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 .
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 13, 15-19 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.
Claim 13 recites the limitation “the feature matching” in line 3. There is insufficient antecedent basis for this limitation in the claims.
Claim 15 recites the limitation “the discriminator error” in line 2. There is insufficient antecedent basis for this limitation in the claims.
Claim 16 recites the limitation “the VGG19 pre-trained CNN model” in line 3. There is insufficient antecedent basis for this limitation in the claims. This feature is found in claim 13, but not in claims 7, 14, or 15 from which claim 16 depends.
Claim 17 recites several variables without definition. Such variables are y’’, E, D, and G. While G and D are previously defined as the generator and discriminator respectively, their use as functions of various x and y variables has not been previously defined.
Claim 18 recites variables without definition. Such variables are Vgg(i)()
Claim 19 recites variables without definition. Such variables are G, Dk, Vgg, D1, D2
Claim Rejections - 35 USC § 102
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 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)(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.
Claim(s) 1-4, 7, 9, 12, 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nitsch (US 2025/0278458).
Claim 1: Nitsch discloses A process for determining a map of an environment (para 0008, 0015), comprising: receiving millimeter-wave map data associated with an environment, wherein the map data is captured by a millimeter-wave radar (para 0022); processing the millimeter-wave map data with a machine learning network model (para 0021-0023, 0060, 0061, 0066) trained with a LiDAR data and millimeter-wave radar data (para 0011, 0029); and generating a map of the environment based on the processing of the machine learning network model (para 0008, 0015, 0030, 0046-0049)
Claim 2: Nitsch discloses the training of the machine learning network model further comprises the step of correlating the LiDAR data and millimeter-wave radar data each corresponding to the same position on the map of the environment. (para 0021-0023, 0060, 0061, 0066)
Claim 3: Nitsch discloses the training of the machine learning network model further comprises the step of obtaining millimeter-wave radar data containing a relative position within a training environment and signal-to-noise ratio associated with the relative position. (para 0022)
Claim 4: Nitsch discloses the training of the machine learning network model further comprises the step of filtering data with a signal-to-noise ratio exceeding a predetermined threshold. (para 0022, 0040-0042)
Claim 7: Nitsch discloses the machine learning network model comprises a generative adversarial network (GAN) comprising: - a generator (G) configured to generate estimated map info associated with a training environment; and - a discriminator (D) configured to determine the accuracy of the estimated map info generated by the generator (G). (para 0029, 0032-0035)
Claim 9: Nitsch discloses the generator (G) comprises a plurality of down-sampled convolutional layers with residual modules, a plurality of up-sampled transposed convolutional layers, and a bottom residual module. (para 0037, 0067-0243)
Claim 12: Nitsch discloses the discriminator (D) comprises an input layer, an output layer, and a plurality of continuously deepening convolutional layers. (para 0036, 0067-0243)
Claim 20: Nitsch discloses the step of deriving a navigational path of a robot based on the map of the environment generated by the machine learning network model. (para 0014, 0048)
Allowable Subject Matter
Claims 5, 6, 8, 10, 11, 14 are 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.
Claims 13, 15-19 would be allowable if rewritten or amended 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 if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The additionally cited prior art comprises further examples of GAN used to evaluate radar sensor data against a trained neural network for mapping and target feature extraction.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER M BYTHROW whose telephone number is (571)270-1468. The examiner can normally be reached on Monday-Friday 830am-5pm.
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, Resha Desai can be reached at (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER M BYTHROW/Primary Examiner, Art Unit 3648
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