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 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 limitation(s) 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 limitation(s) is/are: “machine learning unit” in claims 1-2 and 11; “volume extraction unit” in claims 1, 3, 8 and 11; “sample data input unit” in claim 2; “upscaling unit” in claim 3; “volume calculation unit” in claim 3; “first detection unit” in claim 5-7 and 15-17; “second detection unit” in claim 5-7 and 15-17; “accommodation unit” in claims 10 and 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/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 limitation(s) recite(s) 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.
Claim Rejections - 35 USC § 112
Claim 2-3 and 12-13 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 2, line 4 recites the limitation “a model type generator” which is indefinite – see MPEP 2173.05(b), section III. E, which states “The addition of the word ‘type’ to an otherwise definite expression ... extends the scope of the expression so as to render it indefinite.” For the purpose of this examination, the limitation will be interpreted as “a model generator”. Claim 3 is rejected due to its dependence on claim 2.
Claim 12, line 4 recites the limitation “model type generating” which is indefinite – see MPEP 2173.05(b), section III. E, which states “The addition of the word ‘type’ to an otherwise definite expression ... extends the scope of the expression so as to render it indefinite.” For the purpose of this examination, the limitation will be interpreted as “model generating”. Claim 13 is rejected due to its dependence on claim 12.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Han et al. (US 2021/0336141 A1) in view of Lutz et al. (US 2018/0342044 A1).
Regarding claim 1, Han discloses an apparatus (Fig. 1) for manufacturing a display device, the apparatus comprising:
a plurality of droplet ejection units (140: 141-143), each comprising a nozzle for ejecting droplets (Fig. 1-2; [0079] [0081]);
a detection unit (150: 151, 152) arranged on a fall path of a plurality of droplets falling from the plurality of droplet ejection units and configured to detect data comprising a shape of the plurality of droplets (Fig. 1-2; [0082]; [0089]); and
a control unit (180) configured to determine a volume of each of the plurality of droplets based on the detected data ([0093]), the control unit comprising:
a volume extraction unit configured to determine a volume of the plurality of droplets ([0093]; [0104]; [0120]).
Han does not explicitly disclose
detecting low-resolution data;
a machine learning unit configured to perform machine learning to determine a correlation between sample low-resolution data and sample high-resolution data, the sample low-resolution data comprising a shape of a plurality of sample low-resolution droplets, the sample high-resolution data comprising a shape of high-resolution droplets corresponding to each of the plurality of sample low-resolution droplets input to the machine learning unit; and
a volume extraction unit configured to determine a volume of the plurality of droplets by upscaling the detected low-resolution data into upscaled high-resolution data based a result of learning by the machine learning unit, and
wherein the sample high-resolution data has a magnification greater than a magnification of the sample low-resolution data.
However, Lutz, in the field of endeavor of systems and methods for enhancing the resolution of imaging data in imaging systems, discloses
detecting low-resolution data (Abstract; [0018]-[0019]);
a machine learning unit (Fig. 9; [0129]; [0137]) configured to perform machine learning to determine a correlation between sample low-resolution data and sample high-resolution data, the sample low-resolution data comprising a shape of a plurality of sample low-resolution droplets, the sample high-resolution data comprising a shape of high-resolution droplets corresponding to each of the plurality of sample low-resolution droplets input to the machine learning unit (Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031] – where Lutz discloses a training process where the correlation of low-resolution imaging data with high-resolution data is used to enhance the resolution of images in the imaging system; though Lutz doesn’t explicitly disclose that the data comprises the shape of droplets, it would be obvious to one of ordinary skill in the art that the method can be used in any imaging system where higher-resolution images would allow for improved accuracy in measurements of object features being imaged, including the shape and/or volume of droplets imaged in Han’s manufacturing apparatus); and
a volume extraction unit configured to determine a volume of the plurality of droplets by upscaling the detected low-resolution data into upscaled high-resolution data based a result of learning by the machine learning unit (Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031]), and
wherein the sample high-resolution data has a magnification greater than a magnification of the sample low-resolution data (Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031] – where scaling of the low-resolution image to use in the training process for output as a high resolution image is interpreted as implying that the resulting high-resolution image has a magnification greater than the input low-resolution image).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Han with a method which provides upscaled high-resolution images to a measurement system, allowing for improved accuracy in measurements of droplets and in the overall quality of the manufacturing process.
Regarding claim 2, Han in view of Lutz discloses the apparatus of claim 1, as outlined above, and further discloses wherein the machine learning unit comprises:
a sample data input unit to which the sample low-resolution data and the sample high-resolution data are input (Lutz: Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031]); and
a model generator configured to generate an upscaling model by analyzing the correlation between the sample low-resolution data and the sample high-resolution data (Lutz: Abstract; [0033]).
Regarding claim 3, Han in view of Lutz discloses the apparatus of claim 2, as outlined above, and further discloses wherein the volume extraction unit comprises:
an upscaling unit configured to upscale the detected low-resolution data with the upscaled high-resolution data by substituting the detected low-resolution data into the upscaling model (Lutz: Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031]); and
a volume calculation unit configured to calculate a volume of each of the plurality of droplets by extracting a three-dimensional image of the plurality of droplets (Han: [0104]; [0120]) based on the upscaled high-resolution data (Lutz: Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031]).
Regarding claim 4, Han in view of Lutz discloses the apparatus of claim 1, as outlined above, and further discloses wherein a number of the droplet ejection units is greater than a number of the detection units (Han: Fig. 1-2; [0071]-[0072]; [0084]-[0085]).
Regarding claim 5, Han in view of Lutz discloses the apparatus of claim 1, as outlined above, and further discloses wherein the detection unit (150) comprises:
a first detection unit (151) (Han: Fig. 1-2; [0082]; [0089]); and
a second detection unit (152) facing the first detection unit with the plurality of droplets therebetween (Han: Fig. 1-2; [0082]; [0089]).
Regarding claim 6, Han in view of Lutz discloses the apparatus of claim 5, as outlined above, and further discloses wherein the first detection unit and the second detection unit are each configured to detect a shape of a part of an outer surface of the droplets projected on an arbitrary plane (Han: Fig. 3; [0013]; [0099]-[0100]).
Regarding claim 7, Han in view of Lutz discloses the apparatus of claim 6, as outlined above, and further discloses wherein the control unit (180) is configured to calculate the outer surface of the droplets by connecting portions other than the shape of the part of the outer surface of the droplets detected by the first detection unit (151) and the second detection unit (152) (Han: Fig. 1-2; [0103]).
Regarding claim 8, Han in view of Lutz discloses the apparatus of claim 7, as outlined above, and further discloses wherein the volume extraction unit is configured to calculate a three-dimensional shape of the droplets by rotating the calculated outer surface of the droplets based on the fall path of the droplets and to calculate the volume of the droplets by using the calculated three-dimensional shape of the droplets (Han: [0104]; [0120]).
Regarding claim 9, Han in view of Lutz discloses the apparatus of claim 1, as outlined above, and further discloses wherein the detection unit (150) comprises a confocal microscope or a confocal sensor (Han: Fig. 1-2; [0083]).
Regarding claim 10, Han in view of Lutz discloses the apparatus of claim 1, as outlined above, and further discloses an accommodation unit (160) configured to store the plurality of droplets emitted from the plurality of droplet ejection units (140) (Han: Fig. 1-2; [0080]; [0086]).
Regarding claim 11, Han discloses a method of manufacturing a display device, the method comprising:
ejecting droplets by using each of a plurality of droplet ejection units along a fall path, each of which comprises a nozzle (Fig. 1-2; [0079] [0081]);
detecting, by a detection unit, data comprising a shape of a plurality of droplets falling from the plurality of droplet ejection units (Fig. 1-2; [0082]; [0089]); and
extracting, by a volume extraction unit, a volume of the plurality of droplets ([0093]),
Han does not explicitly disclose inputting, into a machine learning unit, sample low-resolution data comprising a shape of a plurality of sample low-resolution droplets and sample high-resolution data comprising a shape of high-resolution droplets corresponding to each of the plurality of sample low-resolution droplets to perform machine learning to determine a correlation between the sample low-resolution data and the sample high-resolution data;
detecting, by a detection unit, low-resolution data;
extracting, by a volume extraction unit, a volume of the plurality of droplets by upscaling the detected low-resolution data into upscaled high-resolution data based a result of learning by the machine learning unit,
wherein the sample high-resolution data has a magnification greater than a magnification of the sample low-resolution data.
However Lutz, in the same field of endeavor of systems and methods for enhancing the resolution of imaging data, discloses inputting, into a machine learning unit, sample low-resolution data comprising a shape of a plurality of sample low-resolution droplets and sample high-resolution data comprising a shape of high-resolution droplets corresponding to each of the plurality of sample low-resolution droplets to perform machine learning to determine a correlation between the sample low-resolution data and the sample high-resolution data (Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031] – where Lutz discloses a training process where the correlation of low-resolution imaging data with high-resolution data is used to enhance the resolution of images in the imaging system; though Lutz doesn’t explicitly disclose that the data comprises the shape of droplets, it would be obvious to one of ordinary skill in the art that the method can be used in any imaging system where higher-resolution images would allow for improved accuracy in measurements of object features being imaged, including the shape and/or volume of droplets imaged in Han’s manufacturing apparatus);
detecting, by a detection unit, low-resolution data (Abstract; [0018]-[0019]);
extracting, by a volume extraction unit, a volume of the plurality of droplets by upscaling the detected low-resolution data into upscaled high-resolution data based a result of learning by the machine learning unit (Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031]),
wherein the sample high-resolution data has a magnification greater than a magnification of the sample low-resolution data (Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031] – where scaling of the low-resolution image to use in the training process for output as a high resolution image is interpreted as implying that the resulting high-resolution image has a magnification greater than the input low-resolution image).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Han with a method which provides upscaled high-resolution images to a measurement system, allowing for improved accuracy in measurements of droplets and in the overall quality of the manufacturing process.
Regarding claim 12, Han in view of Lutz discloses the method of claim 11, as outlined above, and further discloses wherein the machine learning comprises:
inputting sample data comprising the sample low-resolution data and the sample high-resolution data (Lutz: Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031]); and
model generating an upscaling model by analyzing the correlation between the sample low-resolution data and the sample high-resolution data (Lutz: Abstract; [0033]).
Regarding claim 13, Han in view of Lutz discloses the method of claim 12, as outlined above, and further discloses wherein the volume extracting comprises:
upscaling, in which the detected low-resolution data is upscaled into the upscaled high-resolution data by substituting the detected low-resolution data into the upscaling model (Lutz: Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031]); and
volume calculating in which a volume of each of the plurality of droplets is calculated by extracting a three-dimensional image of the plurality of droplets (Han: [0104]; [0120]) based on the upscaled high-resolution data (Lutz: Abstract; [0018]-[0019]; [0025]; [0028]; [0030]-[0031]).
Regarding claim 14, Han in view of Lutz discloses the method of claim 11, as outlined above, and further discloses wherein a number of the droplet ejection units is greater than a number of the detection units (Han: Fig. 1-2; [0071]-[0072]; [0084]-[0085]).
Regarding claim 15, Han in view of Lutz discloses the method of claim 11, as outlined above, and further discloses wherein the detection unit comprises:
a first detection unit (151) (Han: Fig. 1-2; [0082]; [0089]); and
a second detection unit facing the first detection unit with the plurality of droplets therebetween (Han: Fig. 1-2; [0082]; [0089]).
Regarding claim 16, Han in view of Lutz discloses the method of claim 15, as outlined above, and further discloses wherein the first detection unit and the second detection unit are each configured to detect a shape of a part of an outer surface of the droplets projected on an arbitrary plane (Han: Fig. 3; [0013]; [0099]-[0100]).
Regarding claim 17, Han in view of Lutz discloses the method of claim 16, as outlined above, and further discloses wherein the volume extracting comprises calculating the outer surface of the droplets by connecting portions other than the shape of the part of the outer surface of the droplets detected by the first detection unit (151) and the second detection unit (152) (Han: Fig. 1-2; [0103]).
Regarding claim 18, Han in view of Lutz discloses the method of claim 17, as outlined above, and further discloses wherein the volume extracting comprises calculating a three-dimensional shape of the droplets by rotating the calculated outer surface of the droplets based on the fall path of the droplets and calculating the volume of the droplets by using the calculated three-dimensional shape of the droplets (Han: [0104]; [0120]).
Regarding claim 19, Han in view of Lutz discloses the method of claim 11, as outlined above, and further discloses wherein the detection unit (150) comprises a confocal microscope or a confocal sensor (Han: Fig. 1-2; [0083]).
Regarding claim 20, Han in view of Lutz discloses the method of claim 11, as outlined above, and further discloses storing, in an accommodation unit (160), the plurality of droplets falling from the plurality of droplet ejection units (140) (Han: Fig. 1-2; [0080]; [0086]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHER YAZBACK whose telephone number is (703)756-1456. The examiner can normally be reached Monday - Friday 8:30 am - 5:30 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, Michelle Iacoletti can be reached at (571)270-5789. 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.
/MAHER YAZBACK/Examiner, Art Unit 2877 /MICHELLE M IACOLETTI/Supervisory Patent Examiner, Art Unit 2877
Prosecution Insights