DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The IDS’ of 04/12/24 and 04/21/26 have been considered.
Drawings
The drawings filed on 04/12/24 are accepted.
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 1-13 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.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to an image sensor data control system, which is a machine. All other claims depend on independent claim 1. As such, claims 1-13 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
a learning unit configured to learn a motion feature index of the image sensor data in each spatial region in the real space as indicating a feature related to a movement of a static object or a dynamic object in each spatial region in the real space, based on the image sensor data that has been aggregated by the aggregation unit and is composed of point clouds in the real space (Paragraph 0043 of the applicant’s original specification states, “the learning unit 23 calculates the temporal average of the number of point clouds in the image sensor data … When this temporal average exceeds a predetermined threshold, it may determine that the spatial region contains a high number of dynamic objects in motion.” As a general matter, learning a simple motion feature index of image sensor data in various spatial regions is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. More sophisticated learning that is based on specific mathematical calculations of complex data may not be able to be performed in the human mind. However, as seen in the applicant’s specification, such learning is defined by specific mathematical calculations and relationships. This limitation recites an abstract mental process and/or mathematical concepts.)
a control unit configured to set a priority of the image sensor data in each spatial region in the real space, based on the information on the motion feature index of the image sensor data in each spatial region in the real space (Setting a priority for some data over others is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. Setting a priority value of one data variable over another also reflects an abstract mathematical relationship. This limitation recites an abstract mental process and/or mathematical concepts.)
Dependent claims 2-13 depend on independent claim 1. They also recite the independent claims’ abstract limitations, by virtue of their dependence. In addition, some of the claims also recite their own abstract mathematical concepts and/or mental processes.
Claim 2
synthesizing the image sensor data in chronological order (Chronological order creates mathematical relationships, as a function of time. This limitation therefore recites abstract mathematical concepts.)
Claim 3
wherein the learning unit utilizes the number of point clouds of the image sensor data in each spatial region in the real space as the motion feature index (Utilizing the number of point clouds as a variable in a calculation recites an abstract mathematical concept.)
Claim 4
wherein the learning unit determines whether the spatial region relates to a static object or a dynamic object by learning temporal changes in the motion feature index of the image sensor data in each spatial region in the real space (Such a determination is an observation, evaluation, judgment, and/or opinion that can be performed by the human mind. It recites an abstract mental process.)
Claim 5
wherein the learning unit determines that the spatial region relates to the static object when a deviation of the motion feature index of the image sensor data in a predetermined spatial region in the real space is within a predetermined range, while the learning unit determines that the spatial region relates to the dynamic object when the deviation of the motion feature index of the image sensor data in the predetermined spatial region is outside the predetermined range (Such a determination is an observation, evaluation, judgment, and/or opinion that can be performed by the human mind. Also, the disclosure of various acceptable ranges recites specific mathematical relationships, in terms of whether a data variable is within or outside a range. This limitation therefore recites abstract mental processes and/or mathematical concepts.)
Claim 6
wherein the learning unit determines that there are many dynamic objects moving in the spatial region when an average of the motion feature indexes of the image sensor data in the predetermined spatial region in the real space exceeds a predetermined threshold, while the learning unit determines that there are fewer dynamic objects moving in the spatial region when the average of the motion feature indexes of the image sensor data in the predetermined spatial region in the real space is below or equal to the predetermined threshold (This limitation recites abstract mathematical concepts.)
Claim 7
wherein the control unit sets a higher priority for the image sensor data in the spatial region determined to be a dynamic object by the learning unit, while the control unit sets a lower priority for the image sensor data in the spatial region determined to be a static object by the learning unit (Setting a priority for some data over others is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. Setting a priority value of one data variable over another also reflects an abstract mathematical relationship. This limitation recites an abstract mental process and/or mathematical concepts.)
Claim 8
wherein the control unit sets a higher priority for the image sensor data in the spatial region determined by the learning unit to have a larger number of moving dynamic objects, while the control unit sets a lower priority for the image sensor data in the spatial region determined by the learning unit to have a smaller number of moving dynamic objects (Setting a priority for some data over others is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. Setting a priority value of one data variable over another also reflects an abstract mathematical relationship. This limitation recites an abstract mental process and/or mathematical concepts.)
Claim 9
wherein with respect to the image sensor data acquired in real time by the terminal device, the control unit determines that a moving speed of a dynamic object in a predetermined spatial region is high and sets a higher priority to the image sensor data in the spatial region when a rate of change of the motion feature index of the image sensor data in the spatial region in the real space in a predetermined period of time exceeds a predetermined threshold, while the control unit determines that a moving speed of the dynamic object in the spatial region is low and sets a lower priority to the image sensor data in the spatial region when the rate of change of the motion feature index of the image sensor data in the specified spatial region in the real space in the predetermined time period of time is less than or equal to the predetermined threshold (This limitation recites an abstract mental process and/or mathematical concepts, for similar reasons as given above. Prioritization and speed determination are abstract mental processes. Priority relationships and threshold define specific mathematical relationships.)
Claim 11
wherein the control unit controls the terminal-side transmission unit to preferentially transmit the image sensor data in a spatial region that is set to a higher priority, to the server device, with respect to the image sensor data acquired in real time by the terminal device (prioritizing an action, such as where to send data, based on some sort of priority setting, is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. Also, the existence of priority defines a specific mathematical relationship between two options. This limitation recites an abstract mental process and/or mathematical concepts.)
Claim 12
wherein the terminal-side transmission unit receives the priority of the image sensor data in each spatial region from the server device in advance, and preferentially transmits the image sensor data in the spatial region assigned a higher priority to the server device (prioritizing an action, such as where to send data, based on some sort of priority setting, is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. Also, the existence of priority defines a specific mathematical relationship between two options. This limitation recites an abstract mental process and/or mathematical concepts.)
Claim 13
wherein the real space is composed of a plurality of cells arranged in a grid as spatial regions (This limitation recites mathematical relationships of objects relative to each other in spatial dimensions.)
wherein the control unit sets a priority of the image sensor data for each cell (Priority is defined by abstract mental processes and/or mathematical concepts, for the reasons discussed above.)
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
An image sensor data control system (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
one or more terminal devices (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
a server device (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
wherein the one or more terminal devices and the server device are connected in a communicable manner (This limitation is not indicative of integration into a practical application because it appears to generally and generically describe some sort of computer-implementable data connection. Mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
wherein the server device controls image sensor data acquired by each of the terminal devices (This limitation does not describe the nature of the control. Based on the applicant’s specification (paragraph 0009), it appears to simply be some sort of data processing control, where certain data is prioritized. This merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). It is not indicative of integration into a practical application.)
wherein the terminal device (A generic terminal device merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).) includes
a sensor unit configured to acquire the image sensor data composed of point clouds in real space (Using sensors to collect data that will be processed merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
a terminal-side transmission unit configured to transmit the image sensor data to the server device, the image sensor data being composed of point clouds in the real space and having been acquired by the sensor unit (Transmitting data from one computerized device to another merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Furthermore, the limitation merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
wherein the server device includes a receiving unit configured to receive the image sensor data that is composed of point clouds in the real space and has been transmitted from each of the terminal devices (A computerized device receiving data from another device merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Furthermore, the limitation merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
an aggregation unit configured to aggregate the image sensor data, the image sensor data being composed of point clouds in the real space and having been received by the receiving unit (The aggregation of data is a computer processing operation. The limitation merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
a motion feature index information storage unit configured to store information on the motion feature index of the image sensor data in each spatial region in the real space, the motion feature index having been learned by the learning unit (Storing data is a computer processing operation. The limitation merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
the information having been stored in the motion feature index information storage unit (Storing data is a computer processing operation. The limitation merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
Dependent claims 2-13 depend on independent claim 1. They also recite the independent claims’ limitations that are not indicative of integration into a practical application, by virtue of their dependence. In addition, some of the claims also recite their own limitations that are not indicative of integration into a practical application.
Claim 2
wherein a plurality of the terminal devices is provided to acquire the image sensor data composed of point clouds from different directions with respect to the same real space (Acquiring data merely adds insignificant extra-solution activity to the judicial exception. The presence of terminal devices merely use a computer as a tool to perform an abstract idea.)
wherein the aggregation unit aggregates the image sensor data, the image sensor data having been acquired by each terminal device and being composed of point clouds (This limitation merely uses a computer as a tool to perform an abstract idea.)
Claim 10
wherein a server-side transmission unit is provided on an output side of the aggregation unit (This limitation is not indicative of integration into a practical application because transmitting data after it has been aggregated is a routine and conventional computer processing operation. It is considered to add insignificant extra-solution activity to the judicial exception. Also, the limitation merely uses a computer as a tool to perform an abstract idea.)
wherein the control unit controls the server-side transmission unit to preferentially transmit image sensor data in a spatial region that is set to a higher priority, to a predetermined moving object, with respect to the image sensor data acquired in real time by the terminal device (Transmitting data is a computer processing operation. The limitation merely uses a computer as a tool to perform an abstract idea.)
Examiner’s Note: Part of the prong two analysis considers whether the claim, as a whole, is more than a drafting effort designed to monopolize the exception. Here, the claims are directed to generic image sensor data control systems, without giving any specific technological context or field of use details on how the data control systems are used in a specific industry or technology. In their current form, the claims are considered to monopolize the exception across any industry that involves image sensors, which is many industries.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
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.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sato et al NPL (Sato Keiichiro et al: “Prioritized Transmission control of Point Cloud Data Obtained by LIDAR Devices”, IEEE Access, 30 June 2020, Vol. 8, pages 113779-113789) (This reference was listed in the 04/21/26 IDS).
With respect to claim 1, Sato et al NPL discloses:
An image sensor data control system (abstract; figure 1; note controllers)
one or more terminal devices (figure 1, reference “Device(s)”)
a server device (figure 1, reference “Edge server”)
wherein the one or more terminal devices and the server device are connected in a communicable manner (figure 1, note arrows between devices and edge server)
wherein the server device controls image sensor data acquired by each of the terminal devices (figure 1, note controller in edge server; page 113781 states, “The edge server controller extracts the spatial importance information from the spatial model and sends it to each device.”)
wherein the terminal device (figure 1, reference “Device(s)”) includes
a sensor unit configured to acquire the image sensor data composed of point clouds in real space (figure 1, see “LIDAR” and “Point cloud data”; abstract states, “A network of light detection and ranging (LIDAR) sensors, which generates point cloud data in real time, can be used to detect people’s mobility in smart monitoring.”)
a terminal-side transmission unit configured to transmit the image sensor data to the server device, the image sensor data being composed of point clouds in the real space and having been acquired by the sensor unit (figure 1; see “Transmitter”)
wherein the server device (figure 1; see “Edge server”) includes
a receiving unit configured to receive the image sensor data that is composed of point clouds in the real space and has been transmitted from each of the terminal devices (figure 1, see “Receiver”)
an aggregation unit configured to aggregate the image sensor data, the image sensor data being composed of point clouds in the real space and having been received by the receiving unit (figure 1, “Structured data” module broadly serves as aggregation unit. Page 113781, column 1, lines 1-2 state, “The edge server receives and decompresses the compressed data from the devices.”)
a learning unit configured to learn a motion feature index of the image sensor data in each spatial region in the real space as indicating a feature related to a movement of a static object or a dynamic object in each spatial region in the real space, based on the image sensor data that has been aggregated by the aggregation unit and is composed of point clouds in the real space (figure 1, “Spatial model” module is broadly construed to serve as the claimed “learning unit”; Page 113781, column 2, first paragraph states, “The object recognizer recognizes moving objects … from the structure data and scores the spatial importance of each spatial region on the basis of how dynamically the spatial regions change. The spatial model in the edge server represents the scores of spatial regions.” The “scoring” of the spatial importance of each region is broadly interpreted to serve as the claimed “motion feature index.”)
a control unit configured to set a priority of the image sensor data in each spatial region in the real space, based on the information on the motion feature index of the image sensor data in each spatial region in the real space (page 113781, first paragraph of section “B. METHODOLOGY” states, “Therefore, spatial regions in point cloud data that change more dynamically are considered to be of higher importance when the data is transmitted and classified on the basis of the importance.”)
With respect to claim 1, Sato et al NPL differs from the claimed invention in that is does not explicitly disclose:
a motion feature index information storage unit configured to store information on the motion feature index of the image sensor data in each spatial region in the real space, the motion feature index having been learned by the learning unit
the information having been stored in the motion feature index information storage unit
With respect to claim 1, the following limitation(s) is/are obvious, in view of the total disclosure of Sato et al NPL:
a motion feature index information storage unit configured to store information on the motion feature index of the image sensor data in each spatial region in the real space, the motion feature index having been learned by the learning unit (Some sort of memory is obvious to the system shown in figure 1. Data is passed between the various modules, such as from the object recognizer to the spatial model, and from the spatial model to the controller. Memory to store the data to pass between the components of an edge server would be obvious to one of ordinary skill in the art. Page 113783, first paragraph of “C. TWO CASES OF SPATIAL IMPORTANCE CLASSIFICATION” specifically mentions a computer system with 32 GB memory.)
the information having been stored in the motion feature index information storage unit (storing pertinent data into some sort of memory is obvious, for the reasons discussed in the preceding limitation.)
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Sato et al NPL. The motivation for the skilled artisan in doing so is to gain the benefit of storing data that needs to be passed between various computer components/modules.
With respect to claim 2, Sato et al NPL, as modified, discloses:
wherein a plurality of the terminal devices is provided to acquire the image sensor data composed of point clouds from different directions with respect to the same real space (obvious in view of disclosure of 3D data sensing; Page 113780, column 2, second paragraph of “II. RELATED WORK” section states, “Point cloud compression has been an important research orientation since the increasing capability of 3D data sensing devices such as LIDAR and depth cameras.”)
wherein the aggregation unit aggregates the image sensor data by synthesizing the image sensor data in chronological order, the image sensor data having been acquired by each terminal device and being composed of point clouds (obvious in view of disclosure of real-time monitoring; see abstract and page 113779, column 2, paragraph 2, which states, “Light detection and ranging (LIDAR) is a type of sensor that generates point cloud data in real time …”; Figure 1 processing of real-time data suggests real-time/chronological processing.)
With respect to claim 3, Sato et al NPL, as modified, discloses:
wherein the learning unit utilizes the number of point clouds of the image sensor data in each spatial region in the real space as the motion feature index (figure 1; page 113781, column 2, paragraph 1)
With respect to claim 4, Sato et al NPL, as modified, discloses:
wherein the learning unit determines whether the spatial region relates to a static object or a dynamic object by learning temporal changes in the motion feature index of the image sensor data in each spatial region in the real space (figure 1; page 113781, column 2, first paragraph of “B. METHODOLOGY” section states, “The proposed scheme is based on the principle that point cloud data has spatial characteristics; some spatial regions change dynamically, while others rarely change … spatial regions in point cloud data that change more dynamically are considered to be of higher importance when the data is transmitted and classified on the basis of the importance.”)
With respect to claim 5, Sato et al NPL, as modified, discloses:
wherein the learning unit determines that the spatial region relates to the static object when a deviation of the motion feature index of the image sensor data in a predetermined spatial region in the real space is within a predetermined range, while the learning unit determines that the spatial region relates to the dynamic object when the deviation of the motion feature index of the image sensor data in the predetermined spatial region is outside the predetermined range (obvious in view of Sato’s teaching of classification based on dynamic changes being of “higher importance.”; see page 113781, section “B. METHODOLOGY.” Data classified within the “higher importance” category can be broadly construed as “outside a predetermined range”)
With respect to claim 6, Sato et al NPL, as modified, discloses:
wherein the learning unit determines that there are many dynamic objects moving in the spatial region when an average of the motion feature indexes of the image sensor data in the predetermined spatial region in the real space exceeds a predetermined threshold, while the learning unit determines that there are fewer dynamic objects moving in the spatial region when the average of the motion feature indexes of the image sensor data in the predetermined spatial region in the real space is below or equal to the predetermined threshold (figures 4a-b; page 113784, column 1, paragraph on “2) VOXEL BASED CASE” states, “In the voxel-based case, the high-importance region was the voxel whose importance calculated from the object label information was higher than the threshold … “)
With respect to claim 7, Sato et al NPL, as modified, discloses:
wherein the control unit sets a higher priority for the image sensor data in the spatial region determined to be a dynamic object by the learning unit, while the control unit sets a lower priority for the image sensor data in the spatial region determined to be a static object by the learning unit (page 113781; see section “B. METHODOLOGY”)
With respect to claim 8, Sato et al NPL, as modified, discloses:
wherein the control unit sets a higher priority for the image sensor data in the spatial region determined by the learning unit to have a larger number of moving dynamic objects, while the control unit sets a lower priority for the image sensor data in the spatial region determined by the learning unit to have a smaller number of moving dynamic objects (page 113781; see section “B. METHODOLOGY”)
With respect to claim 9, Sato et al NPL, as modified, discloses:
wherein with respect to the image sensor data acquired in real time by the terminal device, the control unit determines that a moving speed of a dynamic object in a predetermined spatial region is high and sets a higher priority to the image sensor data in the spatial region when a rate of change of the motion feature index of the image sensor data in the spatial region in the real space in a predetermined period of time exceeds a predetermined threshold, while the control unit determines that a moving speed of the dynamic object in the spatial region is low and sets a lower priority to the image sensor data in the spatial region when the rate of change of the motion feature index of the image sensor data in the specified spatial region in the real space in the predetermined time period of time is less than or equal to the predetermined threshold (obvious in view of Sato’s methodology and total disclosure; page 113783, column 1, first paragraph of “2) DRACO” section states, “Draco does not rely on a single compression algorithm but uses multiple techniques … on the basis of … speed …”; Sato page 113779, column 2, paragraph 1 states, “accidents at intersections are amongst the most complex since they involve different types of … speeds …” One of ordinary skill in the art recognizes the relationship between speed and static/dynamic objects. Setting a threshold to classify dynamic/static based on moving speed would be obvious to one of ordinary skill in the art.)
With respect to claim 10, Sato et al NPL, as modified, discloses:
wherein a server-side transmission unit is provided on an output side of the aggregation unit (figure 1; page 113781, column 2)
wherein the control unit controls the server-side transmission unit to preferentially transmit image sensor data in a spatial region that is set to a higher priority, to a predetermined moving object, with respect to the image sensor data acquired in real time by the terminal device (figure 1; page 113781, column 2)
With respect to claim 11, Sato et al NPL, as modified, discloses:
wherein the control unit controls the terminal-side transmission unit to preferentially transmit the image sensor data in a spatial region that is set to a higher priority, to the server device, with respect to the image sensor data acquired in real time by the terminal device (figure 1; page 113781, column 2)
With respect to claim 12, Sato et al NPL, as modified, discloses:
wherein the terminal-side transmission unit receives the priority of the image sensor data in each spatial region from the server device in advance, and preferentially transmits the image sensor data in the spatial region assigned a higher priority to the server device (obvious in view of Sato’s proposed scheme and system model; page 113781, column 1, last paragraph states, “The devices continuously collect point cloud data frame-by-frame using the LIDAR sensors. The controller of each device receives information from the edge server regarding spatial importance and then compresses the point cloud data based on spatial importance.”)
With respect to claim 13, Sato et al NPL, as modified, discloses:
wherein the real space is composed of a plurality of cells arranged in a grid as spatial regions, and wherein the control unit sets a priority of the image sensor data for each cell (obvious representation of 3D point cloud models; see also reference [26] on page 113788, which is incorporated by reference)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Akbarzadeh et al (US PgPub 20230204383) discloses map creation and localization for autonomous driving applications.
Yang et al (US PgPub 20210004613) discloses annotating high definition map data with semantic labels.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
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/LEONARD S LIANG/Examiner, Art Unit 2857 06/13/26
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