DETAILED ACTION
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 § 101
2. 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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In view of the new 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register Vol. 84, No. 4, January 7, 2019), the Examiner has considered the claims and has determined that under step 1, claims 1-13 are to a machine, claim 14 is to a machine, and claim 15 is to an article of manufacture. Next under the new step 2A prong 1 analysis, the claims are considered to determine if they recite an abstract idea (judicial exception) under the following groupings: (a) mathematical concepts, (b) certain methods of organizing human activity, or (c) mental processes. The independent claims contain at least the following bolded limitations (see representative independent claims) that fall into the grouping of mathematical concepts and/or mental processes:
1. An information processing device that displays information related to manufacturing of a molding product by an injection molding machine, the information processing device comprising:
an acquisition unit that acquires information related to a shot in injection molding and estimated data of information indicating a molding result in the shot estimated based on information obtained in the shot; and
a display unit that displays, for each shot, the information related to the shot and the estimated data in the shot in association with each other, based on the information acquired by the acquisition unit.
14. An injection molding machine comprising:
an injection unit;
a mold clamping unit;
a data processing device that estimates estimated data of information indicating a molding result in a shot for each shot in injection molding based on information related to an operation of the injection molding by the injection unit and the mold clamping unit; and
a display unit that displays, for each shot, information related to the shot and the estimated data in the shot in association with each other.
15. A non-transitory computer readable medium storing a program, the program when executed by a processor, cause the processor to:
acquire information related to an operation of injection molding from an injection unit and a mold clamping unit;
estimate estimated data of information indicating a molding result in a shot based on the information acquired for each shot in the injection molding; and
display, for each shot, the information related to the acquired shot and the estimated data in the shot in association with each other.
The estimation of the estimated data could be as simple as a mental process to compare and evaluate data to form an estimation of the estimated data, or a mathematical concept if the estimated data requires a more complex mathematical analysis. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula."(see MPEP 2106.04(a)(2) I.). Thus the limitations of "estimated data of information indicating a molding result in the shot estimated based on information obtained in the shot," "estimates estimated data of information indicating a molding result for each shot in injection molding based on information related to an operation of the injection molding by the injection unit and the mold clamping unit," and "estimate estimated data of information indicating a molding result in a shot based on the information acquired for each shot in the injection molding," are all considered as words serving the same purpose as a formula. The claim limitations amount to a description in words for calculating estimated data of information based on information acquired by the acquisition unit, or information related to an operation of the injection molding, or information acquired for each shot in the injection molding. The end result is an abstract informational-based set of estimated data.
Next in step 2A prong 2, the independent claims are analyzed to determine whether there are additional elements or combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception such that it is more than a drafting effort designed to monopolize the exception, in order to integrate the judicial exception into a practical application. These limitations have been identified and underlined above, and are not indicative of integration into a practical application because: (1) the recitations of an information processing device that displays information, an acquisition unit, a display unit that displays, a data processing device that estimates, a non-transitory computer readable medium storing a program, and the program when executed by a processor, are all limitations that amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); (2) the recitations of acquiring information related to a shot in injection molding or acquiring information related to an operation of injection molding from an injection unit and a mold clamping unit, amount to adding insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)); (3) the recitations of "related to manufacturing of a molding product by an injection molding machine," and "an injection molding machine comprising: an injection unit; mold clamping unit," amount to generally linking the use of the judicial exception to a particular environment or field of use (see MPEP 2106.05(h); and (4) the recitations of displaying for each shot, the information related to the shot and the estimated data in the shot in association with each other, amount to insignificant post-solution displaying of an output result (see MPEP 2106.05(g)).
Next in step 2B, the independent claims are considered to determine if they recite additional elements that amount to an inventive concept (“significantly more”) than the recited judicial exception. The recitations of an information processing device that displays information, an acquisition unit, a display unit that displays, a data processing device that estimates, a non-transitory computer readable medium storing a program, and the program when executed by a processor, are all limitations that do not add something significantly more as they amount to mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The use of generic computer equipment is considered insignificant additional elements. As recited in the MPEP, 2106.07(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94). Selecting a particular generic function for computer hardware to perform (e.g., buffering content, storing and retrieving data from memory) from within a range of well- known, routine, conventional functions performed by the hardware is not significantly more, (see Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264, 120 USPQ2d 1201, 1208 (Fed. Cir. 2016)(MPEP 2106.05(a)II last paragraph). The recitations of acquiring information related to a shot in injection molding or acquiring information related to an operation of injection molding from an injection unit and a mold clamping unit, do not amount so significantly more because they describe insignificant extra-solution data gathering activity to the judicial exception (see MPEP 2106.05(g)), and do not describe any gathering of data in an unconventional way. The recitations of "related to manufacturing of a molding product by an injection molding machine," and "an injection molding machine comprising: an injection unit; mold clamping unit," do not add significantly more because they amount to generally linking the use of the judicial exception to a particular environment or field of use (see MPEP 2106.05(h), and do not describe any applied improvement to the physical operation of the injection molding machine or injection unit or molding clamping unit. The recitations of displaying for each shot, the information related to the shot and the estimated data in the shot in association with each other, do not add significantly more because they amount to insignificant post-solution displaying of an output result (see MPEP 2106.05(g)). The MPEP states that when “Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output)”, the limitations can be mere data gathering or data output (see MPEP 2106.05(g) Insignificant Extra- Solution Activity, in particular item (3)).
Dependent claims 2-10 contain additional limitations that do not provide an integration into a practical application or significantly more as they describe further insignificant data gathering and mere informational-based displaying of output results (see MPEP 2106.05(g)). Dependent claim 11-12 describe the estimation of estimated data using an estimation model trained by machine learning, but patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101 (see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025)), as such limitations amount to merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). In other words, just like "using a processor" is not enough to make a claim patent eligible, "using machine learning" or general "training…using training data" is not enough to make a claim patent eligible, no matter how many words go into describing the (generic) machine learning techniques in the claim. Dependent claim 13 describes storing the estimated data in a storage device, which amounts to generic computer limitations that merely use a computer as a tool to perform an abstract idea (se MPEP 2106.05(f)).
3. An invention is not rendered ineligible for patent simply because it involves an abstract concept. Applications of such concepts "to a new and useful end" remain eligible for patent protection (see Alice Corp., 134 S. Ct. at 2354 (quoting Benson, 409 U.S. at 67)). However, "a claim for a new abstract idea is still an abstract idea" (see Synopsys v. Mentor Graphics Corp. _F.3d_, 120 U.S.P.Q. 2d1473 (Fed. Cir. 2016)). There needs to be additional elements or combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception or render the claim as a whole to be significantly more than the exception itself in order to demonstrate “integration into a practical application” or an “inventive concept.” For instance, particular physical arrangements for actively obtaining sensor data, or further physical applications using the calculated information related to the shot and the estimated data in the shot to drive a transformation, change in physical operation, or repair/maintenance of a technology or technical process could provide integration into a practical application to demonstrate an improvement to the technology or technical field.
Claim Rejections - 35 USC § 102
4. 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.
5. 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.
Claims 1-10 and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Atsuta (US Pat. Pub. 2023/0173726, hereinafter "Atsuta").
In regards to claim 1, Atsuta teaches an information processing device that displays information related to manufacturing of a molding product by an injection molding machine, the information processing device (Atsuta abstract teaches an injection molding machine management system including a display unit to display an operation status related to manufacturing of a molding product by an injection molding machine) comprising:
an acquisition unit that acquires information related to a shot in injection molding (Atsuta paragraphs [0016] and [0027] teach a first control unit (acquisition unit) that acquires measured physical quantity information related to a shot in the injection molding as measured by various sensors) and estimated data of information indicating a molding result in the shot estimated based on information obtained in the shot (Atsuta paragraph [0027] teaches the first control unit also obtaining estimated information indicating whether an abnormality in the injection shot is present based on the inspection data and detection values of the various sensors); and
a display unit that displays, for each shot, the information related to the shot and the estimated data in the shot in association with each other, based on the information acquired by the acquisition unit (Atsuta Figs. 2, Figs. 4-5, and paragraphs [0038]-[0040] teach a display output unit that displays for each injection molding shot unit side by side, the information related to the shot (such as an identification of the shot unit) and the estimated operating state data (such as an abnormality indication) in association with each other, based on the acquired information).
In regards to claim 2, Atsuta teaches the information processing device (Atsuta abstract), wherein
the information related to the shot includes identification information of the shot that identifies the shot (Atsuta paragraph [0041] teaches where information related to the shot includes identification ("U1," "U2,", etc.) that identifies each shot), and
the display unit displays the identification information and the estimated data in the shot identified by the identification information in association with each other (Atsuta Figs. 2 and 4-5 and paragraph [0041] teach where the display screen of the display unit displays the identification information ("U1", "U2", etc.) of each unit shot and the estimated data of an abnormality in the shot as identified by the identification, in association with each other along the same row).
In regards to claim 3, Atsuta teaches the information processing device (Atsuta abstract), wherein
the acquisition unit further acquires an actual value of information indicating a molding result obtained by an operation of the injection molding in the shot (Atsuta Figs. 2 and 4-5 and paragraph [0039] teach acquiring an actual value of a defective rate indicating a molding result obtained by an operation of the injection molding), and
the display unit displays the actual value together with the estimated data in association with the identification information of the shot (Atsuta Figs. 2 and 4-5 teach displaying the defective rate with the estimated data of an abnormality in association with the identification information of the shot).
In regards to claim 4 Atsuta teaches the information processing device (Atsuta abstract), wherein
the acquisition unit further acquires setting information of an operation of the injection molding in the shot, which is used in the operation (Atsuta Figs. 2-5 and paragraph [0042] teach acquiring a setting information of an operation of the injection molding in the shot which is used in operation, such as a "stopped," "operating," "paused," or "power-off" setting), and
the display unit displays the setting information together with the estimated data in association with the identification information of the shot (Atsuta Figs. 2-5 teach displaying the setting information as an icon together with the estimated data in association with the identification information of the shot).
In regards to claim 5, Atsuta teaches the information processing device (Atsuta abstract), wherein
the acquisition unit acquires statistical information obtained by performing statistical processing on the estimated data (Atsuta Figs. 2 and 4-5 and paragraph [0039] teach acquiring a statistical value of a defective rate in parts per million out based on processing the estimated data), and
the display unit displays the statistical information of the estimated data (Atsuta Figs. 2 and 4-5 teach displaying the statistical defective rate in parts per million).
In regards to claim 6, Atsuta teaches the information processing device (Atsuta abstract), wherein
the display unit displays the estimated data satisfying a predetermined condition regarding a state of the molding product in a display mode different from other display information when the estimated data to be displayed satisfies the condition (Atsuta Fig. 4 teaches having a display indication of an "abnormality" when the estimated data satisfies a predetermined condition indicative of an abnormality, where such a display mode is different from other display information when there is no abnormality and no "abnormality" indicator is shown).
In regards to claim 7, Atsuta teaches the information processing device (Atsuta abstract), wherein
the display unit displays the information related to the shot and the estimated data associated with the shot side by side for a plurality of shots, (Atsuta Figs. 2, 4-5 and paragraph [0041] teach where the display unit displays label information related to the shot and the estimated abnormality information of the shot side by side for a plurality of shots, such as between U1 and U2 which are side by side) and
in the display on the display unit, items of data to be displayed in association with the identification information of the shot and a display order of each item are able to be set by a user operation (Atsuta paragraph [0041] teaches where the label in association with the identification information and viewing order of the shot are set by a user as input via an input unit provided in a management device).
In regards to claim 8¸ Atsuta teaches the information processing device (Atsuta abstract), wherein
the display unit displays a setting value of a molding condition for the shot, which is the information related to the shot (Atsuta Figs. 2-5 and paragraph [0042] teach displaying a setting value of a molding condition for the shot, such as a "stopped," "operating," "paused," or "power-off" setting), and
the estimated data of the same item as the setting value in comparison with each other (Atsuta Figs. 2-5 teach displaying the setting information as an icon together with the estimated data in comparison with each other).
In regards to claim 9, Atsuta teaches the information processing device (Atsuta abstract), wherein
the estimated data displayed by the display unit includes at least any one of a value representing a state of a product, a value representing a state of a mold, a value representing a state of a machine, a setting value of a new molding condition, and an amount of change in the molding condition (Atsuta Fig. 2 or 4-5 teach where the estimated data displayed by the display unit includes at least a display value indication representing a state of a product (such as "abnormality" for an abnormality state detected), or a value representing a state of a machine (such as "container replacement" or "material replenishment" indicating the need for a new container or additional material).
In regards to claim 10, Atsuta teaches the information processing device (Atsuta abstract), further comprising:
a processing unit that acquires information related to an operation of injection molding from an injection unit and a mold clamping unit (Atsuta paragraphs [0014] and [0016] teach one or more processors in the first control unit for acquiring sensor information related to an operation of injection molding from an injection device and a mold clamping device) and that estimates estimated data of information indicating a molding result in a shot in injection molding based on the acquired information (Atsuta paragraph [0027] teaches estimating data indicating an abnormality in a molding result based on the acquired inspection data and detection values of the various sensors),
wherein the display unit displays, for each shot, the information acquired by the acquisition unit and the estimated data in the shot estimated by the processing unit in association with each other (Atsuta Figs. 2, Figs. 4-5, and paragraphs [0038]-[0040] teach where the display unit displays for each injection molding shot unit side by side, the acquired information related to the shot (such as an operation rate) and the estimated operating state data (such as an abnormality indication) in association with each other).
In regards to claim 13, Atsuta teaches the information processing device (Atsuta abstract), wherein
the processing unit stores the estimated data in a storage device as a data file in association with actual values of information indicating a molding result for each shot and setting values of a molding condition (Atsuta paragraph [0035] teaches storing the estimated detection result of the abnormality in a storage unit as a data file with actual values of signal data obtained from the injection molding unit, and paragraph [0027] teaches where the signal data from the injection molding unit includes data from a third control unit (including a molding result as described in paragraph [0022]) and a data from a first control unit (including a setting value as described in paragraph [0016]).
In regards to claim 14, Atsuta teaches an injection molding machine (Atsuta abstract and paragraph [0014] teach an injection molding machine) comprising:
an injection unit (Atsuta paragraph [0014] teaches an injection device as part of the injection molding machine);
a mold clamping unit (Atsuta paragraph [0014] teaches a mold clamping device as part of the injection molding machine);
a data processing device (Atsuta teaches one or more processors in a first control unit as part of the injection molding machine) that estimates estimated data of information indicating a molding result in a shot for each shot in injection molding (Atsuta paragraph [0027] teaches the first control unit estimating estimated information indicating whether an abnormality (molding result) in the injection shot is present based on the inspection data and detection values of various sensors) based on information related to an operation of the injection molding by the injection unit and the mold clamping unit (Atsuta paragraphs [0016] and [0027] teach where the information used to determine the estimated data is based on information acquired from various sensors related to the operation of the injection molding by the injection unit and mold clamping unit); and
a display unit that displays, for each shot, information related to the shot and the estimated data in the shot in association with each other (Atsuta Figs. 2, Figs. 4-5, and paragraphs [0038]-[0040] teach a display output unit that displays for each injection molding shot unit side by side, the information related to the shot (such as an identification of the shot unit) and the estimated operating state data (such as an abnormality indication) in association with each other).
In regards to claim 15, Atsuta teaches a non-transitory computer readable medium storing a program, the program when executed by a processor (Atsuta paragraph [0031] teaches a storage unit storing programs that are executed by a CPU processor), cause the processor to:
acquire information related to an operation of injection molding from an injection unit and a mold clamping unit (Atsuta paragraphs [0016] and [0027] teach acquiring information from various sensors related to an operation of the injection molding by an injection device and mold clamping device);
estimate estimated data of information indicating a molding result in a shot based on the information acquired for each shot in the injection molding (Atsuta paragraph [0027] teaches the first control unit estimating estimated information indicating whether an abnormality (molding result) in the injection shot is present based on the inspection data and detection values of the various sensors); and
display, for each shot, the information related to the acquired shot and the estimated data in the shot in association with each other (Atsuta Figs. 2, Figs. 4-5, and paragraphs [0038]-[0040] teach a display output unit that displays for each injection molding shot unit side by side, the information related to the shot (such as an identification of the shot unit) and the estimated operating state data (such as an abnormality indication) in association with each other).
Claim Rejections - 35 USC § 103
6. 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.
7. Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Atsuta (US Pat. Pub. 2023/0173726) as applied to claim 10 above, and further in view of Horiuchi (Us Pat. Pub. 2020/0254670).
In regards to claim 11, Atsuta teaches the information processing device as explained in the rejection of claim 10 above. Atsuta fails to expressly teach wherein the processing unit estimates the estimated data using an estimation model trained by machine learning, and input data in the estimation model is a representative value of actual values of information indicating, for each shot, a molding result in the shot.
Horiuchi paragraph [0011] teaches determining a state of an injection molding machine by introducing a high-accuracy learning model by performing machine learning with time-series data involving changes of the operating or operational state of the injection molding machine. Horiuchi paragraph [0014] teaches where a learning device can comprise a learning model storage unit to store the learning model generated by the machine learning, and an estimation unit configured to perform estimation of the state of the industrial machine using the learning model. Horiuchi paragraph [0015] teaches where the estimation unit may estimate an abnormality degree related to the operating state of the industrial machine, and the state determination device may display a warning message on a display device if a predetermined threshold is exceeded by the abnormality degree estimated by the estimation unit. Horiuchi paragraph [0056] teaches where a worker can set the extraction conditions so that suitable data as the data for learning can be extracted according to the purpose of machine learning to improve the accuracy of determination of the operating state of the injection molding.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further combine the teachings of Horiuchi because the use of machine learning allows for building an effective estimation model based on selected time-series of actual data involving changes of the operating or operational state of the injection molding machine. Therefore, the use of machine learning using actual values of information provides for improved accuracy in estimating a molding result in the shot, including any degrees of abnormality.
In regards to claim 12, Atsuta teaches the information processing device as explained in the rejection of claim 10 above. Atsuta fails to expressly teach wherein the processing unit estimates the estimated data using an estimation model trained by machine learning, and input data in the estimation model is time-series data of actual values of information indicating, for each shot, a molding result in the shot.
Horiuchi paragraph [0011] teaches determining a state of an injection molding machine by introducing a high-accuracy learning model by performing machine learning with time-series data involving changes of the operating or operational state of the injection molding machine. Horiuchi paragraph [0014] teaches where a learning device can comprise a learning model storage unit to store the learning model generated by the machine learning, and an estimation unit configured to perform estimation of the state of the industrial machine using the learning model. Horiuchi paragraph [0015] teaches where the estimation unit may estimate an abnormality degree related to the operating state of the industrial machine, and the state determination device may display a warning message on a display device if a predetermined threshold is exceeded by the abnormality degree estimated by the estimation unit. Horiuchi paragraph [0056] teaches where a worker can set the extraction conditions so that suitable data as the data for learning can be extracted according to the purpose of machine learning to improve the accuracy of determination of the operating state of the injection molding.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to further combine the teachings of Horiuchi because the use of machine learning allows for building an effective estimation model based on selected time-series of actual data involving changes of the operating or operational state of the injection molding machine. Therefore, the use of machine learning using actual time-series values of information provides for improved accuracy in estimating a molding result in the shot, including any degrees of abnormality.
Pertinent Art
8. Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith. The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the of the passage as taught by the prior art or disclosed by the Examiner. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
B. Catoen et al. (US Pat. Pub. 2011/0106284) discloses a System for Use in Performance of Injection Molding Operations. C. Maruyama et al. (US Pat. Pub. 2019/0121596) discloses Display Control Device and Display Control Method. D. Mitsuma (US Pat. Pub. 2023/0202087) discloses Injection Molding Machine Management System.
E. Hosotsubo (US Pat. Pub. 2022/0266491) discloses Injection Molding Machine and Inspection Method of Injection Molding Machine.
F. Shimada et al. (US Pat. Pub. 2020/0307053) discloses Injection Molding System, Molding Condition Correction System, and Injection Molding Method.
G. Nishizawa et al. (US Pat. Pub. 2006/0247821) discloses Control Device of Injection Molding Machine.
H. Konishi (US Pat. Pub. 2007/0009627) discloses Molding Machine and Molding Management System.
I. Murata (US Pat. Pub. 2013/0142899) discloses Control Device of Injection Molding Machine.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL D LEE whose telephone number is (571)270-1598. The examiner can normally be reached on M to F, 9:30 am to 6 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAUL D LEE/Primary Examiner, Art Unit 2857 2/17/2026