DETAILED ACTION
Acknowledgements
The present application is being examined under the pre-AIA first to invent provisions.
This office action is in response to the claims filed August 18, 2025.
Claims 1-20 are pending and have been examined.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In the Instant case, claims 1-13 are directed to a process and claims 14-20 are directed to a product. Therefore, these claims fall within the four statutory categories of invention.
According to the MPEP 2106.04(d), the first prong of the second step of the § 101 analysis (STEP 2A-1) is to determine whether the claim recites an abstract idea, laws of nature or natural phenomena.
Claim 1, for example, recites series of steps for suggesting an order entry based on past care/treatment plan, which is an abstract idea that falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas including managing personal behavior or relationships or interactions between people.
The limitations that set forth the abstract idea are:
generating, […], a rule set for obtaining suggested entries for each of a plurality of components of the CPOE;
obtaining, […], information specific to the patient;
detecting a trigger of a user selection of a component of the plurality of components of the CPOE;
in response to the trigger, determining, […], a suggested entry for the selected component of the CPOE, a support metric for the suggested entry, and a confidence metric for the suggested entry; and
[…]
The above-noted limitations—obtaining and analyzing past care data of a patient and making and displaying suggestions based on the patient’s past care or treatment data—constitute activities that can be performed mentally or manually by a person using pen and paper, without the need for a computer or other machine.
The second prong of the second step of the § 101 analysis (STEP 2A-2) is to determine whether the claim elements, when viewed individually and as an ordered combination, contain an inventive concept sufficient to integrate the claimed abstract idea into a practical application.
The claim elements in addition to the abstract idea are:
a processor of the adaptive suggestion system
an algorithm to apply the generated rule set to the obtained information specific to the patient with respect to the selected component of the CPOE
displaying the suggested entry for the component of the CPOE on a display of the adaptive suggestion system.
The additional elements noted above do not integrate the judicial exception into a practical application. More particularly, the claims do not recite additional limitations that: (i) improve the functionality of a computer or other technology or technical field, see MPEP § 2106.05(a); (ii) use a “particular machine” to apply or use the judicial exception, see MPEP § 2106.05(b); (iii) transform an article to a different thing or state, see MPEP§ 2106.05(c); or (iv) provide any other meaningful limitation, see MPEP § 2106.05(e). See also 84 Fed. Reg. at 55.
The processor, algorithm and user display of the adaptive suggestion system, are recited at a high level of generality, and comprises only a microprocessor and memory to simply perform the generic computer functions such as obtaining and analyzing past care data of a patient and making and showing suggestions based on the patient’s past care/treatment data.
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer/system, or merely uses a computer/system as a tool to perform an abstract idea, does not transform the abstract idea into a practical application - see MPEP 2106.05(f)
The claim does not improve the functioning of any computerized device nor improves another technology or technical process, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment or mere instructions to implement an abstract idea on a computer.
The use of the additional elements noted above as tools to implement/automate the abstract idea does not render the claim patent eligible because it does not provide meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment and requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. See MPEP 2106.05.
Furthermore, the additional claimed elements, noted above, when viewed individually and as an ordered combination does not integrate the abstract idea into a practical application.
STEP-2B of the 101 analysis is to determine whether the claim elements, when viewed individually and as an ordered combination, contain “an inventive concept sufficient to transform the claimed abstract idea into a patent-eligible application.” Alice, 134 S. Ct. at 2357.
The claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, using the additional element noted above to perform the generic computer functions amount to no more than mere instructions to apply the abstract idea using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The dependent claim further recites computer generic functions that do not integrate the abstract idea into a practical application. These functions include: obtaining dataset of a patient, displaying/showing confidence/support metrics, compiling a list of rules and determining confidence metric for the complied list rules.
Furthermore, the features of the dependent claims when viewed individually and as an ordered combination do not integrate the abstract idea into a practical application. The claims do not improve the functioning of any computerized device nor improves another technology or technical process, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment or mere instructions to implement an abstract idea on a computer. The claims also do not include additional elements that are sufficient to amount to significantly more than the abstract idea.
Accordingly, claims 1-20 are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mulligan et al. (US 20210057098 Al) (“Mulligan”) in view of Garimella (US 20230161952 A1) (“Garimella”).
As per claims 1 & 14 & 16, Mulligan discloses:
generating, by a processor of the adaptive suggestion system, a rule set (guidelines/pathways) for obtaining suggested entries (e.g. optimal medical actions) for each of a plurality of components of the CPOE (¶ [0025]-[0027]);
obtaining, by the adaptive suggestion system, information specific to the patient (e.g. patient presentation profile) (¶ [0014]; the patient clustering component 530 may receive data relating to a patient presentation profile 550 (e.g., symptoms reported by a patient). The patient clustering component 530 may cluster patients based on named entities extracted from one or more data sources (e.g., a document/text such as notes from care workers) and other structured data (e.g., vitals, gender, age, etc.))
[…] determining, by the processor of the adaptive suggestion system using an algorithm to apply the generated rule set to the obtained information specific to the patient with respect to the selected component of the CPOE, a suggested entry for a the selected component (e.g. medical action) of the CPOE, a support metric (confidence score) for the suggested entry and a confidence metric for the suggested entry (¶ [0105]- the actions recommendation component 520 cognitively recommend one or more useful medical actions 580 for impacting a health state of a user according to historical data 522 collected from one or more data sources, one or more user profiles ( e.g., patient presentation profile 550, the domain knowledge 524, feedback data 526, or a combination thereof.); ¶ [0107 ]- the usefulness evaluation component 540 may rank and/or re-rank the one or more useful medical actions 580 to maximize a scoring criteria for determining a usefulness of the one or more useful medical actions 580, which a machine learning operation may be used.)
displaying the suggested entry (OMA) for the first component of the CPOE on a user display of the adaptive suggestion system (¶ [0122]- the ranked OMAs may be displayed with the matching SECGs via a graphical user interface ("GUI"));
Mulligan describes a system that receives a “patient presentation profile” and generates “optimal medical actions” (OMAs) for the patient. The system may recommend actions for “each stage” of a plan, or for “each component.”
The system uses a GUI and allows user interaction, but the reference does not explicitly describe that the system waits for a user to select a component (e.g., a particular CPOE field) and then, in response to that specific user selection, generates a suggestion for that selected component.
However, Mulligan does not explicitly teach:
Detecting a trigger of a user selection of a component of the plurality of components of the CPOE, and, in response to the trigger, determining a suggested entry for that selected component.
Garimella, however, teaches this missing limitation.
Garimella is directed to extracting semantic labels for form fields and, in the context of form filling, describes that when a user selects a form field (e.g., focuses or clicks on a field), the system identifies the field’s semantic label and, in response, presents an auto-suggested or auto-filled value for that field based on the user’s profile or history. See ¶ [0029] (“when a widget for a form field in the digital form is selected, created, or otherwise identified, fill assist tool 120 may identify a semantic label for the widget… and auto-fill the widget of the form field or auto-suggests filling the form field with the form field completion data 125.”), and claims 5 and 14.
It would have been obvious to one of ordinary skill in the art, at the time of the invention, to modify the system of Mulligan to incorporate the known user interface pattern from Garimella, such that the system detects when a user selects a component (field) of an electronic form (e.g. CPOE) and, in response to that trigger, determines and presents a suggested entry for the selected component.
The motivation for such a modification would be to improve workflow efficiency and user experience by providing contextually relevant suggestions at the point of user interaction, as taught by Garimella for form fields and as is well known in the art of online form filling and user interface design.
The adaptation would have required only routine skill, as both references are directed to systems for providing user-specific suggestions in electronic forms, and the modification would yield predictable results.
As per claims 2, 9 & 17, Mulligan/Garimella discloses as shown above.
Mulligan further discloses receiving, by the adaptive suggestion system a dataset, the dataset comprising past care plans for each of a plurality of patients ( (e.g., electronic data from one or more electronic health care records) (¶ [0020]); and extracting and normalizing the received data (¶ [0113], the patient pathways are extracted as an ordered sequence of events from the historical patient profiles data.)
As per claims 3 & 10, Mulligan/Garimella discloses as shown above.
Mulligan further discloses displaying the determined confidence metric for the determined suggested entry (¶¶ [0109], [0029], One or more useful medical actions may be recommended at a selected time period for positively impacting a health state of a user according to historical data collected from one or more data sources, one or more user profiles, a domain knowledge, feedback data, a confidence score, or a combination thereof; ¶ [0129], A confidence score may be assigned to the one or more useful medical actions indicating an effectiveness and safety upon the health state of the user ( e.g., a positive or negative impact upon the health state of the patient).
As per claims 4 & 11, Mulligan/Garimella discloses as shown above.
Mulligan further discloses displaying the determined support metric for the determined suggested entry alongside the displayed suggested entry (¶ [0107]- In one aspect, scoring criteria may include, but not limited, to a determination of how useful and/or necessary an action ( e.g., treatment, medication, etc.) based on statistical analysis of historically similar cases, how safe and/or effective an action is on the patient (e.g., action that positively affects the health state of the user) based on patient data and/or the statistical analysis of historically similar cases, and/or a financial cost/constraint of the action is upon the patient. The scoring criteria may be
assigned a value such as, for example a percentage and/or a value within a range of values. ¶ [0027], the feedback may include approvals, rejections, and/or rankings of previous recommendations. Thus, the system for intelligent recommendation of usefulness medical actions provides an interactive responsive system that reacts when a domain knowledge expert decides on one or more medical actions for a patient and suggests more optimal alternative when available. A machine learning mechanism may use the heterogeneous historical input and/ or feedback information to build the recommendation model and also learn the health state of one or more patients.)
As per claims 5 , 12 & 18, Mulligan/Garimella discloses as shown above.
Mulligan further discloses wherein the rule set is comprised of a plurality of rules each having a left hand side and a right hand side wherein the left hand side corresponds to the information specific to the patient and the right hand side corresponds to a possible suggested entry for the component of the CPOE. (¶¶ [0116]-[0117], [0122]-[0124]]
The examiner notes that the location of data withing a GUI is a matter of design choice and has not patentable weight (In re Wolfe, 116 USPQ 443, 444 (CCPA 1961)).
As per claims 6 , 13 & 20, Mulligan/Garimella discloses as shown above.
Mulligan further discloses compiling a list of all possible rules from the rule set that could be applied to the information specific to the patient and determining a confidence metric for each of the possible rules (¶ [0126]- One or more
of the recommended user medical actions may be matched to one or more selected portions of clinical practice guidelines (CPGs), as in block 806. The one or more of the
recommended user medical actions may be selected and ranked, as in block 808. The one or more of the recommended user medical actions may be added, according to the
selecting and ranking, as an additional CPG or as an enhancement to one or more of the matching portions of the recommended user medical actions, as in block 810.)
As per claim 7, Mulligan/Garimella discloses as shown above.
Mulligan further discloses the step of removing rules from the rule set that do not have a determined confidence metrics that meets a pre-determined threshold (¶ [0109]- the usefulness evaluation component 540 may use
a set of rules to boost medical actions 580 that are more important as compared to other possible medical actions, and to assign a negative score to the one or more useful
medical actions 580 that are known to be ineffective and/or unsafe in that particular context. For example, the set of rules may include, but not limited to, assigning a positive, normalized score (e.g., confidence score) to a medical action if the outcome of the medical action applied to previous cases was successful. The confidence score may be increased for those cases having a greater similarity to the user (e.g., a confidence score may have a greater weight, rank, and/or percentage for success on the user).
As per claim 8, Mulligan/Garimella discloses as shown above.
Mulligan further discloses:
completing the entry for the component of the CPOE (¶ [0123]- the CPG section generation component 416 may add the OMAs as one or more new CPG and/or one or more sections of the SECGs may be added with the OMAs, as in block 724. That is, the CPG section generation component 416 may add the OMA's as an additional CPG upon the ranking score exceeding a predetermined threshold, and/or enhancing the matching SECGs with the OMA's matching the one or more selected portions of the matching SECGs upon the ranking score exceeding a predetermined threshold.);
determining, by the processor of the adaptive suggestion system using an algorithm to apply the generated rule set to the obtained information and the completed entry for the first component of the CPOE, a suggested entry for a second component of the CPOE a support metric for the suggested entry for the second component of the CPOE and a confidence metric for the suggested entry for the second component of the CPOE (fig. 8; ¶ [0126 ]- One or more of the recommended user medical actions may be matched to one or more selected portions of clinical practice guidelines (CPGs), as in block 806. The one or more of the recommended user medical actions may be selected and ranked, as in block 808. The one or more of the recommended user medical actions may be added, according to the selecting and ranking, as an additional CPG or as an enhancement to one or more of the matching portions of the recommended user medical actions, as in block 810; ¶ [0128]- The operations of method 800 may rank or re-rank the one or more useful medical actions according to a scoring criteria, historical data, selected evidence data, domain experts, a domain knowledge, or a combination thereof. ¶ [0108] In one aspect, if a care worker/medical professional (e.g., domain knowledge expert 560) has provided medical actions 570 as input, the usefulness evaluation component 540 may merge the medical actions 570 provided by the care giver with the useful medical actions 580 determined by the actions recommendation component 520. The ranking of the one or more useful medical actions 580 may be determined and/or computed based on configurable scoring criteria 590, the domain knowledge 524, and feedback data 526 (e.g., historical feedback data) from the domain knowledge experts 560 about previously computed results. ); and
displaying the suggested entry for the second component of the CPOE on the user display of the adaptive suggestion system (fig. 7, ¶¶ [0122], [0123]; displaying medical actions after adding previous or selected medical actions in which the system was re-ranked/retrained based on the previous medical actions).
As per claim 15, Mulligan/Garimella discloses as shown above.
Mulligan further discloses a user interface, wherein the user interface is configured to receive an entry for the component of the CPOE (¶ [0026], [0051]).
Response to Arguments
Applicant’s arguments with respect to at least claims 1, 14 & 16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
35 U.S.C. § 101
Applicant argues (page 8-10):“In particular, MPEP § 2106.04(a)(2)(III)(A) specifically states that: ‘Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations.’ … The human mind is not equipped to detect a trigger of a user selection of a component of the plurality components of Computerized Physician Orders Entries (CPOE). … The human mind is not equipped to perform such generation of a rule set. Thus, claim 1, when considered as a whole, cannot be performed manually or mentally using a pen and paper without requiring a machine.”. … “Indeed, the claimed invention improves the technical field of medical operations. In particular, the claimed invention improves the medical order entry process for a physician by suggesting an entry for a user selected component of the CPOE.”
The examiner, however, respectfully disagrees.
While the claims recite machine learning and computer-implemented steps, the core of the invention is directed to organizing human activity (i.e., suggesting entries for form fields based on user or patient data), which is an abstract idea. The recited computer elements (e.g., processor, GUI, memory) are generic and perform their conventional functions. The claims do not recite additional elements that amount to significantly more than the abstract idea itself, nor do they provide a specific improvement to computer technology as required by Alice Step 2 and MPEP 2106. The use of machine learning, as claimed, is recited at a high level of generality and does not integrate the abstract idea into a practical application. The steps of detecting a trigger or generating a rule set, even if performed by a computer, do not transform the nature of the claim into patent-eligible subject matter.
35 U.S.C. § 103 –
Applicant argues (page 13):“Mulligan does not disclose a user selection of a component of a CPOE and that Mulligan's recommending of the medical action is in response to a detected trigger of such user selection of a component of a CPOE. Further, Mulligan does not disclose that Mulligan's recommended medical action is a suggested entry for such selected component of the CPOE.”
The examiner, however, respectfully disagrees.
Mulligan discloses a system for generating and presenting patient-specific recommendations for components of a CPOE based on patient data and rules. Garimella teaches that it is well known in the art to detect user selection of a form field and, in response, provide a contextually relevant suggestion or auto-fill value for that field. It would have been obvious to one of ordinary skill in the art to combine these teachings by modifying Mulligan’s system to detect when a user selects a CPOE component and, in response, generate and present a suggestion for that component, as taught by Garimella, see the rejection above. The motivation for such a combination would be to improve user workflow and efficiency, and such a modification would have been a routine application of known UI design patterns yielding predictable results.
The examiner further notes that Garimella teaches the general principle of providing field-specific suggestions in response to user selection, which is applicable to any electronic form, including CPOE systems. The adaptation of this well-known pattern to the CPOE context, as in Mulligan, would have been within the routine skill of a person of ordinary skill in the art and yields predictable improvements in usability and efficiency.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Wang et al (ClinicNet: machine learning for personalized clinical order
set recommendations, 2020), discloses:
Scientific advances have led to a wealth of advances in medicine, but
the escalating complexity makes it difficult for clinicians to learn
how to efficiently use all patient information and optimize practice
to the highest quality possible. In this study we develop ClinicNet, a
recommender algorithm that anticipates clinical items (medications,
procedures, consults, etc.) a clinician will order in the hospital based
on prior similar cases. This is similar to online recommender systems
that automatically anticipate your interests and needs. With Clinic-
Net, we can automatically generate lists of clinical order suggestions.
Koutkias et al (Knowledge engineering for adverse drug event prevention: On the design and development of a uniform, contextualized and sustainable
knowledge-based framework, 2012), discloses:
The primary aim of this work was the development of a uniform, contextualized and sustainable knowledge-based framework to support adverse drug event (ADE) prevention via Clinical Decision Support Systems (CDSSs). In this regard, the employed methodology involved first the systematic analysis and formalization of the knowledge sources elaborated in the scope of this work, through which an application-
specific knowledge model has been defined. The entire framework architecture has been then specified and implemented by adopting Computer Interpretable Guidelines (CIGs) as the knowledge engineering formalism for its construction. The framework integrates diverse and dynamic knowledge sources in the form of rule-based ADE signals, all under a uniform Knowledge Base (KB) structure, according to the defined knowledge model. Equally important, it employs the means to contextualize the encapsulated knowledge, in order to provide appropriate support considering the specific local environment (hospital, medical department, language, etc.), as well as the mechanisms for knowledge querying, inference, sharing, and management. In this paper, we present thoroughly the establishment of the proposed knowledge framework by presenting the employed methodology and the results obtained as regards
implementation, performance and validation aspects that highlight its applicability and virtue in medication
safety.
US 20140046696 Al- discloses:
The present invention provides methods and systems or apparatuses,
to analyze multiple molecular and clinical variables from an individual diagnosed with a psychiatric disorder, such as post-traumatic stress disorder (PTSD), in order to
optimize medication selection for therapeutic response. Molecular co-variables include polymorphisms in genes including those involved in central control and mediation of
the hypothalamic-pituitary axis (HPA) stress response, the density of methylation in regulatory regions of said polymorphic genes, polymorphisms in genes that encode cytochrome P450 enzymes responsible for drug metabolism, and drugdrug
and drug-gene interactions. Clinical co-variables include but are not limited to the sex, age and ethnicity of that individual, medication history, family history, diagnostic
codes, Pittsburgh insonmia rating score, and Charlson index score. The system makes a determination based on unstructured and structured data types derived from internal and external knowledge resources to determine psychotropic drug choice that best matches the molecular and clinical variation profile of an individual patient. The decision support system provides a therapeutic recommendation for a clinician based on the patient's variation profile.
US 20060149416 Al- discloses
A system, software, and methods related to enhanced pharmaceutical
order entry and administration by medical personnel, and enhanced pharmaceutical inventory control within a medical institution are provided. An embodiment of
the system includes a pharmaceutical information management server having memory and a medication administration program product including a set of instructions stored in the memory of the pharmaceutical information management server to enhance provision of pharmacy services. The system also includes medical institution physician computers to provide for computerized physician medication order entry, pharmacy computers to provide for review and verification by a pharmacist of electronic medication orders placed through the physician computers, and medical institution
nursing unit computers, to provide for review of and input to electronic medication administration records.
US 20160260035 Al- discloses:
Pharmacy workflow management with alert integration. A pharmacy workflow management application may obtain alert data from an alert generation platform. In turn, the alert information may be provided to a user of the workflow management
application within the application without having to divert from use of the application. The user may further utilize the pharmacy workflow application to access the alert generation platform. In this regard, the user of the pharmacy workflow management application may be in bidirectional communication with the alert generation platform to, for example, exchange resolution information in relation to an alert. The alert data may comprise any pertinent data related to pharmacy activity managed by the pharmacy workflow management application and in particular may include data related to infection control or antimicrobial stewardship.
US 20200185098 Al- discloses:
Techniques for evaluating dynamically modified plans are provided. A selection of a treatment plan template is received, where the treatment plan template specifies a
plurality of treatment stages, where each treatment stage defines a plurality of treatment options. A plurality of modifications to the treatment plan template is generated. It
is determined, for each respective modification of the plurality of modifications, whether the respective modification is permissible, based on one or more predefined institutional
criteria. Upon determining that a first modification of the plurality of modifications is permissible, a first treatment plan is generated based on the first modification to the
treatment plan template. Further, a first predicted efficacy measure is generated for the first treatment plan based on analyzing a knowledge graph. Finally, the first treatment
plan is provided, along with at least an indication of the first predicted efficacy measure.
US 20190392924 Al- discloses:
Embodiments for intelligent recommendation of useful medical actions to a user by a processor. One or more useful medical actions may be cognitively recommended with
evidence in support thereof for impacting a health state of a user according to historical data collected from one or more data sources, one or more user profiles, a domain knowledge, feedback data, or a combination thereof. The one or more useful medical actions may be ranked according to a scoring criteria, the domain knowledge, the historical data, a set of rules, previously recommended medical actions, or
a combination thereof.
US 20200013515 Al- discloses:
The present invention is directed to a drug interaction checking tool for use without medication orders or CPOE which includes an interface sub-system to receive messages from healthcare information technology systems with information
about the patient such as patient allergies, medications the patient is currently taking, patient factors and co-morbidities, where the interface sub-system also receives
messages from a decentralized pharmacy system re withdrawn medications, the clinician, and the patient; a data repository sub-system to store the received data such as patient allergies, medications prescriber, administered or currently in use, patient factors and co-morbidities, medications withdrawn, and the identity of the clinician; access to a medication interactions database; a guidance engine sub-system which performs the medication interaction checks for the patient and generates alerts if a risk of medication errors or adverse reaction is identified; and a notification sub-system which disseminates the alerts to one or more devices and/or systems.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 MAMON OBEID whose telephone number is (571)270-1813. The examiner can normally be reached 8 AM- 5 PM.
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, Namrata Boveja can be reached at (571) 272-8105. 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.
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687