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 the Claims
This first non-final action is in response to applicant's filing on June 18, 2025. Claims 1-19 are pending and have been considered as follows.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 19 is rejected under 35 U.S.C. 101 because the claimed invention is not to a statuary category.
Analysis of claim 19
STEP 1: Does claim 1 fall within one of the statutory categories? No. claim 19 is directed to non-statutory subject matter.
Claim 19 recites “a computing device-readable recording medium”. The Broadest Reasonable Interpretation (BRI) of machine readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the BRI encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a “machine-readable medium” were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves). Thus, claim 19 is clearly not within one of the four categories (Step 1: NO) and is directed to non-statutory subject matter.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis of claim 1
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 1 is directed toward non-statutory subject matter, as shown below:
STEP 1: Does claim 1 fall within one of the statutory categories? Yes. The claim is directed toward a process (method) which falls within one of the statutory categories.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim is directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claim 1. A method for localizing an autonomous vehicle by fusing a plurality of localization technologies, which is performed by a computing device, the method comprising:
calculating a plurality of localization values by performing localization for a vehicle located in a predetermined region using a plurality of localization technologies for performing localization according to different localization methods ; and
determining a position and orientation of the vehicle by fusing the plurality of calculated localization values as a result of localizing the vehicle.
The method in claim 1 is a mental process that can be practicably performed in the human mind and, therefore, an abstract idea. The limitations of claim 1 highlighted above merely consist of determining a position and orientation of the vehicle based on collected data, e.g. vehicle sensor data or GPS data. This is equivalent to a person, upon receiving sensor/GPS/map data mentally determining 1) location values and 2) vehicle’s position and orientation. Thus, the claims recite a mental process.
In addition, calculating step uses mathematical concepts such as mathematical relationships, mathematical formulas or equations, mathematical calculations to calculate localization values. This step is considered as mathematical algorithm being applied on a general purpose computer. See MPEP § 2106.05(f) Mere Instructions To Apply An Exception. Thus, the claims recite Mathematical concepts.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim 1. A method for localizing an autonomous vehicle by fusing a plurality of localization technologies, which is performed by a computing device, the method comprising:
calculating a plurality of localization values by performing localization for a vehicle located in a predetermined region using a plurality of localization technologies for performing localization according to different localization methods ; and
determining a position and orientation of the vehicle by fusing the plurality of calculated localization values as a result of localizing the vehicle.
Claim 1 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. “a computing device” merely includes instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated into a practical application.
As such, the additional limitations of claim 1 do not integrate the abstract idea into practical application.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim 1 does not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. Estimating and determining data are fundamental, i.e. WURC, activities performed by general purpose computing devices, such as the devices in claim 1.
CONCLUSION
Thus, since claim 1 is: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claim 1 is directed towards non-statutory subject matter.
In addition, independent claims 18 and 19 have similar limitation to claim 1. Claim 18 recites a processor, a network interface and a memory. They merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated into a practical application. claims 18 and 19 are rejected under 35 USC 101 as being drawn to an abstract idea without significantly more, and thus are ineligible
Dependent claims 2-17 further limit the abstract idea without integrating the abstract idea into practical application or adding significantly more, as the limitations are either further part of the mental process or are additional elements that do not integrate the abstract idea into practical application using a similar analysis as applied to claim 1 above.
As such, claims 1-19 are rejected under 35 USC 101 as being drawn to an abstract idea without significantly more, and thus are ineligible.
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 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)(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.
(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.
Claims 1-4 and 17-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Demir (US 20210010814 Al)
Regarding claim 1, Demir teaches a method for localizing an autonomous vehicle by fusing a plurality of localization technologies, which is performed by a computing device ( abstract, Fig. 1 and corresponding paragraphs including at least [0003], [0027], [0035] ), the method comprising:
calculating a plurality of localization values by performing localization for a vehicle located in a predetermined region using a plurality of localization technologies for performing localization according to different localization methods ([0042], a predetermined region: around vehicle or certain road [0046]; [0050], [0054], Fig.1 and corresponding paragraphs); and
determining a position and orientation of the vehicle by fusing the plurality of calculated localization values as a result of localizing the vehicle ( a positioning method for an autonomous vehicle performed by a computer device, the method comprising the steps in which: a plurality of sensors mounted on a vehicle (a positioning method for an autonomous vehicle performed by a computer device, the method comprising the steps in which: a plurality of sensors mounted on a vehicle collect sensor data to perform positioning; a scan matcher using an NDT algorithm for positioning determines scan confidence; and a map-to-odometer conversion result is generated on the basis of GPS data and the scan confidence, wherein the map-to-odometer conversion result means a vehicle position, and the matching result can be used to update an initial pose guess value; [0003], and claims 1 and 6, Fig1 and corresponding paragraphs).
Regarding claims 18 and 19, please see the rejection above with regarding claim 1, which is commensurate in scope to claims 18 and 19. Demir teaches a network interface, a memory, and a computer program loaded into the memory and executed by the processor ([0028]-[0029]) in claim 18.
Regarding claim 2, Demir teaches the calculating of the plurality of localization values includes calculating a first localization value for the vehicle using a first localization technology for performing localization according to a GNSS/INS-based localization method; and calculating a second localization value for the vehicle using a second localization technology for performing localization according to a normal distribution transform (NDT) map-based localization method, the NDT map being generated by post-processing a point cloud for the predetermined region, and the determining of the position and orientation of the vehicle includes deriving position information of the vehicle and orientation information of the vehicle by fusing the calculated first localization value and the calculated second localization value (a plurality of sensors mounted on a vehicle collect sensor data to perform positioning; a scan matcher using an NDT algorithm for positioning determines scan confidence; and a map-to-odometer conversion result is generated on the basis of GPS data and scan confidence; [0003], and claims 1 and 6, Fig1 and corresponding paragraphs).
Regarding claim 3, Demir teaches wherein the deriving of the position information of the vehicle and the orientation information of the vehicle includes determining a localization technology-specific weight for each of a plurality of regions based on regional characteristics of each of the plurality of regions, and generating a localization technology-specific weight map using the determined localization technology-specific weight; assigning a first weight corresponding to the first localization technology to the calculated first localization value and a second weight corresponding to the second localization technology to the calculated second localization value based on the generated localization technology-specific weight map; and deriving the position information of the vehicle and the orientation information of the vehicle by fusing the first localization value to which the first weight is assigned and the second localization value to which the second weight is assigned ( [0055] The transform maintainer 106 may generate a map-to-odometer transformation output 132 based on the scan confidence, the GPS data 120 , and the matched sensor scan point cloud output and map tile point cloud data. For example, the transform maintainer 106 may generate the map-to-odometer transformation output 132 based on the GPS data 120 (first technology’s weight is 1; second technology’s weight is 0) when the scan confidence is less than a scan confidence threshold and when the localization confidence is less than a localization confidence threshold. Conversely, the transform maintainer 106 may generate the map-to-odometer transformation output 132 based on the matched sensor scan point cloud output (first technology’s weight is 0; second technology’s weight is 1) and map tile point cloud data when the scan confidence is greater than a scan confidence threshold and when the localization confidence is greater than a localization confidence threshold. The transform maintainer 106 may also fuse dead reckoning results with scan matcher 104 results).
Regarding claim 4, Demir teaches calculating a first localization value for the vehicle using a first localization technology for performing localization according to a GNSS/INS-based localization method ([0055]);
calculating a second localization value for the vehicle using a second localization technology for performing localization according to a normal distribution transform (NDT) map-based localization method, the NDT map being generated by post-processing a point cloud for the predetermined region ([0009]); and
calculating a third localization value for the vehicle using a third localization technology for performing localization according to a lane matching-based localization method ([0011]-[0016]), and
the determining of the position and orientation of the vehicle includes deriving position information of the vehicle and orientation information of the vehicle by fusing the calculated first localization value, the calculated second localization value, and the calculated third localization value (Fig. 1 and corresponding paragraphs).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 5-17 are rejected under 35 U.S.C. 103 as being obvious over by Demir (US 20210010814 Al) in view of Kim (US 20230194268 A1)
Regarding claim 5, Demir does not explicitly teach but Kim teaches wherein the calculating of the third localization value includes generating a lane precision map for the predetermined region; generating real-time lane information using a real-time point cloud acquired from the vehicle; and matching the generated lane precision map with the generated real-time lane information to calculate the third localization value for the vehicle ([0014] Generally, localization of the AV with respect to lane line features are performed by matching LIDAR point cloud data to lane markers that may be dashed, solid, double, or unmarked. Localization to a lane line feature can be performed using point-to-point iterative closet point (ICP), or GICP, Fig 5 and corresponding paragraphs).
It would have been obvious to one of ordinary skill in the art before the effective date of the present invention to modify, vehicle localization, as taught by Demir, matching the generated lane precision map with the generated real-time lane information to calculate the third localization value for the vehicle, as taught by Kim, as Demir and Kim are directed to vehicle localization (same field of endeavor), and one of ordinary skill in the art would have recognized the established utility using matching the generated lane precision map with the generated real-time lane information to calculate a localization value for the vehicle and predictably applied it to Demir’s teaching to maneuver safely between lanes.
Regarding claim 6, Demir does not explicitly teach but Kim teaches wherein the matching of the generated lane precision map with the generated real-time lane information to calculate the third localization value for the vehicle includes deriving the position information of the vehicle and the orientation information of the vehicle by matching information included in the generated lane precision map with information included in the generated real-time lane information based on a vehicle coordinate system with a point in the vehicle as an origin ( [0014], Fig 5 and corresponding paragraphs).
The same motivation to combine as the parent claim applies here.
Regarding claim 7, Demir teaches wherein the generating of the lane precision map includes extracting only points corresponding to a ground surface from the point cloud acquired by scanning the predetermined region to generate a ground surface point cloud for the predetermined region ([0041]-[0043]);
defining a range of interest (ROI) corresponding to the lane in the point cloud acquired by scanning the predetermined region to generate an ROI map for the predetermined region ( [0042] The map tile point cloud data may be represented as a voxel associated with a mean and a covariance. For example, map tiles 114 may be ‘tiles’ around the vehicle which may be loaded at runtime (e.g., as the vehicle is passing through the operating environment). These ‘tiles’ may be of a predetermined size (e.g., 1 km2 ) and stored and loaded in a NDT compatible forma; ROI: around vehicle ); and
extracting only points matched with points included in the generated ROI map and having an intensity equal to or greater than a threshold value from among a plurality of points included in the generated ground surface point cloud to generate the lane precision map for the predetermined region(Fig. 1 and corresponding paragraphs).
Regarding claim 8, Demir teaches extracting only points corresponding to a ground surface from the point cloud acquired by scanning the predetermined region to generate a ground surface point cloud for the predetermined region; defining a range of interest (ROI) corresponding to the lane in the point cloud acquired by scanning the predetermined region to generate an ROI map for the predetermined region; and extracting only points matched with points included in the generated ROI map and having an intensity equal to or greater than a threshold value from among a plurality of points included in the generated ground surface point cloud to generate the lane precision map for the predetermined region (Fig 1 and corresponding paragraphs, [0041] The scan matcher 104 may receive map tile point cloud data from a map tile server 112 in addition to the sensor scan point cloud output from the scan accumulator 102 . The map tile point cloud data may be indicative of transformed point cloud data associated with a coarse vehicle location and be built using a reference set of sensors 110 . The point cloud data 116 from the map tile server 112 (e.g., map tile point cloud data) may be represented in a local coordinate frame, but the transformation from local to UTM coordinate frame may also be available. The map tile point cloud data may include static features, ground surfaces, features of the environment, layout of the environment, etc. from the real-world environment or operating environment to be localized. The map tile point cloud data will generally exclude dynamic objects from the real-world environment or operating environment to be localized. In other words, during generation or creation of the map tile point cloud data, dynamic objects may be filtered from this dataset; [0042] The map tile point cloud data may be represented as a voxel associated with a mean and a covariance. For example, map tiles 114 may be ‘tiles’ around the vehicle(ROI) which may be loaded at runtime (e.g., as the vehicle is passing through the operating environment).).
Regarding claim 9, Demir does not explicitly teach but Kim teaches wherein the generating of the ROI map includes labeling lanes on the point cloud acquired by scanning the predetermined region to define a road structure for the predetermined region, thereby generating a road network map for the predetermined region; and setting an area having a predetermined size including lanes labeled on the generated road network map as the ROI to generate the ROI map for the predetermined region (Fig. 5 and corresponding paragraphs).
The same motivation to combine as the parent claim applies here.
Regarding claim 10, Demir teaches wherein the generating of the lane precision map includes extracting only points corresponding to a ground surface from the point cloud acquired by scanning the predetermined region to generate a ground surface point cloud for the predetermined region; labeling lanes in the point cloud acquired by scanning the predetermined region to define a road structure for the predetermined region, thereby generating a road network map for the predetermined region; and extracting only points located on the lane labeled on the generated road network map from among the plurality of points included in the generated ground surface point cloud to generate the lane precision map for the predetermined region (claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 11, Demir teaches wherein the generating of the lane precision map includes: extracting a plurality of points corresponding to the lane from the point cloud acquired by scanning the predetermined region; approximating the plurality of extracted points into a line shape to acquire direction information of each of the plurality of extracted points; and generating a lane precision map including position information of each of the plurality of extracted points and the direction information of each of the plurality of extracted points (claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 12, Demir teaches wherein the generating of the real-time lane information includes acquiring a point cloud collected in real time through a sensor included in the vehicle; setting a range of interest (ROI) on the acquired point cloud; extracting a plurality of points included in a predefined range from among the points included in the set ROI, the predefined range including a longitudinal range, a lateral range, a height range, and an intensity range; and connecting the plurality of extracted points based on a gradient between the plurality of extracted points to generate the real-time lane information(claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 13, Demir teaches wherein the setting of the ROI includes setting the ROI in a three-dimensional space shape having a predetermined size in the acquired point cloud with reference to any one of a position of a center point of the vehicle and a position of the sensor included in the vehicle(claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 14, Demir teaches wherein the setting of the ROI includes setting the ROI in the three-dimensional space shape having a predetermined size at a position corresponding to a direction in which the vehicle travels in the acquired point cloud with reference to the direction in which the vehicle travels(claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 15, Demir teaches wherein the setting of the ROI includes acquiring video data generated by filming a region in the direction in which the vehicle travels through a camera sensor included in the vehicle; analyzing the acquired video data to identify a lane ([0015]); and determining a position relative to the identified lane with reference to the vehicle, determining a position at which the ROI is set based on the determined relative position, and setting the ROI in the three-dimensional space shape having the predetermined size at the determined position at which the ROI is set in the acquired point cloud (claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 16, Demir teaches wherein the generating of the real-time lane information includes acquiring a point cloud collected in real time through a sensor included in the vehicle; setting a range of interest (ROI) on the acquired point cloud; defining a ground surface within the set ROI by approximating points included in the set ROI into a plane shape; extracting a plurality of points included in a predefined range from among points located on the defined ground surface, the predefined range including a longitudinal range, a lateral range, a height range, and an intensity range; and connecting the plurality of extracted points based on a gradient between the plurality of extracted points to generate the real-time lane information(claims 1 and 6, Fig.1 and corresponding paragraphs).
Regarding claim 17, Demir teaches wherein the generating of the real-time lane information includes acquiring the point cloud collected in real time through a sensor included in the vehicle; setting a range of interest (ROI) in the acquired point cloud; extracting a plurality of points included in a predefined range from among the points included in the set ROI, the predefined range including a longitudinal range, a lateral range, a height range, and an intensity range; acquiring direction information of each of the plurality of extracted points by approximating the plurality of extracted points into a line shape; and generating real-time lane information including t position information for each of the plurality of extracted points and the direction information of each of the plurality of extracted points (claims 1 and 6, Fig.1 and corresponding paragraphs).
Prior Art
Please refer to form 892 for cited references.
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275,277 (CCPA 1968)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JINGLI WANG whose telephone number is (571)272-8040. The examiner can normally be reached on Mon-Fri 9 am-5 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Anne Antonucci can be reached on (313)446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.W./ Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/ Supervisory Patent Examiner, Art Unit 3666
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