DETAILED ACTION
1. This office action is a response to the Application/Control Number:18/284,271 filed on 09/26/2023.
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/16/2026 has been entered.
Claims Status
3. This office action is based upon claims received on 06/16/2026, which replace all prior or other submitted versions of the claims.
-Claims 1, 3, 5-7 are amended.
-Claims 1-7 are pending.
-Claims 1-7 are rejected.
Notice of Pre-AIA or AIA Status
4. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
5. Acknowledgment is made of a 371 of PCT/JP2021/014540, filed 04/05/2021.
Response to Amendments/Remarks
6. Applicant's remarks/arguments, see page 5, filed on 06/16/2026, with respect to Objections to claims have been considered in light of applicant’s amendments. The claim objections pertaining to claims 1, 5-7 as presented in the previous office action have been withdrawn.
7. Applicant's remarks/arguments, see page 7-10, filed on 6/16/2026, with respect to REMARKS/ARGUMENTS, Claim Rejections – 35 USC § 103 have been considered but are moot because the arguments do not apply to the new grounds of rejection being used in the current rejection.
Furthermore, remarks with respect to any applicable Dependent Claims have been considered, and are moot and not persuasive at least via dependency to the independent claims and via individual rejections addressing the specific claims.
The rejection has been revised and set forth below according to the amended claims (see Office Action).
Claim Rejections - 35 USC § 103
8. 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102
and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory
basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and
the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the
examiner presumes that the subject matter of the various claims was commonly owned as of the
effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised
of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that
was not commonly owned as of the effective filing date of the later invention in order for the examiner
to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art
against the later invention.
9. Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over SHANKHAR et al. (US 20210274358 A1), i.e. “ SHANKAR” in view of Rubio (US 20170019797 A1), i.e. “Rubio”, further in view of ZHANG et al. (US 20160323753 A1) i.e. “ZHANG”.
Regarding Claim 1. (Currently Amended) SHANKAR teaches: A communication design support apparatus (SHANKAR FIG.2 & FIG. 5 & ¶0171 […] In order to construct the 3D model of the environment, the user device 202 may send image information, which may be collected from image information recording unit 216, to server 224; ¶0226 […] method in which a user device 502 and server 524 are in communication […] ; ¶0228 […] user device and server may be in communication across an interface such as interface 226 shown schematically in FIG. 2; NOTE- DISCLOSURE & TEACHING: per FIG. 2, FIG. 5 & ¶0171 i.e. server 224 and per ¶0228 i.e. server 224 reads on: A communication design support apparatus )
comprising: a memory configured to store a propagation model (SHANKAR FIG. 2 & FIG. 5 & ¶0168 […] server device 224 may comprise at least at least one data processing entity 234, at least one memory 236, and other possible components for use in software and hardware aided execution of tasks it is designed to perform […] ; ¶0232 […] At S3, other measurements such as movement information, location information and radio signal measurement information may also be sent. This information may be used to calibrate the radio propagation model generated at S5. For example, signal strength and an estimated position in the environment (estimated using a localization and mapping technique) may be used to update the radio propagation model. This information could also be used to update information regarding an AP type […] ; NOTE- DISCLOSURE & TEACHING: per ¶0168 i.e. at least one memory 236 reads on: comprising: a memory configured to , where i.e. tasks include per ¶0232 i.e. to calibrate the radio propagation model generated at S5 and i.e. used to update the radio propagation model reads on: to store a propagation model );
and a processor configured to (SHANKAR FIG. 2 & FIG. 5 & ¶0168 […] See above […] ; ¶0232 see above ; NOTE- DISCLOSURE & TEACHING: per ¶0168 i.e. server device 224 may comprise at least at least one data processing entity 234 reads on: and a processor configured to ):
obtain a set of data including three-dimensional [[CAD]] computer aided design data including point cloud data (SHANKAR FIG.2 & FIG. 5 & ¶0172 […] to construct the 3D model of the environment, localization and mapping techniques (for example the simultaneous localization and mapping (SLAM) algorithm) and deep learning-based object recognition techniques (for example, convolutional neural networks (ConvNets)) are used; ¶0173 […] As mentioned above, an exemplary localization and mapping technique is the SLAM algorithm. SLAM can be used to construct or update a map of an unknown environment while simultaneously keeping track of a device's location within it. A SLAM algorithm may be termed a “visual SLAM algorithm” when the solution(s) is/are based on visual information alone. The outputs of a visual SLAM algorithm may comprise a 3D point cloud of the environment around the user device as well as the device's own position and viewpoint with respect to the environment.; ¶0234 […] At S4, a 3D model of the environment shown in the image information is constructed as described above. The user device may be located in the environment of which the 3D model is constructed. As described above, this may be achieved by using a localization and mapping technique, such as SLAM, and an object recognition technique, such as ConvNets; NOTE- DISCLOSURE & TEACHING: Per FIG. 5 & per ¶0172 i.e. to construct the 3D model of the environment and per ¶0234 i.e. At S4, a 3D model of the environment shown in the image information is constructed where per ¶0173 an exemplary localization and mapping technique is the SLAM algorithm. SLAM can be used to construct or update a map of an unknown environment while simultaneously keeping track of a device's location within it. A SLAM algorithm may be termed a “visual SLAM algorithm” when the solution(s) is/are based on visual information alone. i.e. The outputs of a visual SLAM algorithm may comprise a 3D point cloud of the environment reads on: obtain a set of data including three-dimensional [[CAD]] computer aided design data including point cloud data ),
coordinate information of structures in a communication utilization environment (SHANKAR – FIG. 2 & FIG. 5 & ¶0176 In some examples, by combining localization and mapping techniques and object recognition techniques it is therefore possible to generate a 3D model of the environment, from which at least some of the following information can be obtained to generate a radio propagation model: ¶0177 A user device's trajectory within the 3D environment; ¶0178 A user device's viewpoint within the 3D environment; [0179] A position of one or more obstacles in the 3D environment; ¶0235 […] Exemplary possible outputs of the 3D model construction of the environment at S4 comprise: information of a user device's position within the 3D environment; information of a user device trajectory and viewpoint; a 3D map of the environment; information of a position and shape of the main obstacles (objects) in the environment that may reflect or block radio waves and information of the surface material of the main obstacles. These outputs can be used to extract (obtain) information to generate a radio propagation model of the environment (“a digital twin of the environment” ; NOTE- DISCLOSURE & TEACHING: Per ¶0176 by combining localization and mapping techniques and object recognition techniques it is therefore possible to generate a 3D model of the environment, from which at least some of the following information can be obtained to generate a radio propagation model: and per ¶0178 i.e. A position of one or more obstacles and per ¶0235 possible outputs of the 3D model construction of the environment at S4 comprise i.e. a 3D map of the environment; information of a position and shape of the main obstacles (objects) in the environment that may reflect or block radio waves reads on: coordinate information of structures per ¶0178 i.e. in the 3D environment and per ¶0235 i.e. the environment reads on: in a communication utilization environment ),
and propagation loss data at respective locations in the communication utilization environment (SHANKAR FIG. 2 & FIG. 5 & ¶0175 […] SLAM may be able to determine an obstacle, but may not be able to determine some of the physical properties of the obstacle. An example of this is that SLAM may not be able to differentiate whether an obstacle is wooden or metallic. A metallic obstacle will attenuate a signal to a higher degree when compared to a wooden obstacle. ConvNets can be used to identify from an image the properties of an object such as its material.; ¶0224 […] 3D key target reconstruction can be used to construct obstacles (objects) in the map.[…] to use the information included in the target class label (e.g., the materials or reflection surface) to reconstruct the 3D object (the propagation obstacle) ; NOTE- DISCLOSURE & TEACHING: per ¶0175 using i.e. SLAM to determine an obstacle, and i.e. ConvNets can be used to identify from an image the properties of an object such as its material, and per ¶0224 i.e. to use the information i.e. to reconstruct the 3D object (the propagation obstacle) reads on: and propagation loss data i.e. to construct obstacles (objects) in the map reads on: at respective locations in the communication utilization environment ),
wherein the point cloud data is generated by point cloud data processing (SHANKAR FIG. 2, FIG. 4 & FIG. 5 & ¶0173 see above; NOTE- DISCLOSURE & TEACHING: Per FIG. 4 & 461 map points Key targets 465 depicting 3D model of the Environment & per ¶0173 i.e. The outputs of a visual SLAM algorithm may comprise a 3D point cloud of the environment reads on: wherein the point cloud data is generated by point cloud data processing ),
and the coordinate information of structures is based on object recognition using a convolutional neural network and further based on extraction of coordinate data using simultaneous localization and mapping (SHANKAR & FIG. 2 & FIG. 5 & ¶ 0176 See above; ¶0178 see above; ¶0235 see above; NOTE- DISCLOSURE & TEACHING: Per ¶0176 by combining localization and mapping techniques and object recognition techniques it is therefore possible to generate a 3D model of the environment, from which at least some of the following information can be obtained to generate a radio propagation model: and per ¶0178 i.e. A position of one or more obstacles and per ¶0235 possible outputs of the 3D model construction of the environment at S4 comprise i.e. a 3D map of the environment; information of a position and shape of the main obstacles (objects) reads on: and the coordinate information of structures, where per ¶0176 i.e. it is therefore possible to generate a 3D model of the environment, from which at least some of the following information can be obtained to generate a radio propagation model: and per ¶0178 i.e. A position of one or more obstacles i.e. by combining localization and mapping techniques (per ¶0172 i.e. SLAM) and object recognition technique (per ¶0172 i.e. convolutional neural networks (ConvNets))) reads on: is based on object recognition using a convolutional neural network and further based on extraction of coordinate data using simultaneous localization and mapping);
tuning and updating (SHANKAR & FIG. 2 & FIG. 5 & ¶0232 see above; NOTE- DISCLOSURE & TEACHING: i.e. ¶0232 […] At S3, other measurements such i.e. as movement information, location information and radio signal measurement information may also be sent. This information may be used to calibrate the radio propagation model generated at S5 reads on: tuning and updating a parameter of the propagation model . For example, signal strength and an estimated position in the environment (estimated using a localization and mapping technique) may be used to update the radio propagation model reads on: to obtain an updated propagation model );
and estimate a radio field intensity in the communication utilization environment by applying the updated propagation model (SHANKAR FIG. 5 & ¶239 Ray tracing may be used to generate radio propagation channels and to generate virtual radio maps using the radio propagation model. Ray tracing is a method of calculating the path of waves or particles through a system with regions of varying propagation velocity, absorption characteristics, and reflecting surfaces; ¶0240 For network planning, the server 524 may use information regarding a preferred type of AP or installed AP sent from the user device at S6. The server 524 may also use the AP preferred type and/or AP installed type and the radio propagation model to generate a virtual radio coverage map. The server 524 may additionally use a location of the AP in the environment to generate the virtual radio coverage map; NOTE- DISCLOSURE & TEACHING: per ¶0240 i.e. The server 524 may also use the AP preferred type and/or AP installed type and the radio propagation model to generate a virtual radio coverage map and i.e. additionally use a location of the AP in the environment to generate the virtual radio coverage map reads on: and estimate a radio field intensity in the communication utilization environment , where per ¶0240 i.e. use the AP preferred type and/or AP installed type and the radio propagation model to generate reads on: by applying the updated propagation model ).
SHANKAR does not appear to explicitly teach or strongly suggest (Specifically i.e. See italicized portions of listed claim limitations): propagation loss data converted from radio field intensity measurements; select an environment label based on the three-dimensional [[CAD]] computer aided design data and the propagation loss data; the tuning and the updating being performed according to the selected;
Rubio which also teaches: A communication design support apparatus (Rubio ¶0115 carrying out signal quality analysis 21 using a propagation modeling software tool one may assign a propagation model within the propagation modeling software to use the object clutter data set 19 by performing the following within the propagation modeling software: […] ; NOTE- DISCLOSURE & TEACHING: i.e. carrying out signal quality analysis 21 i.e. using a propagation modeling software tool i.e. computational device well known in prior art to utilize software tool reads on: A communication design support apparatus)
comprising: a memory configured to store a propagation model (Rubio ¶0115 see above; NOTE- DISCLOSURE & TEACHING: i.e. carrying out signal quality analysis 21 i.e. using a propagation modeling software tool i.e. computational device well known in prior art to utilize software tool where a processor is well known to execute instructions of a software tool stored in memory reads on: comprising: a memory configured to store . Furthermore per ¶0115 i.e. a propagation modeling software tool one may assign a propagation model within the propagation modeling software i.e. software tool running on computational tool reads on: to store a propagation model where i.e. computational device well known in prior art to utilize software tool where a processor is well known to execute instructions of a software tool stored in memory);
and a processor configured to: obtain a set of data including three-dimensional [[CAD]] computer aided design data including coordinate information of structures in a communication utilization environment (Rubio FIG. 19 & ¶0042 […] a color-coded map illustrating the result of a signal quality analysis carried out based on object clutter classes for portions of a wireless communication network; FIG. 21 & ¶0115 See above; ¶0116 1. Instruct the propagation modeling software that buildings are objects having a height extend from ground level to a height z where z is the building height; ¶0117 2. Identify trees as a clutter class n and height z where z represents average tree height in the area (in meters) and n represents the raster row id which gives x and y location of the pixel identified as trees; NOTE- DISCLOSURE & TEACHING: per ¶0115 i.e. assign a propagation model within the propagation modeling software to use the object clutter data set 19 reads on: and a processor configured to: obtain a set of data , furthermore per ¶0115 i.e. by performing the following within the propagation modeling software: i.e. per ¶0116 Instruct the propagation modeling software that buildings are objects having a height extend from ground level to a height z where z is the building height and per ¶0117 i.e. 2. Identify trees as a clutter class n and height z where z represents average tree height in the area (in meters) and n represents the raster row id which gives x and y location of the pixel identified as trees i.e. heights z and corresponding raster id which give x and y location i.e. x, y, z coordinate or location data in 3 dimensions utilized by propagation model within the propagation modeling software to use the object clutter data set 19 and i.e. well known to be executed on or aided by a computer for design and analysis reads on: including three-dimensional [[CAD]] computer aided design data including coordinate information. Furthermore per ¶0116 i.e. 1. Instruct the propagation modeling software that buildings are objects and per ¶0117 Identify trees as a clutter class n reads on: of structures in a communication utilization environment i.e. comprising buildings, trees, etc. as applied to a clutter environment such as FIG. 19),
and propagation loss data in the communication utilization environment (Rubio Fig. 19 & ¶0042 see above; FIG. 21 & ¶0115 - ¶0117 see above; ¶0121 i.e. Assign an appropriate propagation model and assign attenuations based on the real world object types represented in the object clutter data set 19; NOTE- DISCLOSURE & TEACHING: per ¶0120 i.e. assign attenuations reads on: and propagation loss data i.e. based on the real world object types represented in the object clutter data applied by the appropriate propagation model such as for FIG. 19 reads on: in the communication utilization environment);
select an environment label based on the three-dimensional [[CAD]] computer aided design data and the propagation loss data (Rubio FIG. 21 Object clutter classes; ¶0115 - ¶0117 see above; ¶0118 […] 3. Identify water and grass as clutter classes n and m; ¶0120 See above; NOTE- DISCLOSURE & TEACHING: per i.e. As depicted in FIG. 21 and per ¶0120 i.e. Assign an appropriate propagation model and assign attenuations based on the real world object types represented in the object clutter data set 19 i.e. per ¶0115-¶0118 FIG. 21 i.e. building, trees, water, grass object clutter classes as utilized reads on: select an environment label , furthermore per ¶0116 & ¶0117 & ¶0118 i.e. i.e. x, y, z coordinate or location data in 3 dimensions utilized by propagation model within the propagation modeling software to use the object clutter data set 19 reads on: based on the three-dimensional [[CAD]] computer aided design data, where per ¶0120 i.e. assign attenuations reads on: and the propagation loss data which are i.e. based on the real world object types represented in the object clutter data set);
tune and update a parameter of the propagation model to obtain an updated propagation model, the tuning and updating being performed according to the selected environment label (Rubio FIG. 21 & ¶0115 - ¶0117 see above; ¶0118 See above; ¶0120 See above; NOTE- DISCLOSURE & TEACHING: per ¶0120 i.e. Assign an appropriate propagation model i.e. and assign attenuations reads on: tune and update a parameter of the propagation model comprising at least attenuations associated with clutter object data. Furthermore per ¶0120 i.e. the appropriate propagation model i.e. with clutter attenuation applied reads on: to obtain an updated propagation model . Furthermore, per FIG. 21 & ¶0120 i.e. based on the real world object types represented in the object clutter data set 19 reads on: the tuning and updating being performed according to the selected environment label );
and estimate a radio field intensity in the communication utilization environment(Rubio FIG. 21 & ¶0115 - ¶0117 see above; ¶0118 See above; ¶0120 See above; ¶0121 […] Once the propagation model has been assigned and attenuations specified for each object type represented in the object clutter data set 19 the software can represent the results of the signal quality analysis 21 in a map such as the coverage maps of FIG. 11 and FIG. 19 and/or can generate maps for path loss, signal strength and/or interference ; NOTE- DISCLOSURE & TEACHING: per ¶0121 i.e. the software can represent the results of the signal quality analysis 21 in a map such as the coverage maps of FIG. 11 and FIG. 19 and/or can generate maps for path loss, signal strength reads on: and estimate a radio field intensity i.e. corresponding to the clutter object classes utilized for coverage area maps such as FIG. 19 reads on: in the communication utilization environment ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of SHANKAR with teachings of Rubio, since Rubio enables clutter data set be represented in a clutter raster file. This is particularly advantageous because data formatted as a raster file is readily loaded into commercially available signal propagation modeling tools (Rubio - ¶0013).
While SHANKAR in view of Rubio teaches: A communication design support apparatus comprising: a memory configured to store a propagation model; and a processor configured to: obtain a set of data including three-dimensional [[CAD]] computer aided design data including point cloud data, coordinate information of structures in a communication utilization environment, and propagation loss data at respective locations in the communication utilization environment, wherein the point cloud data is generated by point cloud data processing, and the coordinate information of structures is based on object recognition using a convolutional neural network and further based on extraction of coordinate data using simultaneous localization and mapping; select an environment label based on the three-dimensional [[CAD]] computer aided design data and the propagation loss data; tuning and updating the updating being performed according to the selected; and estimate a radio field intensity in the communication utilization environment by applying the updated propagation model,
SHANKAR in view of Rubio does not appear to explicitly teach or strongly suggest (Specifically i.e. See italicized portions of listed claim limitations): propagation loss data converted from radio field intensity measurements;
ZHANG from a similar field of endeavor involving propagation models teaches limitations not explicitly taught or strongly suggested by SHANKAR in view of Rubio, as noted below i.e.
ZHANG which also teaches: A communication design support apparatus (ZHANG – FIG. 1 as depicted 103 Selecting several points in the target building as the testing points, and actually measuring signal intensity at the testing positions & ¶0002 An indoor wireless signal fingerprint database is established […] applied for judging indoor weak coverage area of the wireless communication network, positioning defective devices of an outdoor macro base station and an indoor distributed system, analyzing wireless network optimization and maintenance field such as wireless network interference and […] ; ¶0084 […] 103—selecting 30 testing points at positions 1 meter distance from the horizontal plane of the second floor of the building, as shown in FIG. 4, a testing terminal is carried to measure on site wireless signal fingerprint information of the selected testing positions; ¶0085 […] testing terminal involved […] a personal cell phone, a hand-held spectrum analyzer, a personal digital assistant (PDA); NOTE-DISCLOSURE & TEACHING: Per ¶0002 i.e. fingerprint database […] applied for judging indoor weak coverage area of the wireless communication network, positioning defective devices of an outdoor macro base station and an indoor distributed system reads on: A communication design support and per ¶0084 i.e. a testing terminal is carried to measure on site wireless signal fingerprint information reads on: A communication design support apparatus where per ¶0085 i.e. […] testing terminal involved […] a personal cell phone, a hand-held spectrum analyzer, a personal digital assistant (PDA))
comprising: a propagation model (ZHANG - FIG. 1 & ¶0078 […] a method for rapidly establishing an indoor wireless signal fingerprint database;¶0084 See above; ¶0087 […] (4) 104—theoretically predicting wireless signal fingerprint data at the 30 positions labeled in the step (3) using the ray tracing propagation model algorithm […]; NOTE-DISCLOSURE & TEACHING: per ¶0084 a testing terminal is carried to measure on site wireless signal fingerprint information where per ¶0078 method for establishing i.e. terminal to measure, involves per ¶0087 i.e. 104—theoretically predicting wireless signal fingerprint data at the 30 positions labeled in i.e. the step (3) using the ray tracing propagation model algorithm reads on: comprising: a propagation model where storing and memory are implied and well known in art for computational devices);
furthermore ZHANG (specific to limitations not explicitly taught or strongly suggested by Rubio) teaches: and a processor configured to: a processor configured to: obtain a set of data including three-dimensional [[CAD]] computer aided design data of structures in a communication utilization environment (ZHANG - ¶0078 see above; ¶0079 […] (1) 101—extracting 3D spatial building data of the target building of which an indoor wireless signal fingerprint data is required to be established […] 3D spatially modeling CAD format drawings […] and separately storing 3D spatial building data of each floor, the 3D spatial building data including a vertical storey height of the floor, horizontal area of the floor, building material data of the floor and layout structure data of the floor; NOTE-DISCLOSURE & TEACHING: per ¶0084 a testing terminal is carried to measure on site wireless signal fingerprint information where per ¶0078 method for establishing i.e. terminal to measure such as mobile phone PDA device are well known in the art to comprise a processor reads on: and a processor configured to: and i.e. involves per ¶0079 i.e. extracting 3D spatial building data of the target building i.e. 3D spatially modeling CAD format drawings and separately storing 3D spatial building data of each floor, the 3D spatial building data including a vertical storey height of the floor, horizontal area of the floor, building material data of the floor and layout structure data of the floor reads on: obtain a set of data including three-dimensional [[CAD]] computer aided design data of structures where coordinate data is implied as part of CAD format drawings. Furthermore i.e. where i.e. building of which an indoor wireless signal fingerprint data is required to be established reads on: in a communication utilization environment ),
and propagation loss data converted from radio field intensity measurements at respective locations in the communication utilization environment (ZHANG FIG. 1 & ¶0038 […] process of correcting the 3D ray tracing propagation model in the step (5) may be a process in which the building material wireless propagation loss parameters are adjusted using a simulated annealing algorithm ; ¶0078 see above; ¶0079 See above; ¶0084 See above; ¶0099 […] (5) 105—According to analysis through comparison between the actually measured value measured in the step (3) and the theoretical value calculated in the step (4), the propagation model parameters are corrected using the simulated annealing algorithm, so that the mean square error between the actually measured value and the theoretical value is the minimum. […] the simulated annealing algorithm is a process in which the building wireless propagation loss parameters are adjusted using the simulated annealing algorithm; NOTE-DISCLOSURE & TEACHING: per ¶0084 a testing terminal is carried to measure on site wireless signal fingerprint information where per ¶0078 method for establishing where the method involves per ¶0099 i.e. comparison between the actually measured value measured in the step (3) and the theoretical value calculated in the step (4), the propagation model parameters are corrected using the simulated annealing algorithm, so that the mean square error between the actually measured value and the theoretical value is the minimum. […] the simulated annealing algorithm is a process in which i.e. the building wireless propagation loss parameters are adjusted reads on: and propagation loss data, where per ¶0099 i.e. comparison between the actually measured value measured in the step (3) and the theoretical value calculated in the step (4) reads on: converted from radio field intensity measurements at respective locations, as applied to per ¶0079 i.e. building of which an indoor wireless signal fingerprint data is required to be established reads on: in the communication utilization environment );
tuning and updating (ZHANG FIG. 1 & ¶0038 see above ; ¶0078 see above; ¶0079 see above; ¶0084 see above; ¶0099 See above; NOTE-DISCLOSURE & TEACHING: per ¶0099 i.e. comparison between the actually measured value measured in the step (3) and the theoretical value calculated in the step (4), the propagation model parameters are corrected using the simulated annealing algorithm, so that the mean square error between the actually measured value and the theoretical value is the minimum. […] the simulated annealing algorithm is a process in which i.e. the building wireless propagation loss parameters are adjusted reads on: tuning and updating a parameter of the propagation model, where per ¶0038 process of correcting the 3D ray tracing propagation model in the step (5) reads on: the propagation model to obtain an updated propagation model may be a process in which the building material wireless propagation loss parameters are adjusted using a simulated annealing algorithm ),
and estimate a radio field intensity in the communication utilization environment by applying the updated propagation model (ZHANG FIG. 1 & ¶0078 see above; ¶0079 See above; ¶0084 See above; ¶0099 See above; ¶0116 […] (6) 106—by using the propagation model parameters corrected in the step (5), recalculating wireless signal coverage intensity information generated […] by the 15 transmitting antennas and the 5 WiFi access devices of one WCDMA wireless access device in the 3D building of the five-floor building in the step (1) using the ray tracing propagation model algorithm […] ; NOTE-DISCLOSURE & TEACHING: per ¶0084 a testing terminal is carried to measure on site wireless signal fingerprint information where per ¶0078 method for establishing where the method involves per FIG. 1 Step 106 and ¶0116 i.e. (6) 106—by using the propagation model parameters corrected in the step (5), recalculating wireless signal coverage intensity information generated […] in the 3D building reads on: estimate a radio field intensity in the communication utilization environment where i.e. by using the propagation model parameters corrected in the step (5) and using the ray tracing propagation model algorithm reads on: by applying the updated propagation model i.e. by using the propagation model parameters corrected).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of SHANKAR in view of Rubio with teachings of ZHANG, since ZHANG enables procedures for rapidly establishing an indoor wireless signal fingerprint database, and is advantageous in rapidly establishing an indoor wireless signal fingerprint database (ZHANG - ¶0008).
Regarding Claim 2. (Previously Presented) SHANKAR in view of Rubio and ZHANG teaches: The communication design support apparatus according to claim 1,
furthermore ZHANG teaches: wherein the processor is configured to: receive input of information indicating an estimated base station position(ZHANG – FIG. 1 & ¶0078 see Claim 1; ¶0079 See claim 1; ¶0081 […] (2) 102—recording and storing the wireless access device information that can be received in the building as described in the step (1) […] the building in the embodiment is provided with a WCDMA system and a WiFi wireless local area network […] and thus is provided with the total of 5 network access points, recording data of the transmitting antennas of the WCDMA indoor distributed system and the WiFi access points in the building, respectively […] transmitting antenna data includes specific position information of each transmitting antenna in the building, signal frequency of the transmitting antenna, transmitting power of the transmitting antenna, 3D radiation parameters of the transmitting antenna, an inclination angle of the transmitting antenna; NOTE-DISCLOSURE & TEACHING: i.e. per ¶0081 the wireless access device information […] recording data of the transmitting antennas of the WCDMA indoor distributed system and the WiFi access points includes specific position information of each transmitting antenna in the building, signal frequency of the transmitting antenna, transmitting power of the transmitting antenna etc. reads on: wherein the processor site survey unit is configured to: receive input of information indicating an estimated base station position ),
an estimation system (ZHANG – FIG. 1 & ¶0078 see Claim 1; ¶0079 See claim 1; ¶0081 See above; ¶0087 […] (4) 104—theoretically predicting wireless signal fingerprint data at the 30 positions labeled in the step (3) using the ray tracing propagation model algorithm, the process of predicting fingerprint data is performed one wireless communication system after another, herein comprising predicting the one WCDMA indoor distributed system in the step (2) and predicting wireless signal intensity information at the 30 testing points of 3 wireless access devices of one WiFi system in the step (3). Specific steps are as described in the steps (4-1) to (4-8); ¶0089 […] (4-1) determining all propagation paths; ¶0091 (4-2) calculating propagation loss of each propagation path in free space […] ; ¶0092 […](4-3) calculating loss of each ray path under the influence of the building material […];.. ¶0095 […] (4-6) calculating wireless signal intensity of the reception point i, assuming that P.sub.i is the signal intensity (dBm) of the i-th reception point; P.sub.t is transmitting power (dBm) of the wireless signal transmitting antenna; G.sub.t and G.sub.r are antenna gains (dBi) of the wireless signal transmitting antenna and the reception point respectively ; NOTE-DISCLOSURE & TEACHING: i.e. predicting wireless signal intensity information at the 30 testing points of 3 wireless access devices of one WiFi system in the step (3). Specific steps are i.e. as described in the steps (4-1) to (4-8) reads on: an estimation system ) ,
and a use environment (ZHANG – FIG. 1 & ¶0078 see Claim 1; ¶0079 See claim 1; ¶0081 See above; NOTE-DISCLOSURE & TEACHING: per ¶0079 the 3D spatial building data including, horizontal area of the floor, and layout structure data of the floor, furthermore applied to per ¶0081 i.e. the building reads on: and a use environment ),
and estimate the radio field intensity in the structures in the communication utilization environment on the basis of the information having been input (ZHANG – FIG. 1 & ¶0078 see Claim 1; ¶0079 See claim 1; ¶0081 See above; ¶0089-¶0095 See above ; NOTE-DISCLOSURE & TEACHING: per ¶0095 […] (4-6) calculating wireless signal intensity of the reception point reads on: estimate the radio field intensity. Furthermore per ¶0079 i.e. building of which an indoor wireless signal fingerprint data is required to be established where the building information comprises i.e. per ¶0079 i.e. the 3D spatial building data including, i.e. horizontal area of the floor, and layout structure data of the floor reads on: in the structures in the communication utilization environment where i.e. P.sub.t is transmitting power (dBm) of the wireless signal transmitting antenna; G.sub.t and G.sub.r are antenna gains (dBi) of the wireless signal transmitting antenna and the reception point respectively reads on: on the basis of the information having been input which corresponds to input or recorded information from ¶0081 i.e. data of the transmitting antennas of the WCDMA indoor distributed system and the WiFi access points in the building, respectively […] transmitting antenna data includes specific position information of each transmitting antenna in the building, signal frequency of the transmitting antenna, transmitting power of the transmitting antenna, 3D radiation parameters of the transmitting antenna, an inclination angle of the transmitting antenna).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of SHANKAR in view of Rubio and ZHANG, further with the teachings of ZHANG, since ZHANG enables procedures for rapidly establishing an indoor wireless signal fingerprint database, and is advantageous in rapidly establishing an indoor wireless signal fingerprint database (ZHANG - ¶0008).
Regarding Claim 3. (Currently Amended) SHANKAR in view of Rubio and ZHANG teaches: The communication design support apparatus according to claim 1,
wherein the processor is configured to: acquire relative coordinate information (SHANKAR ¶0178 See claim 1; ¶0235 see above; NOTE- DISCLOSURE & TEACHING: Per ¶0176 by combining localization and mapping techniques and object recognition techniques it is therefore possible to generate a 3D model of the environment, from which at least some of the following information can be obtained to generate a radio propagation model: and per ¶0178 i.e. A position of one or more obstacles and per ¶0235 possible outputs of the 3D model construction of the environment at S4 comprise i.e. a 3D map of the environment; information of a position and shape of the main obstacles (objects) in the environment that may reflect or block radio waves reads on: wherein the processor is configured to: acquire relative coordinate information), the point cloud data(SHANKAR FIG.2 & FIG. 5 & ¶0172 See claim 1; ¶0173 See claim 1; ¶0234 See claim 1; NOTE- DISCLOSURE & TEACHING: Per FIG. 5 & per ¶0172 i.e. to construct the 3D model of the environment and per ¶0234 i.e. At S4, a 3D model of the environment shown in the image information is constructed where per ¶0173 an exemplary localization and mapping technique is the SLAM algorithm. SLAM can be used to construct or update a map of an unknown environment while simultaneously keeping track of a device's location within it. A SLAM algorithm may be termed a “visual SLAM algorithm” when the solution(s) is/are based on visual information alone. i.e. The outputs of a visual SLAM algorithm may comprise a 3D point cloud of the environment reads on: the point cloud data), and radio field intensity data ( SHANKAR ¶0232 See claim 1; NOTE- DISCLOSURE & TEACHING: i.e. At S3, other measurements such as movement information, location information and radio signal measurement information may also be sent reads on: and radio field intensity data ), and estimate the radio field intensity in the structures (SHANKAR FIG. 5 & ¶See Claim 1 ; ¶0240 See Claim 1; NOTE- DISCLOSURE & TEACHING: per ¶0240 i.e. The server 524 may also use the AP preferred type and/or AP installed type and the radio propagation model to generate a virtual radio coverage map and i.e. additionally use a location of the AP in the environment to generate the virtual radio coverage map reads on: and estimate the radio field intensity in the structures)
on the basis of acquired data (HANKAR FIG. 5 & ¶See Claim 1 ; ¶0240 See Claim 1; NOTE- DISCLOSURE & TEACHING: ¶0240 ¶0240 to generate a virtual radio coverage map i.e. the server 524 may use information regarding a preferred type of AP or installed AP sent from the user device at S6. The server 524 may also use the AP preferred type and/or AP installed type reads on: on the basis of acquired data).
furthermore ZHANG also teaches: wherein the processor is configured to: acquire relative coordinate information and data, and estimate the radio field intensity in the structures on the basis of acquired data (ZHANG – FIG. 1 & ¶0078 see Claim 1; ¶0079 See claim 1; ¶0081 See above; ¶0089-¶0095 See above ; NOTE-DISCLOSURE & TEACHING: ¶er ¶0081 i.e. recording i.e. transmitting antenna data includes specific position information of each transmitting antenna in the building, signal frequency of the transmitting antenna, transmitting power of the transmitting antenna etc. reads on: wherein the processor site survey unit is configured to: acquire relative coordinate information and data and per ¶0095 […] (4-6) calculating wireless signal intensity of the reception point reads on: estimate the radio field intensity per ¶0079 i.e. the 3D spatial building data including, i.e. horizontal area of the floor, and layout structure data of the floor reads on: in the structures where per ¶0095 i.e. P.sub.t is transmitting power (dBm) of the wireless signal transmitting antenna; G.sub.t and G.sub.r are antenna gains (dBi) of the wireless signal transmitting antenna and the reception point respectively reads on: on the basis of acquired data which corresponds to input or recorded information from ¶0081 i.e. data of the transmitting antennas of the WCDMA indoor distributed system and the WiFi access points in the building, respectively […] transmitting antenna data includes specific position information of each transmitting antenna in the building, signal frequency of the transmitting antenna, transmitting power of the transmitting antenna, 3D radiation parameters of the transmitting antenna, an inclination angle of the transmitting antenna);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of SHANKAR in view of Rubio and ZHANG, further with the teachings of ZHANG, since ZHANG enables procedures for rapidly establishing an indoor wireless signal fingerprint database, and is advantageous in rapidly establishing an indoor wireless signal fingerprint database (ZHANG - ¶0008).
Regarding Claim 4. (Previously Presented) SHANKAR in view of Rubio and ZHANG teaches: The communication design support apparatus according to claim 3,
furthermore ZHANG teaches: wherein the processor is configured to: receive input of data indicating shape of structures in a new environment and select data indicating classification of a characteristic of a communication environment in the structures on the basis of the data having been input (ZHANG - ¶0079 See claim 1; NOTE-DISCLOSURE & TEACHING: per ¶0079 i.e. extracting 3D spatial building data of the target building of which an indoor wireless signal fingerprint data is required to be established i.e. as applied to per ¶0079 i.e. the 3D spatial building data including, i.e. horizontal area of the floor, and layout structure data of the floor reads on: wherein the processor is configured to: receive input of data indicating a shape of structures i.e. and per ¶0079 i.e. and separately storing 3D spatial building data of each floor reads on: in a new environment comprised in each separate floor, and per ¶0079 i.e. the 3D spatial building data including a vertical storey height of the floor, horizontal area of the floor, building material data of the floor reads on: and select data indicating classification of a characteristic of i.e. and i.e. horizontal area of the floor, layout structure data of the floor i.e. including within spaces depicted in each floor reads on: a communication environment in the structures),
and estimate the radio field intensity in the structure by applying the propagation model according to the characteristic of the communication environment having been selected (ZHANG FIG. 1 & ¶0089 See claim1; ¶0091-0095 see claim 1; ; NOTE-DISCLOSURE & TEACHING: per ¶0095 […] (4-6) calculating wireless signal intensity of the reception point reads on: estimate the radio field intensity in the structure , where per ¶0087 i.e. predicting wireless signal intensity information at the 30 testing points of 3 wireless access devices of one WiFi system in the step (3). Specific steps are as described in the steps (4-1) to (4-8), where per ¶0092 […](4-3) calculating loss of each ray path under the influence of the building material where i.e. the losses of the above ray paths a, b and c due to the influence of the building material are calculated reads on: by applying the propagation model according to the characteristic of the communication environment, where per ¶0079 the stored i.e. the 3D spatial building data including a vertical storey height of the floor, horizontal area of the floor, building material data of the floor reads on: having been selected per ¶0092 correlated to (4-3) calculating loss of each ray path under the influence of the building material).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of SHANKAR in view of Rubio and ZHANG, further with the teachings of ZHANG, since ZHANG enables procedures for rapidly establishing an indoor wireless signal fingerprint database, and is advantageous in rapidly establishing an indoor wireless signal fingerprint database (ZHANG - ¶0008).
Regarding Claim 5. (Currently Amended) SHANKAR in view of Rubio and ZHANG teaches: The communication design support apparatus according to claim 4,
furthermore ZHANG teaches: wherein the processor is configured to perform a function selected (ZHANG – FIG. 1 & ¶0078 see Claim 1; ¶0079 See claim 1; ¶0081 See claim 2; NOTE-DISCLOSURE & TEACHING: i.e. per ¶0081 the wireless access device information […] recording data of the transmitting antennas of the WCDMA indoor distributed system and the WiFi access points includes specific position information of each transmitting antenna in the building, signal frequency of the transmitting antenna, transmitting power of the transmitting antenna etc. reads on: wherein the processor site survey unit is configured to: to perform a function selected)
in accordance with a user operation (ZHANG – FIG. 1 & ¶0084 See claim 1 & ¶0085 See claim 1; NOTE-DISCLOSURE & TEACHING: per ¶0084 a testing terminal is carried to measure on site wireless signal fingerprint information of the selected testing positions, where per ¶0085 i.e. testing terminal involved is i.e. a personal cell phone, a hand-held spectrum analyzer, a personal digital assistant (PDA) reads on: in accordance with a user operation of i.e. a personal cell phone, a hand-held spectrum analyzer, a personal digital assistant (PDA) well known to function with user input and operation)
from among (note: limitations subsequent to recitation “a function selected” […] “from among” and subsequently separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of carrying patentable weight):
collecting data obtained by measuring the structures (note: limitations subsequent to recitation “a function selected” […] “from among” and subsequently separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of carrying patentable weight),
or (note: limitations separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of patentable weight)
receiving input of information indicating an estimated base station position (ZHANG – FIG. 1 & ¶0078 see Claim 1; ¶0079 See claim 1; ¶0081 See claim 2; NOTE-DISCLOSURE & TEACHING: i.e. per ¶0081 the wireless access device information […] recording data of the transmitting antennas of the WCDMA indoor distributed system and the WiFi access points includes specific position information of each transmitting antenna in the building, signal frequency of the transmitting antenna, transmitting power of the transmitting antenna etc. reads on: receiving input of information indicating an estimated base station position),
an estimation system(ZHANG – FIG. 1 & ¶0078 see Claim 1; ¶0079 See claim 1; ¶0081 See claim 2; ¶0087 See claim 2; ¶0089 See claim 2; ¶001-¶0095 See claim 2; NOTE-DISCLOSURE & TEACHING: i.e. predicting wireless signal intensity information at the 30 testing points of 3 wireless access devices of one WiFi system in the step (3). Specific steps are i.e. as described in the steps (4-1) to (4-8) reads on: an estimation system),
and a use environment(ZHANG – FIG. 1 & ¶0078 see Claim 1; ¶0079 See claim 1; ¶0081 See claim 2; ¶0087 See claim 2; ¶0089 See claim 2; ¶001-¶0095 See claim 2; NOTE-DISCLOSURE & TEACHING: per ¶0081 i.e. the building reads on: and a use environment ),
or (note: limitations separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of patentable weight)
acquiring the relative coordinate information, the point cloud data, and the radio field intensity data(note: limitations below that follow, which are subsequent to recitation “a function selected” […] “from among” and subsequently separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of carrying patentable weight);
updating the parameter of the propagation model to obtain an updated parameter (note: limitations below that follow, which are subsequent to recitation “a function selected” […] “from among” and subsequently separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of carrying patentable weight –
ZHANG FIG. 1 & ¶0038 see claim 1; ¶0078 see claim 1; ¶0079 see claim 1; ¶0084 see claim 1; ¶0099 see claim 1; NOTE-DISCLOSURE & TEACHING: per ¶0099 i.e. comparison between the actually measured value measured in the step (3) and the theoretical value calculated in the step (4), the propagation model parameters are corrected using the simulated annealing algorithm, so that the mean square error between the actually measured value and the theoretical value is the minimum. […] the simulated annealing algorithm is a process in which i.e. the building wireless propagation loss parameters are adjusted reads on: updating the parameter of the propagation model i.e. propagation loss parameters are adjusted reads on: to obtain an updated parameter, where per ¶0038 process of correcting the 3D ray tracing propagation model in the step (5) may be a process in which the building material wireless propagation loss parameters are adjusted using a simulated annealing algorithm );
and estimating a radio field intensity in the structures by applying the propagation model having the updated parameter (note: limitations below that follow, which are subsequent to recitation “a function selected” […] “from among” and subsequently separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of carrying patentable weight –
ZHANG FIG. 1 & ¶0078 see claim 1; ¶0079 see claim 1; ¶0084 see claim 1; ¶0099 see claim 1; ¶0116 see claim 1 ; NOTE-DISCLOSURE & TEACHING: per ¶0084 a testing terminal is carried to measure on site wireless signal fingerprint information where per ¶0078 method for establishing where the method involves per FIG. 1 Step 106 and ¶0116 i.e. (6) 106—by using the propagation model parameters corrected in the step (5), recalculating wireless signal coverage intensity information generated […] in the 3D building reads on: and estimating a radio field intensity i.e. as applied to per ¶0079 i.e. the 3D spatial building data including, i.e. horizontal area of the floor, and layout structure data of the floor reads on: in the structures where i.e. by using the propagation model parameters corrected in the step (5) and using the ray tracing propagation model algorithm reads on: by applying the propagation model having the updated parameter i.e. by using the propagation model parameters corrected)),
or (note: limitations separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of patentable weight)
estimating the radio field intensity in the structure on the basis of the information having been input(note: limitations separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of patentable weight),
or (note: limitations separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of patentable weight) estimating the radio field intensity in the structures on the basis of acquired data (note: preceding limitations subsequent to recitation “a function selected” […] “from among” and subsequently separated by a recitation “or” are interpreted as presented in the alternative and not required together i.e. for the purposes of carrying patentable weight).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of SHANKAR in view of Rubio and ZHANG, further with the teachings of ZHANG, since ZHANG enables procedures for rapidly establishing an indoor wireless signal fingerprint database, and is advantageous in rapidly establishing an indoor wireless signal fingerprint database (ZHANG - ¶0008).
Regarding claim 6. (Currently Amended) SHANKAR teaches: A communication design support method performed by a computer (SHANKAR FIG.2 & FIG. 5 & ¶0171 […] In order to construct the 3D model of the environment, the user device 202 may send image information, which may be collected from image information recording unit 216, to server 224; ¶0226 […] method in which a user device 502 and server 524 are in communication […] ; ¶0228 […] user device and server may be in communication across an interface such as interface 226 shown schematically in FIG. 2; NOTE- DISCLOSURE & TEACHING: per FIG. 5 & ¶0226 i.e. method in which a user device 502 and server 524 are in communication reads on: A communication design support method per FIG. 2, FIG. 5 & ¶0171 i.e. server 224 and per ¶0228 i.e. server 224 reads on: performed by a computer ) storing a propagation model (SHANKAR FIG. 2 & FIG. 5 & ¶0168 See claim 1 ; ¶0232 See claim 1 ; NOTE- DISCLOSURE & TEACHING: per ¶0168 i.e. at least one memory 236, where i.e. tasks include per ¶0232 i.e. to calibrate the radio propagation model generated at S5 and i.e. used to update the radio propagation model reads on: storing a propagation model)
(See the rejection of Claim 1, Claim 6 recites similar and parallel features to Claim 1, and the rationale for the rejection of claim 1 applies similarly to claim 6. Where applicable, minor differences between claims are noted as appropriate) the method comprising: obtaining a set of data including three-dimensional [[CAD]] computer aided design data including point cloud data, coordinate information of structures in a communication utilization environment, and propagation loss data converted from radio field intensity measurements at respective locations in the communication utilization environment, wherein the point cloud data is generated by point cloud data processing, and the coordinate information of structures is based on object recognition using a convolutional neural network and further based on extraction of coordinate data using simultaneous localization and mapping; selecting an environment label based on the three-dimensional [[CAD]] computer aided design data and the propagation loss data; tuning and updating a parameter of the propagation model to obtain an updated propagation model, the tuning and the updating being performed according to the selected; and estimating a radio field intensity in the communication utilization environment by applying the updated propagation model (See the rejection of Claim 1, Claim 6 recites similar and parallel features to Claim 1, and the rationale for the rejection of claim 1 applies similarly to claim 6. Where applicable, minor differences between claims are noted as appropriate).
Regarding Claim 7. (Currently Amended) SHANKAR teaches: A non-transitory computer-readable recording medium storing a propagation model and a program for causing a computer (SHANKAR FIG. 2 & FIG. 5 & ¶0168 See claim 1 ; ¶0232 See claim 1 ; NOTE- DISCLOSURE & TEACHING: per ¶0168 i.e. at least one memory 236 reads on: comprising: A non-transitory computer-readable recording medium. Furthermore per ¶0168 i.e. use in software and hardware aided execution of tasks it is designed to perform, and the tasks include per ¶0232 i.e. to calibrate the radio propagation model generated at S5 and i.e. used to update the radio propagation model reads on: storing a propagation model and a program for causing a computer )
(See the rejection of Claim 1, Claim 7 recites similar and parallel features to Claim 1, and the rationale for the rejection of claim 1 applies similarly to Claim 7. Where applicable, minor differences between claims are noted as appropriate) to: obtain a set of data including three-dimensional [[CAD]] computer aided design data including point cloud data, coordinate information of structures in a communication utilization environment, and propagation loss data converted from radio field intensity measurements at respective locations in the communication utilization environment, wherein the point cloud data is generated by point cloud data processing, and the coordinate information of structures is based on object recognition using a convolutional neural network and further based on extraction of coordinate data using simultaneous localization and mapping; select an environment label based on the three-dimensional [[CAD]] computer aided design data and the propagation loss data; tuning and updating the updating being performed according to the selected; and estimate a radio field intensity in the communication utilization environment by applying the updated propagation model (See the rejection of Claim 1, Claim 7 recites similar and parallel features to Claim 1, and the rationale for the rejection of claim 1 applies similarly to Claim 7. Where applicable, minor differences between claims are noted as appropriate).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MALICK A SOHRAB whose telephone number is (571)272-4347. The examiner can normally be reached on Mo-Thu & Alternate Fri 7:30 am - 5:30 pm.
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/M.A.S./
Examiner, Art Unit 2414
07/09/2026
/EDAN ORGAD/Supervisory Patent Examiner, Art Unit 2414
Prosecution Insights