DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/280268 and the preliminary amendment filed therewith. This communication is the first action on the merits. Claims 1-9 are currently pending and have been rejected as follows.
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 .
Information Disclosure Statement
Applicant filed an Information Disclosure Statement (IDS) on 9/5/2023. This filing is in compliance with 37 C.F.R. 1.97.
As required by M.P.E.P. 609(C), the applicant's submission of the Information Disclosure Statement is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609(C), a copy of the PTOL -1449 form, initialed and dated by the examiner, is attached to the instant office action.
Drawings
The drawings filed on 9/5/2023 are acceptable as filed.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for generating a travel plan for traveling a plurality of points by a plurality of mobile bodies. Examiner formulates an abstract idea analysis, following the framework described in the MPEP, as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claim 7) and "system" (claims 1-6 and 8-9).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
… generator a travel plan for traveling a plurality of points by a plurality of mobile bodies by performing, at each output step, processing of selecting any one point out of the plurality of points and any one mobile body out of the plurality of mobile bodies by using a recurrent neural network configured to output visiting probabilities at the plurality of points and use probabilities of the plurality of mobile bodies when point information regarding the plurality of points and mobile body information regarding the plurality of mobile bodies are input; and … output the travel plan
Independent claim 7 recites substantially similar claim language covering substantially similar topics.
Dependent claims 2-6, 8, and 9 recite the same or similar abstract idea(s) as independent claims 1 and 7 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-9 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to generating a travel plan for traveling a plurality of points by a plurality of mobile bodies and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
"Mental processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)" as the limitations identified above include mere data observations, evaluations, judgements, and/or opinions, e.g. including user observation and evaluation through generating a travel plan for traveling a plurality of points by a plurality of mobile bodies, which is capable of being performed mentally and/or using pen and paper.
Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; as the claims recite processing performance indicators that include mathematical formulas, functions, and/or calculations including generating a travel plan for traveling a plurality of points by a plurality of mobile bodies through calculations and equations.
Step 2A - Prong 2: Claims 1-9 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" A travel plan generation device, comprising: generation circuitry configured to … output circuitry configured to output the travel plan… / A non-transitory computer readable medium storing a program for causing a computer to serve as each element included in the travel plan generation device according to claim 1 / A non-transitory computer readable medium storing a program for causing a computer to perform the steps of claim 7," (claims 1, 8, and 9) however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of processing data from a generic "circuitry" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1-9 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of generating a travel plan for traveling a plurality of points by a plurality of mobile bodies as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to generating a travel plan for traveling a plurality of points by a plurality of mobile bodies.
Claims 1-9 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication Number 2021/0383213 to Kawasaki (hereafter referred to as Kawasaki) in view of U.S. Patent Number 11466998 to Williams et al. (hereafter referred to as Williams).
As per claim 1, Kawasaki teaches:
A travel plan generation device, comprising: generation circuitry configured to generator (Paragraph Number [0019] teaches a prediction device according to an embodiment includes one or more hardware processors. The hardware processors acquire moving object information indicating the positions of one or more moving objects including a first moving object to be predicted. The hardware processors generate cumulative map information expressing, on a map, a plurality of positions indicated by the moving object information acquired at a plurality of first time points equal to or earlier than the reference time point. The hardware processors predict a position of the first moving object at a second time point later than the reference time point based on environment map information expressing, on a map, an environment around the first moving object at the reference time point, moving object information acquired at the reference time point, and the cumulative map information).
a travel plan for traveling a plurality of points by a plurality of mobile bodies by performing, at each output step, processing of ... any one point out of the plurality of points and any one mobile body out of the plurality of mobile bodies (Paragraph Number [0025] teaches the moving object is, for example, a vehicle such as an automobile or a motorbike that moves along a lane provided on a road. The moving object is not limited to an automobile or a motorbike, and may be a robot that moves along a lane, for example. The moving object may be an object moving in a lane on the water, such as a ship. The following description will be given mainly using an exemplary case where the moving object is a vehicle. Paragraph Number [0040] teaches here, an example of map information will be described. FIG. 3 is a view illustrating an example of predicting the trajectory of a vehicle (an example of a moving object) based on map information prepared in advance including lane information. FIG. 4 is a diagram illustrating an example of predicting the trajectory by using only the information (sensor information) from the sensor device 24 without using the map information prepared in advance. The lane information is information indicating a trajectory on which the moving object is to travel. The trajectory is information indicating the positions of moving objects at a plurality of time points. Paragraph Number [0045] teaches the moving object information acquisition unit 101 acquires moving object information indicating the positions of one or more moving objects including a moving object to be predicted (first moving object). For example, the moving object information acquisition unit 101 acquires moving object information by using the sensor device 24, vehicle-to-vehicle communication, road-to-vehicle communication, or the like. Road-to-vehicle communication is communication between an external device such as a roadside device and the vehicle 10. The method of acquiring the moving object information is not limited to this, and any method may be used).
by using a recurrent neural network configured to output visiting probabilities at the plurality of points and use probabilities of the plurality of mobile bodies (Paragraph Number [0084] teaches applicable examples of compression of information include principal component analysis, an EM algorithm, and a recurrent neural network (RNN). For example, the cumulative map generator 104 inputs the information set in the grid and the acquired moving object information into the recurrent neural network, and then sets multidimensional information of a predetermined number of dimensions including the mixture distribution output from the recurrent neural network, as new information for the grid. By compressing and accumulating information, it is possible to reduce the storage capacity required for the cumulative map. Paragraph Number [0064] teaches the obstacle map 821 includes map information indicating the presence or absence of obstacles when observed from above the vehicle 801 using a plurality of grids. Each of the grids is associated with information that expresses the presence or absence of obstacles with a probability value of 0 to 1. In the obstacle map 821, a grid with no obstacles is illustrated in white (probability=0), a grid with obstacles is illustrated in black (probability=1), and a grid in which the presence of obstacles is unknown is illustrated in gray having density corresponding to the probability).
when point information regarding the plurality of points and mobile body information regarding the plurality of mobile bodies are input (Paragraph Number [0061] teaches the obstacle map generator 301 inputs the environmental information acquired by the environmental information acquisition unit 102 and the moving object information acquired by the moving object information acquisition unit 101, and then generates an obstacle map 311. The attribute map generator 302 generates an attribute map 312 indicating attributes of the environment around the moving object. The route map generator 303 generates a route map 313 indicating a route on which the moving object is to travel. Paragraph Number [0087] teaches the time-series feature extraction unit 501 extracts time-series features from the moving object information and outputs the time-series features. The time-series feature extraction unit 501 inputs data (input data) as a result of acquisition of a one-dimensional vector for one time point or more including at least one of pieces of moving object information acquired by the moving object information acquisition unit 101, such as position, angle, speed, angular velocity, acceleration, and angular acceleration. The time-series features output by the time-series feature extraction unit 501 are information that characterizes a time-series movement change amount of the moving object).
output circuitry configured to output the travel plan. (Paragraph Number [0055] teaches the predictor 105 predicts the future position of the moving object from the moving object information, the environment map, and the cumulative map, and outputs a predicted trajectory of the moving object. The future position may be represented by the coordinates of the position, or may be represented, for example, by the movement amount from the current position. For example, the predictor 105 predicts the position of the moving object based on the environment map at the reference time point and the cumulative map that has accumulated the moving object information acquired at the reference time point and the moving object information acquired up to the reference time point. The predictor 105 predicts the position of the moving object by using a model such as a neural network that inputs an environment map and moving object information, and a cumulative map, and outputs a prediction result of the position of the moving object, for example).
Kawasaki teaches generating a travel plan for traveling a plurality of points by a plurality of mobile bodies, but does not explicitly teach making selections of specific points and moving objects to alter information or update information about them which is taught by the following citations from Williams:
processing of selecting any one point (Col. 33 line 50 - Col. 34 line 3 teaches exemplary vehicle routes 400 and 450, respectively. More specifically, FIG. 4A illustrates a first vehicle route 400 that may be generated using current delivery systems. First vehicle route 400 may be implemented using a vehicle (e.g., vehicle 100, shown in FIG. 1). In the illustrated embodiment, first vehicle route 400 includes two tasks, which may have been selected and scheduled by a vehicle user associated with the vehicle. For example, the vehicle may deliver cargo including a commuting person. Specifically, the vehicle may pick up a passenger A at a location 1 at 8:00 AM, and may drop off passenger A at a location 2 at 9:00 AM, which represents a first task. The vehicle may pick up passenger A at location 2 at 5:00 PM and drop off passenger A at location 1 at 6:00 PM, which represents a second task. First vehicle route 400 does not take advantage of the time between 9:00 AM and 5:00 PM, in which the vehicle could be completing additional tasks. Moreover, first vehicle route 400 does not enable concurrent tasks, nor tasks associated with cargo including objects (e.g., package delivery)).
Both Kawasaki and Williams are directed to generating travel plans for a plurality of moving bodies. Kawasaki discloses generating a travel plan for traveling a plurality of points by a plurality of mobile bodies. Williams improves upon Kawasaki by disclosing making selections of specific points and moving objects to alter information or update information about them. One of ordinary skill in the art would be motivated to further include making selections of specific points and moving objects to alter information or update information about them, to efficiently modify and optimize data in a computer model as new information becomes available. Accordingly, 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 and method of generating a travel plan for traveling a plurality of points by a plurality of mobile bodies in Kawasaki to further utilize making selections of specific points and moving objects to alter information or update information about them as disclosed in Williams, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 7, claim 7 recites a method that is substantially similar to that performed by the system of claim 1 and is rejected for the same reasons put forth in regard to claim 1.
As per claim 2, the combination of Kawasaki and Williams teaches each of the limitations of claims 1.
In addition, Kawasaki teaches:
wherein the recurrent neural network includes: an encoder that generates a first embedded vector corresponding to the point information and a second embedded vector corresponding to the mobile body information (Paragraph Number [0094] teaches the spatial feature extraction unit 502 inputs the normalized environment map and the cumulative map to a neural network. The neural network is constituted with a CNN, for example, and outputs spatial features reduced to a one-dimensional vector. The neural network may be configured to weight (draw attention to) each of grids of the environment map based on the values obtained in the cumulative map. For example, in a case where the speed of the moving object is set for each of grids of the cumulative map, a weight having a value varying with the speed is assigned to the corresponding grid of the environment map. Furthermore, the neural network may be configured to handle the environment map and the cumulative map by concatenating them in the channel direction. Paragraph Number [0106] teaches the time-series feature extraction unit 501b inputs a true value of moving object information (a true value of a trajectory of a moving object), and outputs a time-series feature corresponding to the true value of the moving object information. The true value of the moving object information is data as a result of acquisition, for one time point, of a one-dimensional vector including at least one of the position, angle, speed, angular velocity, acceleration, and angular acceleration of the moving object at a future time point. For example, after sequentially inputting moving object information to the time-series feature extraction unit 501, by performing inputting the true value to the recurrent neural network of the time-series feature extraction unit 501b having the same weight as the recurrent neural network of the time-series feature extraction unit 501, it is possible to extract time-series features of the trajectory corresponding to the true value).
a decoder that generates a hidden vector on the basis of information regarding a point and a mobile body selected at a previous output step (Paragraph Number [0100] teaches the trajectory generator 505 obtains a predicted trajectory using a neural network that inputs the above input data and outputs the predicted trajectory. Since both the spatiotemporal features and latent variables are one-dimensional vectors, it is possible to use a neural network that inputs input data in which these vectors are concatenated in the dimensional direction and outputs the predicted trajectory. Paragraph Number [0106] teaches the time-series feature extraction unit 501b inputs a true value of moving object information (a true value of a trajectory of a moving object), and outputs a time-series feature corresponding to the true value of the moving object information. The true value of the moving object information is data as a result of acquisition, for one time point, of a one-dimensional vector including at least one of the position, angle, speed, angular velocity, acceleration, and angular acceleration of the moving object at a future time point. For example, after sequentially inputting moving object information to the time-series feature extraction unit 501, by performing inputting the true value to the recurrent neural network of the time-series feature extraction unit 501b having the same weight as the recurrent neural network of the time-series feature extraction unit 501, it is possible to extract time-series features of the trajectory corresponding to the true value).
attention circuitry configured to calculate the visiting probabilities at the plurality of points and the use probabilities of the plurality of mobile bodies on the basis of the first embedded vector, the second embedded vector, and the hidden vector. (Paragraph Number [0019] teaches a prediction device according to an embodiment includes one or more hardware processors. The hardware processors acquire moving object information indicating the positions of one or more moving objects including a first moving object to be predicted. The hardware processors generate cumulative map information expressing, on a map, a plurality of positions indicated by the moving object information acquired at a plurality of first time points equal to or earlier than the reference time point. The hardware processors predict a position of the first moving object at a second time point later than the reference time point based on environment map information expressing, on a map, an environment around the first moving object at the reference time point, moving object information acquired at the reference time point, and the cumulative map information. Paragraph Number [0084] teaches applicable examples of compression of information include principal component analysis, an EM algorithm, and a recurrent neural network (RNN). For example, the cumulative map generator 104 inputs the information set in the grid and the acquired moving object information into the recurrent neural network, and then sets multidimensional information of a predetermined number of dimensions including the mixture distribution output from the recurrent neural network, as new information for the grid. By compressing and accumulating information, it is possible to reduce the storage capacity required for the cumulative map. Paragraph Number [0064] teaches the obstacle map 821 includes map information indicating the presence or absence of obstacles when observed from above the vehicle 801 using a plurality of grids. Each of the grids is associated with information that expresses the presence or absence of obstacles with a probability value of 0 to 1. In the obstacle map 821, a grid with no obstacles is illustrated in white (probability=0), a grid with obstacles is illustrated in black (probability=1), and a grid in which the presence of obstacles is unknown is illustrated in gray having density corresponding to the probability).
As per claim 3, the combination of Kawasaki and Williams teaches each of the limitations of claims 1 and 2.
In addition, Kawasaki teaches:
generate a first context vector indicating a weighted sum of the point information on the basis of the first embedded vector and the hidden vector (Paragraph Number [0094] teaches the spatial feature extraction unit 502 inputs the normalized environment map and the cumulative map to a neural network. The neural network is constituted with a CNN, for example, and outputs spatial features reduced to a one-dimensional vector. The neural network may be configured to weight (draw attention to) each of grids of the environment map based on the values obtained in the cumulative map. For example, in a case where the speed of the moving object is set for each of grids of the cumulative map, a weight having a value varying with the speed is assigned to the corresponding grid of the environment map. Furthermore, the neural network may be configured to handle the environment map and the cumulative map by concatenating them in the channel direction).
generate a second context vector indicating a weighted sum of the mobile body information on the basis of the second embedded vector and the hidden vector (Paragraph Number [0106] teaches the time-series feature extraction unit 501b inputs a true value of moving object information (a true value of a trajectory of a moving object), and outputs a time-series feature corresponding to the true value of the moving object information. The true value of the moving object information is data as a result of acquisition, for one time point, of a one-dimensional vector including at least one of the position, angle, speed, angular velocity, acceleration, and angular acceleration of the moving object at a future time point. For example, after sequentially inputting moving object information to the time-series feature extraction unit 501, by performing inputting the true value to the recurrent neural network of the time-series feature extraction unit 501b having the same weight as the recurrent neural network of the time-series feature extraction unit 501, it is possible to extract time-series features of the trajectory corresponding to the true value).
calculate the visiting probabilities at the plurality of points on the basis of the first embedded vector, the first context vector, and the second context vector (Paragraph Number [0084] teaches applicable examples of compression of information include principal component analysis, an EM algorithm, and a recurrent neural network (RNN). For example, the cumulative map generator 104 inputs the information set in the grid and the acquired moving object information into the recurrent neural network, and then sets multidimensional information of a predetermined number of dimensions including the mixture distribution output from the recurrent neural network, as new information for the grid. By compressing and accumulating information, it is possible to reduce the storage capacity required for the cumulative map. Paragraph Number [0064] teaches the obstacle map 821 includes map information indicating the presence or absence of obstacles when observed from above the vehicle 801 using a plurality of grids. Each of the grids is associated with information that expresses the presence or absence of obstacles with a probability value of 0 to 1. In the obstacle map 821, a grid with no obstacles is illustrated in white (probability=0), a grid with obstacles is illustrated in black (probability=1), and a grid in which the presence of obstacles is unknown is illustrated in gray having density corresponding to the probability).
calculate the use probabilities of the plurality of mobile bodies on the basis of the second embedded vector, the first context vector, and the second context vector (Paragraph Number [0084] teaches applicable examples of compression of information include principal component analysis, an EM algorithm, and a recurrent neural network (RNN). For example, the cumulative map generator 104 inputs the information set in the grid and the acquired moving object information into the recurrent neural network, and then sets multidimensional information of a predetermined number of dimensions including the mixture distribution output from the recurrent neural network, as new information for the grid. By compressing and accumulating information, it is possible to reduce the storage capacity required for the cumulative map. Paragraph Number [0064] teaches the obstacle map 821 includes map information indicating the presence or absence of obstacles when observed from above the vehicle 801 using a plurality of grids. Each of the grids is associated with information that expresses the presence or absence of obstacles with a probability value of 0 to 1. In the obstacle map 821, a grid with no obstacles is illustrated in white (probability=0), a grid with obstacles is illustrated in black (probability=1), and a grid in which the presence of obstacles is unknown is illustrated in gray having density corresponding to the probability).
As per claim 4, the combination of Kawasaki and Williams teaches each of the limitations of claim 1.
In addition, Kawasaki teaches:
excluding a point specified according to first mask information indicating an unselectable point out of the plurality of points on the basis of the visiting probabilities at the plurality of points output from the recurrent neural network (Paragraph Number [0066] teaches the obstacle map generator 301 may generate an obstacle map so as to include ambiguity in the position where the obstacle exists. For example, the presence or absence of an obstacle may be represented by a probability distribution centered on a grid in which an object exists. Furthermore, the obstacle map generator 301 may generate an obstacle map indicating the presence/absence of only an object excluding the moving object to be predicted. Paragraph Number [0084] teaches applicable examples of compression of information include principal component analysis, an EM algorithm, and a recurrent neural network (RNN). For example, the cumulative map generator 104 inputs the information set in the grid and the acquired moving object information into the recurrent neural network, and then sets multidimensional information of a predetermined number of dimensions including the mixture distribution output from the recurrent neural network, as new information for the grid. By compressing and accumulating information, it is possible to reduce the storage capacity required for the cumulative map. Paragraph Number [0064] teaches the obstacle map 821 includes map information indicating the presence or absence of obstacles when observed from above the vehicle 801 using a plurality of grids. Each of the grids is associated with information that expresses the presence or absence of obstacles with a probability value of 0 to 1. In the obstacle map 821, a grid with no obstacles is illustrated in white (probability=0), a grid with obstacles is illustrated in black (probability=1), and a grid in which the presence of obstacles is unknown is illustrated in gray having density corresponding to the probability).
excluding a mobile body specified according to second mask information indicating an unselectable mobile body out of the plurality of mobile bodies (Paragraph Number [0066] teaches the obstacle map generator 301 may generate an obstacle map so as to include ambiguity in the position where the obstacle exists. For example, the presence or absence of an obstacle may be represented by a probability distribution centered on a grid in which an object exists. Furthermore, the obstacle map generator 301 may generate an obstacle map indicating the presence/absence of only an object excluding the moving object to be predicted. Paragraph Number [0084] teaches applicable examples of compression of information include principal component analysis, an EM algorithm, and a recurrent neural network (RNN). For example, the cumulative map generator 104 inputs the information set in the grid and the acquired moving object information into the recurrent neural network, and then sets multidimensional information of a predetermined number of dimensions including the mixture distribution output from the recurrent neural network, as new information for the grid. By compressing and accumulating information, it is possible to reduce the storage capacity required for the cumulative map. Paragraph Number [0064] teaches the obstacle map 821 includes map information indicating the presence or absence of obstacles when observed from above the vehicle 801 using a plurality of grids. Each of the grids is associated with information that expresses the presence or absence of obstacles with a probability value of 0 to 1. In the obstacle map 821, a grid with no obstacles is illustrated in white (probability=0), a grid with obstacles is illustrated in black (probability=1), and a grid in which the presence of obstacles is unknown is illustrated in gray having density corresponding to the probability).
on the basis of the use probabilities of the plurality of mobile bodies output from the recurrent neural network (Paragraph Number [0084] teaches applicable examples of compression of information include principal component analysis, an EM algorithm, and a recurrent neural network (RNN). For example, the cumulative map generator 104 inputs the information set in the grid and the acquired moving object information into the recurrent neural network, and then sets multidimensional information of a predetermined number of dimensions including the mixture distribution output from the recurrent neural network, as new information for the grid. By compressing and accumulating information, it is possible to reduce the storage capacity required for the cumulative map. Paragraph Number [0064] teaches the obstacle map 821 includes map information indicating the presence or absence of obstacles when observed from above the vehicle 801 using a plurality of grids. Each of the grids is associated with information that expresses the presence or absence of obstacles with a probability value of 0 to 1. In the obstacle map 821, a grid with no obstacles is illustrated in white (probability=0), a grid with obstacles is illustrated in black (probability=1), and a grid in which the presence of obstacles is unknown is illustrated in gray having density corresponding to the probability).
updating the first mask information and the second mask information (Paragraph Number [0101] teaches the neural network used by the trajectory generator 505 includes a recurrent neural network and a fully connected layer, for example. The recurrent neural network repeats arithmetic operations at each of time steps until a designated predicted time is reached. The input of the recurrent neural network at each of time steps is the same input data concatenating spatiotemporal features and latent variables. Internal variables in the recurrent neural network are updated sequentially by performing iterative operations).
Kawasaki teaches generating a travel plan for traveling a plurality of points by a plurality of mobile bodies, but does not explicitly teach making selections of specific points and moving objects to alter information or update information about them which is taught by the following citations from Williams:
selecting one point out of the plurality of points (Col. 33 line 50 - Col. 34 line 3 teaches exemplary vehicle routes 400 and 450, respectively. More specifically, FIG. 4A illustrates a first vehicle route 400 that may be generated using current delivery systems. First vehicle route 400 may be implemented using a vehicle (e.g., vehicle 100, shown in FIG. 1). In the illustrated embodiment, first vehicle route 400 includes two tasks, which may have been selected and scheduled by a vehicle user associated with the vehicle. For example, the vehicle may deliver cargo including a commuting person. Specifically, the vehicle may pick up a passenger A at a location 1 at 8:00 AM, and may drop off passenger A at a location 2 at 9:00 AM, which represents a first task. The vehicle may pick up passenger A at location 2 at 5:00 PM and drop off passenger A at location 1 at 6:00 PM, which represents a second task. First vehicle route 400 does not take advantage of the time between 9:00 AM and 5:00 PM, in which the vehicle could be completing additional tasks. Moreover, first vehicle route 400 does not enable concurrent tasks, nor tasks associated with cargo including objects (e.g., package delivery)).
selecting one mobile body out of the plurality of mobile bodies (Col. 33 line 50 - Col. 34 line 3 teaches exemplary vehicle routes 400 and 450, respectively. More specifically, FIG. 4A illustrates a first vehicle route 400 that may be generated using current delivery systems. First vehicle route 400 may be implemented using a vehicle (e.g., vehicle 100, shown in FIG. 1). In the illustrated embodiment, first vehicle route 400 includes two tasks, which may have been selected and scheduled by a vehicle user associated with the vehicle. For example, the vehicle may deliver cargo including a commuting person. Specifically, the vehicle may pick up a passenger A at a location 1 at 8:00 AM, and may drop off passenger A at a location 2 at 9:00 AM, which represents a first task. The vehicle may pick up passenger A at location 2 at 5:00 PM and drop off passenger A at location 1 at 6:00 PM, which represents a second task. First vehicle route 400 does not take advantage of the time between 9:00 AM and 5:00 PM, in which the vehicle could be completing additional tasks. Moreover, first vehicle route 400 does not enable concurrent tasks, nor tasks associated with cargo including objects (e.g., package delivery)).
adding the selected point to a route of the selected mobile body (Col. 33 line 50 - Col. 34 line 3 teaches exemplary vehicle routes 400 and 450, respectively. More specifically, FIG. 4A illustrates a first vehicle route 400 that may be generated using current delivery systems. First vehicle route 400 may be implemented using a vehicle (e.g., vehicle 100, shown in FIG. 1). In the illustrated embodiment, first vehicle route 400 includes two tasks, which may have been selected and scheduled by a vehicle user associated with the vehicle. For example, the vehicle may deliver cargo including a commuting person. Specifically, the vehicle may pick up a passenger A at a location 1 at 8:00 AM, and may drop off passenger A at a location 2 at 9:00 AM, which represents a first task. The vehicle may pick up passenger A at location 2 at 5:00 PM and drop off passenger A at location 1 at 6:00 PM, which represents a second task. First vehicle route 400 does not take advantage of the time between 9:00 AM and 5:00 PM, in which the vehicle could be completing additional tasks. Moreover, first vehicle route 400 does not enable concurrent tasks, nor tasks associated with cargo including objects (e.g., package delivery). Col. 67 lines 57-67 teach the method 1250 may include, while the delivery AV is floating along the route, dynamically updating the route by calculating the lowest cost route that includes the intermediate pick-up and drop-off points within the additional service requests received as additional waypoints along the overall route to the final destination 1264. The dynamically updated route, in addition to accounting for, and adding, one or more additional service request's intermediate pick-up/drop-off points as waypoints, may also be updated to account for the new AV, infrastructure, and/or other computing device condition data received).
on the basis of a result of adding the selected point to the route of the selected mobile body (Col. 33 line 50 - Col. 34 line 3 teaches exemplary vehicle routes 400 and 450, respectively. More specifically, FIG. 4A illustrates a first vehicle route 400 that may be generated using current delivery systems. First vehicle route 400 may be implemented using a vehicle (e.g., vehicle 100, shown in FIG. 1). In the illustrated embodiment, first vehicle route 400 includes two tasks, which may have been selected and scheduled by a vehicle user associated with the vehicle. For example, the vehicle may deliver cargo including a commuting person. Specifically, the vehicle may pick up a passenger A at a location 1 at 8:00 AM, and may drop off passenger A at a location 2 at 9:00 AM, which represents a first task. The vehicle may pick up passenger A at location 2 at 5:00 PM and drop off passenger A at location 1 at 6:00 PM, which represents a second task. First vehicle route 400 does not take advantage of the time between 9:00 AM and 5:00 PM, in which the vehicle could be completing additional tasks. Moreover, first vehicle route 400 does not enable concurrent tasks, nor tasks associated with cargo including objects (e.g., package delivery). Col. 67 lines 57-67 teach the method 1250 may include, while the delivery AV is floating along the route, dynamically updating the route by calculating the lowest cost route that includes the intermediate pick-up and drop-off points within the additional service requests received as additional waypoints along the overall route to the final destination 1264. The dynamically updated route, in addition to accounting for, and adding, one or more additional service request's intermediate pick-up/drop-off points as waypoints, may also be updated to account for the new AV, infrastructure, and/or other computing device condition data received).
A person of ordinary skill in the art would have been motivated to combine these references as described in regard to claim 1.
As per claim 5, the combination of Kawasaki and Williams teaches each of the limitations of claims 1 and 4.
In addition, Kawasaki teaches:
the point information includes positions (Paragraph Number [0061] teaches the obstacle map generator 301 inputs the environmental information acquired by the environmental information acquisition unit 102 and the moving object information acquired by the moving object information acquisition unit 101, and then generates an obstacle map 311. The attribute map generator 302 generates an attribute map 312 indicating attributes of the environment around the moving object. The route map generator 303 generates a route map 313 indicating a route on which the moving object is to travel. Paragraph Number [0087] teaches the time-series feature extraction unit 501 extracts time-series features from the moving object information and outputs the time-series features. The time-series feature extraction unit 501 inputs data (input data) as a result of acquisition of a one-dimensional vector for one time point or more including at least one of pieces of moving object information acquired by the moving object information acquisition unit 101, such as position, angle, speed, angular velocity, acceleration, and angular acceleration. The time-series features output by the time-series feature extraction unit 501 are information that characterizes a time-series movement change amount of the moving object).
the mobile body information includes positions (Paragraph Number [0061] teaches the obstacle map generator 301 inputs the environmental information acquired by the environmental information acquisition unit 102 and the moving object information acquired by the moving object information acquisition unit 101, and then generates an obstacle map 311. The attribute map generator 302 generates an attribute map 312 indicating attributes of the environment around the moving object. The route map generator 303 generates a route map 313 indicating a route on which the moving object is to travel. Paragraph Number [0087] teaches the time-series feature extraction unit 501 extracts time-series features from the moving object information and outputs the time-series features. The time-series feature extraction unit 501 inputs data (input data) as a result of acquisition of a one-dimensional vector for one time point or more including at least one of pieces of moving object information acquired by the moving object information acquisition unit 101, such as position, angle, speed, angular velocity, acceleration, and angular acceleration. The time-series features output by the time-series feature extraction unit 501 are information that characterizes a time-series movement change amount of the moving object).
the updating of the first mask information and the second mask information includes: adding the selected point to the first mask information as the unselectable point (Paragraph Number [0066] teaches the obstacle map generator 301 may generate an obstacle map so as to include ambiguity in the position where the obstacle exists. For example, the presence or absence of an obstacle may be represented by a probability distribution centered on a grid in which an object exists. Furthermore, the obstacle map generator 301 may generate an obstacle map indicating the presence/absence of only an object excluding the moving object to be predicted. Paragraph Number [0084] teaches applicable examples of compression of information include principal component analysis, an EM algorithm, and a recurrent neural network (RNN). For example, the cumulative map generator 104 inputs the information set in the grid and the acquired moving object information into the recurrent neural network, and then sets multidimensional information of a predetermined number of dimensions including the mixture distribution output from the recurrent neural network, as new information for the grid. By compressing and accumulating information, it is possible to reduce the storage capacity required for the cumulative map. Paragraph Number [0064] teaches the obstacle map 821 includes map information indicating the presence or absence of obstacles when observed from above the vehicle 801 using a plurality of grids. Each of the grids is associated with information that expresses the presence or absence of obstacles with a probability value of 0 to 1. In the obstacle map 821, a grid with no obstacles is illustrated in white (probability=0), a grid with obstacles is illustrated in black (probability=1), and a grid in which the presence of obstacles is unknown is illustrated in gray having density corresponding to the probability).
adding the selected mobile body to the second mask information as the unselectable mobile body (Paragraph Number [0066] teaches the obstacle map generator 301 may generate an obstacle map so as to include ambiguity in the position where the obstacle exists. For example, the presence or absence of an obstacle may be represented by a probability distribution centered on a grid in which an object exists. Furthermore, the obstacle map generator 301 may generate an obstacle map indicating the presence/absence of only an object excluding the moving object to be predicted. Paragraph Number [0084] teaches applicable examples of compression of information include principal component analysis, an EM algorithm, and a recurrent neural network (RNN). For example, the cumulative map generator 104 inputs the information set in the grid and the acquired moving object information into the recurrent neural network, and then sets multidimensional information of a predetermined number of dimensions including the mixture distribution output from the recurrent neural network, as new information for the grid. By