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 .
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 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is not supported by either a process, machines, manufactures and composition of matter asserted utility or a well established utility.
Claim 58 claims “A computer-readable media having computer executable instructions for causing a computer to scan a frequency spectrum.” However, claims 18-20 do not clearly define a computer-readable media to be a memory/disk, see ¶[0056] “ [0056] “Computer-readable storage media” refers to media and/or devices that enable persistent and/or non-transitory information storage in contrast to mere signal transmission, carrier waves, or signals per se. Thus, computer-readable storage media refers to non-signal-bearing media. The computer-readable storage media includes hardware such as volatile and non-volatile, removable and non-removable media, and/or storage devices implemented in a method or technology suitable for storage of information such as computer-readable instructions, data structures, program modules, logic elements/circuits, or other data.” The and/or statements make it unclear of this application and is thus non-statutory for that reason. Applicant’s specification , further does not exclude “computer-readable storage media” from other forms of propagated signals that computer program product may be formatted. Moreover, the claims do not define a computer executable instructions to be a functional descriptive material encoded on a memory/disk, and is thus non-statutory for that reason (i.e., “When functional descriptive material is recorded on some computer-readable medium it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized”). Furthermore, “Computer program product” or “Instructions” is neither a process (“action”), nor machine, nor manufacture, nor composition of matter (i.e., tangible “thing”) and therefore non-statutory.
Therefore, the full scope of claim 18 as properly read in light of the disclosure encompasses non-statutory subject matter, i.e., signal, the claim as a whole is non-statutory, under the present USPTO Interim Guidelines, 1300 Official Gazette Patent and Trademark Office 142 (Nov. 22, 2005).
The Examiner suggests amending the claim to include the disclosed tangible computer readable media, while at the same time excluding the intangible media such as signals, carrier waves, etc…
Any amendment to the claim should be commensurate with its corresponding disclosure.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 11-12, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Meilland (US 2021/0225074).
As per claims 1, 11, and 18 Meilland teaches, a method and system and computer-readable media comprising: receiving, by a processing device, signed distance values of voxels in a first voxel grid having a first resolution to represent a three-dimensional (3D) object (Meilland, ¶[0012] “ In some implementations, the exemplary method further involves determining whether to represent 3D positions as voxels having the first resolution or voxels having the second resolution based on distance of surfaces nearest the voxels from a source (e.g., depth camera position) of the depth data. In some implementations, voxels of the first set of voxels have a first size and voxels of the second set of voxels have a second size, where the first size is larger than the second size.” This represents signed distance values of voxels in a first voxel grid having a first resolution to represent a three-dimensional (3D) object ); for each voxel in the first voxel grid, determining, by the processing device and based on the signed distance values of first neighboring voxels (Meilland, ¶ [0066] FIG. 6 is a block diagram illustrating an example environment 600 for a multi-resolution hashing data structure in accordance with some implementations. The example environment 600 includes an 8×8×8 single resolution orthogonal (uniform) voxel grid 602. The voxel grid 602 represents all the information in a volume by a fixed 3D grid of voxels that is pre-allocated in memory. While the voxel grid 602 results in a constant access time for each voxel to store and retrieve its TSDF values, the fact that the memory has to be pre-allocated makes it impractical to store large volumes, which can run into tens of gigabytes even for a decent sized room. Each voxel (e.g., voxel 610) can include the global (x,y,z) coordinates, and the TSDF values. An even more compact representation can be made using a hash function H(x,y,z) that maps world coordinates to voxels. Thus, the information in voxel 610 is shown in the example environment 600 as stored in the hash table 620. The hash table 620 stores hash entries, each hash entry containing a pointer to an allocated voxel block (e.g., voxel 610). For each voxel, the information is stored in buckets based on different parameters (e.g., stored information may include the world coordinates (x,y,z), TSDF values, and an offset value for bucket excess entries). For example, the hash table 620 may be broken down into a set of buckets, where each slot is either unallocated or contains an entry (shaded grey). Each bucket is then stored as a voxel block array 630 for every ordered voxel entry, removing all excess entries.” This represents having for each voxel in the first voxel grid, determining, by the processing device and based on the signed distance values of first neighboring voxels ), whether the voxel is located near a surface of the 3D object; for each target voxel near the surface of the 3D object, upsampling, using a machine-learning model and based on the signed distance values of second neighboring voxels (Meilland, ¶[0061] “In some implementations, the machine learning model is a neural network (e.g., an artificial neural network), decision tree, support vector machine, Bayesian network, or the like.” upsampling, using a machine-learning model and based on the signed distance values of second neighboring voxels and the 3D voxel object is already being used see ¶ [0062] According to some implementations, a third hash table may be generated to store 3D positions of a third set of voxels having a third resolution and signed distance values representing distances to the surfaces of the physical environment based on the depth data.), the signed distance value of the target voxel into the signed distance values of multiple voxels in a second voxel grid having a second resolution that is greater than the first resolution ( Meilland, ¶ [0063] At block 408, the method 400 generates a mesh representing the surfaces based on the first hash table and the second hash table, where the mesh is generated by positioning a vertices of the mesh along a line connecting a first voxel of the first set of voxels with a second voxel of the second set of voxels. For example, the mesh may be generated by positioning a vertices of the mesh along a line connecting a first voxel (e.g., a position at the center of the first voxel) of the first set of voxels with a second voxel (e.g., a position at the center of the second voxel) of the second set of voxels. In some implementations, the mesh may be generated using a dual marching cubes meshing algorithm technique that identifies lines connecting points associated with the voxels in each hash table and interpolates to identify vertices along those lines that correspond to the surfaces” this represents the second voxel grid); and rendering, by the processing device and using the signed distance values of the multiple voxels in the second voxel grid, the 3D object (Meilland, ¶ [0033] “In some implementations, the user wears the device 120 on his/her head. As such, the device 120 may include one or more displays provided to display content. For example, the device 120 may enclose the field-of-view of the user. In some implementations, the device 120 is a handheld electronic device (e.g., a smartphone or a tablet) configured to present content to the user. In some implementations, the device 120 is replaced with a chamber, enclosure, or room configured to present content in which the user does not wear or hold the device 120.” This display is the rendering).
As per claims 2 and 12, Meilland teaches, the method of claim 1, wherein: the machine-learning model is a multilayer perceptron; for each target voxel, an input to the multilayer perceptron is an input vector that includes the signed distance values for the second neighboring voxels of the target voxel; and for each target voxel, an output from the multilayer perceptron is an output vector that includes the signed distance values of the multiple voxels upsampled from the target voxel (Meilland, ¶[0061] “In some implementations, semantic labeling uses a machine learning model, where a semantic segmentation model may be configured to identify semantic labels for pixels or voxels of image data. For example, if a voxel is labeled as a “wall,” regardless of how far away the object (wall) is, the system can select a bigger/sparser resolution based on the assumption or specification that a wall type object will have a consistent or insignificant texture that need not be represented using fine resolution. If the voxel is labeled as a “tea cup,” the system can select a smaller/finer resolution based on the assumption or specification that a tea cup type object will have fine details that are worth attempting to represent regardless of potential noise. In some implementations, the machine learning model is a neural network (e.g., an artificial neural network), decision tree, support vector machine, Bayesian network, or the like.” This represents the machine-learning model is a multilayer perceptron; for each target voxel, an input to the multilayer perceptron is an input vector that includes the signed distance values for the second neighboring voxels of the target voxel; and for each target voxel, an output from the multilayer perceptron is an output vector that includes the signed distance values of the multiple voxels unsampled from the target voxel ).
Allowable Subject Matter
Claims 3-10, 13-17, and 19-20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Regarding 19-20 the 101 rejection must be addressed in order for these to be allowable when placed in an independent claim. The subject matter in these claims were not found in the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTIAGO GARCIA whose telephone number is (571)270-5182. The examiner can normally be reached Monday-Friday 9:30am-5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
Prosecution Insights