DETAILED ACTION
This action is in response to the application filed on November 25th, 2025. Claims 1-10 are pending and have been examined.
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 .
Terminal Disclaimer
The terminal disclaimer filed on November 25th, 2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of patent application #18/258,860 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Arguments
Applicant has overcome the previously set forth 35 U.S.C. 101 rejections. Applicant’s amendment of claim 9 to include “non-transitory” had narrowed the broadest reasonable interpretation to NOT include signals and carrier waves. Claim 9 is therefore now directed to patentable subject matter. Furthermore, independent claims 1 and 6 have been amended in a way that overcomes the previously set forth 35 U.S.C. 101 rejection. These claims now require that the quantity of hue valid pixels be determined by a number of pixels. Given that modern images generally contain thousands of pixels, it would not be practical for a human observer to count a number of pixels.
Applicant's arguments filed November 25th, 2025 have been fully considered but they are not persuasive.
Applicant alleges that “amended claims 1 and 6 more clearly define the “quantity of hue valid pixels” Examiner respectfully disagrees. Applicant’s amendment more clearly defines that a quantity of hue valid pixels is a number of pixels within a preset hue interval, however this does not clearly define how a pixel is determined to be “hue valid.” The specification states “The preset hue interval may be [15, 95], and is not limited in this embodiment. If pixels with hue values in the preset hue interval in the candidate weeding region image are selected as hue valid pixels, the number of hue valid pixels is the quantity of hue valid pixels,” however, it is unclear if the hue valid pixels must be within the range [15, 95], or if there are other suitable ranges of pixel values which would be considered “valid.” To overcome this rejection, the applicant must clearly set forth what criteria must be met for a pixel to be considered “valid”. Therefore, the rejection is maintained.
Claim Objections
Claims 4, 8 and 9 are objected to because of the following informalities:
Claim 8 line 5 and claim 9 lines 2-3: “the obstacle recognition method” lacks antecedent basis. This should read “the method.”
Claim 4 lines 5-6 and 10: “the step of determining whether there is an exposed region in the candidate weeding region image” lacks antecedent basis. This should read “the step of determining whether an obstacle or a boundary exist, or whether a lawn region exposed to strong light exists in the candidate weeding region image.” Furthermore, line 10 then refers to “the exposed region”, this should likely read “the lawn region exposed to strong light.”
Appropriate correction is required.
Claim Interpretation
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.
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 limitations 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 limitations are: an information obtaining module, a histogram generation module, an information determination module, a pixel quantity determination module, an obstacle determination module, a histogram generation unit, and a sudden change peak point determination unit in claims 6 and 7. Prong 1: Each of these limitations uses a generic placeholder “module” or “unit” for performing the relevant function. Prong 2: The “modules” and “units” are modified by functional language such as “information obtaining” and “histogram generation”, and each of these limitations is linked with the transition phrase “configured to”. Prong 3: There is not sufficient structure, material, or acts in the claims for performing the claimed function.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they 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 limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid 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 limitations recites 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.
In claims 1 and 6, the claim language of “…so that a weeding robot may work in the lawn region exposed to strong light and avoid working at the obstacle or at the boundary” does not limit the claim any further. The claim language is interpreted as an intended use, and MPEP 2103 I C states that “Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. The following types of claim language may raise a question as to its limiting effect: (A) statements of intended use or field of use, including statements of purpose or intended use in the preamble.” This claim language merely states that the method and apparatus of distinguishing an obstacle or boundary from a region exposed to strong light is intended to be used so that the weeding robot may work in any lawn region exposed to strong light and avoid working at any obstacle or any boundary. If applicant wishes for this claim language to be limiting, it should be positively recited in such a way that requires that method and apparatus to perform these steps.
Claim Rejections - 35 USC § 112
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.
Claims 1, 4, and 6 are 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.
The claim limitation “hue valid pixels” is indefinite because the metes and bounds of “valid” are not clear. The specification defines hue valid pixels as pixels within “the preset hue interval [15,95], and is not limited in this embodiment”. Due to this fact, it is not certain whether hue valid pixels must be within the range [15, 95], or some other range this is not described within the specification. Furthermore, the specification does not provide any additional detail as to how to determine whether a pixel of a certain hue is valid, and why. For examination purposes, this limitation is being interpreted as pixels within any hue interval.
Claims 2-3, 5, and 7-10 are also rejected due to their dependency on a previously rejected claim.
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.
Claims 1-3 and 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over US20100111362 (herein after referred to by its primary author, Huang) in view of “SHADOW REMOVAL OF SINGLE TEXTURE REGION USING HISTOGRAM MATCHING AND COLOR MODEL RECOVERY” (herein after referred to by its primary author, Yali)
In regards to claim 1, Huang teaches a method for distinguishing, in a weeding region, an obstacle or a boundary from a lawn region exposed to strong light (Huang Paragraph [0003] “The present invention generally relates to a digital image processing method, and more particularly, to a shadow detection method.” Examiner note: This reference teaches determining a shadowed region, with the remaining region being considered the exposed region.), so that a weeding robot may work in the lawn region exposed to strong light and avoid working at the obstacle or at the boundary (Examiner note: As previously discussed with regards to claim interpretation, this claim language does not further limit the claim.), the method comprising the steps of: obtaining hue information; (Huang Paragraph [0031] “Each connected component (i.e., each entity) in the foreground is distinguished through connected component labeling method, and then the color characteristics of all the pixels in the connected component are obtained (referred to as observation) to be used for establishing a histogram subsequently”) generating a hue histogram of the candidate weeding region image according to the hue information (Huang Paragraph [0033] “Then, a distribution of the color variation of the pixels in the moving object is calculated to obtain a histogram of the color variation of the moving object. ”), and obtaining peak information of the hue histogram, wherein the peak information comprises a hue value of a sudden change peak point and a peak value of the sudden change peak point; (Huang Paragraph [0038] “Assuming that a mean value m of the Gaussian function curve falls at the maximum peak of the cumulative histogram,”) determining, as a quantity of hue valid pixels, a number of pixels in a preset hue interval in the candidate weeding region image; (Huang Paragraph [0045] “For example, if the color variation of the pixels is distributed between 0 and 255, the color variation of each pixel in the object image of the moving object can be calculated, and the histogram 530 of the color variation can be rendered according to the distribution of the color variation by taking the color variation (i.e., 0.about.255) as the abscissa and the number of pixels as the ordinate.” Examiner note: The hue interval is 0-255, and the number of pixels in each hue is determined. The number of pixels is then used to assist with the creation of the hue histogram.) determining, according to Huang Paragraph [0042] “Shadow detection can be carried out by using the estimated Gaussian function curve. According to the definition of statistics, a pixel is determined to belong to the shadow when the characteristic value of the pixel falls within multiples of the standard deviation (.sigma.) of the Gaussian function curve. Herein 2.5 times of the standard deviation is taken; however, the present invention is not limited thereto.” Examiner note: This reference determines a shadow based on the hue histogram, and that hue histogram is based off the quantity of hue valid pixels as determine with respect to Huang Paragraph [0045] above. When a shadow is determined, the remaining region can be considered the “exposed” region, therefore this reference teaches a method for finding the “exposed” region.), so that the weeding robot may work in any lawn region exposed to strong light and avoid working at any obstacle or at any boundary (Examiner note: As previously discussed with regards to claim interpretation, this claim language does not further limit the claim.).
Huang does not teach obtaining a number of exposed state pixels and a number of non-exposed state white pixels of a candidate weeding region image; determining target pixel position information and target pixel value information of the candidate weeding region image according to the hue value of the sudden change peak point; determining according to the number of exposed state pixels, the number of non-exposed state white pixels,
However, Yali does teach obtaining a number of exposed state pixels (Yali Figure 2; “Section 2.2 “We can make masks fulfilled with 0 and 1 to mark up the shadowed and unshaded area respectively, as in Fig.2” Examiner note: Exposed state pixels is being interpreted as pixels which are exposed to something, for example bright, not shadowed, or visible pixels. The black pixels of figure 2 represent pixels which are exposed to sunlight.) and a number of non-exposed state white pixels (Yali Figure 2; “Section 2.2 “We can make masks fulfilled with 0 and 1 to mark up the shadowed and unshaded area respectively, as in Fig.2” Examiner note: Non-exposed state white pixels is being interpreted as pixels which are not exposed to sunlight, and are therefore shaded. The white pixels of figure 2 represent pixels which are shaded.) of a candidate weeding region image; determining target pixel position information and target pixel value information of the candidate weeding region image according to the hue value of the sudden change peak point (Yali Figure 2; Section 2.2 “We can make masks fulfilled with 0 and 1 to mark up the shadowed and unshaded area respectively, as in Fig.2” Examiner note: The target pixel information is identified and then the image is filled with 1s or 0s based on the location being in or out of the target area. The target pixels are the pixels filled with 0s in this mapping, as they represent pixels which are exposed.); determining according to the number of exposed state pixels, the number of non-exposed state white pixels, Yali 2.2 “Shadow detection based on histogram matching” Examiner note: This reference also finds a shaded region, but again the shaded region is complementary to eh exposed region, so when one is found the other is also found.)
Yali is considered to be analogous to the claimed invention because they are both in the same field of shade and sunlight detection. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Huang to include the teachings of Yali, to provide the advantage of simple shadow detection that performs well (Yali Section 2.2 “This method of shadow detection is not complex but plays a good performance.”)
In regards to claim 2, Huang in view of Yali teaches the method according to claim 1, wherein the step of generating a hue histogram of the candidate weeding region image according to the hue information, and obtaining peak information of the hue histogram comprises the steps of: performing histogram statistics on the hue information to generate the hue histogram of the candidate weeding region image (Huang Paragraph [0033] “To be specific, in the present embodiment, images in the same regions of the background image are subtracted from the object image to obtain a color variation of each pixel between the moving object and the background image. Then, a distribution of the color variation of the pixels in the moving object is calculated to obtain a histogram of the color variation of the moving object.”); and determining the sudden change peak point according to differences between adjacent frequencies in the hue histogram, and obtaining the peak information of the sudden change peak point (Huang Paragraph [0038] “Assuming that a mean value m of the Gaussian function curve falls at the maximum peak of the cumulative histogram” Examiner note: The “sudden change peak” of this disclosure is being interpreted as any peak in a histogram. A peak, by definition, must have a frequency higher than the two adjacent frequencies, which meets the BRI of this claim. This reference uses a gaussian curve of the peak and its surrounding values (see Paragraph [0037] “peak region”), therefore this peak is based on adjacent frequencies.)
In regards to claim 3, Huang in view of Yali teaches the method according to claim 1, wherein the step of determining target pixel position information and target pixel value information of the candidate weeding region image according to the hue value of the sudden change peak point comprises the steps of: determining target pixels according to the hue value of the sudden change peak point; (Yali Figures 1 and 2 Examiner note: The peak of the histograms of Yali are used to identify relevant shadow areas.) determining the target pixel position information of the candidate weeding region image according to the target pixels and position information of the target pixels (Yali Section 2.2 “We can make masks fulfilled with 0 and 1 to mark up the shadowed and unshaded area respectively, as in Fig.2.” Examiner note: By marking the target pixels as 1, the area’s position is identified.); and determining the target pixel value information of the candidate weeding region image according to the target pixels and value information of the target pixels. (Yali Figures 1 and 2; Zhu Paragraph [0072] “S203: determining the peak and the valley in the second color component histogram based on the preset chrominance interval and the preset peak and valley setting conditions” Examiner note: The value of the peak is taken as the target pixel value information, as it represents the hue of the pixels which need to be marked as 1 or 0.)
In regards to claim 5, Huang in view of Yali teaches the method according to claim 1, wherein the step of determining whether there is an obstacle in the candidate weeding region image comprises the steps of: obtaining a hue segmentation image of the candidate weeding region image according to a preset hue segmentation interval if there is the exposed region (Yali Figure 2 Examiner note: This figure shows a binarized image identifying the shaded and exposed regions, which is analogous to a hue segmentation image.); and determining whether there is an obstacle in the candidate weeding region image according to the hue segmentation image (Yali Figure 2 Examiner note: The obstacle in this image is the identified shadow).
In regards to claim 6, Huang in view of Yali renders obvious the claim limitations as in the consideration of claim 1.
In regards to claim 7, Huang in view of Yali renders obvious the claim limitations as in the consideration of claim 2.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Yali, and further in view of “Finding Self-Cast Shadows in Aerial Camera Images” (herein after referred to by its primary author, Gatter) and “Combining shadow detection and simulation for estimation of vehicle size and position” (herein after referred to by its primary author, Johansson) and.
In regards to claim 4, Huang in view of Yali teaches the method according to claim 1, wherein the target pixel value information comprises an average value of the target pixel (Yali Section 2.2 “In Fig.l… the histogram of the unshaded piece in (d) has the expectation of 180 and the variance of 9.6.” Examiner note: The expectation of a histogram is analogous to the mean, or expected value, of the histogram.); and the step of determining whether there is an exposed region in the candidate weeding region image according to the number of exposed state pixels, the number of non-exposed state white pixels, the peak value of the sudden change peak point, the target pixel position information, the target pixel value information, and the quantity of hue valid pixels comprises the step of: determining that there is the exposed region in the candidate weeding region image if the peak value of the sudden change peak point is greater than a preset peak value threshold of the sudden change peak point (Huang Paragraph [0042] “Shadow detection can be carried out by using the estimated Gaussian function curve. According to the definition of statistics, a pixel is determined to belong to the shadow when the characteristic value of the pixel falls within multiples of the standard deviation (.sigma.) of the Gaussian function curve. Herein 2.5 times of the standard deviation is taken; however, the present invention is not limited thereto.” Examiner note: The gaussian curve (which is based on the peak region) is used to detect the shadow, and the threshold in this reference is the standard deviations by which the pixel must fall in to be considered a shadow. The lower limit (2.5 standard deviations from the mean) is considered the threshold value that the peak point must be greater than.), the average value of the target pixels is greater than a preset average value threshold of the target pixels (Yali Section 2.2 “Based upon this principle, whether a pixel belongs to the shadow or not can be judged by whether its gray value is within the scale of [-3, +3] in the histogram of the shadow.” Examiner note: The value of the pixels must be greater than the variance minus 3 in the histogram.), the number of exposed state pixels is greater than a preset number threshold of exposed state pixels (Yali Figure 2 Examiner note: The number of exposed state pixels must be greater than 0, because if the number of pixels that have an exposed state was equal to 0, then there would be no exposed region to detect. Therefore, this reference does teach that there must be at least one exposed region pixel for the exposed region to be detected.), and the number of non-exposed state white pixels is less than a preset number threshold of non-exposed state white pixels (Yali Figure 2 Examiner note: The number of non-exposed state white pixels must be less than the amount of pixels within the image, because if the number of pixels that have a non-exposed state was equal to or greater than the amount of pixels within the image, then there would be no exposed region to detect. Therefore, this reference teaches that there cannot be a number of non-exposed state white pixels equal to or greater than the number of pixels within the image.).
Huang in view of Yali does not teach the average position of the target pixels is less than a preset average position threshold of the target pixels and the quantity of hue valid pixels is greater than a preset quantity threshold of hue valid pixels.
However, Gatter teaches the average position of the target pixels is less than a preset average position threshold of the target pixels, (Gatter Figure 8; Section II B “Because of that, a confidence area is introduced in which the votes from the template matching are amplified with a constant factor. This area is originated at the center of the estimated shadow in the camera image and projected onto the ground.” Examiner note: This reference teaches an imaging system that can detect its own shadow. The position of this shadow is estimated then refined, and a refined estimate shadow must be within a confidence area that is originated from a rough estimate shadow. This is analogous to determining that target pixels are less than an average position because the position of the refined estimate shadow must be within a range of the rough estimate shadow, which includes an upper bound that the target region must be less than. As in figure 8, the target region must not be above the confidence area to be considered proper.)
Gatter is considered to be analogous to the claimed invention because they are both in the same field of shade and sunlight detection. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Huang in view of Yali to include the teachings of Gatter, to provide the advantage of a precise estimation system which accounts for errors and yields meaningful results within the system that it is being applied to (Gatter Section II B “This area is originated at the center of the estimated shadow in the camera image and projected onto the ground. If the maximal errors of all involved sensors as well as the condition of the surface are known, the size and shape of the confidence area can be set accordingly. If this is not the case, a generous estimation of the area size and a circular shape still yields good results.”)
Furthermore, Johansson teaches the quantity of hue valid pixels is greater than a preset quantity threshold of hue valid pixels. (Johansson Section 5 “We employ a fairly simple optimization method. Basically, the algorithm is initiated with a predicted box state (from e.g. a Kalman filter) and tries different changes of size and position iteratively to find the box state that gives the highest similarity measure. The changes in size are not arbitrary, but chosen from a list of common sizes, see Table 1 for examples. The iteration loops over box sizes and position transformations, with gradually decreasing step size, see Algorithms 1 and 2 for details. Fig. 4 shows some example results. For comparison, the same figure also shows the result when simulating, but not detecting shadows, as well as when detecting and removing shadow pixels (and without simulating shadows).” Examiner note: This reference compares the size of a target region (and therefore the number of pixels) to the size of preset bounding boxes, the hue valid pixels of this reference are pixels which are estimated to be shadows, and therefore have a lower hue. The size comparison ensures that the shadow is given the correct size based on what the object is, for example a box truck will have a larger shadow than a sedan. This is analogous to determining a quantity of hue valid pixels, as the hue valid pixels are the shadow pixels, and the quantity must be similar in area to the bounding box area (and therefore greater than a smaller bounding box.)
Johansson is considered to be analogous to the claimed invention because they are both in the same field of shade and sunlight detection. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Huang in view of Yali and Gatter to include the teachings of Johansson, to provide the advantage of a system that is more consistent on average compared to other similar systems (Johansson Section 5 “Fig. 4 shows some example results. For comparison, the same figure also shows the result when simulating, but not detecting shadows, as well as when detecting and removing shadow pixels (and without simulating shadows). As we show, the other methods are in some cases somewhat better, but the new proposed method does better on average”)
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Yali, and further in view of “The Control System Design of Automatic Weeding Robot Based on Visual Navigation” (herein after referred to by its primary author, Qin)
In regards to claim 8, Huang in view of Yali teaches the method according to claim 1, but fails to teach one or more processors; and a storage apparatus configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, enable the one or more processors to implement the obstacle recognition method.
However, Qin teaches one or more processors; and a storage apparatus configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, enable the one or more processors to implement the obstacle recognition method (Qin Section III B “Video capture board system structure is shown in Fig. 4. TI Da Vinci DM6446 integrated two processor cores: 297MHz ARM926EJ-S core and 594MHz C64x+ core… The 8bit data transmission was moved from VPIF to DDR2 using the EDMA3(Enhanced Direct Memory Access 3).”)
Qin is considered to be analogous to the claimed invention because they are both in the same field of a weeding robots which uses image processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Huang in view of Yali to include the teachings of Qin, to provide the advantage of a robot which can accurately perform its job, including movement, just based off of visual sensors (Qin Abstract “And the results show that weeding robot can correctly achieve line-changed action and meets fast, accurate weeding job requirements.”)
In regards to claim 9, Huang in view of Yali and Qin renders obvious the claim limitations as in the consideration of claim 8.
In regards to claim 10, Huang in view of Yali and Qin teaches a weeding robot (Qin Figure 1 “Weeding robot”) and renders obvious the remaining claim limitations as in the consideration of claim 8.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US20190346848 teaches an autonomous lawn mower that can avoid obstacles and detect shaded regions of an area.
“Embedded Robust Visual Obstacle Detection on Autonomous Lawn Mowers” teaches an autonomous lawn mower that uses computer vision to detect obstacles.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CALEB LOGAN ESQUINO whose telephone number is (703)756-1462. The examiner can normally be reached M-Fr 8:00AM-4:00PM EST.
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, Andrew Bee can be reached at (571) 270-5183. 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.
/CALEB L ESQUINO/Examiner, Art Unit 2677
/JAYESH A PATEL/Primary Examiner, Art Unit 2677
Prosecution Insights