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 Claims
This action is in reply to the application filed on 18 April 2023.
Claims 1-12 are currently pending and have been examined.
IDS
The information disclosure statement (IDS) submitted on 04/18/23 has been considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Foreign Priority
Acknowledgment is made of Applicant's claim for foreign priority based on an application filed in Japan on 05 July 2022. A certified copy of the JP2022-108331 application as required by 37 CFR 1.55 has been received on 26 May 2023. Therefore, a priority date of 05 July 2022 has been given to the instant application.
Claim Interpretation/Notice to Applicant
The claims appear to be a translation from a foreign language and include ambiguous language. Examiner has included her interpretation of such instances here and/or with the relevant claim mappings in 103 section, and has examined the instant application as best understood. Examiner recommends reviewing and amending the claim language to clarify the scope of the invention and what is actually being claimed.
Claims 1, 5 and 9 contain recitation of “stores/storing a learned model that has undergone machine learning to output a demand prediction result that is a prediction result of the demand for the medical device by inputting electronic chart data describing information showing a necessity of use of the medical device, using learning data including lending record data indicating a record of the medical device that has been lent and the electronic chart data describing information indicating the necessity of the use of the medical device that has been lent”. Examiner interprets “learned model” to be synonymous with “trained model”; Examiner interprets the portion of the limitation beginning with “a learned model that has undergone machine learning to output a demand prediction result …” as a trained machine learning model that has been trained to output a demand prediction result; Examiner interprets the portion of the limitation beginning with “using learning data including lending record data…” as specifying the types of data that were used to train the “learned model”.
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 1-12 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1
Claims 1-4 are drawn to a system, Claims 5-8 are drawn to a method, and Claims 9-12 are drawn to a non-transitory storage medium, each of which are within the four statutory categories. Claims 1-12 are further directed to an abstract idea on the grounds set out in detail below.
Step 2A Prong 1
Claim 1 recites implementing the steps of:
inputting chart data describing the information indicating the necessity of the use of a medical device into a model to acquire a demand prediction result;
inputting an inventory prediction result that is a prediction result of an inventory of the medical device, the prediction result being predicted based on a present inventory and reservation information; and
comparing the acquired demand prediction result with the input inventory prediction result, and
providing a notification when the demand exceeds the inventory.
These steps amount to managing personal behavior or relationships or interactions
between people and therefore recite certain methods of organizing human activity. Predicting a demand of a medical device and providing a notification when demand exceeds inventory are personal behaviors that may be performed by a hospital personnel or healthcare providers.
Independent claim 5 and claim 9 recites similar limitations and also recites an abstract idea under the same analysis.
The above claims are therefore directed to an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application because the additional
elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The independent claims additionally recite:
electronic chart data (Claims 1, 5, 9)
learned model (Claims 1, 5, 9)
medical device lending system (Claims 1, 5, 9) as the entity to which the notification is provided when demand exceeds the inventory
a computer (Claim 5) as implementing the steps of the abstract idea
a non-transitory storage medium storing a program causing a computer (Claim 9) as implementing the steps of the abstract idea
The broad recitation of the above-mentioned general purpose computing elements at a high level of generality only amounts to mere instructions to implement the abstract idea using computing components as tools.
Regarding “electronic” chart data, this only amounts to mere instructions to apply the abstract idea on a computer, e.g., using a computer to maintain chart data electronically.
Regarding “learned” model, the specification does not appear to provide any particulars of the model other than “machine learning”. The broad recitation of a learned (interpreted as “trained”) machine learning model, in this case to determine a prediction of demand for medical devices, only amounts to using the machine learning model as a tool to apply data to a model and generate a result (see MPEP 2106.05(f)(2)).
Regarding the medical device lending system, para. [0058] discloses “The device lending system 30 is a system that manages a lending schedule (management information) indicating a lending date and time and a lending destination (a use place, a user, or the like) for each of the lending devices. The device lending system 30 may be a server connected to the host management device 10, and exchanges data with the host management device 10. Thus, the host management device 10 can obtain the lending schedule of the lending device managed by the device lending system 30. The device lending system 30 may be distributed and arranged in the host management device 10, or may be installed in the host management device 10”. No particulars of the device lending system or server are provided; therefore, this element is given its broadest reasonable interpretation as a general purpose computing device such as a server computer functioning in its ordinary capacity.
Regarding the computer, para. [0057] teaches on a server being the device lending system; as stated in preceding paragraph, no particulars of the device lending system or server are provided; therefore, this element is given its broadest reasonable interpretation as a general purpose computing device functioning in its ordinary capacity. Para. [0054] further teaches on a “user terminal” which may be “a tablet computer, a smartphone, or the like, but may be an installation-type computer. The user terminal 400 only needs to be an information processing device capable of wireless or wired communication”. This element is therefore given its broadest reasonable interpretation as a general purpose computing device functioning in its ordinary capacity.
Regarding the non-transitory medium, no particulars are provided. Per para. [0192], this element is given its broadest reasonable interpretation as a general purpose computing device functioning in its ordinary capacity.
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claims 1, 5, 9 additionally recite
stores/storing a learned model that has undergone machine learning to output a demand prediction result that is a prediction result of the demand for the medical device by inputting electronic chart data describing information showing a necessity of use of the medical device, using learning data including lending record data indicating a record of the medical device that has been lent and the electronic chart data describing information indicating the necessity of the use of the medical device that has been lent;
This element amounts to insignificant extra-solution activity. As explained above, the independent claims are directed to an abstract idea in the form of predicting a demand for a medical device in a medical device lending system and providing a notification when a predicted demand exceeds an inventory prediction. As stated in MPEP 2106.05(g), "[t]he term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim." In the present claim, the function of stores/storing a learned model that has undergone machine learning to output a demand prediction result is only nominally or tangentially related to predicting a demand for a medical device and providing a notification when demand exceeds inventory, and accordingly constitutes insignificant extra-solution activity. Examiner prospectively notes that the entirety of this limitation is included as an additional element, as elements beyond the “store/storing a learned model” only serve to further describe the model that is being stored.
These elements in Sections An and B above are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
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:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1, 5 and 9 only recite the aforementioned computing elements as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f).
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Likewise, as explained above, the step of stores/storing a learned model that has undergone machine learning to output a demand prediction result that is a prediction result of the demand for the medical device by inputting electronic chart data describing information showing a necessity of use of the medical device, using learning data including lending record data indicating a record of the medical device that has been lent and the electronic chart data describing information indicating the necessity of the use of the medical device that has been lent, only amounts to insignificant application of the abstract idea.
C. Well-Understood, Routine and Conventional Activities. MPEP 2106.05(d)
In addition to amounting to insignificant extra-solution activity the elements in Section B above constitute well-understood, routine and conventional activity. The element of store/storing a model that has undergone machine learning to output a demand prediction result only amounts to storing/retrieving information in memory, which has been previously held to be well-understood, routine and conventional when claimed at a high level of generality or as insignificant extra-solution activity. See MPEP 2106.05(d)(II).
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.
Depending Claims
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims:
Claims 2-4, 6-8, 10-12 recite limitations which further narrow the scope of the independent claims.
Claims 2, 6 and 10 additionally recites limitations pertaining to temporarily reserving lending of the medical device, which are also certain methods of organizing human activity including managing personal behavior or relationships or interactions between people as placing a reservation to temporarily borrow a medical device is a personal behavior that may be performed by a healthcare provider or other hospital/healthcare personnel. Claims 2, 6 and 10 recite additional elements consistent with those discussed above with respect to the independent claims, which only amount to using tools to apply the abstract idea, e.g., a “reservation system” of the medical device lending system, which is understood to be server functioning in its ordinary capacity per para. [0058] and Fig. 3. See MPEP 2106.5(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the abstract idea.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101 as they include all of the limitations of claims 1, 5 or 9 respectively. The additional recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea. Beyond the limitations which recite the abstract idea, the claims recite additional elements consistent with those identified above with respect to the independent claims which encompass adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 2-4, 6-8, 10-12, recite additional subject matter which amounts to additional elements consistent with those identified in the analysis of the independent claims above. As discussed above with respect independent claims and integration of the abstract idea into a practical application, recitation of these additional elements only amounts to invoking computers as a tool to perform the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Dependent claims 2-4, 6-8, 10-12, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
For the reasons stated, Claims 1-12 fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1, 3, 5, 7, 9, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laster et. al. US Publication 20170061375A1 in view of Ravazzolo (US Publication 20070198357 A1).
Regarding Claim 1, Laster discloses: A prediction system for predicting a demand for a medical device in a medical device system, the prediction system being a system that: stores a learned model that has undergone machine learning ([0142], teaching on a “predictive model” which may be a machine learning classifier, an artificial neural network, a support vector machine, a kernel machine or other machine learning model; [0145] teaches on how the model may be trained, which is interpreted as the model being a “learned model”) to output a demand prediction result that is a prediction result of the demand for the medical device by inputting electronic chart data describing information showing a necessity of use of the medical device ([0067] teaches on “predictive models” which determine recommended levels of inventory that medical facilities should maintain; the predictive models can provide predictions of inventory levels that are closely aligned with likely future needs (interpreted as reading on “demand prediction result”); the predictive models can be trained to determine recommended inventory levels based on information about procedures performed at a specific facility, various facilities having similar characteristics, or general information about different medical facilities; [0128], [0130] and Fig. 3b further teach on inputting patient profile data (“electronic chart information”) for a plurality of patients having upcoming procedures (interpreted as describing “necessity of use”, if a procedure is planned for the patient) to the predictive model to predict an inventory level needed to satisfy the likely needs (“demand prediction result”) of a medical facility needing a particular medical device for the upcoming planned procedures; the predictive model indicates predicted quantities of items that should be stocked to meet the needs predicted for the upcoming procedures; the predictive model determines “quantities of implants needed” to complete a set of procedures for 10 different patients – see Fig. 3b, Chart 330 showing predicted quantities of different size implants; Examiner interprets “implants” to read on the broadest reasonable interpretation of “medical devices”), using learning data including record data indicating a record of the medical device that has been [used] and the electronic chart data describing information indicating the necessity of the use of the medical device that has been [used] (Examiner interprets “learning data” to be synonymous with “training data”; [0123] teaches on the predictive model being trained with data indicating the sets of instruments and supplies used in or provided for use in various procedures (record data of medical device that has been used); training data may include information about prior surgeries performed by the same surgeon or performed at the same hospital (interpreted as “electronic chart data indicating the necessity of the use of the medical device”; [0133] further teaches on training the predictive models with historical data about actual implant procedures, including size and type of implant – also interpreted as “electronic chart data indicating the necessity of the use of the medical device”);
inputs the electronic chart data describing the information indicating the necessity of the use of the medical device into the learned model to acquire the demand prediction result ([0128]/Fig. 3 teach on inputting patient profiles, which include patient data for each of a set of scheduled knee replacement procedures scheduled for the next month, into predictive model; where [0118] teaches on the patient profile including physical characteristics of the patient such as height, age, race, sex, comorbidities; BMI, type of surgery, surgery location, etc. – all interpreted as “electronic chart data” which has information indicating the necessity of use of a particular medical device, e.g., implants for a total knee replacement); [0130] teaches on the output of the predictive model indicating predicted quantities of items that should be stocked to meet the needs predicted for the upcoming procedures; the predictive model determines “quantities of implants needed” to complete a set of procedures for 10 different patients; see Chart 330 in Fig. 3 which shows quantity of different size implants needed, e.g., demand prediction result obtained via the model);
inputs an inventory prediction result that is a prediction result of an inventory of the medical device, the prediction result being predicted based on a present inventory and reservation information ([0112], [0114] teach on reserving particular items from an inventory; a specific instance, e.g., a tracking number, of an item of a plurality of the same items may be reserved from an inventory; once the reserved items are assigned to a particular procedure, the reserved items (“reservation information”) are deducted from available inventory (“present inventory information”) so they cannot be assigned to or used in another procedure; each physical item has a unique identifier for tracking purposes; the medical facility’s inventory management system updates the inventory records when an item is reserved – interpreted as “inventory prediction result” based on subtracting reserved items from available inventory); and
compares the acquired demand prediction result with the input inventory prediction result, and notifies the medical device system when the demand exceeds the inventory ([0136] teaches on the system determining differences that indicate that there are implant sizes where the current inventory (“input inventory prediction result”) is less than “predicted need” (interpreted as “acquired demand prediction result”) and subsequently sends an alert indicating the shortfall, or the system may automatically generate an order to purchase the needed items; see Fig. 3C chart 340, showing instances where predicted inventory need (demand) exceeds current inventory, e.g., implant sizes 12 and 13).
Laster does not explicitly teach the following, but Ravazzolo, which is directed to an inventory and patient management system, teaches:
lending / medical device lending system ([0004] teaches on renting/loaning CPAP devices, where CPAP devices are interpreted as “medical devices”; [0092] teaches on a system issuing a CPAP device with a particular serial number to a patient as a “rental unit”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine Laster with these teachings of Ravazzolo, to use the method of Laster for using a machine learning model to predict demand and available inventory for lending a medical device within a medical device lending system, with the motivation of enabling providers to prevent running out of stock of particular devices for loaning/renting to patients (Ravazzolo [0005]).
Regarding Claim 3, Laster/Ravazzolo teach the limitations of Claim 1. Laster further discloses wherein the electronic chart data includes information indicating that medical staff has determined the use of the medical device ([0081] teaches on a surgeon profile including information about the surgeon performing the medical procedure (e.g., medical staff); the profile may include explicit preferences that the surgeon has indicated, such as a specific make, model, or type of implant (device) is preferred; as the instant specification does not appear to disclose what it means for medical staff to “determine the use of the medical device”, Examiner interprets the applied reference to read on the broadest reasonable interpretation of the claim language as it teaches on using a surgeon’s profile (electronic chart data) which specifies which medical device the surgeon prefers to use).
Regarding Claim 5, Laster/Ravazzolo teach the limitations of Claim 1. Claim 5 recites limitations that are the same or substantially similar to Claim 1, and the discussion above with respect to Claim 1 is equally applicable to Claim 5. Claim 5 is directed to a method performed by a computer, which is also taught by Laster (see: Abstract, [0015], [0278]).
Regarding Claim 7, Laster/Ravazzolo teach the limitations of Claim 3. Claim 7 recites limitations that are the same or substantially similar to Claim 3, and the discussion above with respect to Claim 3 is equally applicable to Claim 7.
Regarding Claim 9, Laster/Ravazzolo teach the limitations of Claim 1. Claim 9 recites limitations that are the same or substantially similar to Claim 1, and the discussion above with respect to Claim 1 is equally applicable to Claim 9. Claim 9 is directed to a non-transitory storage medium storing a program to cause a computer to execute the steps of the process, which is also taught by Laster (see: [0216], [0278]).
Regarding Claim 11, Laster/Ravazzolo teach the limitations of Claim 3. Claim 11 recites limitations that are the same or substantially similar to Claim 3, and the discussion above with respect to Claim 3 is equally applicable to Claim 11.
Claim(s) 2, 6, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laster et. al. US Publication 20170061375A1 in view of Ravazzolo (US Publication 20070198357 A1) as applied to Claims 1, 5, and 9 above, respectively, and further in view of Okada et. al. (WIPO Publication WO2023170974A1).
Regarding Claim 2, Laster/Ravazzolo teach the limitations of Claim 1. Laster does not disclose, but Okada, which is directed to a reservation server and system for a medical device, further teaches the medical device lending system includes a reservation system for temporarily reserving lending of the medical device (Page 11, para. 4 teaches on the reservation server accepting a temporary reservation from the user terminal for a medical device); and the lending record data includes data in which information indicating the medical device temporarily reserved by the reservation system is associated with information indicating a record of actual lending based on a temporary reservation (Page 13 as printed, paras. 5-6, teach on a provisional (temporary) reservation; the system determines if license information has been received and if so, the reservation system moves the next step and changes the tentative reservation selected in the reservation application into a final reservation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Laster/Ravazzolo with these teachings of Okada, to incorporate Okada’s method of temporarily reserving a medical device and including data that includes the medical device is temporarily reserved in association with information indicating a record of actual lending, with the motivation of verifying the correct information (e.g., license information) prior to confirming a reservation (page 14, para. 5).
Regarding Claim 6 and Claim 10, Laster/Ravazzolo teach the limitations of Claim 2. Claims 6 and 10 recite limitations that are the same or substantially similar to Claim 2, and the discussion above with respect to Claim 4 is equally applicable to Claims 6 and 10.
Claim(s) 4, 8, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laster et. al. US Publication 20170061375A1 in view of Ravazzolo (US Publication 20070198357 A1) as applied to Claims 1, 5, and 9 above, respectively, and further in view of Mineo (US Publication 20160217347 A1).
Regarding Claim 4, Laster/Ravazzolo teach the limitations of Claim 1 but do not teach the following. Mineo, which is directed to a medical image classification system, teaches wherein the lending record data includes information indicating an end time or a return time of the use of the medical device ([0099] teaches on a patient being lent a beacon B from a plurality of beacons in a hospital; [0175] teaches on accepting return of a beacon B (interpreted as the medical device) in which the current time of the return is regarded as the “return time of use of the medical device”; per [0082]-[0083], the beacon transmits information to pass to a medical image acquisition apparatus and as it is involved in the transfer of medical images, is interpreted as being a medical device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the combined teachings of Laster/Ravazzolo with these teachings of Mineo, to include information indicating a return time of a loaned medical device in a lending record, with the motivation of using the return time in conjunction with a time the device was lent to a patient to calculate a usage time period of the medical device (Mineo [0175]).
Regarding Claim 8 and Claim 12, Laster/Ravazzolo teach the limitations of Claim 4. Claims 8 and 12 recite limitations that are the same or substantially similar to Claim 4, and the discussion above with respect to Claim 4 is equally applicable to Claims 8 and 12.
Conclusion
Examiner respectfully requests that Applicant provides citations to relevant paragraphs of specification for support for amendments in future correspondence.
The following relevant prior art not cited is made of record:
US Publication 20220293254 A1, teaching on automated data aggregation with predictive modeling for predicting future inventory of medical items
US Publication 20210174963 A1, teaching on using a machine learning model to forecast demand for donated blood and output an alert when predicted demand exceeds supply
US Publication 20230307117 A1, teaching on a system for tracking use of medical devices to update the remaining life and forecast a future demand for the medical devices to facilitate inventory management
US Publication 20200364660 A1, teaching on a system for inventory management and fulfillment for medical items dispensed in conjunction with treatment of a patient at a medical facility
US Publication 20090144078 A1, teaching on methods for predicting appropriate inventory of medical supplies to be adequately prepared for widespread illness
US Publication 20080027751 teaching on medical product reservation, distribution and purchasing system
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE-MARIE K ALDERSON whose telephone number is (571)272-3370. The examiner can normally be reached on Mon-Fri 9:00am-5:00pm EST and generally schedules interviews in the timeframe of 2:00-5:00pm EST.
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, Fonya Long, can be reached on 571-270-5096. 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.
/ANNE-MARIE K ALDERSON/Primary Examiner, Art Unit 3682