CTNF 18/863,156 CTNF 101534 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Specification 06-14 AIA Applicant is reminded of the proper content of an abstract of the disclosure. A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art. If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives. Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps. Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length. See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts. The abstract of the disclosure is objected to because it is not a single paragraph. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b). 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a first map creation unit configured” recited in Line 2, “a second map creation unit configured” recited in Line 5, and “a third map creation unit configured” recited in Line 9, all in Claim 17. Regarding Claim 17, “a first map creation unit configured to create…”, “a second map creation unit configured to… create…”, and “a third map creation unit configured to combine”. The corresponding structure in the disclosure for performing the claimed map creation is: (P[0026] “The occupancy map creation unit ("OM creation unit") 110 creates the occupancy map 400 on the basis of the environmental information 300 transmitted from the user terminal UT and received via the communication unit 160. The OM creation unit 110 outputs the created occupancy map 400 to a waypoint setting unit (hereinafter referred to as the "WP setting unit") 120, the preliminary map creation unit 140, and the map information storage unit 150.”). Fig 3 also discloses map creation functions in elements 110, 140, 230 and 180, and P[0051] describes the preliminary map creation unit 140 which corresponds to first map creation unit, intermediate map creation unit 230 which corresponds to a second map creation unit, and final map creation unit 180, which corresponds to the third map creation unit. Additionally, Fig 19 shows the structure of a CPU executing the program stored in memory to perform these functions. This discloses structure for receiving environmental information, generating a map on said information, and outputting the generated map to additional map processing units. Therefore, “a first map creation unit configured to create…”, “a second map creation unit configured to… create…”, and “a third map creation unit configured to combine” are interpreted as hardware components that receive information, generate a map, and output the map. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 4-5 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Regarding Claims 4-5, they recite “the observation point” in line 5, but there is no observation point defined prior to this in Claims 4 or 5 or any claims they depends from. The closest is “multiple observation points” recited in Claim 2, but that is a plurality of points, while Claims 4-5 only target one observation point. Thus, Claims 4-5 are unclear and indefinite. For purposes of examination, the examiner will interpret the term to mean a singular observation point. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1 and 15-18 are rejected under 35 U.S.C. 102( a ) as being anticipated by Hieida et al. (WO 2017038291 A1), hereinafter referred to as Hieida (from English translation) . Regarding Claim 1, Hieida teaches an information processing method comprising: creating, on the basis of environmental information that indicates an environment of a target region, a first map that indicates a movable region and an immovable region in the target region; Hieida [Page 3 Paragraph 2] "The three-dimensional shape information 101 of the area for creating the environment map is acquired and given to the travelable region extracting unit 102 that determines whether or not any vehicle can travel from the three-dimensional shape information. At this time, the three-dimensional shape information includes unmanned aircraft, manned aircraft, flying objects such as artificial satellites, cameras, LIDAR (laser ranging sensors), millimeter wave radars, ultrasonic sensors and similar environmental shapes or environmental brightness and colors. It is assumed that a sensor capable of acquiring information and temperature information is attached, measured, and acquired." Examiner Note: Three-dimensional shape information is the environmental information. A travelable region is movable, and a non-travelable region is immovable. The target region is the area that the environment map is created from. Hieida [Page 3 Paragraph 7] "The 3D shape information is stored in the 3D shape information storage unit 14. Since these techniques are known techniques, they are omitted here. Subsequently, a travelable area is extracted from the three-dimensional shape information using the travelable area extraction unit 102.” Hieida [Page 3 Paragraph 8] “FIG. 3 shows an example of the principle of extracting the travelable area and an example of extracting the travelable area of an arbitrary vehicle. The road surface area 301 is represented by a grid, and the obstacle 302 and the obstacle 303 are not travelable road surfaces because the vehicle cannot travel." on the basis of the first map, moving a mobile object including a sensor in the target region and observing the environment of the target region, thereby creating a second map that indicates a movable region and an immovable region in the target region; and Hieida [Page 6 Paragraph 5] “According to the present embodiment, when the environment is measured by UAV or the like, occlusion due to the blocking of the tunnel or trees occurs in the environment, or the road is temporarily blocked at the time of measurement by the temporary stop of the vehicle or the gate. Even in such a case, it is possible to identify the possible location and present it to the user. In addition, from the three-dimensional shape information created using information acquired using UAV, etc., a location (supplementary measurement location) where there is a possibility that sufficient environmental information cannot be obtained when an autonomous vehicle or the like travels is presented to the user. can do. Based on this information, an environment map necessary for autonomously traveling vehicles to autonomously travel can be created by measuring again with a sensor attached to the UAV or the like and a sensor attached to the vehicle. ” Examiner Note: An autonomously traveling vehicle in the mobile object. The map data from this vehicle is the second map. The region being mapped is the same region as the first map (the target region). combining the first map and the second map, thereby creating a third map. Hieida [Page 7 Paragraph 5] "Measured information integration unit 1203 uses matching and integrated 3D shape information as new map data in map update unit 1204. In other words, the map update unit 1204 updates the environment map based on the 3D shape information matched by the measurement information integration unit 1203." Examiner Note: New map data is used to update the environment map (combine with the first map), creating a third map. Regarding Claim 15, Hieida teaches the information processing method according to claim 1, further comprising detecting a difference between the first map and the second map and placing, in the third map, information indicating the detected difference. Hieida [Page 7 Paragraph 5] “At this time, if there is a discrepancy between the three-dimensional shape information newly measured by the supplementary part measurement unit 1201 and the data stored in the three-dimensional shape information storage unit, the information of the supplementary part measurement unit is reflected with priority.” Examiner Note: Difference between first map and second map (data stored and newly measured data by the mobile object) is detected. Then, information pertaining to the difference is reflected in the third map. The incorporated information represents and indicates the detected difference. Regarding Claim 16, Hieida teaches the information processing method according to claim 1, wherein the sensor is any of a monocular camera, a stereo camera, a depth sensor, and a LiDAR, or a combination thereof. Hieida [Page 3 Paragraph 2] “At this time, the three-dimensional shape information includes unmanned aircraft, manned aircraft, flying objects such as artificial satellites, cameras, LIDAR (laser ranging sensors), millimeter wave radars, ultrasonic sensors and similar environmental shapes or environmental brightness and colors. It is assumed that a sensor capable of acquiring information and temperature information is attached, measured, and acquired.” Examiner Note: Sensor capable of getting LIDAR would be a LiDAR sensor (or any other one of the forms of sensing). Regarding Claim 17, it recites similar limitations to Claim 1, except it contains an information processing device comprising; (Hieida [Page 3 Paragraph 1] “The environment map automatic creation device 1000”) a first map creation unit; (Hieida [Page 3 Paragraph 2] “The three-dimensional shape information 101 of the area for creating the environment map is acquired and given to the travelable region extracting unit 102 ” Examiner Note: Travelable region extracting unit corresponds to the first map creation unit.) a second map creation unit; (Hieida [Page 7 Paragraph 2] “The complement location measurement unit 1201 measures the complement location presented by the supplement measurement location presentation unit 105 in accordance with the command. At this time, the measuring means used by the complementary part measuring unit 1201 is a camera, LIDAR, millimeter wave radar, ultrasonic sensor, and the external environment sensor that can acquire the shape of the environment or the luminance, color information, and temperature information of the environment.” Examiner Note: Complement(Supplement) location measurement unit corresponds to the second map creation unit.) and a third map creation unit; (Hieida [Page 7 Paragraph 5] “Measured information integration unit 1203 uses matching and integrated 3D shape information as new map data in map update unit 1204. In other words, the map update unit 1204 updates the environment map based on the 3D shape information matched by the measurement information integration unit 1203.“ Examiner Note: Map update unit corresponds to the third map creation unit.) Other recited limitations are similar to Claim 1, and are therefore rejected under similar rationale. Regarding Claim 18, it recites similar limitations to Claim 1, except it contains an information processing system; (Hieida [Page 3 Paragraph 1] “FIG. 1 is a schematic diagram showing the configuration of this embodiment. The environment map automatic creation device 1000 extracts a travelable area where a vehicle can travel in an arbitrary area based on the three-dimensional shape information of the area for creating an environment map acquired by a sensor in the flying object. Extraction unit 102, region category determination unit 103 that determines the category of the travelable region, complement location determination unit 104 that determines whether or not to measure the supplemental location that complements the travelable region, and complement location presentation unit 105 that presents the supplemental measurement location.” Examiner Note: The components shown working together here form an information processing system.) a mobile object having a sensor configured to observe an environment of a target region; (Hieida [Page 6 Paragraph 5] “Based on this information, an environment map necessary for autonomously traveling vehicles to autonomously travel can be created by measuring again with a sensor attached to the UAV or the like and a sensor attached to the vehicle. ”) an information processing device capable of communicating with the mobile object, the information processing device including; . (Hieida [Page 3 Paragraph 1] “The environment map automatic creation device 1000”) a first map creation unit; (Hieida [Page 3 Paragraph 2] “The three-dimensional shape information 101 of the area for creating the environment map is acquired and given to the travelable region extracting unit 102 ” Examiner Note: Travelable region extracting unit corresponds to the first map creation unit.) a second map creation unit; (Hieida [Page 7 Paragraph 2] “The complement location measurement unit 1201 measures the complement location presented by the supplement measurement location presentation unit 105 in accordance with the command. At this time, the measuring means used by the complementary part measuring unit 1201 is a camera, LIDAR, millimeter wave radar, ultrasonic sensor, and the external environment sensor that can acquire the shape of the environment or the luminance, color information, and temperature information of the environment.” Examiner Note: Complement(Supplement) location measurement unit corresponds to the second map creation unit.) and a third map creation unit; (Hieida [Page 7 Paragraph 5] “Measured information integration unit 1203 uses matching and integrated 3D shape information as new map data in map update unit 1204. In other words, the map update unit 1204 updates the environment map based on the 3D shape information matched by the measurement information integration unit 1203.“ Examiner Note: Map update unit corresponds to the third map creation unit.) Other recited limitations are similar to Claim 1, and are therefore rejected under similar rationale . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 2, 3, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hieida et al. (WO 2017038291 A1), hereinafter referred to as Hieida, in view of Maeda et al. (WO 2020144970 A1), hereinafter referred to as Maeda . Regarding Claim 2, Hieida teaches the information processing method according to claim 1, further comprising: setting multiple observation points in the movable region in the first map on the basis of a range of observation by the sensor; producing a first action plan to have the mobile object move through the multiple observation points and have the sensor make observation at the multiple observation points ; and Hieida [Page 6 Paragraph 5] “Based on this information, an environment map necessary for autonomously traveling vehicles to autonomously travel can be created by measuring again with a sensor attached to the UAV or the like and a sensor attached to the vehicle.” Examiner Note: Sensor measuring and making observations. having the mobile object operate on the basis of the first action plan , thereby creating the second map. Hieida [Page 7 Paragraph 5] "Measured information integration unit 1203 uses matching and integrated 3D shape information as new map data in map update unit 1204.” Examiner Note: Second map creation (new map data). However, Hieida doesn’t teach setting multiple observation points in the movable region in the first map on the basis of a range of observation by the sensor; a first action plan. producing a first action plan to have the mobile object move through the multiple observation points Maeda teaches setting multiple observation points in the movable region in the first map on the basis of a range of observation by the sensor; Maeda [Page 7 Paragraph 2] “For example, FIGS. 5A and 5B are explanatory diagrams illustrating a higher-level action plan having a distribution created based on the optimum route. 5A and 5B, similarly to FIG. 3, the action plan map is a two-dimensional obstacle map, and the action target is the arrival of the robot device 10 at the destination D.” Examiner Note: Figs 5A and 5B both show points for a mobile device to move through, and correspond to setting multiple observation points. Since the points are set on the road, it is in the movable area of the first map. producing a first action plan to have the mobile object move through the multiple observation points and have the sensor make observation at the multiple observation points; and having the mobile object operate on the basis of the first action plan, thereby creating the second map. Maeda [Page 5 Paragraph 3] “For example, the plan map creation unit 130 can create the action plan map by embedding the evaluation information according to the airframe characteristic or the action characteristic of the robot apparatus 10 in the map showing the external environment. By using such an action plan map, the action plan unit 140 in the subsequent stage can create an action plan according to the machine body characteristic or the action characteristic of the robot apparatus 10. Note that the plan map creation unit 130 may create a plurality of different types of action plan maps according to the use, type, or condition.” Examiner Note: Plan is meant for the robot apparatus (mobile object) to move, and is the first action plan. Hieida and Maeda are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, and Maeda teaches an action plan for this mobile object to operate on. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Maeda’s action planning into Hieida’s mobile object remapping process so Hieida’s system could remap on the basis of an action plan with observation points. Doing so would provide a systemic and optimized procedure for collecting environmental information, ensuring map completeness, and to reduce the likelihood of unobserved regions. Regarding Claim 3, Maeda further teaches the information processing method according to claim 2, wherein the first map indicates a probability of whether each unit region is the movable region, the unit region being obtained by dividing the target region into multiple regions on the basis of the environmental information, the movable region in the first map is a region having a probability of being the immovable region, the probability being a probability of a first threshold or less, and the immovable region in the first map is a region having a probability of being the immovable region, the probability being a probability of a second threshold or greater. Maeda [Page 1 Paragraph 4] “For example, when the robot device tries to move to the destination, the robot device first creates an external map that maps the existence probability of the obstacle by observing the obstacle with a sensor or the like. Next, the robot apparatus uses a graph search algorithm or the like to search for an optimal movement route that avoids an obstacle region where the probability of existence of an obstacle is high, and creates an action plan for moving the searched optimal movement route . .. Accordingly, the robot device can move to the destination while avoiding obstacles by acting based on the created action plan.” Examiner Note: The existence of an obstacle region shows the map has been split into regions based on environment information, and movable and immovable (traversable and non-traversable) regions are detected based on a probability of an obstacle compared to a certain threshold. Hieida and Maeda are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, and Maeda teaches an action plan for this mobile object to operate on, with obstacle regions being defined based on a probability. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Maeda’s probability into Hieida’s mobile object remapping process so Hieida’s system could differentiate its traversable and non-traversable regions using a probability of it being occupied. Doing so allow for more reliable selection of supplemental regions, improving map accuracy. Regarding Claim 14, Hieida, in view of Maeda, teaches the information processing method according to claim 3, the immovable region having a probability that is the second threshold or greater in the first map is set as the immovable region in the third map even in a case where the region is the movable region in the second map. Hieida [Page 7 Paragraph 5] “At this time, if there is a discrepancy between the three-dimensional shape information newly measured by the supplementary part measurement unit 1201 and the data stored in the three-dimensional shape information storage unit, the information of the supplementary part measurement unit is reflected with priority.” Examiner Note: Hieida teaches using one map for priority over another in the case of differences. This teaches the same concept as the claimed limitation, where when combining two sets of data, one is given priority over the other . 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Hieida et al. (WO 2017038291 A1), hereinafter referred to as Hieida, in view of Maeda et al. (WO 2020144970 A1), hereinafter referred to as Maeda, in further view of Takahashi et al. (US 11687089 B2), hereinafter referred to as Takahashi . Regarding Claim 6, Hieida, in view of Maeda, teaches the information processing method according to claim 3, wherein the immovable region is a region having an obstacle therein, and for each of the unit regions, the probability is set on the basis of the presence or absence of the obstacle and a possibility of the obstacle moving. Hieida [Page 3 Paragraph 8] “FIG. 3 shows an example of the principle of extracting the travelable area and an example of extracting the travelable area of an arbitrary vehicle. The road surface area 301 is represented by a grid, and the obstacle 302 and the obstacle 303 are not travelable road surfaces because the vehicle cannot travel ." Examiner Note: For each region on the grid (unit region), the probability of travelability is determined (determining if the area has an obstacle or not). However, Hieida and Maeda don’t teach and a possibility of the obstacle moving. Takahashi teaches and a possibility of the obstacle moving. Takahashi [Col 6 Lines 59-67] “ If observation information regarding the moving obstacle 210 (dynamic obstacle) continues to be stored, the locus thereof is left as a huge obstacle 220 like a wall. FIG. 6 is a schematic diagram illustrating the obstacle 220 generated by continuing to store a dynamic obstacle. As illustrated in FIG. 6, if the dynamic obstacle 210 continues to be stored, the obstacle 220 like a wall remains in front of the mobile robot 100 in the environmental map 500 in storage of the mobile robot 100. Since a region 230 obtained by removing the current position of the obstacle 210 from the obstacle 220 is a ghost, the environmental map 500 is created by removing the ghost region 230 from the obstacle 220.” Examiner Note: Takahashi teaches that an obstacle may be a moving obstacle. Hieida, Maeda, and Takahashi are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, Maeda teaches an action plan for this mobile object to operate on, with obstacle regions being defined based on a probability, and Takahashi teaches considering if an obstacle is a moving obstacle. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Takahashi’s possibility of obstacle movement into Hieida in view of Maeda’s probability based obstacle mapping system so that the probability assigned to each unit region would be based on both existence of an obstacle and the possibility that it’s moving. Doing so would allow for temporary or dynamic obstacles to be treated differently from fixed ones, reducing false classifications, thereby improving accuracy of the mapping system . 07-21-aia AIA Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Hieida et al. (WO 2017038291 A1), hereinafter referred to as Hieida, in view of Maeda et al. (WO 2020144970 A1), hereinafter referred to as Maeda, in further view of Wheeler et al. (US 10794711 B2), hereinafter referred to as Wheeler . Regarding Claim 13, Hieida, in view of Maeda, teaches the information processing method according to claim 2, further comprising: comparing the first map and the second map to detect a region unobserved by the mobile object; Hieida [Page 6 Paragraph 5] “According to the present embodiment, when the environment is measured by UAV or the like, occlusion due to the blocking of the tunnel or trees occurs in the environment, or the road is temporarily blocked at the time of measurement by the temporary stop of the vehicle or the gate . Even in such a case, it is possible to identify the possible location and present it to the user . Examiner Note: An area occluded is a region unobserved by the mobile object, and is found in an examination of the map producing a second action plan to have the mobile object observe an environment of the detected region; and on the basis of the second action plan, having the mobile object operate to update the second map. Hieida [Page 6 Paragraph 5] “According to the present embodiment, when the environment is measured by UAV or the like, occlusion due to the blocking of the tunnel or trees occurs in the environment, or the road is temporarily blocked at the time of measurement by the temporary stop of the vehicle or the gate . Even in such a case, it is possible to identify the possible location and present it to the user . In addition, from the three-dimensional shape information created using information acquired using UAV, etc., a location (supplementary measurement location) where there is a possibility that sufficient environmental information cannot be obtained when an autonomous vehicle or the like travels is presented to the user… Based on this information, an environment map necessary for autonomously traveling vehicles to autonomously travel can be created by measuring again with a sensor attached to the UAV or the like and a sensor attached to the vehicle .” Examiner Note: A mobile object (traveling vehicle) measures again to update a map with the occluded information (supplementary information). However, Hieida doesn’t teach producing a second action plan to have the mobile object observe an environment of the detected region; and on the basis of the second action plan, Maeda teaches producing a second action plan to have the mobile object observe an environment of the detected region; and on the basis of the second action plan, Maeda [Page 5 Paragraph 3] “For example, the plan map creation unit 130 can create the action plan map by embedding the evaluation information according to the airframe characteristic or the action characteristic of the robot apparatus 10 in the map showing the external environment. By using such an action plan map, the action plan unit 140 in the subsequent stage can create an action plan according to the machine body characteristic or the action characteristic of the robot apparatus 10. Note that the plan map creation unit 130 may create a plurality of different types of action plan maps according to the use, type, or condition.” Examiner Note: Maeda shows the capabilities of producing action plans for mobile objects to move through. Hieida and Maeda are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, and Maeda teaches an action plan for this mobile object to operate on. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Maeda’s action planning into Hieida’s mobile object remapping process so Hieida’s system could remap on the basis of a second action plan to update a map with missing information. Doing so would provide a systemic and optimized procedure for collecting environmental information, ensuring map completeness, and to reduce the likelihood of unobserved regions. However, Hieida and Maeda don’t teach comparing the first map and the second map Wheeler teaches comparing the first map and the second map Wheeler (Col 2 Lines 1-5) “The autonomous vehicles detect map discrepancies based on differences in the surroundings observed using sensor data compared to the high definition map and send messages describing these map discrepancies” Examiner Note: Teaches comparing maps to get map discrepancies. Hieida, Maeda, and Wheeler are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, and Maeda teaches an action plan for this mobile object to operate on, and Wheeler teaches finding discrepancies, including occluded areas, by comparing maps. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wheeler’s map comparison for discrepancies into Hieida in view of Maeda’s mobile object remapping process with a second action plan as an obvious way to find the discrepancies the mobile object would look for. Doing so would provide a systemic and optimized procedure for collecting environmental information, ensuring map completeness, and to reduce the likelihood of unobserved regions . 07-21-aia AIA Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hieida et al. (WO 2017038291 A1), hereinafter referred to as Hieida, in view of Maeda et al. (WO 2020144970 A1), hereinafter referred to as Maeda, in further view of Sakai et al. (JP 2009053561 A), hereinafter referred to as Sakai . Regarding Claim 7, Hieida, in view of Maeda, teaches the information processing method according to claim 3, wherein the environmental information includes a photographed image of an environment of the target region, the unit region corresponds to a pixel in the image, a line segment is detected from the image, and the probability is set for the unit region on the basis of to whether the pixel corresponding to the unit region is included in the line segment detected by the line segment detection. Hieida [Page 3 Paragraph 4] “In this configuration, first, the monocular camera 2 is attached to the UAV 1 to fly over the location where the environmental map is to be created. At this time, continuous shooting is performed with the monocular camera 2. At this time, it is desirable to shoot so that the captured image overlaps with the other captured images about 80% before and after, and about 60% lateral.” Examiner Note: Photos shot from the camera are the photographed image of the environmental information. However, Hieida doesn’t teach the unit region corresponds to a pixel in the image, a line segment is detected from the image, and the probability is set for the unit region on the basis of to whether the pixel corresponding to the unit region is included in the line segment detected by the line segment detection. Maeda teaches the probability is set for the unit region Maeda [Page 1 Paragraph 4] “For example, when the robot device tries to move to the destination, the robot device first creates an external map that maps the existence probability of the obstacle by observing the obstacle with a sensor or the like. Next, the robot apparatus uses a graph search algorithm or the like to search for an optimal movement route that avoids an obstacle region where the probability of existence of an obstacle is high, and creates an action plan for moving the searched optimal movement route . .. Accordingly, the robot device can move to the destination while avoiding obstacles by acting based on the created action plan.” Examiner Note: Maeda demonstrates the functionality of a probability being set for a region. Hieida and Maeda are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, and Maeda teaches an action plan for this mobile object to operate on, with obstacle regions being defined based on a probability. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Maeda’s probability into Hieida’s mobile object remapping process so Hieida’s system could differentiate its traversable and non-traversable regions using a probability of it being occupied. Doing so allow for more reliable selection of supplemental regions, improving map accuracy. However, Hieida and Maeda don’t teach the unit region corresponds to a pixel in the image, a line segment is detected from the image, and… on the basis of to whether the pixel corresponding to the unit region is included in the line segment detected by the line segment detection. Sakai teaches the unit region corresponds to a pixel in the image, a line segment is detected from the image, and… on the basis of to whether the pixel corresponding to the unit region is included in the line segment detected by the line segment detection. Sakai [Page 5 Paragraph 6] “In the partial map in FIG. 4 described above, for example, the line segment LS1 is acquired as the partial map G3, and the line segment LS2 is acquired as the map information in the partial map G4. The line segments LS1 and LS2 are map information indicating a part of the boundary W1 shown in FIG. 3, and when the boundary W1 is constituted by a flat wall or the like, these are line segments arranged on the same straight line. It is. The line segment is an environment fixed object shown in a line segment on the map, and is represented by the coordinates of both end points of the line segment, the coordinates of one end point and the angle with respect to the coordinate axis, and the like.” Examiner Note: Teaches a line segment being acquired from the image, where a line segment corresponds to an immovable area like a wall or boundary. Since it is an image, the existence of pixels are implicit, and they correspond to unit regions. Hieida, Maeda, and Sakai are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, Maeda teaches an action plan for this mobile object to operate on, with obstacle regions being defined based on a probability, and Sakai teaches extracting line segments from image data, containing pixels, and using them as map information for indications of obstacles such as walls. Since these line segments are derived from image data, it would follow that each pixel in the image would corresponds to a unit region, and would indicate if the unit region is part of a line segment. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sakai’s line segment detection into Hieida in view of Maeda’s probability based obstacle mapping system so that the probability assigned to each unit region could be based on whether the pixel corresponding to the unit region is contained within a detected line segment. Doing so would allow for structural features and boundaries such as walls be reflected in the environment map, and allows the probability determination of a unit region (pixel) to be based off whether it is included in an immovable region or not, therefore improving classification accuracy of the mapping system . 07-21-aia AIA Claim s 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Hieida et al. (WO 2017038291 A1), hereinafter referred to as Hieida, in view of Maeda et al. (WO 2020144970 A1), hereinafter referred to as Maeda, in further view of Toyoura (WO 2022259621 A1), hereinafter referred to as Toyoura . Regarding Claim 8, Maeda further teaches the information processing method according to claim 3, wherein the environmental information includes the location of an object existing in the target region and meta information about the object, and on the basis of the meta information, the probability is set for the unit region included in a region where the object is located. Maeda [Page 1 Paragraph 4] “For example, when the robot device tries to move to the destination, the robot device first creates an external map that maps the existence probability of the obstacle by observing the obstacle with a sensor or the like. Next, the robot apparatus uses a graph search algorithm or the like to search for an optimal movement route that avoids an obstacle region where the probability of existence of an obstacle is high , and creates an action plan for moving the searched optimal movement route. .. Accordingly, the robot device can move to the destination while avoiding obstacles by acting based on the created action plan.” Examiner Note: Location and probability is recorded for an obstacle region (unit region included in a region where the object is located). Hieida and Maeda are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, and Maeda teaches obstacle regions being defined based on a probability with location information. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Maeda’s probability and location information into Hieida’s mobile object remapping process so Hieida’s system could differentiate its traversable and non-traversable regions using a probability of it being occupied. Doing so allow for more reliable selection of supplemental regions, improving map accuracy. However, Hieida and Maeda don’t teach and meta information about the object, and on the basis of the meta information, Toyoura teaches and meta information about the object, and on the basis of the meta information, Toyoura [Page 5 Paragraph 3] “ The type of object indicates the type of object that occupies the voxel 510, such as floor, wall, obstacle, roadway, sidewalk, and sign. The type of this object is determined based on other types of analysis result information. For example, the type of object is determined from the image data of the RGB camera 313 using an image recognition technique such as semantic segmentation. Also, the type of object may be determined based on the inclination, flatness, reflection intensity, color, brightness, etc. of the object.” Examiner Note: Meta information is the listed types of information related to the object. Hieida, Maeda, and Toyoura are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, Maeda teaches an action plan for this mobile object to operate on, with obstacle regions being defined based on a probability and associated with the location of an obstacle, and Toyoura teaches storing meta information about obstacles including type of object, and additional characteristics, and determining such object type using semantic segmentation. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Toyoura’s meta information into Hieida in view of Maeda’s probability based obstacle mapping system so that in addition to the location of an object in the target region, meta information about the object would also be used to set the probability for a unit region. Doing so would allow probability to also reflect the characteristics of an object, enabling more accurate classification. Regarding Claim 10, Toyoura further teaches the information processing method according to claim 8, wherein the environmental information includes a photographed image of an environment of the target region, and semantic segmentation is performed on the image to obtain the location of the object and the meta information about the object. Toyoura [Page 5 Paragraph 3] “The type of object indicates the type of object that occupies the voxel 510, such as floor, wall, obstacle, roadway, sidewalk, and sign. The type of this object is determined based on other types of analysis result information. For example, the type of object is determined from the image data of the RGB camera 313 using an image recognition technique such as semantic segmentation. Also, the type of object may be determined based on the inclination, flatness, reflection intensity, color, brightness, etc. of the object.” Examiner Note: Occupying a voxel shows location information, and the meta information is collected from image data with semantic segmentation. Hieida, Maeda, and Toyoura are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, Maeda teaches an action plan for this mobile object to operate on, with obstacle regions being defined based on a probability and associated with the location of an obstacle, and Toyoura teaches storing meta information about obstacles including type of object, and additional characteristics, and determining such object type using semantic segmentation. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Toyoura’s meta information into Hieida in view of Maeda’s probability based obstacle mapping system so that in addition to the location of an object in the target region, meta information about the object would also be used to set the probability for a unit region, and obtained using semantic segmentation. Doing so would allow would allow for precise object identification, with probability that reflects characteristics of an object, enabling more accurate classification . 07-21-aia AIA Claim s 9 is rejected under 35 U.S.C. 103 as being unpatentable over Hieida et al. (WO 2017038291 A1), hereinafter referred to as Hieida, in view of Maeda et al. (WO 2020144970 A1), hereinafter referred to as Maeda, and Toyoura (WO 2022259621 A1), hereinafter referred to as Toyoura, and in further view of Takahashi et al. (US 11687089 B2), hereinafter referred to as Takahashi . Regarding Claim 9, Toyoura further teaches the information processing method according to claim 8, wherein the meta information includes information indicating a possibility of the object being an obstacle and information indicating a possibility of the location of the object varying. Toyoura [Page 5 Paragraph 3] “ The type of object indicates the type of object that occupies the voxel 510, such as floor, wall, obstacle, roadway, sidewalk, and sign. The type of this object is determined based on other types of analysis result information. For example, the type of object is determined from the image data of the RGB camera 313 using an image recognition technique such as semantic segmentation. Also, the type of object may be determined based on the inclination, flatness, reflection intensity, color, brightness, etc. of the object.” Examiner Note: Meta information is classifying if the type of object is an obstacle. Hieida, Maeda, and Toyoura are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, Maeda teaches an action plan for this mobile object to operate on, with obstacle regions being defined based on a probability and associated with the location of an obstacle, and Toyoura teaches storing meta information about obstacles including type of object, and additional characteristics, and determining such object type using semantic segmentation. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Toyoura’s meta information into Hieida in view of Maeda’s probability based obstacle mapping system so that in addition to the location of an object in the target region, meta information about the object would also be used to set the probability for a unit region. Doing so would allow probability to also reflect the characteristics of an object, enabling more accurate classification. However, Hieida, Maeda, and Toyoura don’t teach and information indicating a possibility of the location of the object varying. Takahashi teaches and information indicating a possibility of the location of the object varying. Takahashi [Col 6 Lines 59-67] “ If observation information regarding the moving obstacle 210 (dynamic obstacle) continues to be stored, the locus thereof is left as a huge obstacle 220 like a wall. FIG. 6 is a schematic diagram illustrating the obstacle 220 generated by continuing to store a dynamic obstacle. As illustrated in FIG. 6, if the dynamic obstacle 210 continues to be stored, the obstacle 220 like a wall remains in front of the mobile robot 100 in the environmental map 500 in storage of the mobile robot 100. Since a region 230 obtained by removing the current position of the obstacle 210 from the obstacle 220 is a ghost, the environmental map 500 is created by removing the ghost region 230 from the obstacle 220.” Examiner Note: Dynamic obstacle is the possibility of the location of the object varying, and information about it is stored. Hieida, Maeda, Toyoura, and Takahashi are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, Maeda teaches an action plan for this mobile object to operate on, with obstacle regions being defined based on a probability of an obstacle existing at its location, Toyoura teaches storing meta information about obstacles including type of object, and additional characteristics, and determining such object type using semantic segmentation, and Takahashi teaches storing information regarding if an obstacle is moving or not. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Takahashi’s dynamic obstacle information into Hieida in view of Maeda in further view of Toyoura’s probability based obstacle mapping system so that meta information would not only include object type, but also indicate if the obstacle is moving or not. Doing so would allow probabilities to account both for the type and movement of an object, improving map accuracy . 07-21-aia AIA Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Hieida et al. (WO 2017038291 A1), hereinafter referred to as Hieida, in view of Maeda et al. (WO 2020144970 A1), hereinafter referred to as Maeda, in further view of Li et al. (US 11501492 B1), hereinafter referred to as Li . Regarding Claim 12, Hieida and Maeda don’t teach the information processing method according to claim 3, further comprising: Detecting a plane on the basis of a photographed image of an environment of the target region; detecting a wall on the basis of an area of the plane detected by the plane detection and a normal direction of the plane; and setting the probability for the unit region on the basis of whether the wall is present in the unit region. However, Li teaches detecting a plane on the basis of a photographed image of an environment of the target region; detecting a wall on the basis of an area of the plane detected by the plane detection and a normal direction of the plane; and setting the probability for the unit region on the basis of whether the wall is present in the unit region. Li [Col 9 Lines 39-50] “such as by using SLAM techniques for multiple video frame images and/or other SfM techniques for a ‘dense’ set of images that are separated by at most a defined distance (such as 6 feet) to generate a 3D point cloud for the room including 3D points along walls of the room and at least some of the ceiling and floor of the room and optionally with 3D points corresponding to other objects in the room, etc.) and/or by determining and aggregating information about planes for detected features and normal (orthogonal) directions to those planes to identify planar surfaces for likely locations of walls and other surfaces of the room ” Examiner Note: A plane is detected from the image, and a wall is identified from the plane by applied a normal direction to the plane. Hieida, Maeda, and Li are in the analogous art of autonomous navigation on the basis of environment maps. Hieida teaches generating an environment map from a mobile object, Maeda teaches obstacle regions being defined based on a probability, and Li teaches generating a dense set of images of the environment, aggregating information about planes, and using the normal direction of the planes to detect surfaces such as walls. It would be obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Li’s wall detection into Hieida in view of Maeda’s probability based obstacle mapping system so that probabilities assigned to unit regions could be based on whether a wall is detected in the region. Doing so would allow probability to more accurately reflect the characteristics of an object, improving the mapping system . Allowable Subject Matter 07-43-02 Since no prior art is being applied, Claims 4-5 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. Regarding Dependent Claim 4, Hieida (WO 2017038291 A1), in view of Maeda (WO 2020144970 A1) previously taught the information processing method according to claim 2, wherein the first map indicates a probability of whether each unit region is the movable region, the unit region being obtained by dividing the target region into multiple regions on the basis of the environmental information, the movable region in the first map is a region having a probability of being the immovable region, the probability being a probability of a first threshold or less, and the immovable region in the first map is a region having a probability of being the immovable region, the probability being a probability of a second threshold or greater (Maeda [Page 1 Paragraph 4] “For example, when the robot device tries to move to the destination, the robot device first creates an external map that maps the existence probability of the obstacle by observing the obstacle with a sensor or the like. Next, the robot apparatus uses a graph search algorithm or the like to search for an optimal movement route that avoids an obstacle region where the probability of existence of an obstacle is high, and creates an action plan for moving the searched optimal movement route . .. Accordingly, the robot device can move to the destination while avoiding obstacles by acting based on the created action plan.”), as shown in Claim 3. However, the prior art of record, taken alone or in combination, fails to teach or fairly suggest the information processing method according to claim 3, further comprising performing corner detection on the basis of a region having a probability of being the movable region, the probability being a probability of a threshold or greater; producing a polygonal mesh using the corner detected by the corner detection as a vertex; and setting the observation point at the center of gravity of the mesh, as shown in Claim 4. Therefore, Claim 4 is considered to be allowable. Claim 5 contains allowable subject matter because it depends on Claim 4 that contains allowable subject matter. 12-151-08 AIA 07-43 12-51-08 Claim 11 is 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 Dependent Claim 11, Li (US 11501492 B1) further teaches on the basis of multiple images of the target region photographed from multiple viewpoints, projecting feature points mutually corresponding between the multiple images onto a space representing the environment of the target region and producing a point cloud; (Li [Col 9 Lines 39-44] “such as by using SLAM techniques for multiple video frame images and/or other SfM techniques for a ‘dense’ set of images that are separated by at most a defined distance (such as 6 feet) to generate a 3D point cloud for the room including 3D points along walls of the room ”), Yao (US 20260120425 A1) further teaches performing processing to increase density of the point cloud; (Yao [0030] “The three-dimensional point cloud segmentation device according to the present embodiment receives the three-dimensional point cloud A as an input, and generates a point cloud (hereinafter referred to as a “densified point cloud B”) obtained by densifying the three-dimensional point cloud A using an image. That is, the density of points included in each point cloud is higher in the densified point cloud B than in the three-dimensional point cloud A. Therefore, in the present embodiment, in comparing the three-dimensional point cloud A and the densified point cloud B, the density of the three-dimensional point cloud A is defined as low density, and the density of densified point cloud B is defined as high density.”) However, the prior art of record, taken alone or in combination, fails to teach or fairly suggest projecting the point cloud on multiple voxels obtained by dividing the space according to the size of the unit region, thereby counting the number of points included in the voxel; and on the basis of the number of points included in the voxel, setting the probability for the unit region corresponding to the voxel. Therefore, Claim 11 is considered to be allowable. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID W SOON whose telephone number is (571)272-8113. The examiner can normally be reached M-F 7:30-5:00. 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, Alicia Harrington can be reached at (571) 272-2330. 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. /DAVID W SOON/Examiner, Art Unit 2615 /KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611 Application/Control Number: 18/863,156 Page 2 Art Unit: 2615 Application/Control Number: 18/863,156 Page 3 Art Unit: 2615 Application/Control Number: 18/863,156 Page 4 Art Unit: 2615 Application/Control Number: 18/863,156 Page 5 Art Unit: 2615 Application/Control Number: 18/863,156 Page 6 Art Unit: 2615 Application/Control Number: 18/863,156 Page 7 Art Unit: 2615 Application/Control Number: 18/863,156 Page 8 Art Unit: 2615 Application/Control Number: 18/863,156 Page 9 Art Unit: 2615 Application/Control Number: 18/863,156 Page 10 Art Unit: 2615 Application/Control Number: 18/863,156 Page 11 Art Unit: 2615 Application/Control Number: 18/863,156 Page 12 Art Unit: 2615 Application/Control Number: 18/863,156 Page 13 Art Unit: 2615 Application/Control Number: 18/863,156 Page 14 Art Unit: 2615 Application/Control Number: 18/863,156 Page 15 Art Unit: 2615 Application/Control Number: 18/863,156 Page 16 Art Unit: 2615 Application/Control Number: 18/863,156 Page 17 Art Unit: 2615 Application/Control Number: 18/863,156 Page 18 Art Unit: 2615 Application/Control Number: 18/863,156 Page 19 Art Unit: 2615 Application/Control Number: 18/863,156 Page 20 Art Unit: 2615 Application/Control Number: 18/863,156 Page 21 Art Unit: 2615 Application/Control Number: 18/863,156 Page 22 Art Unit: 2615 Application/Control Number: 18/863,156 Page 23 Art Unit: 2615 Application/Control Number: 18/863,156 Page 24 Art Unit: 2615 Application/Control Number: 18/863,156 Page 25 Art Unit: 2615 Application/Control Number: 18/863,156 Page 26 Art Unit: 2615 Application/Control Number: 18/863,156 Page 27 Art Unit: 2615 Application/Control Number: 18/863,156 Page 28 Art Unit: 2615 Application/Control Number: 18/863,156 Page 29 Art Unit: 2615 Application/Control Number: 18/863,156 Page 30 Art Unit: 2615 Application/Control Number: 18/863,156 Page 31 Art Unit: 2615 Application/Control Number: 18/863,156 Page 32 Art Unit: 2615 Application/Control Number: 18/863,156 Page 33 Art Unit: 2615 Application/Control Number: 18/863,156 Page 34 Art Unit: 2615
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