DETAILED ACTION
Introduction
Claims 1-7 and 9 have been examined in this application. Claims 1, 2, and 4-7 are amended. Claims 3 and 9 are original. Claim 8 is cancelled. This is the First Action On the Merits (FAOM). The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Office Action Formatting
The following is an explanation of the formatting used in the instant Office Action:
• [0001] – Indicates a paragraph number in the most recent, previously cited source;
• [0001, 0010] – Indicates multiple paragraphs (in example: paragraphs 1 and 10) in the most recent, previously cited source;
• [0001-0010] – Indicates a range of paragraphs (in example: paragraphs 1 through 10) in the most recent, previously cited source;
• 1:1 – Indicates a column number and a line number (in example: column 1, line 1) in the most recent, previously cited source;
• 1:1, 2:1 – Indicates multiple column and line numbers (in example, column 1, line 1 and column 2, line 2) in the most recent, previously cited source;
• 1:1-10 – Indicates a range of lines within one column (in example: all lines spanning, and including, lines 1 and 10 in column 1) in the most recent, previously cited source;
• 1:1-2:1 – Indicates a range of lines spanning several columns (in example: column 1, line 1 to column 2, line 1 and including all intervening lines) in the most recent, previously cited source;
• p. 1, ln. 1 – Indicates a page and line number in the most recent, previously cited source;
• ¶1 – The paragraph symbol is used solely to refer to Applicant's own specification (further example: p. 1, ¶1 indicates first paragraph of page 1); and
• BRI – the broadest reasonable interpretation.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on application JP2023-070470 filed in Japan on 4/21/2023. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/23/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Such claim limitations are:
(a) “a global planning unit” that computes a low-cost path, in Claim 9
(b) “a local planning unit” that specifies a velocity or angular velocity, in Claim 9,
(c) “a sensing unit” that acquires sensor information, in Claim 9,
(d) “a re-planning unit” that re-computes the low-cost path, in Claim 9,
(e) “a storage unit” that stores a time series, in Claim 9,
(f) “a timing determination unit” that determines, in Claim 9,
(g) “a communication unit” that notifies the moving body, in Claim 9.
The limitation(s) invoke 112(f) because the claim limitation(s) use the generic placeholder “unit“ that is coupled with the above functional language, without reciting sufficient structure to perform the recited function and without the generic placeholder being preceded by a structural modifier.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation:
(a) through (g): specification ¶0024 states that the units correspond to a CPU and algorithms for the functions.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-7 and 9 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.
Regarding Claims 1, 7, and 9, the limitation regarding specifying “for each time, a velocity or angular velocity of the moving body in order to follow the low-cost path” renders the claims indefinite. There is no further detail regarding “each time.” It is not clear whether the limitation is referring to a velocity being specified each time a path is generated, or whether the path is being divided into time steps or increments, or for example whether velocity for particular segments (therefore covering all times of the path) would read on the limitation. Additionally it is not clear whether the sensor information being acquired “at each time” is referring to the same time parameter as the specified velocity, or is merely referring to continuously acquiring sensor data over time, or something else. The scope of the claims is therefore indefinite. For the purposes of examination, the limitations are interpreted as specifying of velocity for particular discrete times and any sensor data acquired at any time is understood to read on the sensor data at each time.
Claims 2-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being dependent on rejected Claim 1 and for failing to cure the deficiencies listed above.
Regarding Claim 1, the limitation to “determine whether or not re-computing is to be performed” renders the claim indefinite. It is not stated what the re-computing is of. It is not clear whether the re-computing is referring to the previously recited re-computing of the low-cost path based on the map and the sensor information, or alternatively whether something else such as the obstacle information or trajectory or something else. The scope of the claim is therefore indefinite. For the purposes of examination, the limitation is interpreted as re-computing of the low-cost path based on the map and the sensor information.
Claims 2-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being dependent on rejected Claim 1 and for failing to cure the deficiencies listed above.
Regarding Claim 2, the recitation of “movement history data including the sensor information at each time, the trajectory of the moving body, the low-cost path at each time, and the goal point” renders the claim indefinite. The recitation of “history data” would appear to mean that the history data is from previous times that the moving body has traveled paths. However, the data include refers to “the sensor information” and “the trajectory” and “the low-cost path” and “the goal point” i.e. the same data from Claim 1, which would appear to describe the present path and use of the model after training, and not historical, past data used to train the model. The scope of the historical data and claim is therefore indefinite. For the purposes of examination, the recitation is understood to be of historical sensor data, historical trajectory, historical path, and historical goal point.
Claims 3-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being dependent on rejected Claim 2 and for failing to cure the deficiencies listed above.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(101 Analysis - Step 1 - Statutory Category) Regarding Claims 1-7, the claims are directed to one of the statutory categories of subject matter as the claims recite a process, machine, manufacture or composition of matter.
(101 Analysis - Step 2A, Prong I - Judicial Exception) Regarding Independent Claim 1, the claim recites a path planning device comprising:
a memory; and
at least one processor coupled to the memory, the at least one processor being configured to:
compute a low-cost path from a starting point to a goal point based on a pre-constructed map including positions of obstacles;
specify, for each time, a velocity or angular velocity of a moving body in order to follow the low-cost path;
acquire sensor information measured for obstacles in a periphery of the moving body at each time;
re-compute the low-cost path based on the map and the sensor information;
store a time series of the sensor information and a trajectory of the moving body; and
determine whether or not re-computing is to be performed using a pre-trained determination model for determining whether or not it is a timing for re-computing to be performed based on the time series of the sensor information, the trajectory of the moving body, the low-cost path, and the goal point.
The limitations indicated in BOLD above, under their broadest reasonable interpretation, are an abstract idea of a mental process, capable of being performed in a human mind or manually, using pen and paper (see MPEP 2106.04(a)(2)(III)).
Particularly, a person is capable of mentally or manually performing functions to:
compute a low-cost path from a starting point to a goal point based on a pre-constructed map including positions of obstacles (for example a person drawing possible paths on an indoor floor plan (with obstacles such as furniture or equipment) and computing a shortest path between two places (lowest distance cost));
specify, for each time, a velocity or angular velocity of a moving body in order to follow the low-cost path (the person deciding on a speed for the robot to travel the whole route, and direction per the drawn path (speed and direction forming velocity));
re-compute the low-cost path based on the map and sensor information (for example the person deciding on a new path given updated sensor information about the location of a new obstacle);
store a time series of the sensor information and a trajectory of the moving body (the person memorizing information about the obstacle and travel of the robot); and
determine whether or not re-computing is to be performed by determining whether or not it is a timing for re-computing to be performed based on the time series of the sensor information, the trajectory of the moving body (the person making a decision to re-plan or not, by deciding whether the obstacle information is going to be relevant to the robot’s travel i.e. based on present trajectory), the low-cost path (the trajectory being part of the overall path), and the goal point (the trajectory and path being how the robot proceeds to the goal point).
Thus, the claim recites an abstract idea.
(101 Analysis - Step 2A, Prong II - Practical Application) This judicial exception is not integrated into a practical application. The limitations indicated with underlining above are additional elements in the claim. That is, the additional elements in the claim are the path planning device comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured to perform the functions, the function to acquire sensor information measured for obstacles in a periphery of the moving body at each time, and the determining whether or not re-computing is to be performed using a pre-trained determination model.
For the path planning device comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured to perform the functions, these elements are all recitations of generic computer components and their use, recited at a high level of generality. The claims do not provide an improvement in computer hardware or computing technology. Therefore, the claims act as mere instructions to “apply” the abstract idea using generic computer components as tools to perform the functions. This does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
For the function to acquire sensor information measured for obstacles in a periphery of the moving body at each time, this is determined to be insignificant extra-solution activity, because it corresponds to mere data gathering which is necessary to perform the abstract idea. This does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
For the determining whether or not re-computing is to be performed using a pre-trained determination model, this is recited broadly, without any details of the model. Therefore, the claim merely generally ties the abstract idea to the field of machine learning, and does not limit the claim or abstract idea in a meaningful way. Therefore, it does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Additionally, the ordered combination of additional elements and claim as a whole are not determined to integrate the abstract idea into a practical application as the ordered combination does not add anything already present when the elements are considered separately and merely recites receiving and evaluation of data by a processor at a high level of generality.
(101 Analysis - Step 2B - Significantly More / Inventive Concept) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As above, the additional elements in the claim are the path planning device comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured to perform the functions, the function to acquire sensor information measured for obstacles in a periphery of the moving body at each time, and the determining whether or not re-computing is to be performed using a pre-trained determination model.
For the path planning device comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured to perform the functions, for the same reasons as presented above, these elements are all recitations of generic computer components and their use, at a high level of generality, such that the claims act as mere instructions to “apply” the functions using a generic computer components as tools to perform the functions. This does not amount to significantly more than the abstract idea (see MPEP 2106.05(f)). Additionally, such elements are well-understood, routine, and conventional in the art (see MPEP 2106.05(d) computer functions which are recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity include: ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)).
For the function to acquire sensor information measured for obstacles in a periphery of the moving body at each time which was determined to be insignificant extra-solution activity, this is re-evaluated in step 2B and determined to be well-understood, routine, and conventional in the art (see e.g. US20090254216A1 at [0002]).
For the determining whether or not re-computing is to be performed using a pre-trained determination model, for the same reasons as recited above, this is recited broadly, and generally ties the abstract idea to the field of machine learning, and does not limit the claim or abstract idea in a meaningful way. Therefore, it does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)). Additionally it is determined to be well-understood, routine, and conventional in the art (see e.g. US20090234783A1 at [0002])
Additionally, the ordered combination of additional elements and claim as a whole are not determined to amount to significantly more as the ordered combination recites input and processing of data at a high level of generality and additionally is well-understood, routine, and conventional in the art (see e.g. US20200166896A1 at [0002]).
Thus, the claim is not patent eligible.
Regarding Independent Claim 7, the claim recites the same abstract idea. The additional elements are the method performed by a computer. This is the recitation of generic computer components at a high level of generality and therefore the additional elements do not integrate the abstract idea into a practical application or amount to significantly more for the same reasons as presented above with respect to Claim 1.
Dependent Claims 2-6 do not recite further limitations that integrate the judicial exception into a practical application or amount to significantly more.
Claims 2-6 recites training and reward features of the model with particular data and particular data sources, however, this is an integral part of use of a reinforcement learning model and there is not particular detail or improvement in reinforcement learning as a technology. Thus, the claim only generally ties the abstract idea to the field of reinforcement learning and does not integrate the abstract idea into a practical application or amount to significantly more for the same reasons as presented above with respect to Claim 1.
Thus, the claims are not patent eligible.
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 1-7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Publication US2021/0325891A1 (Young et al.) in view of Publication US2021/0107648A1 (Augugliaro et al.), further in view of Published Application US2025/0191475A1 (Kiles et al.).
Regarding Claim 1, Young et al. discloses a path planning device (see Figure 1, asset 102) comprising:
a memory (see Figure 10, [0123, 0125] implemented as computer system); and
at least one processor coupled to the memory, the at least one processor being configured to (see [0124]):
compute a low-cost path (see Figure 4, [0045] path generation ML technique 120 generates graph, [0091] trained based on A* (i.e. low cost paths) and [0058] executive ML technique 124 determines next node (series of nodes being computed path)) from a starting point to a goal point (see Figure 4, [0041, 0053] starting location and target) based on a pre-constructed map including positions of obstacles (see [0058] mission environment 408, [0034] environment being 3D obstacle data);
acquire sensor information measured for obstacles in a periphery of the moving body (see [0061] during mission operation, new obstacle detected by sensor of asset) at each time (see [0058] Fig. 4 operations during runtime as asset maneuvers to subsequent nodes, or [0098] respond to obstacles in near real-time);
re-compute the low-cost path based on the map and the sensor information (see [0061] new graph generated with the additional data augmenting environment 408, [0062] for selection by the executive ML technique);
store the sensor information (see [0040]) and a trajectory of the moving body (see [0038, 0125] executed as computer program with memory, i.e. series of node positions at least temporarily stored); and
determine whether or not re-computing is to be performed (see [0062] whether to alter course) using a pre-trained determination model (see [0062] using executive ML technique 124, [0056] “trained using the executive ML technique training data 306”) for determining whether or not it is a timing for re-computing to be performed (see [0058, 0061, 0062] determine whether to alter course as part of runtime and obstacle detection “during flight”) based on the sensor information (see [0061-0062] based on new graph based on obstacle detected), the trajectory of the moving body (see [0061] during mission operation, i.e. based on selected nodes which results in new obstacle being detected), the low-cost path (see [0061] during mission operation, i.e. based on the path which is being traveled which results in the new obstacle), and the goal point (see [0061] based on flight to target).
Young et al. does not explicitly recite the processor configured to:
specify, for each time, a velocity or angular velocity of a moving body in order to follow the low-cost path.
However, Augugliaro et al. teaches a technique to specify an aircraft path, configured to:
specify, for each time, a velocity or angular velocity of a moving body in order to follow the low-cost path (see [0121] “each flight plan may further comprise any one or more of velocity, acceleration, and/or yaw orientation for the flying machine for discrete times over a time period”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the path of Young et al. to include velocity data as taught by Augugliaro et al., with a reasonable expectation of success, with the motivation of enhancing the robustness and flexibility of the system to specify further path data and sensor settings to avoid collisions (see Augugliaro et al., [0121]).
Young et al. does not explicitly recite the processor configured to:
store a time series of the sensor information,
and the decision based on: the time series of the sensor information.
However, Kiles et al. teaches a technique for handling flight data (see Figure 1A), including:
storing a time series of the sensor information (see Figure 1A, sensor data including video (a time series of frames) to database).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the sensor data and decision of Young et al. to use time series data as taught by Kiles et al. with a reasonable expectation of success, with the motivation of improving performance and safety by using cloud resources (see Kiles et al., [0003-0004]).
Regarding Claim 2, Young et al. discloses the path planning device of claim 1, wherein:
the at least one processor is further configured to train the determination model (see [0053] fig. 3 illustrates training for executive ML technique) using reinforcement learning (see [0037, 0058]) from movement history data from past starting points to goal points (see [0053-0054] using start and target from 302), which is movement history data including the sensor information at each time (see [0053] environment 308 [0040] sensor data – at each time per the combination with Kiles et al. in the rejection of Claim 1), the trajectory of the moving body (see [0053] 124 operated “at each node” i.e. series of positions of asset), the low-cost path at each time (see [0057] the series of selected nodes – at each time per the combination with Augugliaro et al. in the rejection of Claim 1), and the goal point (see [0053-0054] target at operation 302).
Regarding Claim 3, Young et al. discloses the path planning device of claim 2, wherein a reward in the reinforcement learning is determined based on whether or not a goal point has been arrived at (see [0057] reward as indication of effectiveness of completion of goal 310 which [0055] can include target) or an arrival time to the goal point.
Regarding Claim 4, Young et al. discloses the path planning device of claim 2, wherein the movement history data is obtained for a moving body that is the same as the moving body (see [0053, 0058] for same asset e.g. 102 for training in Figure 3 process and operation in Figure 4 process).
Regarding Claim 5, Young et al. discloses the path planning device of claim 2, wherein the movement history data is obtained for a moving body that is different from the moving body (see [0053, 0058] for different asset for training in Figure 3 process and operation in Figure 4 process, e.g. asset 102 and 104)..
Regarding Claim 6, Young et al. discloses the path planning device of claim 2, wherein the movement history data is obtained by simulation related to movement of the moving body (see [0091] example of training paths can be simulated).
Regarding Claim 7: all limitations as recited have been analyzed with respect to Claim 1. Claim 7 pertains to a method corresponding to the device of Claim 1. Claim 7 does not teach or define any new limitations beyond Claim 1, and therefore is rejected under the same rationale.
Regarding Claim 9, Young et al. discloses a movement control system including a control device (see Figure 10, [0123, 0125] implemented as computer system) and a moving body (see Figure 1, [0026] asset 102), wherein:
the control device comprises:
a global planning unit (see [0124]) that computes a low-cost path (see Figure 4, [0045] path generation ML technique 120 generates graph, [0091] trained based on A* (i.e. low cost paths) and [0058] executive ML technique 124 determines next node (series of nodes being computed path) from a starting point to a goal point (see Figure 4, [0041, 0053] starting location and target) based on a pre-constructed map including positions of obstacles (see [0058] mission environment 408, [0034] environment being 3D obstacle data);
a sensing unit (see [0124]) that acquires sensor information measured for obstacles in a periphery of the moving body (see [0061] during mission operation, new obstacle detected by sensor of asset) at each time (see [0058] Fig. 4 operations during runtime as asset maneuvers to subsequent nodes, or [0098] respond to obstacles in near real-time);
a re-planning unit (see [0124]) that re-computes the low-cost path based on the map and the sensor information (see [0061] new graph generated with the additional data augmenting environment 408, [0062] for selection by the executive ML technique);
a storage unit (see [0124]) that stores the sensor information (see [0040]) and a trajectory of the moving body (see [0038, 0125] executed as computer program with memory, i.e. series of node positions at least temporarily stored);
a timing determination unit (see [0124]) that determines whether or not computing by the re- planning unit is to be performed (see [0062] whether to alter course) using a pre-trained determination model (see [0062] using executive ML technique 124, [0056] “trained using the executive ML technique training data 306”) for determining whether or not it is a timing for computation to be performed by the re-planning unit (see [0058, 0061, 0062] determine whether to alter course as part of runtime and obstacle detection “during flight”) based on the time series of the sensor information (see [0061-0062] based on new graph based on obstacle detected), the trajectory of the moving body (see [0061] during mission operation, i.e. based on selected nodes which results in new obstacle being detected), the low-cost path (see [0061] during mission operation, i.e. based on the path which is being traveled which results in the new obstacle), and the goal point (see [0061] based on flight to target), and
the moving body:
moves according to the path of the moving body (see [0032-0033]).
Young et al. does not explicitly recite the system comprising:
a local planning unit that specifies, for each time, a velocity or angular velocity of the moving body in order to follow the low-cost path, and
the moving body:
moves according to the notified velocity or angular velocity of the moving body.
However, Augugliaro et al. teaches a technique to specify an aircraft path, configured to:
a local planning unit that specifies, for each time, a velocity or angular velocity of the moving body in order to follow the low-cost path, (see [0121] “each flight plan may further comprise any one or more of velocity, acceleration, and/or yaw orientation for the flying machine for discrete times over a time period”) and
the moving body:
moves according to the notified velocity or angular velocity (see [0115] autonomous flight).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the path of Young et al. to include velocity data as taught by Augugliaro et al., with a reasonable expectation of success, with the motivation of enhancing the robustness and flexibility of the system to specify further path data and sensor settings to avoid collisions (see Augugliaro et al., [0121]).
Young et al. does not explicitly recite the system comprising:
a storage unit that stores a time series of the sensor information
a communication unit that notifies the moving body of the velocity or angular velocity of the moving body specified for each time; and
the moving body:
notifies the control device of sensor information measured for obstacles in the periphery of the moving body at each time.
However, Kiles et al. teaches a technique for handling flight data (see Figure 1A), including:
a storage unit that stores a time series of the sensor information (see Figure 1A, sensor data including video (a time series of frames) to database),
a communication unit that notifies the moving body of the path (see Figure 1A, Claim 13, updating the flight plan from cloud to UAV); and
the moving body:
notifies the control device of sensor information measured for obstacles in the periphery of the moving body at each time (see Figure 1A, [0049] transmitting the sensor data including video, see Claims 17-18, obstacle).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system of Young et al. to use time series data and a separate cloud system and UAV as taught by Kiles et al. with a reasonable expectation of success, with the motivation of improving performance and safety by using cloud resources (see Kiles et al., [0003-0004]).
Conclusion
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/P.A./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
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