DETAILED ACTION
Claims 12 and 14-22 are presented for examination.
Claims 12 and 14-22 have been amended.
Claim 13 has been canceled.
This office action is in response to the amendment submitted on 27-JAN-2026.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed Application No. EP20159822.4, filed on 02/27/2020.
Response to Arguments – 35 USC 101
On pgs. 6 of the Applicant/Arguments Remarks, Applicant argues the amended claims have overcome the rejection under 35 USC 101. The applicant argues the invention integrates into a practical application that enables quick and accurate orientation of a measuring device.
The invention as recited in the claims amounts to nothing more than what a human can do with the aid of a pen and paper except it is being done with the aid of a computer.
Limitations that invoke a computer as a tool to perform an abstract idea fall within the “apply it” category. See MPEP 2106.04(d) referencing MPEP 2106.05(f)(2) — example (i) A commonplace business method or mathematical algorithm being applied on a general purpose computer. Similar to applying a mathematical algorithm on a general purpose computer, performing specific mathematical calculations or mental processes on a general purpose computer is using a computer as a tool to perform an abstract idea. As noted in the cases referenced by MPEP 2106.05(f), when the additional elements are mere instructions to apply the abstract idea on a general purpose computer, the additional elements do not integrate the judicial exception into a practical application. If the claim as a whole integrates the recited judicial exception into a practical application, then it would be patent eligible. Here, the claim is generally linked to the technology of predicting aircraft movement and actions based on predicted movements, but, as drafted, the claim only refers to using machine learning to perform the actions, which is generally linking the use of the judicial exception to a particular field of use. See MPEP 2106.04(d) referencing 2106.05(h).
Additionally as recited in the MPEP 2106.05(f): Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on "the draftsman’s art").
Response to Arguments – 35 USC 103
On pg. 7-8, the applicant argues that Chaplot doesn’t address suitability, distance/angle measurement nor does it assess if additional measurements are required. Moreover, Chaplot doesn’t comprise all the claimed actions/steps.
The examiner notes that while Chaplot may not expand on the detailed specifics of the measurements, the combination of Moller and Chaplot makes obvious the claimed invention. Chaplot provides the ML and probability belief map underpinnings in the context of observation/measurement, while Moller provides the detailed measurement steps. The examiner points to Moller’s Fig. 5 which illustrates the logical flow of the measurements/action. The rejection is updated per the newly amended claims below.
The applicant’s arguments have been fully considered but are not persuasive. The rejection under 35 USC 103 is maintained.
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 12 and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 12
Step 1: Statutory class – process.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes
“3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).” MPEP § 2106.04(a).
The claims are directed to an abstract idea of data processing and analysis. The claim recites:
assess whether a further distance/angle measurement by the measuring device is required and whether a measuring position of the measuring device is suitable
defining a group of actions comprising a first action indicating no further distance/angle measurement by the measuring device is required; a second action indicating further distance/angle measurement by the measuring device is required and the measuring position is unsuitable; a third action indicating further distance/angle measurement by the measuring device is required and the measuring position is suitable
initializing a probability grid for the position and/or orientation of the measuring device in the measuring environment;
performing a sequence of steps, comprising: A first step in which the artificial neural network assesses whether further distance/angle measurement by the measuring device is required and also assesses whether the measuring position is suitable; A second step in which a best action is determined from among the group of actions based on the assessment; A third step in which the sequence is terminated when the best action is determined to be the first action the sequence otherwise proceeds to a fourth step, and the fourth step in which: when the best action is determined to be the second action, the measuring position is adjusted and the sequence returns to the first step when the best action is determined to be the third action, the further distance/angle measurement is carried out by the measuring device at the measuring position,
the probability grid is updated based on the further distance/angle measurement and the sequence returns to the first step: in response to terminating the sequence, calculating the position and/or orientation of the measuring device based on the probability grid.
The assessing, initializing a probability grid, and determining/performing a set of action based on certain conditions are limitations of mental processes of evaluation, judgement and mathematical calculations. By way of example, one can mentally evaluate the current state of a measuring device, create a probability grid that calculates the probability of each location on the grid, and determine based on the probability the best course of action from a set of predefined steps.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The additional elements are:
Providing an artificial neural network configured to
The trained ANN limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer.
Additionally, it is merely indicating a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a
particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. MPEP § 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than judicial exception?
No, as discussed with respect to Step 2A, the additional limitation is mere instructions to apply an exception on a generic computer and a general purpose computer. They do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B. Further, in regards to step 2B and as cited above in step 2A, MPEP 2106.05(g) “Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir.2011)” is merely data gathering. The additional elements have been considered both individually and as an ordered combination in the significantly more consideration. This claim is ineligible.
Claim 14 recites the artificial neural network is configured to assess the measuring position of the measuring device as suitable if an inaccuracy in determining the position and/or orientation of the measuring device is reduced, which is a mental/mathematical process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 15 recites the trained artificial neural network is configured to deny the need for further distance/angle measurement by the measuring device if an inaccuracy in determining the position and/or orientation of the measuring device falls below a specified value, which is a mental/mathematical process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 16 recites further in response to the second action being determined as the best action, at least one image of the measuring environment is recorded in an old measuring position and/or the new measuring position by a camera device, which is mere data gathering under Step 2A Prong Two and 2B. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 17 recites the group of actions also includes a fifth action, which is a mental process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 18 recites when the best action is determined to be the fifth action, the measuring position is adjusted by an adjustment direction and/or an adjustment angle that is different from the second action, and the sequence returns to the first step, which is a mental/mathematical process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 19 recites when the best action is determined to be the fifth action, the further distance/angle measurement is caried out by the measuring device at the measuring position with a measuring time and/or measuring accuracy different from the third action, the probability grid is updated based on the further distance/angle measurement, and the sequence returns to the first step, which is a mental/mathematical process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 20 recites a method for precisely specifying a position and/or orientation of a measuring device, wherein the position and/or orientation of the measuring device has been determined by the method of claim 12, which is a mathematical process under Step 2A Prong One. Therefore, the claim is considered ineligible under 35 USC 101.
Claim 21 recites an apparatus for determining a position and/or orientation of a measuring device in a measuring environment, comprising: (statutory category – machine)
a control device communicatively coupled to the measuring device: and a memory storing software that configures the control device to execute the method of claim 12, which is mere instructions to apply an exception on a generic computer under Step 2A Prong Two and 2B. MPEP § 2106.05(f).
Claim 22 recites a non-transitory computer readable medium storing computer executable software (statutory category – machine)
that when executed by a control device causes the control device to carry out the method of claim 12, which is mere instructions to apply an exception on a generic computer under Step 2A Prong Two and 2B. MPEP § 2106.05(f).
The remaining limitations are similar to claim 1 and are rejected under the same rationale. Therefore, the claim is considered ineligible under 35 USC 101.
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 12 and 14-22 are rejected under 35 U.S.C. 103 as being unpatentable over Moller et al. (US20200065432A1) in view of Chaplot et al. (ACTIVE NEURAL LOCALIZATION).
Regarding claim 12, Moller teaches A computer-implemented method for determining a position and/or orientation of a measuring device in a measuring environment that is mapped in a geometry model, Wherein the measuring device measures distance/angle relative to the target objects, the method comprising ([0013-0014] “Some embodiments of the present invention relate to a method for automatically deriving and/or providing of stationing zones for an electronic measuring and/or marking device in a worksite environment. The device is therein configured for a measuring and/or marking at the worksite environment, in particular by an aimable light beam e.g. by a boning unit. The method comprises a querying of a database or data file for construction plan information for the worksite environment and an acquiring of a worksite-task-information of a worksite-task to be executed. This worksite-task-information in particular comprises spatial points in the construction plan which have to measured and/or marked to accomplish the worksite-task. The method also comprises an acquiring of at least coarse 3D-data of the actual real world worksite environment and a geometrical merging of the at least coarse 3D-data and the construction plan information to form an actual state model of the worksite environment.”)
Providing an artificial neural network configured to assess whether a further distance/angle measurement by the measuring device is required and whether a measuring position of the measuring device is suitable ([0029] “Some aspects of the invention also relate to an electronic measuring and/or marking device, e.g. embodied as mentioned, which comprises a 3d sensor unit for acquiring the at least coarse 3D-data and with a computation unit configured for the method according to the invention. The electronic measuring and/or marking device can therein e.g. comprise an opto-electronic distance measuring unit (EDM) as at least one of the measurement functionalities of the device, a stationing means for stationing the device at the worksite and a pointing hinge for adjusting the direction of the distance measuring. The device can also comprise a powered deflection unit for deflecting the direction of measurement light of the electronic distance measuring unit, for automatically pointing the distance measuring into ascertainable directions to aim the spatial points. This can be done fully automatically, e.g. by motorized hinges with position encoders. Another embodiment without motorized hinges can comprise an indicator for guiding a user to hand-adjust the pointing hinge to point the distance measurement into the direction towards the measurement point. In particular latter can comprise a rough manual adjustment for pointing the measurement substantially in the direction of the spatial point—combined with a powered automatic deflection unit for fine adjusting the direction of the measurement and/or marking light.” Also see [0051] and [0071])
Defining a group of actions comprising:
a first action indicating no further distance/angle measurement by the measuring device is required (Fig 5, 45 forks to 46 when further measurement requirements are canceled. [0063] “Once the fine location is defined within a predefined margin, but still not accurately enough to be considered final, according to the present invention, the further measurement points can be automatically selected at characteristic points within the construction plan information or an actual state information derived from the database.” EN: When it’s accurate enough it’s considered final and hence no more measurements are required. [0096-0097] describe an example where once the measurements are within a certain threshold no more measurements are collected and the next function is performed.)
a second action indicating further distance/angle measurement by the measuring device is required and the measuring position is unsuitable (Fig 5, 45 loops back to 43 for further adjustment when location is unsuitable. [0052] “For achieving such, care has to be taken to station the device 1 at a location at the worksite, from where the device 1 will not be obstructed in its measurement and/or marking function. For example, as illustrated that a view from the device 1 to the marking locations 2 b,2 b, and/or to the measurement points 13 a,13 b,13 c is not obstructed. At the shown stationing location of the device 1, a line of sight of the device 1 to the marking location 2 b is obstructed by the cardboard box 8 b, so this point cannot be marked, wherefore the shown stationing location is not advantageous for the present task of fixing the light 12.” And [0082] “For example, in a special embodiment, the device 1 can at first be randomly stationed by the worker at a location at the worksite, which he assumes to be a good one. The device 1 will then automatically reference itself to the room, (e.g. as in EP 3 222 969) and will in particular try to measure the spatial points for the task. If such does not resolve with the construction plan, the device can automatically calculate a preferred stationing location according to the invention, wherein the measurements taken for the referencing of the device can at least partially form the coarse 3d-data. The device 1 can then e.g. automatically indicate a new stationing location directly at the worksite by its marking functionality and direct the worker to re-station the device 1 accordingly. Optionally, this special embodiment can be looped until an appropriate stationing location is found.” [0082] “For example, in a special embodiment, the device 1 can at first be randomly stationed by the worker at a location at the worksite, which he assumes to be a good one. The device 1 will then automatically reference itself to the room, (e.g. as in EP 3 222 969) and will in particular try to measure the spatial points for the task. If such does not resolve with the construction plan, the device can automatically calculate a preferred stationing location according to the invention, wherein the measurements taken for the referencing of the device can at least partially form the coarse 3d-data. The device 1 can then e.g. automatically indicate a new stationing location directly at the worksite by its marking functionality and direct the worker to re-station the device 1 accordingly. Optionally, this special embodiment can be looped until an appropriate stationing location is found.”)
a third action indicating further distance/angle measurement by the measuring device is required and the measuring position is suitable (Fig 5, 46 provides further measurement when position is suitable. [0060] “The worker 11 can then station the device 1 about the stationing zone 10 provided to him, e.g. in a floor plan view at his mobile device 14. Once stationed, measurement capabilities of the electronic measuring and/or marking device 1 can determine and survey at least one measurement point 13 in a spatial environment of the electronic measuring device 1, preferably substantially automatically by the device 1. The electronic measuring device 1 can thereby derive its correct actual location with and can then measure in the direction towards the measurement and/or marking points 2 c,2 b as defined in the worksite-task. When doing so, in addition also further spatial information can be comprised, for example an orientation derived by a goniometer, a compass, a plumb- or level-direction derived by an electronic level or gravity sensor, or the like.” EN: [0061] further describes the measurement taking from this suitable location)
performing a sequence of steps comprising:
A first stepfurther distance/angle measurement by the measuring device is required and also assesses whether the measuring position is suitable (Fig. 5, 45 and [0060-0061])
A third step in which the sequence is terminated when the best action is determined to be the first action ([0063] and [0096-0097])
the sequence otherwise proceeds to a fourth step, and the fourth step in which: when the best action is determined to be the second action, the measuring position is adjusted and the sequence returns to the first step (EN: [0082] describes the process looping until an acceptable measurement is achieved in which case the process stops )
when the best action is determined to be the third action, the further distance/angle measurement is carried out by the measuring device at the measuring position (Fig. 5, 46 and [0060-0061])
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However, Moller doesn’t appear to explicitly teach:
providing an artificial neural network
initializing a probability grid for the position and/or orientation of the measuring device in the measuring environment
A first step in which the artificial neural network assesses whether further … measurement … is required
A second step in which a best action is determined from among the group of actions based on the assessment
the probability grid is updated based on the further distance/angle measurement and the sequence returns to the first step:
in response to terminating the sequence, calculating the position and/or orientation of the measuring device based on the probability grid
Chaplot teaches:
providing an artificial neural network (Pg. 1, Abstract, "“Active Neural Localizer”, a fully differentiable neural network that learns to localize accurately and efficiently" Pg. 3, Proposed Model, “The overall architecture of the proposed model, Active Neural Localizer (ANL), is shown in Figure 1. It has two main components: the perceptual model and the policy model. At each timestep t, the perceptual model takes in the agent’s observation, st and outputs the Likelihood Map Lik(st). The belief is propagated through time by taking an element-wise dot product with the Likelihood Map at each timestep. Let Bel(yt) be the Belief Map at time t before observing st. Then the belief, after observing st, denoted by Bel(yt),”).
initializing a probability grid for the position and/or orientation of the measuring device in the measuring environment (Pg. 6, Baselines, "We use a geometric variant of Markov localization where the state space is represented by fine-grained, regularly spaced grid, called position probability grids" Pg. 3, Proposed Model, “Representation of Belief and Likelihood Let yt be a tuple Ao,Ax,Ay where Ax,Ay and Ao denote agent’s x-coordinate, y-coordinate and orientation respectively. Let M × N be the map size, and O be the number of possible orientations of the agent. Then, Ax ∈ [1,M], Ay ∈ [1,N] and Ao ∈ [1,O]. Belief is represented as an O × M ×N tensor, where (i,j,k)th element denotes the belief of agent being in the corresponding state, Bel(yt = i,j,k). This kind of grid-based representation of belief is popular among localization methods as if offers several advantages over topological representations”).
A first step in which the artificial neural network assesses whether further … measurement … is required (Pg. 6, Baselines, “The ‘utility’ of an action a at time t is defined as the expected reduction in the uncertainity of the agent state after taking the action a at time t and making the next observation at time t + 1: Ut(a) = H(yt) − Ea[H(yt+1)], where H(y) denotes the entropy of the belief: H(y) = − yBel(y)logBel(y), and Ea[H(yt+1)] denotes the expected entropy of the agent after taking the action a and observing yt+1. The ‘cost’ of an action refers to the time needed to perform the action. In our environment, each action takes a single time step, thus the cost is constant.”)
A second step in which a best action is determined from among the group of actions based on the assessment (Fig. 1 shows the logic and flow for action determination where at each step the best action is determined based on probability. Pg. 3, Background, “Bayesian filters (Fox et al., 2003) are used to probabilistically estimate a dynamic system’s state using observations from the environment and actions taken by the agent. Let yt be the random variable representing the state at time t. Let st be the observation received by the agent and at be the action taken by the agent at time step t. At any point in time, the probability distribution over yt conditioned over past observations s1:t−1 and actions a1:t−1 is called the belief, Bel(yt) = p(yt|s1:t−1,a1:t−1) The goal of Bayesian filtering is to estimate the belief sequentially. For the task of localization, yt represents the location of the agent, although in general it can represent the state of the any object(s) in the environment…. where Lik(st) = p(st|yt) is the likelihood of observing st given the location of the agent is yt, and Z =Σyt Lik(st)Bel(yt) is the normalization constant. The likelihood of the observation, Lik(st) is given by the perceptual model and p(yt|yt−1,at−1), i.e. the probability of landing in a state yt from yt−1 based on the action, at−1, taken by the agent is specified by a state transition function, ft. The belief at time t = 0, Bel(y0), also known as the prior, can be specified based on prior knowledge about the location of the agent. For global localization, prior belief is typically uniform over all possible locations of the agent as the agent position is completely unknown.” Also see Pg. 4, Proposed Model, “The Belief Map, after observing st, is passed through the policy model to obtain the probability of taking any action, π(at|Bel(yt)). The agent takes an action at sampled from this policy. The Belief Map at time t +1 is calculated using the transition function (fT), which updates the belief at each location according to the action taken by the agent, i.e. p(yt+1|yt,at).”)
the probability grid is updated based on the further distance/angle measurement and the sequence returns to the first step (Pg. 4, Model Architecture, “The Belief Map, after observing st, is passed through the policy model to obtain the probability of taking any action, π(at|Bel(yt)). The agent takes an action at sampled from this policy. The Belief Map at time t +1 is calculated using the transition function (fT), which updates the belief at each location according to the action taken by the agent, i.e. p(yt+1|yt,at).” Pg. 9, Figure 4, “Belief maps show the probability of being at a particular location. Darker shades imply higher probability. The belief of its orientation and agent’s true orientation are also highlighted by colors. For example, the Red belief map shows the probability of agent facing east direction at each x-y coordinate.”)
in response to terminating the sequence, calculating the position and/or orientation of the measuring device based on the probability grid (Pg. 5, Experiments, “At the end of the episode, the prediction is the state with the highest probability in the Belief Map. If the prediction is correct, i.e. y∗ = argmaxyt Bel(yt) where y∗ is the true state of the agent, then the agent receives a positive reward of 1.” Fig. 4 shows the orientation and coordinates are included with the probability grid/belief map)
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Moller and Chaplot are analogous art because they are from the same field of endeavor in robotic measurement and localization. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Moller and Chaplot to incorporate Chaplot’s neural network, and probability grid based action assessment and selection to have better results with expected results (Chaplot, Pg. 4, “This kind of grid-based representation of belief is popular among localization methods as if offers several advantages over topological representations”).
Regarding Claim 14 Moller in view of Chaplot teaches the method of claim 12. Chaplot further teaches the artificial neural network is configured to assess the measuring position of the measuring device as suitable if an inaccuracy in determining the position and/or orientation of the measuring device is reduced (Pg 5, Experiments, "At each time step, the agent receives an intermediate reward equal to the maximum probability of being in any state, rt = maxyt (Bel(yt)). This encourages the agent the reduce the entropy of the Belief Map in order to localize as fast as possible").
Regarding Claim 15 Moller in view of Chaplot teaches the method of claim 12. Chaplot further teaches the trained artificial neural network is configured to deny the need for further distance/angle measurement by the measuring device if an inaccuracy in determining the position and/or orientation of the measuring device falls below a specified value (Pg 5, Experiments, "At the end of the episode, the prediction is the state with the highest probability in the Belief Map. If the prediction is correct, i.e. y ∗ = arg maxyt Bel(yt) where y∗ is the true state of the agent, then the agent receives a positive reward of 1" EN: indicating no further measurements will be taken).
Regarding Claim 16 Moller in view of Chaplot teaches the method of claim 12. Chaplot further teaches further in response to the second action being determined as the best action, at least one image of the measuring environment is recorded in an old measuring position and/or the new measuring position by a camera device (Pg. 6, Simulation Environments, "Apart from the map design, the agent also receives a set of images of the visuals seen by the agent at a few locations uniformly placed around the map in all 4 orientations. These images, called memory images, are required by the agent for global localization," and Pg. 2, Related work, "the visual odometry-based methods only predict the relative motion between consecutive frames or with respect to the initial frame using camera images" shows the images are camera images).
Regarding Claim 17 Moller in view of Chaplot teaches the method of claim 12. Moller further teaches the group of actions also includes a fifth action (Fig 5, Measure 1 = Step 41, Measure 2 = Step 45. Adjust 1 = 43 and Adjust 2 = 44. [0082] Also explicitly explains the multiple measurements from multiple locations).
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Regarding Claim 18 Moller in view of Chaplot teaches the method of claim 17. Moller further teaches when the best action is determined to be the fifth action, the measuring position is adjusted by an adjustment direction and/or an adjustment angle that is different from the second action, and the sequence returns to the first step (Fig 4 shows device 1. Device is to be adjusted until it's in the optimum position. Once it's repositioned, the Adjust 1 and Adjust 2 will have different directions/angles. [0082-0083]).
Regarding Claim 19 Moller in view of Chaplot teaches the method of claim 17. Moller further teaches when the best action is determined to be the fifth action, the further distance/angle measurement is caried out by the measuring device at the measuring position with a measuring time and/or measuring accuracy different from the third action, the probability grid is updated based on the further distance/angle measurement, and the sequence returns to the first step (Fig 5 and [0082] show the measurements happening sequentially, thereby having different measuring times. The probability grid being updated at every step is covered by Chaplot).
Regarding Claim 20 Moller in view of Chaplot teaches the method of claim 12. Chaplot further teaches a method for precisely specifying a position and/or orientation of a measuring device, wherein the position and/or orientation of the measuring device has been determined by the method of claim 12 (Pg. 1, Abstract, "Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment…The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to localize accurately while minimizing the number of steps required for localization").
Regarding Claim 21 Moller in view of Chaplot teaches the method of claim 12. Moller further teaches an apparatus for determining a position and/or orientation of a measuring device in a measuring environment, comprising: a control device communicatively coupled to the measuring device: and a memory storing software that configures the control device to execute the method of claim 12 ([0002] “The present invention relates generally to a method for automatically deriving stationing zones for an electronic measuring and/or marking device in a worksite environment, as well as to a corresponding device, system and computer program product” and [0032] “A device or system according to some embodiments of the present invention comprise microcontrollers, microcomputers, DSPs or a programmable or hardwired digital logics, wherefore the present invention can involve a computer program product with program code being stored on a machine readable medium, which implements functionality according to the invention at least partially in software—which therefore is also an embodiment of the invention.”).
Regarding Claim 22 Moller in view of Chaplot teaches the method of claim 12. Moller further teaches a non-transitory computer readable medium storing computer executable software that when executed by a control device causes the control device to carry out the method of claim 12 ([0002] “The present invention relates generally to a method for automatically deriving stationing zones for an electronic measuring and/or marking device in a worksite environment, as well as to a corresponding device, system and computer program product” and [0032] “A device or system according to some embodiments of the present invention comprise microcontrollers, microcomputers, DSPs or a programmable or hardwired digital logics, wherefore the present invention can involve a computer program product with program code being stored on a machine readable medium, which implements functionality according to the invention at least partially in software—which therefore is also an embodiment of the invention.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhao et al (US-10452078-B2): Discloses automated robotic measurements.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMIR DARWISH whose telephone number is (571)272-4779. The examiner can normally be reached 7:30-5:30 M-Thurs.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lewis Bullock can be reached on 571-272-3759. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.E.D./Examiner, Art Unit 2187
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199