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 .
Response to Arguments
Applicant’s arguments, see page 9, filed 04/10/2026, with respect to the objection to Figures 2-3 have been fully considered. The objection to the drawings has been withdrawn in response to the replacement drawings filed 04/10/2026.
Applicant's arguments, see pages 9-10, filed 04/10/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. § 112(a) have been fully considered but they are not persuasive.
Applicant first argues that the amended claims “replaces the generic formulation with the specific, multi-step procedural algorithm executed by the system” and relies upon paragraphs [0051] and [0054] to adequately demonstrate possession.
The Examiner respectfully disagrees.
The amended claims still recite “averaging a plurality of chunk probabilities obtained for the plurality of chunks, respectively, … wherein the plurality of chunk probabilities are obtained by inputting the plurality of I/O statistics into a machine learning model”. The originally filed disclosure still remains silent on how the now-claimed machine learning model is capable of accomplishing the claimed function of obtaining a probability that a first single host-specific stream of the plurality of single host-specific streams is infected by ransomware. The originally filed disclosure is silent with respect to any steps/procedure/algorithm on how to detect ransomware infection, and instead relies upon a “pre-trained machine learning model” that is trained by “any known machine learning algorithm”, without actually disclosing how the ransomware probability is actually obtained. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
The rejection of claims 1, 9, and 17 will be maintained under 35 U.S.C. § 112(a).
Applicant's arguments, see pages 10-12, filed 04/10/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. § 102(a)(1) have been fully considered but they are not persuasive.
Applicant argues that the previously presented Armangau reference does not teach the amended claim features, that the claimed invention “does not analyze host-classified streams as they are”, that the claimed invention tracks “subtle changes in time-series I/O patterns”, that the Armangau reference “fails to teach the claimed technology that uses a plurality of chunks that enable statistical judgement on individual host streams… the claimed chunks are objects quantified to a size suitable for statistical judgement within a stream classified by SQID”, and that “Armangau fails to disclose (1) obtaining queues via an NVMe-of interface and dividing them using SQIDs, and (2) obtaining a probability by dividing a stream into chunks, calculating I/O statistics per chunk, and averaging the chunk probabilities”.
The Examiner respectfully disagrees.
Regarding the argued limitations that are not claimed in independent claim 1, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “does not analyze host-classified streams as they are”, “the claimed chunks are objects quantified to a size suitable for statistical judgement within a stream classified by SQID”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Regarding the amended claims, Armangau anticipates the claimed “divide a plurality of submission queues received from a plurality of hosts into a plurality of single-host-specific streams” by disclosing at least “Host 1”, “Host 2”, “Host N” (host 110) of Figure 1 transmitting I/O requests. Armangau further anticipates the claimed “dividing the first single host-specific stream into a plurality of chunks” by disclosing at least Figure 1 Cache 140 receiving incoming I/O requests (e.g. 112w write request) and arranging the received data into pages 142, so that the Node can track statistics “which may be tracked on a per-data-object (per volume) basis” (Armangau Col. 5 lines 55-67). Armangau further anticipates the claimed “calculating a plurality of I/O statistics for each of the plurality of chunks” by disclosing at least “The cache 140 may also store recently-read data of the data objects and may track statistics 144 related to cache performance. For example, the statistics 114 may include a cache hit rate for writes, which may be tracked on a per-data-object (per volume) basis” (Armangau Col. 5 lines 60-65) and Figure 6 Cache statistics “FIG. 6 shows an example of cache statistics 144 in greater detail. Here, statistics for write hit rate are maintained on a per-volume basis. For example, hit rates 610-1 and 610-2 are maintained for respective volumes V1 and V2, where one of the volumes V1 or V2 may correspond to data object 180. In an example, write hit rate of the volume for data object 180 is provided to the RWPM 170 as an indicator of a write-after-read pattern (WaRP), which may be used in detecting suspected ransomware attacks” (Armangau Col. 12 lines 30-38). Armangau further anticipates “averaging a plurality of chunk probabilities obtained for the plurality of chunks” by disclosing at least averaging aggregate attribute scores for ransomware detection (Armangau Col. 17 lines 29-54). Armangau lastly anticipates “wherein the plurality of chunk probabilities are obtained by inputting the plurality of I/O statistics into a machine learning model” by disclosing at least utilizing machine learning (such as a neural net) to analyze data object changes in order to determine whether a ransomware attack is likely (Armangau Col. 7 lines 46-60).
The rejection of claims 1-20 under 35 U.S.C. § 102(a)(1) will be maintained.
Drawings
The drawings were received on 04/10/2026. These drawings are acceptable.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 1:
Independent claim 1 recites “obtain a probability that a first single host-specific stream of the plurality of single host-specific streams is infected by ransomware, by (i) dividing the first single host-specific stream into a plurality of chunks, (ii) calculating a plurality of I/O statistics for each of the plurality of chunks”. The limitations in question do not satisfy the written description requirement under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph. The specification does not describe the limitations in sufficient detail so that one of ordinary skill in the art would recognize that the applicant had possession of the claimed invention. The only description found for “dividing the first single host-specific stream into a plurality of chunks” and “calculating a plurality of I/O statistics” is found in paragraph [0051] disclosing, “The preprocessing module 212_2a may divide a host-specific read/write stream to chunks and calculate different statistics of the read/write commands for each chunk. For example, the preprocessing module 212_2a may calculate a ratio of write after read commands, a distribution of logical block addresses, a distribution of delays between I/O commands, etc. The calculated statistics, along with the raw data of the host-specific read/write stream are then forwarded to the machine learning model 212_2b”. There is no disclosure regarding how the inventor intended to achieve the desired result of dividing the host-specific stream into chunks, nor any disclosure of achieving the desired results of calculating the claimed genus of “a plurality of I/O statistics”. The division of the stream into chunks and the calculation of the plurality of I/O statistics is critical to the functioning of the claimed invention (as the claims enumerate these two steps are part of how the claimed invention “obtain[s] a probability that a first single host-specific stream … is infected by ransomware”), but the disclosed “preprocessing module 212_2a” comprises a black-box implementation in Figure 5, and there is no disclosure regarding how the division of host-specific streams or calculation of I/O statistics is achieved in the originally field disclosure. Furthermore, a list of example statistics in paragraph [0051] of the originally filed disclosure is not commensurate with the claimed genus of “calculating a plurality of I/O statistics for each of the plurality of chunks” as the originally filed disclosure does not even disclose how the inventor achieved a calculation of a single “I/O statistic”.
In MPEP 2161.01, "computer-implemented functional claim language must still be evaluated for sufficient disclosure under the written description". And MPEP 2161.01(I) "generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed." For computer-implemented inventions, the determination of the sufficiency of disclosure will require an inquiry into the sufficiency of both the disclosed hardware and the disclosed software due to the interrelationship and interdependence of computer hardware and software. The critical inquiry is whether the disclosure of the application relied upon reasonably conveys to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date.
As in MPEP 2161.01 (I), "The description requirement of the patent statute requires a description of an invention, not an indication of a result that one might achieve if one made that invention." It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
AS in MPEP 2161.01 “For instance, generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed. Ariad, 598 F.3d at 1349-50, 94 USPQ2d at 1171 ("[A]n adequate written description of a claimed genus requires more than a generic statement of an invention’s boundaries.") (citing Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1405-06); Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) (holding that generic claim language appearing in ipsis verbis in the original specification did not satisfy the written description requirement because it failed to support the scope of the genus claimed); Fiers v. Revel, 984 F.2d 1164, 1170, 25 USPQ2d 1601, 1606 (Fed. Cir. 1993) (rejecting the argument that "only similar language in the specification or original claims is necessary to satisfy the written description requirement").”
“The Federal Circuit has explained that a specification cannot always support expansive claim language and satisfy the requirements of 35 U.S.C. 112 "merely by clearly describing one embodiment of the thing claimed." LizardTech v. Earth Resource Mapping, Inc., 424 F.3d 1336, 1346, 76 USPQ2d 1731, 1733 (Fed. Cir. 2005). The issue is whether a person skilled in the art would understand applicant to have invented, and been in possession of, the invention as broadly claimed. In LizardTech, claims to a generic method of making a seamless discrete wavelet transformation (DWT) were held invalid under 35 U.S.C. 112, first paragraph, because the specification taught only one particular method for making a seamless DWT and there was no evidence that the specification contemplated a more generic method. "[T]he description of one method for creating a seamless DWT does not entitle the inventor . . . to claim any and all means for achieving that objective." LizardTech, 424 F.3d at 1346, 76 USPQ2d at 1733.”
Independent claim 1 further recites: “averaging a plurality of chunk probabilities obtained for the plurality of chunks, respectively, … wherein the plurality of chunk probabilities are obtained by inputting the plurality of I/O statistics into a machine learning model”. The limitation in question does not satisfy the written description requirement under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph. The specification does not describe the limitation in sufficient detail so that one of ordinary skill in the art would recognize that the applicant had possession of the claimed invention. The only description found for “wherein the plurality of chunk probabilities are obtained by inputting the plurality of I/O statistics into a machine learning model” appears in paragraphs [0051-0054].
However, paragraphs [0051-0054] disclose that “The preprocessing module 212_2a may divide a host-specific read/write stream to chunks and calculate different statistics of the read/write commands for each chunk. For example, the preprocessing module 212_2a may calculate a ratio of write after read commands, a distribution of logical block addresses, a distribution of delays between I/O commands, etc. The calculated statistics, along with the raw data of the host-specific read/write stream are then forwarded to the machine learning model 212_2b. The machine learning model 212_2b may be a pre-trained machine learning model that has been trained on large volumes of both ransomware and benign applications to classify the ransomware attacks. The machine learning model 212_2b may determine a probability that a chunk is infected by ransomware malware. For example, the machine learning model 212_2b may output the probability as a percent. Alternatively, the machine learning model 212_2b may output the probability as one of high, medium, or low. Any known machine learning algorithm may be used for training the machine learning model 212_2b. For example, the machine learning model may include at least one of Fully Connected Neural Network, Convolutional Neural Network, Transformer Network, Decision Trees, Random Forest, etc.”.
There is no disclosure regarding how the inventor intended to achieve the claimed desired result of obtaining a probability of a host stream being infected by malware by inputting the plurality of I/O statistics into a machine learning model. The originally filed disclosure is silent with respect to any steps/procedure/algorithm on how to detect ransomware infection using the claimed machine learning model, and instead relies upon a “pre-trained machine learning model” that is trained by “any known machine learning algorithm”. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
Independent claims 9 and 17 recite substantially the same content and are therefore rejected under the same rationales.
Dependent claims fall together accordingly.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1, 9, and 17 recite the limitation “dividing the first single host-specific stream into a plurality of chunks”. The term “chunk” in claims 1, 9, and 17 is a relative term which renders the claim indefinite. The term “chunk” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term “chunk” as used in the claims is not a term of the art, and one of ordinary skill would not be appraised of the scope as to what a “chunk” would comprise under the broadest reasonable interpretation, and there is no definition of the term provided in the originally filed disclosure as far as dividing a host-specific stream “into a plurality of chunks”.
Independent claims 1, 9, and 17 further recite the limitation “I/O statistics”. The term “I/O”, while not spelled out in the claims or the specification, normally stands for “input/output” and the slash mark (“/”) means “and/or” in the art. However, the claim requires “calculating a plurality of I/O statistics”, and raises a genuine question as to whether the claimed statistics and claimed averaging are calculated either for the input statistics or output statistics; or both the input and output statistics. A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) is considered indefinite, since the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). Note the explanation given by the Board of Patent Appeals and Interferences in Ex parte Wu, 10 USPQ2d 2031, 2033 (Bd. Pat. App. & Inter. 1989), as to where broad language is followed by "such as" and then narrow language. The Board stated that this can render a claim indefinite by raising a question or doubt as to whether the feature introduced by such language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims. Note also, for example, the decisions of Ex parte Steigewald, 131 USPQ 74 (Bd. App. 1961); Ex parte Hall, 83 USPQ 38 (Bd. App. 1948); and Ex parte Hasche, 86 USPQ 481 (Bd. App. 1949).
Dependent claims fall together accordingly.
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) 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Armangau et. al. (US Patent No. US 11,755,733 B1) hereinafter Armangau.
Regarding Claims 1, 9, and 17:
Claim 1. Armangau discloses a storage system comprising: a plurality of non-volatile memory devices; and processing circuitry configured to (Armangau Col. 5 lines 4-31) divide a plurality of submission queues received from a plurality of hosts into a plurality of single host-specific streams (Armangau Fig. 1, Col. 7 lines 24-35 receive incoming I/O requests (e.g. 112w write request) Nodes 1 through N send a plurality of I/O requests; Col. 20 lines 4-18 “An improved technique has been described of identifying hosts 110 suspected of being sources of ransomware infection. The technique includes initiating a tracking interval 1022 in response to a data storage system 116 detecting a suspected ransomware attack. During the tracking interval 1022, write requests 112w received by the data storage system 116 are analyzed and ransomware attributes 960 for those write requests 112w are generated. The ransomware attributes 960 of the write requests 112w indicate risks of ransomware infection and are associated with hosts 110 from which the respective write requests 112w originate. A particular host is identified as a suspected source of ransomware infection based at least in part on the ransomware attributes 960 associated with that host.”), obtain a probability that a first single host-specific stream of the plurality of single host-specific streams is infected by ransomware (Armangau Fig. 1-2; Col. 7 lines 49-60 “The RWPM 170 then determines, based on the attributes, whether a ransomware attack is likely. The determination may take various forms. In some examples, a ransomware score is generated, where a higher score corresponds to a greater likelihood of a ransomware attack and a lower score corresponds to a lower likelihood. The ransomware score may be based on a combination of the attributes, such as an algebraic combination (e.g., weighted sum) or one that uses machine learning, such as a neural net, e.g., one with attributes provided as inputs and weights used to balance the contributions of the attributes.”; Col. 14 lines 46-51 particular hosts may be identified as the source of ransomware infection), by (i) dividing the first single host-specific stream into a plurality of chunks (Armangau Fig. 1; Col. 5 lines 55-67), (ii) calculating a plurality of I/O statistics for each of the plurality of chunks (Armangau Col. 5 lines 60-65 “The cache 140 may also store recently-read data of the data objects and may track statistics 144 related to cache performance. For example, the statistics 114 may include a cache hit rate for writes, which may be tracked on a per-data-object (per volume) basis”), and (iii) averaging a plurality of chunk probabilities obtained for the plurality of chunks, respectively (Armangau Col. 17 lines 29-54 averaging aggregate attribute scores for ransomware detection), and generate a warning signal in response to the probability of the first single host-specific stream being infected by the ransomware (Armangau Col. 15 lines 48-50 and Col. 16 lines 14-21 “In other examples, the node 120 sends an alert to an administrator, informing the administrator of the suspect host and requesting confirmation to block further I/O requests 112 from that host. In still other examples, the node 120 may alert the administrator of the suspect host but rely on the administrator to take remedial action. Various options are contemplated.”), wherein the plurality of chunk probabilities are obtained by inputting the plurality of I/O statistics into a machine learning model (Armangau Col. 7 lines 46-60 machine learning used to analyze data object changes in order to determine whether a ransomware attack is likely).
Claims 9 and 17 recite substantially the same content and are therefore rejected under the same rationales. Armangau further discloses a method (Armangau Fig. 2, claim 1) and further discloses a non-transitory computer-readable storage medium (Armangau Col. 20 lines 53-67, claim 15).
Regarding Claims 2, 10, and 18:
Claim 2. Armangau further discloses the storage system of claim 1 (Armangau Col. 5 lines 4-31), wherein the processing circuitry is further configured to determine a presence of ransomware in the system based on a plurality of probabilities of a plurality of the single host-specific streams (Armangau Col. 17 lines 48-54).
Claims 10 and 18 recite substantially the same content and are therefore rejected under the same rationales.
Regarding Claims 3, 11, and 19:
Claim 3. Armangau further discloses the storage system of claim 1 (Armangau Col. 5 lines 4-31), wherein the processing circuitry is further configured to evaluate a probability of each single host-specific stream based on probabilities from other single host-specific streams of the plurality of hosts (Armangau Col. 17 lines 48-54).
Claims 11 and 19 recite substantially the same content and are therefore rejected under the same rationales.
Regarding Claims 4, 12, and 20:
Claim 4. Armangau further discloses the storage system of claim 1 (Armangau Col. 5 lines 4-31), wherein the processing circuitry is further configured to suspend a host of the plurality of hosts from transmitting read/write commands in response to generating the warning signal (Armangau Col. 16 lines 10-14 “the node 120 may then take action to protect the data storage system 116 from continuing infection. In some examples, the node 120 may unregister a suspect host, such that the suspect host is blocked from issuing any more I/O requests 112 to the data storage system 116.”).
Claims 12 and 20 recite substantially the same content and are therefore rejected under the same rationales.
Regarding Claims 5 and 13:
Claim 5. Armangau further discloses the storage system of claim 1 (Armangau Col. 5 lines 4-31), wherein the processing circuitry is further configured to instantiate an instance of a ransomware detector for each single host-specific stream (Armangau Col. 15 line 59 through Col. 16 line 7 associate write requests with respective initiators; Col. 15 lines 24-34).
Claim 13 recites substantially the same content and is therefore rejected under the same rationales.
Regarding Claims 6 and 14:
Claim 6. Armangau further discloses the storage system of claim 5 (Armangau Col. 5 lines 4-31), wherein the processing circuitry is further configured to close an instance of a ransomware detector in response to a host corresponding with the instance of the ransomware detector not transmitting a read/write command for more than a threshold amount of time (Armangau Col. 15 lines 54-64 tracking may preferably set to be short periods of time, and may depend on circumstances to be longer or shorter).
Claim 14 recites substantially the same content and is therefore rejected under the same rationales.
Regarding Claims 7 and 15:
Claim 7. Armangau further discloses the storage system of claim 1 (Armangau Col. 5 lines 4-31), wherein the storage system is a non-volatile memory express over-fabrics (NVMe-of) storage system (Armangau Col. 4 line 55 through Col. 5 line 3).
Claim 15 recites substantially the same content and is therefore rejected under the same rationales.
Regarding Claims 8 and 16:
Claim 8. Armangau further discloses the storage system of claim 1 (Armangau Col. 5 lines 4-31), wherein the processing circuitry is configured to divide the submission queues based on respective submission queue identifications (SQIDs) corresponding with respective hosts of the plurality of hosts (Armangau Fig. 1 Hosts 1 through N; Col. 15 lines 15-34).
Claim 16 recites substantially the same content and is therefore rejected under the same rationales.
Conclusion
The prior art made of record in the submitted PTO-892 Notice of References Cited and not relied upon is considered pertinent to applicant’s disclosure.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 MIGUEL A LOPEZ whose telephone number is (703)756-1241. The examiner can normally be reached 8:00AM-5:00PM.
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, Jorge Ortiz-Criado can be reached on 5712727624. 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.
/M.A.L./ Examiner, Art Unit 2496
/JORGE L ORTIZ CRIADO/ Supervisory Patent Examiner, Art Unit 2496
Prosecution Insights