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 .
Formal Matters
Applicant's response, filed 16 January 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1, 2, 4-8, 10-16, 21, and 22 are currently pending and have been examined.
Claims 1, 4, 10, 13, 21, and 22 have been amended.
Claims 3, 9, 17-20, 23, and 24 have been canceled.
Claims 1, 2, 4-8, 10-16, 21, and 22 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2022-028697, filed on 19 February 2025.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 25 February 2022 claiming benefit to JP2022-028697.
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 1, 2, 4-8, 10-16, 21, and 22 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.
Step 1 – Statutory Categories of Invention:
Claims1, 2, 4-8, 10-16, 21, and 22 are drawn to an apparatus or method, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites an information processing apparatus, independent claim 13 recites an information processing apparatus, independent claim 21 recites a method, and independent claim 22 recites a non-transitory computer-readable medium each in part performing the steps of receiving measured body information in which a value of a first attribute and a value of a second attribute are missing and in which a value of at least one other attribute is included; and in response to a request for information associated with the measured body information: automatically acquiring an estimated value of the first attribute by inputting body information in which the value of the first attribute is missing to at least one trained model, and automatically acquiring an estimated value of the second attribute by inputting body information in which the value of the second attribute is missing to the at least one trained model, the second attribute being different from the first attribute, determining a recommendation based on the acquired estimated values of the first and second attributes, and wherein the provided information associated with the measured body information includes the acquired estimated missing values for the first and second attributes of the body information and the value of the at least one other attribute included in the measured body information, the at least one trained model is a model that is trained using at least two data sets, and the two data sets include missing attributes different from each other and include a value of at least one common attribute, and the recommendation identifies a change in value in the at least one attribute that correlated to a change in value for the first attribute and/or the second attribute.
These steps amount to functions performable in the mind or with pen and paper and are only concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work (MPEP § 2106.04(a)(2)(III)(B) citing the abstract idea grouping for mental processes with or without physical aid and MPEP § 2106.04(a)(2)(III)(c)(3) citing the abstract idea grouping for mental processes using a computer as a tool to perform the mental process).
Dependent claim 2 recites, in part, wherein the at least two data sets are data sets that are respectively measured in different locations or periods.
Dependent claim 4 recites, in part, wherein the basic information includes height and weight.
Dependent claim 5 recites, in part, wherein the body information includes information regarding a physical condition.
Dependent claim 6 recites, in part, wherein the body information includes information regarding a psychological state.
Dependent claim 7 recites, in part, wherein the estimated value is a value having a predetermined range.
Dependent claim 8 recites, in part, acquires a confidence coefficient of the estimated value.
Dependent claim 10 recites, in part, presents an attribute to be measured to increase a confidence coefficient of the acquired estimates missing values for the first and second attributes of the body information, and reacquires the estimated missing values for the first and second attributes of the body information.
Dependent claim 11 recites, in part, wherein the at least two data sets are data sets that are measured in different situations.
Dependent claim 12 recites, in part, wherein the at least two data sets are data sets including values of different attributes that are acquired in an identical situation.
Dependent claim 14 recites, in part, presents the body information in which the value of the first attribute is missing and the estimated value of the first attribute in a form of table data.
Dependent claim 15 recites, in part, presents the estimated value of the first attribute in a display form different from a display form of other body information.
Dependent claim 16 recites, in part, changes the display form of the estimated value of the first attribute on a basis of a confidence coefficient of the estimated value of the first attribute.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 13 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1 and 13 recite an information processing apparatus, comprising: at least one memory; and at least one processor. Claims 1 and 21 recite a user terminal. Claims 17 and 20 recite a terminal, comprising: at least one memory; and at least one processor. Claim 21 recites a processor. Claim 22 recites a non-transitory computer-readable storage medium having stored thereon instructions. The instant specification offers generic embodiments of the computer hardware for implementing the algorithm in the Detailed Description in ¶ 0116-122. The use of the computer/terminal and corresponding hardware only serves as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claims 1, 13, 21, and 22 recite, in part, providing, for presentation [at the user terminal], the requested information associated with the received body information and the determined recommendation. Claim 10 recites, in part, presents to the user terminal an attribute. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 1 and 13 recite an information processing apparatus, comprising: at least one memory; and at least one processor. Claims 1 and 21 recite a user terminal. Claims 17 and 20 recite a terminal, comprising: at least one memory; and at least one processor. Claim 21 recites a processor. Claim 22 recites a non-transitory computer-readable storage medium having stored thereon instructions
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claims 1, 13, 21, and 22 recite, in part, providing, for presentation [at the user terminal], the requested information associated with the received body information and the determined recommendation. Claim 10 recites, in part, presents to the user terminal an attribute. The courts have decided that presenting generated data as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example iv. presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1, 2, 4-8, 10-16, 21, and 22 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
Claims 1, 2, 4-8, 10-16, 21, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al. (US Patent App No 2019/0130226)[hereinafter Guo] in view of Kleinhanzl et al. (US Patent Application No. 2023/0186115)[hereinafter Kleinhanzl].
As per claim 1, Guo teaches on the following limitations of the claim:
an information processing apparatus, comprising is taught in the Detailed Description in ¶ 0023, ¶ 0034, and in the Figures at fig. 10 (teaching on a medical data imputation model for predicting missing medical data from a patient record)
at least one memory; and at least one processor, wherein the at least one processor executes is taught in the Detailed Description in ¶ 0027-30 (teaching on the imputation model running on a computer with a processor, memory, and corresponding program code)
measured body information in which a value of a first attribute and a value of a second attribute are missing and in which values of other attributes are included is taught in the Detailed Description in ¶ 0034, ¶ 0049, ¶ 0051-52, in the Claims at claim 1, and in the Figures at fig. 10 (teaching on a data record with missing attribute values and provided data fields )
in response to an input from the user terminal to request information associated with the measured body information is taught in the Detailed Description in ¶ 0030 and ¶ 0050 (teaching on obtaining datasets from an EHR computing device with a runtime component to trigger the running of data imputation machine learning model)
automatically acquiring an estimated value of the first attribute by inputting the body information in which the value of the first attribute is missing to at least one trained model, and is taught in the Detailed Description in ¶ 0034, ¶ 0049, ¶ 0051-52, in the Claims at claim 1, and in the Figures at fig. 10 (teaching on imputing a first missing attribute value, F7, (treated as synonymous to an estimated value of a first attribute) by applying modified random forest learning model )
automatically acquiring an estimated value of the second attribute by inputting the body information in which the value of the second attribute is missing to the at least one trained model, the second attribute being different from the first attribute is taught in the Detailed Description in ¶ 0034, ¶ 0049, ¶ 0051-52, in the Claims at claim 2, and in the Figures at fig. 10 (teaching on imputing a second missing attribute value (treated as synonymous to an estimated value of a first attribute) by applying modified random forest learning model wherein multiple missing data points within a patient profile can be imputed if indicated as "significant")
providing, for presentation on the user terminal, the requested information associated with the measured body information and is taught in the Detailed Description in ¶ 0049 and in the Figures at fig. 9 (teaching on outputting the new patient data record with the imputed attribute values)
wherein the provided information associated with the measured body information includes the acquired estimated missing values for the first and second attributes of the body information and the values of the other attributes included in the measured body information is taught in the Detailed Description in ¶ 0049 and in the Figures at fig. 9 (teaching on outputting the new patient data record with the imputed attribute values)
the at least one trained model is a model that is trained using at least two data sets, and is taught in the Detailed Description in ¶ 0060 and in the Figures at fig. 11 (teaching on training the modified random forest learning model with historical patient data wherein each patient data record is a "set" and a plurality of patients' data records is employed)
the two data sets include missing attributes different from each other and include a value of at least one common attribute is taught in the Detailed Description in ¶ 0060 and in the Figures at fig. 11 (teaching on the training sets including missing attribute data that is both significant and not significant)
the first attribute and the second attribute are body information other than basic information and at least some of the other attributes are basic information, wherein the basic information is information regarding a body that is self-recognizable, and is taught in the Detailed Description in ¶ 0023, ¶ 0033, and ¶ 0037 (teaching on the patient attribute being designated as significant (treated as synonymous to not basic information) OR not significant (treated as synonymous to basic information))
the recommendation includes an indication showing that a change in value in one of the included attributes correlates to a change in value for the first attribute and/or the second attribute is taught in the Detailed Description in ¶ 0049 and in the Figures at fig. 9 (teaching on outputting the new patient data record with the imputed attribute values)
Guo fails to teach the following; Kleinhanzl, however, does disclose:
receiving, from a user terminal, measured body information is taught in the Detailed Description in ¶ 0060-63 and ¶ 0094 (teaching on receiving measured body information from a user terminal device)
determining a recommendation based on the acquired estimated values of the first and second attributes, and is taught in the Detailed Description in ¶ 0058 and ¶ 0111-112 (teaching on determining a user recommendation based on the user's determined data)
the determined recommendation is taught in the Detailed Description in ¶ 0058 (teaching on displaying on a user device a user recommendation based on the user's determined data)
One of ordinary skill in the art before the effective filing date would combine the missing medical data imputation model of Guo with the medical recommendation determination and display of Kleinhanzl with the motivation of “individualiz[ing] or personaliz[ing] the user interaction policies for each user by selecting and providing, to each user, user interaction policies that maximize user engagement by that specific user and/or the likelihood that the specific user achieves their goals” (Kleinhanzl in the Detailed Description in ¶ 0060).
Independent claims 13, 21 and 22 are rejected under the same rational.
As per claim 2, the combination of Guo and Kleinhanzl discloses the limitations of claim 1. Guo also discloses the following:
the information processing apparatus according to claim 1, wherein the at least two data sets are data sets that are respectively measured in different locations or periods is taught in the Detailed Description in ¶ 0057, ¶ 0060, and in the Figures at fig. 11 (teaching on the training patient data records include old and new data records (treated as synonymous to records measured in different periods))
As per claim 4, the combination of Guo and Kleinhanzl discloses the limitations of claim 1. Guo also discloses the following:
the information processing apparatus according to claim 1, wherein the basic information includes height and weight is taught in the Detailed Description in ¶ 0023 (teaching on the patient attribute being designated as significant (treated as synonymous to not basic information) wherein the significant attributes could include an electrocardiogram field or a blood glucose value field which are not sex, age, height, or weight fields - Examiner notes that under MPEP § 2111.04, the claim scope is not limited by "wherein" claim language that suggests or makes optional but does not require steps to be performed; herein the wherein clause simply expresses the intended result of a process step of utilizing the "other than basic information" positively recited)
As per claim 5, Guo the combination of Guo and Kleinhanzl discloses the limitations of claim 1. Guo also discloses the following:
the information processing apparatus according to claim 1, wherein the body information includes information regarding a physical condition is taught in the Detailed Description in ¶ 0023 (teaching on the patient attribute could include an electrocardiogram filed or a blood glucose value)
As per claim 6, Guo the combination of Guo and Kleinhanzl discloses the limitations of claim 1. Guo fails to teach the following; Kleinhanzl, however, does disclose:
the information processing apparatus according to claim 1, wherein the body information includes information regarding a psychological state is taught in the Detailed Description in ¶ 0060-63 and ¶ 0094 (teaching on the patient data including missing psychographic (treated as synonymous to psychological state data))
One of ordinary skill in the art before the effective filing date would combine the missing medical data imputation model of Guo with physiological data specifically of Kleinhanzl with the motivation of “Having access to a user's psychographic data plays an important role in providing individualized or personalized user interaction policies to users that maximize user engagement” (Kleinhanzl in the Detailed Description in ¶ 0060).
As per claim 7, the combination of Guo and Kleinhanzl discloses the limitations of claim 1. Guo also discloses the following:
the information processing apparatus according to claim 1, wherein the estimated value is a value having a predetermined range is taught in the Detailed Description in ¶ 0037 (teaching on determining an impact significant criterion value (treated as synonymous to a confidence coefficient) for each attribute to determine if a missing attribute value is predictive or not wherein the attribute's significant criterion value must meet a predetermined p-val range to receive an imputation estimation value)
As per claim 8, the combination of Guo and Kleinhanzl discloses the limitations of claim 1. Guo also discloses the following:
the information processing apparatus according to claim 1, wherein the at least one processor acquires a confidence coefficient of the estimated value is taught in the Detailed Description in ¶ 0037 (teaching on determining an impact significant criterion value (treated as synonymous to a confidence coefficient) for each attribute to determine if a missing attribute value is predictive or not)
As per claim 10, the combination of Guo and Kleinhanzl discloses the limitations of claim 1. Guo fails to teach the following; Kleinhanzl, however, does disclose:
the information processing apparatus according to claim 1, wherein the at least one processor presents to the user terminal an attribute to be measured to increase a confidence coefficient of the acquired estimated missing values for the first and second attributes of the body information, and requires the estimated missing values for the first and second attributes of the body information is taught in the Detailed Description in ¶ 0060, ¶ 0094, and ¶ 0106 (teaching on requesting additional information regarding the patient's missing psychographic data to improve the predictive capability (treated as synonymous to the confidence coefficient) of the imputation value)
One of ordinary skill in the art before the effective filing date would combine the missing medical data imputation model of Guo with requesting additional information to increase the model’s predictive outcome accuracy of Kleinhanzl with the motivation of “refin[ing] the imputation models” (Kleinhanzl in the Detailed Description in ¶ 0106).
As per claim 11, the combination of Guo and Kleinhanzl discloses the limitations of claim 1. Guo also discloses the following:
the information processing apparatus according to claim 1, wherein the at least two data sets are data sets that are measured in different situations is taught in the Detailed Description in ¶ 0060 and in the Figures at fig. 11 (teaching on the training patient data records include old and new data records (treated as synonymous to records measured in different situations))
As per claim 12, the combination of Guo and Kleinhanzl discloses the limitations of claim 1. Guo also discloses the following:
the information processing apparatus according to claim 1, wherein the at least two data sets are data sets including values of different attributes that are acquired in an identical situation is taught in the Detailed Description in ¶ 0043, ¶ 0060, and in the Figures at fig. 11 (teaching on the training patient data records includes a subset of the sample records for a similar condition for imputing missing attributes (treated as synonymous to identical situations) from a subset of the possible sample data)
As per claim 14, the combination of Guo and Kleinhanzl discloses the limitations of claim 13. Guo also discloses the following:
the information processing apparatus according to claim 13, wherein the at least one processor presents the body information in which the value of the first attribute is missing and the estimated value of the first attribute in a form of table data is taught in the Figures in fig. 6 (teaching on presenting the missing values of a sample patient profile data set with missing attribute values in a table format)
As per claim 15, the combination of Guo and Kleinhanzl discloses the limitations of claim 13. Guo also discloses the following:
the information processing apparatus according to claim 13, wherein the at least one processor presents the estimated value of the first attribute in a display form different from a display form of other body information is taught in the Figures in fig. 6 (teaching on presenting the missing values of a sample patient profile data set with missing attribute values in a table format in a hashed box form instead of a solid box design)
As per claim 16, the combination of Guo and Kleinhanzl discloses the limitations of claim 13. Guo also discloses the following:
the information processing apparatus according to claim 13, wherein the at least one processor changes the display form of the estimated value of the first attribute on a basis of a confidence coefficient of the estimated value of the first attribute is taught in the Figures in fig. 9 and in the Detailed Description in ¶ 0037 (teaching on presenting the imputed value of a sample patient profile data set with missing attribute values in a table format in a solid box form instead of a hashed box design once imputed wherein an impact significant criterion value (treated as synonymous to a confidence coefficient) for each attribute is calculated to determine if a missing attribute value is predictive or not wherein the attribute's significant criterion value must meet a predetermined p-val range to receive an imputation estimation value)
Response to Arguments
Applicant's arguments filed 16 January 2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant first asserts that under Step 2A Prong 1, the claims do not recite an abstract idea. Examiner disagrees, but cannot provide further explanation as Applicant has not indicated which limitations or a rational as to why the claim do not recite a mental process as indicated by Examiner.
Next, Applicant asserts that the claim recite a practical application under Step 2A Prong 2 stating the “specific claim features can effectively provide a particular treatment in the form of (i) requested information associated with body information missing an attribute and (ii) a recommendation based on the requested information particularly where the recommendation shows or includes an indication showing that a change in value in a “non-missing” attribute correlated to a change in value for at least the first attribute that was missing from the received body information”. Examiner disagrees that this amounts to a practical application via a “particular treatment”. As there is no positively recited administration step of the treatment, but only “recommending a treatment”, the claim does not qualify as a prophylaxis step under Step 2A Prong 2. In order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used. Here, merely providing a generic recommendation for any ailment does not positively invoke the particular treatment analysis under Step 2A Prong 2.
Finally, Applicant asserts the independent claim effects a transformation of a specific combination of body attribute information initially missing an attribute value under Step 2A Prong 2. Examiner is not persuaded that the claims amount to a transformation to a particular machine - under the factors for consideration, the machine (I) is particular of the elements and can be specifically identified (here a body attribute information is not a machine but an abstract idea), (II) integrates the recited judicial exception into a practical application (here the transformation is merely to the abstraction), and (III) poses meaningful limits on the claim (here there are no meaningful limitations that limit the abstract idea to a particular hardware environment/device; any general purpose computer is sufficient to perform the steps of the abstract idea). Therefore the rejection has been maintained.
Applicant’s arguments filed 16 January 2026 with respect to 35 USC § 102 have been considered and are persuasive in part regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Kleinhanzl, as per the rejection above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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, Arleen M 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.
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.
/JORDAN L JACKSON/Primary Examiner, Art Unit 2857