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 .
Status of Claims
This is in response to Applicant’s case, no. 18/930,788, with an effective filing date of 10/29/2024. Claims 1-13 are currently pending.
Response to Arguments
Examiner acknowledges that the necessary changes were made regarding the Specification and Claim Objection sections in Applicant’s arguments, see page 6, and subsequently withdraws objections to said sections.
Examiner acknowledges that the necessary changes were made regarding the rejection of claim(s) 3 under 35 USC § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter regarded as the invention due to containing relative terminology in Applicant’s arguments, see pg. 7, and subsequently withdraws the 35 USC § 112(b) rejection to said claim.
Regarding the 35 USC § 103 rejection of claims 1-13 as being unpatentable over Doughty et al. (US Pat. Pub. No. 2025/0089606 A1) [hereinafter referred to as Doughty] in view of Simpson (US Pat. No. 12,029,156 B1) and Young et al. (US Pat. Pub. No. 2015/0099580 A1) [hereinafter referred to as Young], the Applicant argues that Doughty, as modified by Simpson, allows merely for a user input of a time window for operation. However, Doughty discloses, as discussed in the previous action, in [0073] s.1, mowing schedule includes information indicative of when to perform mowing operations, such as start times and [0155] s. 3, recommends mowing schedules with start times and durations which is construed as a system establishing operational time windows. Furthermore, Simpson, as discussed in the previous action, teaches in col 8 ln 3-27 that the maintenance model(s) may afford fully autonomous operation (e.g., without user input) of a lawn maintenance machine, in some cases a comprehensive system for turf maintenance and/or management includes techniques for remote supervision, monitoring, and, optionally, remote control, of the lawn maintenance machines. Furthermore, a user may use the server, construed as user evaluation data, to supervise, monitor, and/or control the lawn maintenance machine, although the model affords full autonomy for the lawn maintenance machine. The user can establish and/or schedule mow operations, including selecting mowing start times and dates and patterns for given areas. The server may then cause the mow operations to be executed in accordance with the user selections, including, for example, issuing appropriate commands to one or more mowers to initiate the scheduled or requested mow operations. This is interpreted as the actions a user can perform can be replaced with the trained AI system as taught in Simpson including that of operational scheduling and compiling evaluation data of the time window that a user, or an AI trained system, is able to access and assess.
Therefore, this argument is unpersuasive.
Applicant argues the dependent claims are patentable by virtue of their dependency.
This argument is unpersuasive as each independent claim has been fully rejected for the reasons as given above.
Drawings
Drawings 1-2 are objected to under 37 CFR 1.83(a) because they fail to show: with respect to Figs. 1-2 details regarding the identifications (i.e., Fig. 1 items 200, 210, 220, 230, 300, 401-402, and 500), as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). If the changes are not accepted by the Examiner, the applicant will be notified an informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 103
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Doughty et al. (US Pat. Pub. No. 2025/0089606 A1), hereinafter referred to as Doughty, in view of Simpson (US Pat. No. 12,029,156 B1) and Young et al. (US Pat. Pub. No. 2015/0099580 A1), hereinafter referred to as Young.
Regarding claim 1, Doughty discloses:
A computer- implemented method for determining a time window (TWO) for a garden device (100) for maintaining a lawn, preferably for a mowing robot (101), a garden tractor (102) or a mower (103) ([0003] sentence (s.)1, Robotic lawnmowers can perform mowing operations in which the robotic lawnmowers autonomously navigate about mowable areas to mow vegetation within the mowable areas and users can manually activate and deactivate the robotic lawnmowers to selectively cause the robotic lawnmowers to perform the mowing operations when desired, or they can be preprogrammed to mow on a particular schedule and [0026] s.1, robotic lawnmowers, or operational aspects thereof, can be implemented as/controlled by a computer program product that includes instructions that are stored on one or more non-transitory machine-readable storage media, and that are executable on one or more processing devices to control (e.g., to coordinate) the operations), wherein the time window for operation (TWO) comprises at least a start time (ST) and/or a duration of operation (DO) for the operation of the garden device ([0073] s.1, mowing schedule includes information indicative of when to perform mowing operations, such as start times and [0155] s. 3, recommends mowing schedules with start times and durations), wherein an evaluation query (EQ) is generated for evaluating the time window for operation (TWO) and the evaluation query (EQ) is provided to a user's terminal device (110) ([0023] forwarding information about the fluctuating weather condition to a remote device, and waiting for a prompt from the remote device before adjusting the mowing schedule and [0024] s.2, systems can generate and provide information about conditions and characteristics of the mowable area to a user, enabling the user to make informed decisions about lawn care and Fig. 10B below which provides an evaluation query for a user’s remote device and allows user to adjust the mowing schedule based on the query.).
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Although Doughty discloses in [0073] s.1, mowing schedule includes information indicative of when to perform mowing operations, such as start times and [0155] s. 3, recommends mowing schedules with start times and durations which is construed as a system establishing operational time windows and the evaluation query for data as discussed above in [0023-24], Doughty does not explicitly disclose:
wherein the time window for operation (TWO) is determined based on a trained Al system (202); and
wherein user evaluation data (UED) of the time window for operation (TWO) is retrieved to generate a training data set (TD) for an Al system (202).
However, Simpson teaches in column (col) 3 lines (ln) 8-15 machine learning model (which is a subset of artificial intelligence and is construed as a trained AI system) trained at least in part on a number of simulated mow operations or on a training set including training data from a number of human-piloted mow operations. Furthermore, in col 7 ln 35-37 Simpson teaches a machine learning model trained on a data set comprising date information, mow-area information (e.g., mow area shapes, sizes, attributes, etc.), and cutting patterns. Further in col 8 ln 3-27 Though the maintenance model(s) may afford fully autonomous operation of a lawn maintenance machine, in some cases a comprehensive system for turf maintenance and/or management includes techniques for remote supervision, monitoring, and, optionally, remote control, of the lawn maintenance machines. Furthermore, a user may use the server, construed as user evaluation data, to supervise, monitor, and/or control the lawn maintenance machine, although the model affords full autonomy for the lawn maintenance machine. The user can establish and/or schedule mow operations, including selecting mowing start times and dates and patterns for given areas. The server may then cause the mow operations to be executed in accordance with the user selections, including, for example, issuing appropriate commands to one or more mowers to initiate the scheduled or requested mow operations. This is interpreted as the actions a user can perform can be replaced with the trained AI system as taught in Simpson including that of operational scheduling and compiling evaluation data of the time window that a user, or an AI trained system, is able to access and assess.
Therefore it would have been obvious to one of ordinary skill in the art of vehicle controls and computer science before the effective filing date of the current invention to modify the robotic lawnmower of Doughty, by incorporating the machine learning model utilizing user feedback of Simpson, such that the combination would provide for the predictable result of improving autonomy of the robotic lawnmower and improve navigational capabilities without direct human intervention.
However, although Doughty discloses time window for operation (TWO) is determined based on input data (scheduling as discussed above and [0004] s.2-3 where a vegetation characteristic sensor generates sensor data in response to detecting a vegetation characteristic of the mowable area and the vegetation characteristic is selected from the group consisting of a moisture content, a grass height, and a color, this is construed as a time window for operation being initiated by input data), and Simpson teaches simulating mow operations based on attributes of the lawn, the references do not explicitly disclose:
a grass growth simulation (201)).
However, Young in [0008] teaches a virtual environment, e.g., an ecological system, may be rapidly modeled and may serve as the basis for a graphic rendering engine to simulate complex behaviors such as water flow, grass growth, and cloud formation.
Therefore it would have been obvious to one of ordinary skill in the art of vehicle controls and computer science before the effective filing date of the current invention to further modify the robotic lawnmower of Doughty, as already modified by Simpson‘s machine learning model, by further incorporating the complex environment simulation teachings of Young, such that the combination would provide for the predictable result of simulating complex behaviors, such as grass growth as acknowledged by Young in [0008].
Regarding claim 2, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1, wherein the user evaluation data (UED) comprises at least one of the following elements: a desired time window for operation (DTWO), a desired start time (DST), a desired duration of operation (DDO) and/or a qualitative evaluation (QE) of the time window for operation ([0003] perform the mowing operations when desired).
Regarding claim 3, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1 (see claim 1 and Fig. 10B).
Regarding claim 4, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1, wherein the time window for operation (TWO) or a rectified time window for operation (RTWO) of the garden device (100) is provided to a control interface (see claim 1 and Fig. 10B).
Regarding claim 5, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1, wherein the training
data set (TD') comprises at least one of the following elements: the input data (ID), the time window for operation (TWO), a rectified time window for operation (RTWO), the user evaluation data (UED) and/or operation data (OD) (see claim 1 and Fig. 10B where training data incorporates input data, schedule, and adjustment to the schedule).
Regarding claim 6, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1, wherein the input data (ID) includes at least one of the following elements: weather data (WD) ([0015] s. 6, adjust a mowing schedule based on information about the fluctuating weather condition and [0047] s.1 lawnmower receives information from a remote system about historic, current and anticipated weather conditions at the mowable area), garden device data (GDD), user profile data (UPD) ([0118] user lawn care operations where the system may provide recommendations to modify those operations, which is construed as a user profile where preferences are stored), lawn characteristics data (LCD) (see claim 1 regarding vegetation characteristics), historic operating data (HOD) ([0113] s.2 previously stored position-referenced data generated during previous mowing or boundary teach operations) and/or calendar data (CD) (see Fig 14 which has calendar data and weather data).
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Regarding claim 7, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1, wherein one or more evaluation queries (EQ) are generated before and/or after use of the garden device (100) ([0153] s. 3, electronic processor waits for a prompt from the user device 510 before adjusting the mowing schedule stored in system memory and see claim 1 regarding recommendations and training data which may necessarily be communicated after use of the garden device).
Regarding claim 8, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1 been used (see claim 1 regarding vegetation characteristics and user interface and other training data that is stored either on-board the gardening device or in the user’s device).
Regarding claim 9, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1, wherein operation data (OD) is processed to generate the evaluation query (EQ) and/or to generate the training data set (TD) (see claim 1 regarding generating queries and training data).
Regarding claim 10, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1, wherein a missing user evaluation is evaluated as an implicitly positive evaluation of the time window for operation (lack of a response to the system may necessarily mean the system maintains the current operation schedule and [0059] s.6, the robotic lawnmower automatically executes these recommendations, which cause modifications in, for example, mowing operations and mowing schedules associated with the robotic lawnmower).
Regarding claim 11, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 1, wherein at least one training data set (TD') for training the Al system (202) is stored in a training data base (250) (see claim 1 and [0072] s.1, electronic processor is operable with memory, which stores data and information received from the electronic processor as well as predetermined data and information pertaining to the operations of the robotic lawnmower).
Regarding claim 12, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 11, wherein the Al system (202) is trained with a plurality of training data sets (TD) from the training data base (250) (see claim 11 where at least 2 datasets are incorporated).
Regarding claim 13, Doughty, as modified by Simpson and Young, discloses:
The computer- implemented method according to claim 12, wherein the plurality of training data sets (TD) are filtered by ([0146] forecasted weather conditions, including predicted temperatures and likelihoods for precipitation, which is construed as a plausibility (i.e., likelihood) factor that is accounted for in the machine learning system of claim 1).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see:
Burdoucci (US Pat. Pub. No. 2018/0035606 A1) is directed towards an intelligent interactive apparatus, system and method that aligns with property trimming and cutting tools such as pole saws, lawnmowers or unmanned aerial drones using pattern recognition with machine learning; and
Lee et al. (US Pat. Pub. No. 2023/0292654 A1) is directed towards an external device may be further configured to generate scheduling information for the robotic garden tool based on the first information, and transmit the scheduling information to the robotic garden tool.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEITH ALLEN VON VOLKENBURG whose telephone number is (703)756-5886. The Examiner can normally be reached Monday-Friday 8:30 am-5:00 pm.
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, Erin D. Bishop can be reached at (571) 270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Keith A von Volkenburg/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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