DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11 September 2025 has been entered. Claims 1-20 have been presented in the application, of which, claims 1-3 were previously presented and claims 4-20 are original. Accordingly, pending claims 1-20 are addressed herein.
Response to Arguments
Applicant's arguments filed 11 September 2025 have been fully considered but they are not persuasive.
On page 7, Applicant asserts that “[t]he prior art is silent with respect to each state of the mobile robotic device comprises at least a location of the mobile robotic device within the workspace.” In support of this assertion, Applicant argues that Allen is silent with respect to “…each state of the mobile robotic device comprises at least a location of the mobile robotic device within the work space (as opposed to having states that do not comprise a location as in the prior art)” because “the immediate command method does not include a location requirement.” The examiner disagrees.
It is noted that the words “each state” as recited in the claim are interpreted as referring back to the “current state” and the “next state” of the prior limitation. The claim does not require and the rejection does not rely upon any “immediate command method” to “include a location requirement” as suggested by Applicant’s argument. The cited portion of Allen is evidence that the mobile robot travels a distance from first location (i.e., current state) to a second location (i.e., next state). Each state (i.e., the current state and the next state) comprises at least a location (i.e., current location and the next location, respectively) of the mobile robotic device within the workspace. Accordingly, the anticipation rejection of claim 1 has been maintained.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 10-15 and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Allen et al. (US 5,995,884).
Regarding claim 1, Allen et al. disclose a method for operating a mobile robotic device, comprising: selecting (Figs. 25-26, steps 140, 142-143), by the processor (Figs. 6, 15 and 24, element 9, 121), actions (commands in queue 123 of Fig. 24) of the mobile robotic device (Figs. 1-2, 5-6, 15, element 1) as the mobile robotic device navigates (Fig. 38, path 227) through a workspace (Fig. 38, room 224; column 23, lines 29-55; column 24, lines 64-67; column 25, lines 1-26; column38, lines 39-47), wherein: at least a portion of the actions [one or more actions] transitions the mobile robotic device from a current state (current location) to a next state (next location; Fig. 26, step 144; robot travels a distance from a current state to a next state before executing a command 145; column 25, lines 26-30); and each state of the mobile robotic device comprises at least a location of the mobile robotic device within the workspace (robot travels a distance from a current state to a next state before executing a command 145; column 25, lines 26-30); actuating (Fig. 26, step 155), by the processor, the mobile robotic device to execute the actions (via motors 53 and/or steering 55 in Fig. 24; column 25, lines 26-30); detecting (Fig. 25, step 129), by the processor, whether a collision or a stuck event is experienced by the mobile robotic device (column 24, lines 24-26); and associating, by the processor, a collision or a stuck event to a location within the workspace in which the collision or the stuck event occurred (Fig. 33A; column 4, lines 10-11, column 34, lines 19-22; column 35, lines 9-12).
Regarding claim 2, Allen et al. disclose the method of claim 1, further comprising: determining, by the processor, a movement path of the mobile robotic device based on at least locations of previous collisions or previous stuck events, wherein the mobile robot device avoids locations of previous collisions or previous stuck events (column 34, lines 38-45).
Regarding claim 3, Allen et al. disclose the method of claim 1, wherein a portion of actions are selected based on at least three of: at least a portion of data collected by the plurality of sensors (Figs. 7 and 24, element 56, 59; column 13, lines 41-52; column 17, lines 60-65), locations of obstacles (Fig. 33A; column 35, lines 9-12), a level of debris accumulation in different areas within the workspace (column 47, lines 36-47), locations of previous collisions or previous stuck events within the workspace (column 34, lines 38-45), and floor types of floor surfaces in different areas within the workspace (column 45, lines 34-56).
Regarding claim 10, Allen et al. disclose the method of claim 1, further comprising: indicating locations of collisions or stuck events within a map of the workspace, wherein the locations of the collisions or stuck events correspond with locations in the workspace at which the collisions or stuck events occurred during at least one work session (column 34, lines 38-45).
Regarding claims 11-12, the claims are anticipated by Allen et al. because claims further limit the stuck event and the stuck event is required only as an alternative to a collision, which is disclosed by Allen et al.
Regarding claim 13, Allen et al. disclose a mobile robotic device, comprising at least: a plurality of sensors (Fig. 4, element 56, 59, 61, 81, 84); a processor (Figs. 6, 15, 24, element 9, 121); and a tangible, non-transitory, machine readable medium (Figs. 3 and 6, element 20, 41) storing instructions (Fig. 3, element 16, 17) that when executed by the processor effectuates operations comprising: selecting (Figs. 25-26, steps 140, 142-143), by the processor, actions (command or commands in queue 123 of Fig. 24) of the mobile robotic device (Figs. 1-2, 5-6, 15, element 1) as the mobile robotic device navigates (Fig. 38, path 227) through a workspace (room 224 in Fig. 38; column 23, lines 29-55; column 24, lines 64-67; column 25, lines 1-26; column 38, lines 39-47), wherein: at least a portion of the actions [one or more actions] transitions the mobile robotic device from a current state (current location) to a next state (next location; Fig. 26, step 144; robot travels a distance from a current state to a next state before executing a command at 145; column 25, lines 26-30); and each state of the mobile robotic device comprises at least a location of the mobile robotic device within the workspace (robot travels a distance from a current state to a next state before executing a command at 145; column 25, lines 26-30); actuating (Fig. 26, step 145), by the processor, the mobile robotic device to execute the actions (via motors 53 and/or steering 55 in Fig. 24; column 25, lines 26-30); detecting (Fig. 25, step 129), by the processor, whether a collision or a stuck event is experienced by the mobile robotic device (column 24, lines 24-26); and associating, by the processor, a collision or a stuck event to a location within the workspace in which the collision or the stuck event occurred (Fig. 33A; column 4, lines 10-11; column 34, lines 19-22; column 35, lines 9-12).
Regarding claim 14, Allen et al. disclose the mobile robotic device of claim 13, wherein the operations further comprise: determining, by the processor, a movement path of the mobile robotic device based on at least locations of previous collisions or previous stuck events, wherein the mobile robot device avoids locations of previous collisions or previous stuck events (column 34, lines 38-45).
Regarding claim 15, Allen et al. disclose the mobile robotic device of claim 13, wherein a portion of actions are selected based on at least three of: at least a portion of data collected by the plurality of sensors (Figs. 7 and 24, element 56, 59; column 13, lines 41-52; column 17, lines 60-65), locations of obstacles (Fig. 33A; column 35, lines 9-12), a level of debris accumulation in different areas within the workspace (column 47, lines 36-47), locations of previous collisions or previous stuck events within the workspace (column 34, lines 38-45), and floor types of floor surfaces in different areas within the workspace (column 45, lines 34-56).
Regarding claim 20, Allen et al. disclose the mobile robotic device of claim 13, wherein the operations further comprise: indicating locations of collisions or stuck events within a map of the workspace, wherein the locations of the collisions or stuck events correspond with locations in the workspace at which the collisions or stuck events occurred during at least one work session (column 34, lines 38-45).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4-6, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allen et al. as applied to claims 1 and 13 above, and further in view of Rosenstein et al. (US 2015/0120128 A1).
Regarding claim 4, Allen et al. teach the method of claim 1 above. Allen et al. are silent regarding collecting, with a sensor disposed on the robot, debris data; and determining, with the processor, a level of debris accumulation in different areas of the workspace; and determining, with the processor, a navigation path of the mobile robotic device based on the level of debris accumulation in different areas of the workspace.
Rosenstein et al. teach collecting (Fig. 14, step 1410), with a sensor (Figs. 1, 6 and 11, element 510) disposed on the robot (Figs. 1-3, 6-9 and 11, element 100), debris data (Figs. 1, 6, 8, 10, element 22 via Figs. 6 and 10-13, element 514); and determining, with the processor, a level of debris accumulation (threshold level of dirt) in different areas (Figs. 1, 6, 8-13, element 12) of the workspace (Figs. 1, 6-10, 12-13, element 10); and determining, with the processor, a navigation path (Figs. 7-9, element 700, 710, 720) of the mobile robotic device based on the level of debris accumulation in different areas of the workspace (paragraphs 0005, 0057, 0065-0066, 0068-0069, 0078-0085, 0096).
It would have been obvious to a person having ordinary skill in the art prior to Applicant’s effective filing date to apply the well-known technique taught by Rosenstein et al. to the prior art method taught by Allen et al. That is, it would have been obvious to configure the robot taught by Allen to collect debris data, determine a level of debris accumulation and determine a navigation path based on the level of debris accumulation by applying the well-known technique taught by Rosenstein et al. Application of the well-known technique taught by Rosenstein et al. would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and because such application would have yielded predictable results. The predictable results including: the robot being configured for collecting, with a sensor disposed on the robot, debris data; and determining, with the processor, a level of debris accumulation in different areas of the workspace; and determining, with the processor, a navigation path of the mobile robotic device based on the level of debris accumulation in different areas of the workspace.
Regarding claim 5, the combination of Allen et al. and Rosenstein et al. teach the method of claim 4 above. Rosenstein et al. further teach the navigation path prioritizes coverage of areas with a high level of debris accumulation over coverage of areas with a low level of debris accumulation (Rosenstein et al., paragraph 0079-0080).
Regarding claim 6, the combination of Allen et al. and Rosenstein et al. teach the method of claim 4 above. Rosenstein et al. teach a level of debris accumulation in different areas of the workspace are indicated on a map of the workspace (Rosenstein et al., paragraph 0083).
Regarding claim 16 Allen et al. teach Allen et al. teach the mobile robotic device of claim 13 above. Allen et al. are silent regarding collecting, with a sensor disposed on the robot, debris data; and determining, with the processor, a level of debris accumulation in different areas of the workspace; and determining, with the processor, a navigation path of the mobile robotic device based on the level of debris accumulation in different areas of the workspace, wherein: the navigation path prioritizes coverage of areas with a high level of debris accumulation over coverage of areas with a low level of debris accumulation; and a level of debris accumulation in different areas of the workspace are indicated on a map of the workspace.
Rosenstein et al. teach collecting (Fig. 14, step 1410), with a sensor (Figs. 1, 6 and 11, element 510) disposed on the robot (Figs. 1-3, 6-9 and 11, element 100), debris data (Figs. 1, 6, 8, 10, element 22 via Figs. 6 and 10-13, element 514); and determining, with the processor, a level of debris accumulation (threshold level of dirt) in different areas (Figs. 1, 6, 8-13, element 12) of the workspace (Figs. 1, 6-10, 12-13, element 10); and determining, with the processor, a navigation path (Figs. 7-9, element 700, 710, 720) of the mobile robotic device based on the level of debris accumulation in different areas of the workspace (paragraphs 0005, 0057, 0065-0066, 0068-0069, 0078-0085, 0096), wherein: the navigation path prioritizes coverage of areas with a high level of debris accumulation over coverage of areas with a low level of debris accumulation (paragraph 0079-0080); and a level of debris accumulation in different areas of the workspace are indicated on a map of the workspace (paragraph 0083).
It would have been obvious to a person having ordinary skill in the art prior to Applicant’s effective filing date to apply the well-known technique taught by Rosenstein et al. to the prior art device taught by Allen et al. That is, it would have been obvious to configure the robot taught by Allen to collect debris data, determine a level of debris accumulation and determine a navigation path based on the level of debris accumulation by applying the well-known technique taught by Rosenstein et al. Application of the well-known technique taught by Rosenstein et al. would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and because such application would have yielded predictable results. The predictable results including: the robot being configured for collecting, with a sensor disposed on the robot, debris data; and determining, with the processor, a level of debris accumulation in different areas of the workspace; and determining, with the processor, a navigation path of the mobile robotic device based on the level of debris accumulation in different areas of the workspace, wherein: the navigation path prioritizes coverage of areas with a high level of debris accumulation over coverage of areas with a low level of debris accumulation; and a level of debris accumulation in different areas of the workspace are indicated on a map of the workspace.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allen et al. as applied to claims 1 and 13 above, and further in view of Burlutskiy (US 2013/0206177 A1).
Regarding claim 7, Allen et al. teach the method of claim 1 above. Allen et al. are silent regarding inferring, with the processor, a level of user activity or time of user activity within the workspace; and determining, with the processor, an operational schedule for cleaning the workspace based on the level of user activity or the time of user activity within the workspace.
Burlutskiy teaches inferring a level of user activity (Fig. 6, element 521-524 via Figs. 4 and 8, element 413 and 810) or time of user activity within the workspace (paragraph 0031-0033); and determining, with the processor, an operational schedule for cleaning the workspace based on the level of user activity or the time of user activity within the workspace (paragraph 0052). It would have been obvious to a person having ordinary skill in the art prior to Applicant’s effective filing date to apply the well-known technique taught by Burlutskiy to the prior art method taught by Allen et al. That is, it would have been obvious to configure the robot taught by Allen et al. to infer a level of user activity, determine an operation schedule for cleaning based on the level of user activity. Application of the well-known technique taught by Burlutskiy to the prior art method taught by Allen et al. would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and because such application would have yielded predictable results. The predictable results including: the robot being configured for inferring, with the processor, a level of user activity or time of user activity within the workspace; and determining, with the processor, an operational schedule for cleaning the workspace based on the level of user activity or the time of user activity within the workspace.
Regarding claim 17, Allen et al. teach the mobile robotic device of claim 13 above. Allen et al. are silent regarding inferring, with the processor, a level of user activity or time of user activity within the workspace; and determining, with the processor, an operational schedule for cleaning the workspace based on the level of user activity or the time of user activity within the workspace.
Burlutskiy teaches inferring a level of user activity (Fig. 6, element 521-524 via Figs. 4 and 8, element 413 and 810) or time of user activity within the workspace (paragraph 0031-0033); and determining, with the processor, an operational schedule for cleaning the workspace based on the level of user activity or the time of user activity within the workspace (paragraph 0052). It would have been obvious to a person having ordinary skill in the art prior to Applicant’s effective filing date to apply the well-known technique taught by Burlutskiy to the prior art device taught by Allen et al. That is, it would have been obvious to configure the robot taught by Allen et al. to infer a level of user activity, determine an operation schedule for cleaning based on the level of user activity. Application of the well-known technique taught by Burlutskiy to the prior art method taught by Allen et al. would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and because such application would have yielded predictable results. The predictable results including: the robot being configured for inferring, with the processor, a level of user activity or time of user activity within the workspace; and determining, with the processor, an operational schedule for cleaning the workspace based on the level of user activity or the time of user activity within the workspace.
Claim(s) 8-9 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Allen et al. as applied to claims 1 and 13 above, and further in view of Hillen et al. (US 2014/0166047 A1).
Regarding claim 8, Allen et al. teach the method of claim 1 above. Allen et al. are silent regarding collecting, with a sensor disposed on the robot, floor data; determining, with the processor, a floor type of a floor surface within different areas of the workspace; and determining, with the processor, a height of a cleaning tool in relation to a floor surface based on the floor type of the floor surface.
Hillen et al. teach collecting, with a sensor (Fig. 5, element 7-8) disposed on a robot (Figs. 1-5 and 9, element 1), floor data (Fig. 6, element FA; paragraph 0079-0081, 0088-0089); determining, with [a] processor (Figs. 1-4, element 10), a floor type (Fig. 1, 3-4 and 9, element 2; Fig. 9, element Pa, Fl, La, We) of a floor surface within different areas (rooms) of the workspace (paragraph 0044, 0087, 0099-0105); and determining, with the processor, a height (floor clearance) of a cleaning tool in relation to a floor surface based on the floor type of the floor surface (paragraph 0007-0008, 0108 and 0115). It would have been obvious to a person having ordinary skill in the art to apply the well-known technique taught by Hillen et al. to the prior art robot method taught by Allen et al. Application of the well-known technique to the prior art robot method would have been obvious because such application would have been well within the level of skill in the art and because such application would have yielded predictable results. The predictable results including: the robot being configured for collecting, with a sensor disposed on the robot, floor data; determining, with the processor, a floor type of a floor surface within different areas of the workspace; and determining, with the processor, a height of a cleaning tool in relation to a floor surface based on the floor type of the floor surface.
Regarding claim 9, Allen et al. teach the method of claim 1 above. Allen et al. are silent regarding collecting, with a sensor disposed on the robot, floor data determining, with the processor, a floor type of a floor surface within different areas of the workspace; and determining, with the processor, a vacuuming intensity based on the floor type of the floor surface.
Hillen et al. teach collecting, with a sensor (Fig. 5, element 7-8) disposed on a robot (Figs. 1-5 and 9, element 1), floor data (Fig. 6, element FA; paragraph 0079-0081, 0088-0089); determining, with [a] processor (Figs. 1-4, element 10), a floor type (Fig. 1, 3-4 and 9, element 2; Fig. 9, element Pa, Fl, La, We) of a floor surface within different areas (rooms) of the workspace (paragraph 0044, 0087, 0099-0105); and determining, with the processor, a vacuuming intensity (suction power) based on the floor type of the floor surface (paragraph 0111, 0065, 0080, 0083, 0094, 0097-0098, 0106).It would have been obvious to a person having ordinary skill in the art to apply the well-known technique taught by Hillen et al. to the prior art robot method taught by Allen et al. Application of the well-known technique to the prior art robot method would have been obvious because such application would have been well within the level of skill in the art and because such application would have yielded predictable results. The predictable results including: the robot being configured for collecting, with a sensor disposed on the robot, floor data determining, with the processor, a floor type of a floor surface within different areas of the workspace; and determining, with the processor, a vacuuming intensity based on the floor type of the floor surface.
Regarding claim 18, Allen et al. teach the mobile robotic device of claim 13 above. Allen et al. are silent regarding the operations further comprise: collecting, with a sensor disposed on the robot, floor data; determining, with the processor, a floor type of a floor surface within different areas of the workspace; and determining, with the processor, a height of a cleaning tool in relation to a floor surface based on the floor type of the floor surface.
Hillen et al. teach collecting, with a sensor (Fig. 5, element 7-8) disposed on a robot (Figs. 1-5 and 9, element 1), floor data (Fig. 6, element FA; paragraph 0079-0081, 0088-0089); determining, with [a] processor (Figs. 1-4, element 10), a floor type (Fig. 1, 3-4 and 9, element 2; Fig. 9, element Pa, Fl, La, We) of a floor surface within different areas (rooms) of the workspace (paragraph 0044, 0087, 0099-0105); and determining, with the processor, a height (floor clearance) of a cleaning tool in relation to a floor surface based on the floor type of the floor surface (paragraph 0007-0008, 0108 and 0115). It would have been obvious to a person having ordinary skill in the art to apply the well-known technique taught by Hillen et al. to the prior art robot method taught by Allen et al. Application of the well-known technique to the prior art robot method would have been obvious because such application would have been well within the level of skill in the art and because such application would have yielded predictable results. The predictable results including: the robot being configured for collecting, with a sensor disposed on the robot, floor data; determining, with the processor, a floor type of a floor surface within different areas of the workspace; and determining, with the processor, a height of a cleaning tool in relation to a floor surface based on the floor type of the floor surface.
Regarding claim 19, Allen et al. teach the mobile robotic device of claim 13 above. Allen et al. are silent regarding the operations further comprise: collecting, with a sensor disposed on the robot, floor data determining, with the processor, a floor type of a floor surface within different areas of the workspace; and determining, with the processor, a vacuuming intensity based on the floor type of the floor surface.
Hillen et al. teach collecting, with a sensor (Fig. 5, element 7-8) disposed on a robot (Figs. 1-5 and 9, element 1), floor data (Fig. 6, element FA; paragraph 0079-0081, 0088-0089); determining, with [a] processor (Figs. 1-4, element 10), a floor type (Fig. 1, 3-4 and 9, element 2; Fig. 9, element Pa, Fl, La, We) of a floor surface within different areas (rooms) of the workspace (paragraph 0044, 0087, 0099-0105); and determining, with the processor, a vacuuming intensity (suction power) based on the floor type of the floor surface (paragraph 0111, 0065, 0080, 0083, 0094, 0097-0098, 0106). It would have been obvious to a person having ordinary skill in the art to apply the well-known technique taught by Hillen et al. to the prior art robot method taught by Allen et al. Application of the well-known technique to the prior art robot method would have been obvious because such application would have been well within the level of skill in the art and because such application would have yielded predictable results. The predictable results including: the robot being configured for collecting, with a sensor disposed on the robot, floor data determining, with the processor, a floor type of a floor surface within different areas of the workspace; and determining, with the processor, a vacuuming intensity based on the floor type of the floor surface.
Conclusion
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/Dale Moyer/Primary Examiner, Art Unit 3656