DETAILED ACTION
Notices to Applicant
This communication is a final rejection. Claims 1, 3-9, and 11-16, as filed 01/21/2026, are currently pending and have been considered below.
Priority is generally acknowledged as shown on the filing receipt with the earliest priority date being 12/16/2021.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon and the rationale supporting the rejection would be the same under either status.
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, 3-9, and 11-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter 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
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:
1. A method for interpreting medical detection data, comprising:
obtaining medical detection data to be interpreted, wherein the medical detection data to be interpreted comprises a detection image and indicator data, wherein the indicator data includes medical indicator data; (additional element; insignificant extra-solution activity, mere data-gathering)
performing image recognition on the detection image, to obtain image recognition data; (abstract idea – mental process)
interpreting, using a rule library, the indicator data and the image recognition data, to obtain an interpretation result, wherein an interpretation rule in the rule library is obtained by mining and analyzing a plurality of pieces of interpreted data based on a data mining algorithm, and the rule library is dynamically updated with an interpretation process (abstract idea – mental process).
mining and analyzing the plurality of pieces of interpreted data based on the data mining algorithm, to obtain the interpretation rule, wherein the interpreted data comprises indicator data, the image recognition data, and the interpretation result obtained based on the indicator data and the image recognition data; (abstract idea – mental process; mathematical concept)
updating the rule library based on the interpretation rule (additional element; insignificant extra-solution activity, data output; generally applying the abstract idea with a computer).
3. The method according to claim 2, wherein the mining and analyzing the plurality of pieces of interpreted data based on the data mining algorithm, to obtain the interpretation rule comprises:
classifying the plurality of pieces of interpreted data, to obtain interpreted data of a plurality of categories; (abstract idea – mental process)
selecting, from the interpreted data of the plurality of categories, interpreted data of a first category that meets a preset condition, wherein the preset condition is that a quantity of similar interpreted data in the interpreted data of the category is greater than a quantity threshold, interpretation results of any two pieces of similar interpreted data are the same, and indicator data of the two pieces of similar interpreted data falls within a same value range and/or image recognition data of the two pieces of similar interpreted data falls within a same value range; (abstract idea – mental process)
obtaining the interpretation rule based on the similar interpreted data in the interpreted data of the first category (additional element; insignificant extra-solution activity, mere data-gathering).
4. The method according to claim 1, wherein the detection image comprises a signal wave image, and the performing image recognition on the detection image, to obtain image recognition data comprises:
performing image recognition on the signal wave image, to obtain at least one piece of attribute information of a signal wave waveform in the signal wave image (abstract idea – mental process and mathematical concept).
5. The method according to claim 1, wherein the interpreting, using a rule library, the indicator data and the image recognition data, to obtain an interpretation result comprises:
interpreting, using the rule library, the image recognition data, to obtain at least two candidate interpretation results (abstract idea – mental process); and
selecting the interpretation result from the at least two candidate interpretation results based on the indicator data (abstract idea – mental process).
6. The method according to claim 5, wherein there are at least two pieces of indicator data; and the selecting the interpretation result from the at least two candidate interpretation results based on the indicator data comprises:
separately obtaining, based on value intervals of the at least two pieces of indicator data, candidate interpretation results corresponding to the corresponding value intervals, wherein the value intervals of the at least two pieces of indicator data are divided into a plurality of value intervals, and each value interval corresponds to at least one candidate interpretation result (additional element; insignificant extra-solution activity, mere data-gathering); and
selecting, from all candidate interpretation results obtained based on the at least two pieces of indicator data, a candidate interpretation result that appears most frequently, to obtain the interpretation result (abstract idea – mental process).
7. The method according to claim 5, wherein the selecting the interpretation result from the at least two candidate interpretation results based on the indicator data comprises:
obtaining target indicator data respectively corresponding to the at least two candidate interpretation results, wherein the target indicator data corresponding to any one candidate interpretation result is obtained based on indicator data in interpreted data corresponding to the candidate interpretation result (additional element; insignificant extra-solution activity, mere data-gathering);
separately determining similarities between the indicator data in the medical detection data and the target indicator data corresponding to the at least two candidate interpretation results (abstract idea – mental process); and
selecting a candidate interpretation result corresponding to a highest similarity, to obtain the interpretation result (abstract idea – mental process).
8. The method according to claim 1, wherein before the interpreting, using a rule library, the indicator data and the image recognition data, to obtain an interpretation result, the method further comprises:
determining, based on a quality control rule, that the indicator data and the image recognition data meet a preset quality control specification (abstract idea – mental process).
Claims 1-8 are presented as exemplary claims but the same analysis applies to the other claims.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes like analyzing, interpreting, selecting, and determining information as well as mathematical concepts like data mining algorithms. For example, but for the “electronic device” language of claim 9, these steps can all be performed mentally or are mathematical concepts.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims such as additional mental processes or mathematical concepts as described above.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, updating the rule library based on the interpretation rule amounts to generally applying the abstract idea with a computer, see applicant’s specification [0136], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea. For example, obtaining medical detection data to be interpreted (claim 1) or separately obtaining, based on value intervals of the at least two pieces of indicator data, candidate interpretation results corresponding to the corresponding value intervals (claim 6) amounts to invoking computers as a tool to perform the abstract idea amounts to mere data gathering, and updating the rule library similarly amounts to mere data output, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims as described above. 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 and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. For example, obtaining data and updating a rule library amount to receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. 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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4, 8, 9, 12, and 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lyman (US20210118534A1).
Regarding claim 1, Lyman discloses: A method for interpreting medical detection data, comprising:
--obtaining medical detection data to be interpreted, wherein the medical detection data to be interpreted comprises a detection image (ECG printouts in [0253]-[0265]) and indicator data, wherein the indicator data includes medical indicator data (patient history data in [0265]);
--performing image recognition on the detection image, to obtain image recognition data (performing waveform detection on the ECG printouts in [0275]-[0277]); and
--interpreting, using a rule library (image analysis functions in [0050]), the indicator data and the image recognition data, to obtain an interpretation result (diagnosing function generates diagnosis data, e.g., [0282]; “Performing diagnosing function 4026 can utilize other information, such as additional information 4419, ECG classifier data 4420, and/or patient history data 4430, for example, by utilizing models trained on this other type of information as input labels…” [0283]),
--wherein an interpretation rule in the rule library is obtained by mining and analyzing a plurality of pieces of interpreted data based on a data mining algorithm (training models [0283]; re-training model during the analysis in [0291]; training diagnosing function in [0319]), and
--the rule library is dynamically updated with an interpretation process (improve existing functions in [0171]; remediation in [0118]-[0124]; backpropagation in [0136]; “Alternatively or in addition, the expert user can identify additional parameters and/or rules in the expert feedback data based on the errors made by the inference function in generating the inference data 1110 for the medical scan, and these parameters and/or rules can be applied to update the medical scan inference function, for example, by updating the model type data 622 and/or model parameter data 623,” [0120]);
--mining and analyzing the plurality of pieces of interpreted data based on the data mining algorithm, to obtain the interpretation rule, wherein the interpreted data comprises the indicator data, the image recognition data, and the interpretation result obtained based on the indicator data and the image recognition data (“The diagnosis data 4440 can include measurement data 4443 indicating one or more measurements,” [0267]; patient history data [0069] and [0141]; known diagnosis data [0282]); and
--updating the rule library based on the interpretation rule (“the central server system can train on this information to produce new and/or updated model parameters for transmission back to the medical picture archive integration system 2600 for use on subsequently received medical scans,” [0150]; “the central server system 2640 can train on this data to improve existing models by producing updated model parameters of an existing inference function and/or to generate new models…” [0171]).
Regarding claim 4, Lyman further discloses: wherein the detection image comprises a signal wave image, and the performing image recognition on the detection image, to obtain image recognition data comprises: performing image recognition on the signal wave image, to obtain at least one piece of attribute information of a signal wave waveform in the signal wave image (obtaining various signal data from “pseudo-raw ECG signal data” [0278]-[0282]).
Regarding claim 8, Lyman further discloses: wherein before the interpreting, using a rule library, the indicator data and the image recognition data, to obtain an interpretation result, the method further comprises: determining, based on a quality control rule, that the indicator data and the image recognition data meet a preset quality control specification (input quality assurance function in [0164]).
Claims 9, 12, and 16 are substantially similar to claims 1, 4 and 8 and are rejected with the same reasoning.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 3, 5-7, 11 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Lyman (US20210118534A1) in view of Jia (“A patient-similarity-based model for diagnostic prediction”).
Regarding claim 3, Lyman discloses: wherein the mining and analyzing the plurality of pieces of interpreted data based on the data mining algorithm, to obtain the interpretation rule comprises:
--classifying the plurality of pieces of interpreted data, to obtain interpreted data of a plurality of categories (categories and classifiers in, e.g., [0071] and [0098]; “Thus, the input quality assurance function 1106 can take medical scan image data 410 as input and can generate an inferred scan category as output,” [0114]);
…
--obtaining the interpretation rule based on the similar interpreted data in the interpreted data of the first category (“Each medical scan inference function 1105 in the set can correspond to a scan category 1120, and can be trained on a set of medical scans that compare favorably to the scan category 1120,” [0109], [0114]-[0116]).
Lyman does not expressly disclose but Jia teaches:
--selecting, from the interpreted data of the plurality of categories, interpreted data of a first category that meets a preset condition, wherein the preset condition is that a quantity of similar interpreted data in the interpreted data of the category is greater than a quantity threshold, interpretation results of any two pieces of similar interpreted data are the same, and indicator data of the two pieces of similar interpreted data falls within a same value range and/or image recognition data of the two pieces of similar interpreted data falls within a same value range (sorting patients by level of similarity or dissimilarity as described in Table 1; “The input of this supervised prediction model is the feature vectors of each patient pair in the cohort, which are built by integrating three parts: symptom similarity, lab test similarity, and preliminary diagnosis similarity. The construction of integrated feature vectors is a process that deriving the attribute similarity,” page 2).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Lyman’s AI-based diagnostic methods to include Jia’s identifying and leveraging of quantifiable similar interpreted data because this would generate more accurate prediction (page 6: “This promising work allows an accurate prediction performance while guaranteeing a high success percentage when the data of millions of patients are accessible. Eligible similarome is promising even when the screening criteria are strict. Additional experiments based on a large-scale cohort are expected. The model proposed in this research is a kind of exhaustive comparison with a high computation cost. To effectively evaluate the similarities among pairwise patients and efficiently retrieve similar patients, large-scale patient indexing is essential to handle massive amounts of data.”).
Regarding claim 5, Lyman discloses: wherein the interpreting, using a rule library, the indicator data and the image recognition data, to obtain an interpretation result comprises:
--interpreting, using the rule library, the image recognition data, to obtain at least two candidate interpretation results (“the ECG interpretation system 4002 can determine diagnosis data generated for a captured image requires human review based on corresponding confidence score data 4460 indicating or more probability values generated in generating the diagnosis data comparing unfavorably to one or more corresponding probability thresholds, in response to a random quality assurance process, in response to a request from a client device for a second opinion via user interface 4075, and/or in response to the ECG interpretation system otherwise determining human review is necessary,” [0288]; [0070]).
--selecting the interpretation result(“The diagnosis data 440 of a medical scan can include a binary abnormality identifier 441 indicating whether the scan is normal or includes at least one abnormality. In some embodiments, the binary abnormality identifier 441 can be determined by comparing some or all of confidence score data 460 to a threshold, can be determined by comparing a probability value to a threshold, and/or can be determined by comparing another continuous or discrete value indicating a calculated likelihood that the scan contains one or more abnormalities to a threshold” [0070]).
Lyman discloses techniques for using rules, thresholds, and probabilities to generate diagnosis data but does not expressly disclose selecting between two candidate interpretation results based on the indicator data. Jia teaches this feature. Jia uses clinical reasoning and similarity metrics to generate and reject diagnostic hypotheses, i.e., interpretations, (Abstract). Jia’s positive (similar) and negative (dissimilar) hypotheses are interpreted as indicator data that helps select the appropriate interpretation result (“Rather than attempting to solve the problem of predicting diagnoses just by k-nearest neighbors, we instead explore a two-step method of retrieving positive analogies to generate hypotheses and negative analogies to reject hypotheses,” page 3).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Lyman’s AI-based diagnostic methods to include Jia’s two-step method of retrieving analogies with different indicator values (i.e., positive or negative) because this would increase the prediction accuracy (Jia Abstract).
Regarding claim 6, Lyman does not expressly disclose but Jia teaches: wherein there are at least two pieces of indicator data; and the selecting the interpretation result from the at least two candidate interpretation results based on the indicator data comprises:
--separately obtaining, based on value intervals of the at least two pieces of indicator data, candidate interpretation results corresponding to the corresponding value intervals, wherein the value intervals of the at least two pieces of indicator data are divided into a plurality of value intervals, and each value interval corresponds to at least one candidate interpretation result (the positive and negative analogies are multiplied by additional indicator values, namely weights, such that “confidence = weight * positive + (1-weight) * negative” on page 5); and
--selecting, from all candidate interpretation results obtained based on the at least two pieces of indicator data, a candidate interpretation result that appears most frequently, to obtain the interpretation result (“The top kd_kNN most frequent diagnoses are selected as the prediction results” on page 5).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Lyman’s AI-based diagnostic methods to include Jia’s hypothesis generation and selection because this would generate more accurate prediction (page 6: “This promising work allows an accurate prediction performance while guaranteeing a high success percentage when the data of millions of patients are accessible. Eligible similarome is promising even when the screening criteria are strict. Additional experiments based on a large-scale cohort are expected. The model proposed in this research is a kind of exhaustive comparison with a high computation cost. To effectively evaluate the similarities among pairwise patients and efficiently retrieve similar patients, large-scale patient indexing is essential to handle massive amounts of data.”).
Regarding claim 7, Lyman does not expressly disclose but Jia further teaches: wherein the selecting the interpretation result from the at least two candidate interpretation results based on the indicator data comprises:
--obtaining target indicator data respectively corresponding to the at least two candidate i-interpretation results, wherein the target indicator data corresponding to any one candidate interpretation result is obtained based on indicator data in interpreted data corresponding to the candidate interpretation result (“The input of this supervised prediction model is the feature vectors of each patient pair in the cohort, which are built by integrating three parts: symptom similarity, lab test similarity, and preliminary diagnosis similarity. The construction of integrated feature vectors is a process that deriving the attribute similarity. The supervised model is to assign weight to attributes automatically. The prediction of the target is a process that deriving relational similarity,” page 2; discharge diagnosis similarity, e.g., page 2);
--separately determining similarities between the indicator data in the medical detection data and the target indicator data corresponding to the at least two candidate interpretation results; and selecting a candidate interpretation result corresponding to a highest similarity, to obtain the interpretation result (Table 1 showing how sufficiency of similarity is calculated).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Lyman’s AI-based diagnostic methods to include Jia’s similarity calculations and analogies because this would generate more accurate prediction (page 6: “This promising work allows an accurate prediction performance while guaranteeing a high success percentage when the data of millions of patients are accessible. Eligible similarome is promising even when the screening criteria are strict. Additional experiments based on a large-scale cohort are expected. The model proposed in this research is a kind of exhaustive comparison with a high computation cost. To effectively evaluate the similarities among pairwise patients and efficiently retrieve similar patients, large-scale patient indexing is essential to handle massive amounts of data.”).
Claims 11 and 13-15 are substantially similar to claims 3 and 5-7 (respectively) and are rejected with the same reasoning.
Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lyman (US20210118534A1) in view of Walker (US20180146874A1).
Regarding claim 17, Lyman does not expressly disclose but Walker teaches: wherein the medical indicator data includes unidimensional data comprising at least one of a blood oxygen saturation level or a body temperature (“the device has features additional to what is minimally needed for mere operation as a defibrillator. These features can be for monitoring physiological indicators of a person in an emergency scenario. These physiological indicators are typically monitored as signals, such as a person's full ECG (electrocardiogram) signals, or impedance between two electrodes. Additionally, these signals can be about the person's temperature, non-invasive blood pressure (NIBP), arterial oxygen saturation/pulse oximetry (SpO2),” [0025]; patient context score in [0047]-[0052]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Lyman’s AI-based diagnostic methods to include Walker’s physiological indicators because combining such indicators with ECG would improve diagnostic confidence (see [0006]).
Claim 18 is substantially similar to claim 17 and is rejected with the same reasoning.
Response to arguments
Applicant's arguments filed 01/21/2026 have been fully considered and are discussed below.
Regarding the subject matter ineligibility rejections, Applicant argues that the claimed invention is not directed to a mental process (Step 2A Prong One) because “mining and analyzing” are “computerized algorithmic process[es] involving complex data” that cannot be performed in the human mind. Remarks page 8. The Examiner disagrees. But for a general recitation of a computer, the limitation can be performed by a human reviewing patient information and applying mental rules for analysis.
Applicant argues that the claimed invention integrates any abstract idea into a practical application (Step 2A Prong Two) because it improves “diagnostic accuracy” by “multi-source input-based interpretation”. Remarks page 9. This is not persuasive because improving diagnostic accuracy flows from the abstract idea itself. That is, a person following rules such as those in the claims may achieve the result of improved diagnostic accuracy, but this is an improvement to the thought process a clinician follows. An improvement to the abstract idea is not a technical improvement.
Applicant argues that the data “being sourced from medical sensors or physiological measurements” is not an abstract idea. Remarks page 9. This is not persuasive because these sensors are part of the additional elements and have no bearing on whether other aspects of the claim are directed to an abstract idea. The types of sensors are largely disconnected from the data analysis and are thus insignificant extra-solution activity, namely, data-gathering. Courts have found that the following analogous feature is not significantly more than the abstract idea: “Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process.” MPEP 2106.05(A).
Regarding the prior art rejections, Applicant argues that Lyman fails to disclose medical detection data being “a detection image” and “medical indicator data”. Remarks page 10. This is not persuasive because it relies on reading of medical indicator data that is much narrower than the BRI in view of the specification. Lyman’s patient history data is medical indicator data because it indicates something about a patient’s medical status.
Applicant next argues that Lyman’s ML models do not read on the claimed “rule library”. This is not persuasive because it relies on an interpretation of “rule library” that is narrower than the BRI. There is no claim limitation or requirement from the specification indicating that the data mining algorithm could be “a support vector machine algorithm, a decision tree algorithm, or a naive Bayes algorithm” in [0072], but the BRI of “rule library” plausibly reads on any stored collection of computational parameters or heuristics that map input to outputs, which is precisely what Lyman’s image analysis functions do. Applicant’s comments about Lyman teaching away from a rules-based function are not germane to the question of anticipation.
Conclusion
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office Action (See MPEP 706.07(a)). Accordingly, 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 extension fee 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 JOSHUA BLANCHETTE whose telephone number is (571)272-2299. The examiner can normally be reached on Monday - Thursday 7:30AM - 6:00PM, 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, Shahid Merchant, can be reached on (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSHUA B BLANCHETTE/ Primary Examiner, Art Unit 3624