the processes now result in “claims 1-3 [being] not directed to an abstract idea under Step 2A, Prong One”. This argument is unpersuasive.
The mere incorporation of a neural network into a claimed processed as performed in a method does not restrict that process from being able to be practically performed in the human mind. As discussed above, a human is very capable of performing the mental processes recited. The mere mention that a neural network is used fails to demonstrate how a person is not otherwise able to perform the mental processes as there is no mention of how this neural network is used to perform the mental processes other than its generic recital. Therefore, the claim is considered to recite judicial exceptions.
Applicant argues over the analysis in Step 2A, Prong Two, contending that “the Examiner evaluated the additional elements completely separate from the recited judicial exception elements, thereby failing to take into consideration all the claim limitations and how these limitations interact and impact each other”. This argument is unpersuasive.
The component of “the at least one processor” and “a neural network” are some of the additional elements recited. The “at least one processor” and “a neural network” are described as a very general means of performing the mental processes described. “The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” (see MPEP § 2106.05(f)). The use of a generic processor and a generic neural network amount to no more than the equivalent of the words “apply it” with the judicial exceptions, and thus fail to integrate them into a practical application.
The claims recite the additional element and extra-solution activity of “obtaining, with at least one processor, motion data from a motion sensor of a mobile device”. This element and extra-solution activity performed only interact with the claimed judicial exceptions in that they provides the motion data as necessary to perform them. Given the generic way they are described, they are recognized as mere data gathering.
Applicant further argues that “the ‘estimating steps’ are not mere data gathering but rather are transforming motion data into various parameters for determine pedestrian dead reckoning”. Examiner notes that these previous “estimating steps” were considered mental processes in the analysis of subject matter eligibility. This remains reflected in the current rejection. The only extra-solution activity recognized in the 101 rejections given is the activity of “obtaining, with at least one processor, motion data from a motion sensor of a mobile device”, which is pre-solution activity in the form of mere data gathering.
Applicant further argues that “the claims do not simply recite, without more, the mere desired result of pedestrian dead reckoning, but rather recite ‘a specific solution for accomplishing that goal.’” This argument is unpersuasive.
As described in applicant’s disclosure, “PDR typically includes estimates of speed and direction of travel (DoT) to determined a pedestrian velocity vector in a local inertial reference frame” ([0003]). These principles are included in the independent claims, and the independent claims as a whole are generically described so that the claims remain directed to Pedestrian Dead Reckoning and do not disclose a particular improvement. Applicant’s disclosed improvement in their disclosure is that “the disclosed embodiment enable PDR estimation using inertial sensors of a mobile device for different device placements on a user’s body” ([0006]) However, given the generic way in which the independent claims are described, such as the nondescriptive way in which the device placement is used in this claim, this improvement is not reflected in the independent claims. Furthermore, PDR estimation methods for different device placements are already known in the art, such as shown in the cited prior art in this rejection. Additionally, “the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception… Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field” (see MPEP § 2106.05(a)). Therefore, one of ordinary skill in the art would not have recognized an improvement from the use of a processor and a neural network in the performing of PDR as the use of such components is well known.
Subsequently, the judicial exceptions are not integrated into a practical application.
Applicant argues over the analysis in Step 2B, contending that “the elements labeled by the Examiner as abstract are not insignificant post-solution activity or well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality to the judicial exception. Rather, these elements describe specific implementation steps for pedestrian dead reckoning.” This argument is unpersuasive.
“An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself… Instead, an ‘inventive concept’ is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself” (see MPEP § 2106.05). As stated above, the extra-solution activity and the additional components recited are insufficient to integrate the judicial exceptions into a practical application. When further considering these additional elements when searching for an inventive concept, the generic nature in which they are described fails to demonstrate such an inventive concept beyond what is previously known to the industry. Furthermore, a variety of methods for performing PDR based on the placement of a mobile device on a person are well known in the art. This is reflected in the cited prior art. Therefore, the claims are not considered to amount to significantly more than the judicial exceptions they recite.
Applicant’s arguments regarding the previous rejection under 35 U.S.C 103 have been considered and are persuasive. Therefore, a new rejection under 35 U.S.C 103 has been furnished in the presently filed office action as necessitated by applicant’s amendments to the claims.
Conclusion
The following art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Trigoni et al. (US 20170241787 A1)
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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACK R. BREWER whose telephone number is (571)272-4455. The examiner can normally be reached 10AM-6PM.
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/JACK ROBERT BREWER/Examiner, Art Unit 3663
/ADAM D TISSOT/Primary Examiner, Art Unit 3663
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