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 .
This action is responsive to the Amendment filed on 02/13/2026. Claims 1-20 are pending in the case.
Applicant Response
In Applicant’s response dated 02/13/2026, Applicant amended Claims 1, 11 and 20 and argued against all objections and rejections previously set forth in the Office Action dated 08/27/2025.
Response to Arguments
Claim Rejections - 35 U.S.C. § 101,
Applicant argues that “the proposed amended claims recite more than a mental process as was alleged in the Office Action. For at least these reasons, withdrawal of the pending rejections under 35 U.S.C. § 101 is respectfully requested. Accordingly, the claimed subject matter is not directed to an abstract idea and recite more than a mental process as was alleged in the Office Action. For at least these reasons, withdrawal of the pending rejections under 35 U.S.C. § 101 is respectfully requested.”
Examiner respectfully disagrees:
The argument have been fully considered but are not persuasive. Examiner notes that “without requiring retraining” limitations reflects the use of pre-trained models in inference mode and the improvement is how a mathematical model is applied not the functioning of the computer itself. Examiner notes that reducing retraining frequency improves the efficiency of the abstract predictive model but it does not change how the computer functions. In addition, selecting “efficient frontier” of submodel is a known modeling approach such as ensemble modeling. Therefore, the 35 U.S.C. 101 rejection for being directed non-statutory subject matter has been updated based on the new amended limitation and the 35 U.S.C. 101 rejection has been maintained.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/13/2026 has been entered.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a computer implemented method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
determining, by a first submodel of a plurality of submodels, a first class associated with a first entity of the plurality of entities, the first-class indicative of calculation of a first impact score of the target action (This step involves performing a calculation to produce a class which is understood to be abstract mathematical concept.)
determining a first plurality of weighted attributes associated with the first entity (This step involves assigning weights to attributes and weighting features is a mathematical operation which is understood to be abstract mathematical concept.)
determining a first impact function associated with the first entity based on the first entity, wherein the first impact function based on attributes associated with the first entity and the target action (This step involves defining a mathematical which is understood to be abstract mathematical concept.)
calculating, by the determined first impact function, the first impact score associated with the first entity, wherein the calculation is based on one or more of the first plurality of weighted attributes, the input attributes, and/or a type associated with the target action, wherein the first impact score is indicative of a probability that the first entity will successfully perform the target action (This step involves mathematical modeling and statistical analysis using probability estimation calculation which is understood to be abstract mathematical concept.)
assigning the target action to the first entity among the plurality of entities based at least on the calculated first impact score without requiring retraining of the plurality of submodels when the first impact score is different from a second impact score of a second entity of the plurality of entities. (This step involves mathematical evaluation and comparison operations that can be performed in human mind or using basic mathematical tools, and fall within the mental processes and mathematical concepts grouping of abstract ideas.)
The above limitations in the context of this claim encompass determining a weighted score of an entity and calculating probability that the first entity will successfully perform the target action (The claim recites a mathematical calculation which fall within the mathematical concepts grouping of abstract ideas). Thus, the claims are patent eligible because they do not recite a judicial exception.
Step 2A Prong Two Analysis: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application?
The claim recites additional element “receiving data characterizing input attributes associated with a plurality of entities and a target action (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(all)(i)))); Storing in database, monitoring activity, selecting a submodel training models using standard ML techniques and using standard statistical algorithms are well understood, routine and conventional. These additional limitations fail to integrate the abstract idea into a practical application. The limitations, recited at a high level of generality, only amount to “apply it” using a generic computer component (MPEP 2106.05(f)).
Step 2B Analysis: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
No, the claim recites additional element “receiving data characterizing input attributes associated with a plurality of entities and a target action.” These additional limitations fail to integrate the abstract idea into a practical application. The limitations, recited at a high level of generality, only amount to “apply it” using a generic computer component (MPEP 2106.05(f)).
The dependent claims respectively recite a judicial exception in limitations of: “accessing, using an identity of the first entity, the first impact function from a table of impact functions; determining, using the first plurality of weighted attributes, a value of a true positive, a value of a true negative, a value of a false positive, and/or a value of a false negative; or weighting, using the first plurality of weighted attributes, the value of the true positive, the value of the true negative, the value of the false positive, and/or the value of the first negative. (claims 2, 12); calculating, by a second impact function, a second impact score associated with a second entity of the plurality of entities, wherein the calculation is based on one or more of a second plurality of weighted attributes, the input attributes, and/or the type associated with the target action, wherein the second impact score is indicative of a probability that the second entity will successfully perform the target action. comparing the first impact score with the second impact score; and assigning the target action to the first entity or the second entity based on the comparison between the first impact score and the second impact score ” (claims 3, 13 ), “determining the first class associated with the second entity, the first class indicative of calculation of the second impact score and the second plurality of weighted attributes; and determining the second impact function based on the second plurality of weighted attributes” (claims 4, 14 ), “training, using the received data characterizing input attributes, the first submodel and a second submodel associated with second entity; determining a first performance of the first submodel based on output of the first submodel when a first subset of input attributes is provided as input to the first submodel; and determining a second performance of the second submodel based on output of the second submodel when the first subset of input attributes is provided as input to the second submodel.” (claims 5, 15 ), “wherein the input attributes include one or more of personal information associated with the first entity, performance history of the first entity, and a confidence metric associated with the performance history, wherein the performance history includes one or more of opportunities per year, average opportunity profit, win probability and expected profit.”(claims 6,, “wherein the hybrid data adapted inference model is obtained prior to the portion of the collected data being provided to the entity” (claims 6,16), “receiving personal information associated with the first entity from a user.(claims 7, 17) “monitoring activity by the plurality of entities, the monitoring over time; determining, based on the monitoring, the input attributes associated with the plurality of entities; and storing, within a database, the determined input attributes.” (claims 8, 18); monitoring activity by the plurality of entities, the monitoring over time; determining, based on the monitoring, the input attributes associated with the plurality of entities; and storing, within a database, the determined input attributes.” (claims 9, 19). These additional limitations (in claims 2-9, 11-15 and 17-20) also constitute concepts performed in the human mind which fall within the “Mental Processes” groupings of abstract ideas.
This judicial exception is not integrated into a practical application. Additional elements “non-transitory computer readable medium comprising: computer program code” (in claims 20) and processor; and memory storing instructions (11-19) all amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of non-transitory computer readable medium comprising: computer program code are again insignificant extra-solution activity steps that cannot provide an inventive concept. All of these additional elements as generically claimed are considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all of the dependent claims are also not patent eligible.
Examiner Comments
7. 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 § 103
8. 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.
9. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (Pub. No. US 20200320462 A1 published on 2020-10-08) in view of Telmo (NPL: Title: Classifier calibration: a survey on how to assess and improve predicted class probabilities, Pub. Date: 5 January 2022)
.
Regarding independent Claim 1,
Wang teaches the method comprising:
receiving data characterizing input attributes associated with a plurality of entities and a target action (see Wang: Fig.7, [0088], “collecting (e.g., via weighted organizational distance system 102 and/or data collection component 602) data corresponding to one or more entities of an organization hierarchy.”)
determining, by a first submodel of a plurality of submodels, a first class associated with a first entity of the plurality of entities, the first-class indicative of calculation of a first impact score of the target action (see Wang: Fig.7, [0090], “calculating an entity impact score (e.g., via weighted organizational distance system 102, link weight component 402, and/or entity impact component 502) for entity A and an entity impact score for B (e.g., using the direct connections of entity A and of entity B, differentiating u, d, p links).”);
determining a first plurality of weighted attributes associated with the first entity (see Wang: Fig.7, [0091], “calculating (e.g., via weighted organizational distance system 102, weighted organizational distance component 108, link weight component 402, and/or entity impact component 502) the weighted organizational distance score of the pair of entities, entity A and entity B (e.g., using the entity impact scores of entity A and entity B multiplied by relative rank weights (e.g., person associated with the organization to manager, executive to executive, etc.)).
determining a first impact function associated with the first entity based on the first entity (see Wang: Fig.8, [0098], “calculating (e.g., weighted organizational distance system 102 and/or weighted organizational distance component 108) the weighted organizational distance score of the team (e.g., using a normalized mean of the weighted organizational distance scores of all pairs of entities of the team)”), wherein the first impact function based on attributes associated with the first entity and the target action (see Wang: Fig.8, [0099], “learner component 110 can facilitate generating information based on one or more of the weighted organizational distance scores that can be calculated by weighted organizational distance component 108 as described herein, where such weighted organizational distance scores can be calculated based on the respective entity impact scores of each entity of Project Team 1 represented by organization chart 200b and/or other data (e.g., the data, attributes, and/or actions defined above with reference to FIG. 5) corresponding to one or more of such entities of Project Team 1.”); and
calculating, by the determined first impact function, the first impact score associated with the first entity (see Wang: Fig.7, [0090], “at 706, computer-implemented method 700 can comprise calculating an entity impact score (e.g., via weighted organizational distance system 102, link weight component 402, and/or entity impact component 502) for entity A and an entity impact score for B (e.g., using the direct connections of entity A and of entity B, differentiating u, d, p links).)”, wherein the calculation is based on one or more of the first plurality of weighted attributes (see Wang: Fig.8, [0099], “the weighted organizational distance score of the team;”), the input attributes and/or a type associated with the target action (see Wang: Fig.8, [0099], “ weighted organizational distance score of a pair of entities; the entity impact score of one or more entities of the team; and/or other data corresponding to one or more entities of the team.”), wherein the first impact score is indicative of a probability that the first entity will successfully perform the target action (see Wang: Fig.9, [0109], “computer-implemented method 900 can comprise employing, by the system (e.g., via weighted organizational distance system 102 and/or learner component 110), an artificial intelligence model to generate information (e.g., a recommendation, a prediction, etc.) based on the weighted organizational distance score.” … [0046], “generate at least one of an entity to entity connection recommendation, an entity assignment to a team recommendation, a team of defined entities recommendation, or a team performance prediction based on the weighted organizational distance score.”)
Wang does not explicitly teach the system wherein:
assigning the target action to an entity among the plurality of entities based at least on the calculated first impact score without requiring retraining of the plurality of submodels when the first impact score is different from a second impact score of a second entity of the plurality of entities.
However, Telmo teaches the system wherein:
assigning the target action to an entity among the plurality of entities based at least on the calculated first impact score (see Telmo: page 3211-3212, Section 1, Introduction, “A K-class probabilistic classifier is well-calibrated if among test instances receiving a predicted K-dimensional probability vector s, the class distribution is (approximately) distributed as s. This property is of fundamental importance when using a classifier for cost sensitive classification, for human decision making, or within an autonomous system. It means that the classifier correctly quantifies the level of uncertainty or confidence associated with its predictions.” i.e. predicted probabilities output are used for selecting and assigning an outcome at inference time), without requiring retraining of the plurality of submodels when the first impact score is different from a second impact score of a second entity of the plurality of entities (see Telmo: page 3215, Section 2, A predicted probability (vector) should match empirical (observed) probabilities. In the language of predictive machine learning, given an instance space, a binary target space = {+, −} , and a binary probabilistic classifier 𝖿 ∶ binary classifier is calibrated if ∀s ∈[0, 1]:(Y =+ (X)=s)=s”, i.e. once the model is trained, score comparison and selection are performed at inference time without retraining and trained classifiers output probability vectors that are used directly for decision making at runtime.)
Because both Wang and Telmo address the same issue of AI model predicting optimization, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Wang to include the system that assigning the target action to an entity based at least on the calculated first impact score without requiring retraining as taught by Telmo. One would have been motivated to make such a combination in order to provide efficient real time training and aa reduction in computational; overhead by avoiding retraining of large models.
Regarding Claim 2,
As shown above, Wang and TELMO teaches all the limitations of claim 1. Wang further teaches the method wherein determining the first impact function (see Wang: Fig.7, [0090], “at 706, computer-implemented method 700 can comprise calculating an entity impact score (e.g., via weighted organizational distance system 102, link weight component 402, and/or entity impact component 502) for entity A and an entity impact score for B (e.g., using the direct connections of entity A and of entity B, differentiating u, d, p links).)”, includes:
accessing, using an identity of the first entity, the first impact function from a table of impact functions (see Wang: Fig.5, [0096], “entity impact component 502 can calculate an entity impact score of one or more entities of an organization hierarchy by calculating a direct connection score of each of such entities, where such direct connection score of each entity can respectively account for the direct connections of one or more other entities.”);
determining, using the first plurality of weighted attributes, a value of a true positive, a value of a true negative, a value of a false positive, and/or a value of a false negative (see Wang: Fig.7, [0078], “based on historical data, weighted organizational distance component 108 and/or entity impact component 502 can employ CNN model to optimize the loss function to determine a desired value (e.g., acceptable value within a predefined range of acceptable values) of Beta, Theta, u, d, and p, using equation (1) defined below.”); or
weighting, using the first plurality of weighted attributes, the value of the true positive, the value of the true negative, the value of the false positive, and/or the value of the first negative.
Regarding Claim 3,
As shown above, Wang and TELMO teaches all the limitations of claim 3. Wang further teaches the method comprising:
calculating, by a second impact function, a second impact score associated with a second entity of the plurality of entities, wherein the calculation is based on one or more of a second plurality of weighted attributes, the input attributes, and/or the type associated with the target action (see Wang: Fig.7, [0090], “at 706, computer-implemented method 700 can comprise calculating an entity impact score (e.g., via weighted organizational distance system 102, link weight component 402, and/or entity impact component 502) for entity A and an entity impact score for B (e.g., using the direct connections of entity A and of entity B, differentiating u, d, p links).)”,, wherein the second impact score is indicative of a probability that the second entity will successfully perform the target action comparing the first impact score with the second impact score (see Wang: Fig.9, [0109], “computer-implemented method 900 can comprise employing, by the system (e.g., via weighted organizational distance system 102 and/or learner component 110), an artificial intelligence model to generate information (e.g., a recommendation, a prediction, etc.) based on the weighted organizational distance score.”); and
assigning the target action to the first entity or the second entity based on the comparison between the first impact score and the second impact score (see Wang: Fig.7, [0090], “entity impact component 502 can calculate an entity impact score of an entity of an organization hierarchy (e.g., entity A and entity B of organization chart 200a illustrated in FIG. 2) by calculating a direct connection score of each of such entities, where such direct connection scores of entity A and entity B can respectively account for the direct connections of each entity.”)
Regarding Claim 4,
As shown above, Wang and TELMO teaches all the limitations of claim 3. Wang further teaches the method comprising:
determining the first class associated with the second entity, the first-class indicative of calculation of the second impact score and the second plurality of weighted attributes (see Wang: Fig.5, [0075], “, entity impact component 502 can calculate an entity impact score of at least one entity of an organization hierarchy based on data including, but not limited to: metadata; organizational rank (e.g., person associated with an organization ranking relative to other people associated with the organization, position of the entity within an organization chart, etc.); reporting chain; direct connection to one or more other entities of the organization hierarchy (e.g., direct connection to one or more peers, one or more managers, one or more reporting people associated with the organization, etc.);”; and
determining the second impact function based on the second plurality of weighted attributes (see Wang: Fig.5, [0078], “calculate an entity impact score of an entity of an organization hierarchy based on an online community impact score of the entity that can represent the entity's online community activity (e.g., social network activity such as, for instance, message posts, responses to posts, etc.),”)
Regarding Claim 5,
As shown above, Wang and TELMO teaches all the limitations of claim 3. Wang further teaches the method comprising:
training, using the received data characterizing input attributes, the first submodel and the second submodel (see Wang: Fig.9, [0109], “computer-implemented method 900 can comprise employing, by the system (e.g., via weighted organizational distance system 102 and/or learner component 110), an artificial intelligence model to generate information (e.g., a recommendation, a prediction, etc.) based on the weighted organizational distance score.”)
determining a first performance of the first submodel based on output of the first submodel when the first set of input attributes is provided as input to the first submodel (see Wang: Fig.9, [0103], “weighted organizational distance system 102 can provide technical improvements to a processing unit (e.g., processor 106) associated with a classical computing device and/or a quantum computing device (e.g., a quantum processor, quantum hardware, superconducting circuit, etc.)”; and
determining a second performance of the second submodel based on output of the second submodel when the first subset of input attributes is provided as input to the second submodel (see Wang: Fig.9, [0109] “weighted organizational distance system 102 can provide technical improvements to a processing unit (e.g., processor 106) associated with a classical computing device and/or a quantum computing device (e.g., a quantum processor, quantum hardware, superconducting circuit, etc.)”),
Regarding Claim 6,
As shown above, Wang and TELMO teaches all the limitations of claim 1. Wang further teaches the method wherein:
the first subset of input attributes includes one or more of personal information associated with the first entity, performance history of the first entity, and a confidence metric associated with the performance history, wherein the performance history includes one or more of opportunities per year, average opportunity profit, win probability and expected profit (see Wang: Fig.5, [0088], “data collection component 602 can collect such data, which can include, but is not limited to: metadata; organizational rank; reporting chain; direct connection to one or more other entities of the organization hierarchy; an entity impact score of a directly connected entity of the organization hierarchy; online community activity; and/or other data.”)
Regarding Claim 7,
As shown above, Wang and TELMO teaches all the limitations of claim 6. Wang further teaches the method comprising:
receiving personal information associated with the first entity from a user (see Wang: Fig.5, [0106], “entity impact component 502, and/or data collection component 602 can be more complex than information obtained manually by a human user.”)
Regarding Claim 8,
As shown above, Wang and TELMO teaches all the limitations of claim 1. Wang further teaches the method comprising:
monitoring activity by the plurality of entities, the monitoring over time (see Wang: Fig.5, [0105], “the amount of data processed, the speed of processing such data, or the types of data processed by weighted organizational distance system 102 over a certain period of time can be greater, faster, or different than the amount, speed, or data type that can be processed by a human mind over the same period of time.”);
determining, based on the monitoring, the input attributes associated with the plurality of entities (see Wang: Fig.7, [0092] “weighted organizational distance scores can be calculated based on the respective entity impact scores of entity A and entity B of organization chart 200a and/or other data (e.g., the data, attributes, and/or actions defined above with reference to FIG. 5) corresponding to entity A and/or entity B.”); and
storing, within a database, the determined input attributes (see Wang: Fig.6, [0085], “data collection component 602 can store (e.g., via processor 106) collected data on a memory (e.g., memory 104) where it can be retrieved and/or used by any components of weighted organizational distance system 102 (e.g., weighted organizational distance component 108, learner component 110, link weight component 402, entity impact component 502, etc.).
Regarding Claim 9,
As shown above, Wang and TELMO teaches all the limitations of claim 1. Wang further teaches the method comprising:
receiving data characterizing a first capacity of the first entity (see Wang: Fig.5, [0094], “data collection component 602 can collect such data, which can include, but is not limited to: metadata; organizational rank; reporting chain; direct connection to one or more other entities of the organization hierarchy; an entity impact score of a directly connected entity of the organization hierarchy; online community activity; and/or other data.”); and
selecting the first submodel from the plurality of submodels based on the first capacity (see Wang: Fig.1, [00068], “learner component 110 can comprise and/or employ an AI model to generate information based on one or more of the weighted organizational distance scores that can be calculated by weighted organizational distance component 108 according to one or more embodiments of the subject”), wherein the selected first submodel is for use to determine the first plurality of weighted attributes (see Wang: Fig.1, [0068], “earner component 110 can comprise and/or employ an AI model to generate information based on one or more of the weighted organizational distance scores that can be calculated by weighted organizational distance component 108.”).
Regarding Claim 10,
As shown above, Wang and TELMO teaches all the limitations of claim 9. Wang further teaches the method comprising:
determining the first capacity (see Wang: Fig.9, [0099], “calculating (e.g., weighted organizational distance system 102 and/or weighted organizational distance component 108) the weighted organizational distance score of the team (e.g., using a normalized mean of the weighted organizational distance scores of all pairs of entities of the team).”), by at least:
monitoring historical activity of the first entity and predicting the first capacity based on the monitored historical activity (see Wang: Fig.9, [0099], “generating (e.g., via weighted organizational distance system 102 and/or learner component 110) information (e.g., a recommendation, prediction, etc.)”; or
monitoring a future schedule of the first entity and predicting the first capacity based on the monitored schedule.
Regarding independent Claim 11 and 20,
Claims 11 is directed to a system claim and claim 20 is directed non-transitory computer readable medium claim and the claims have same/similar claim limitation as claim 1 and are rejected under the same rationale.
Regarding Claim 12-19,
Claims 12-19 are directed system claim and the claims have same/similar claim limitation as claim 2-10 and is rejected under the same rationale.
Response to amendments
Claim Rejections - 35 U.S.C. § 103,
Applicant’s arguments with respect to claim amendments have been considered but are moot considering the new combination of references being used in the current rejection. The new combination of references was necessitated by Applicant’s claim amendments. Therefore, the claims are rejected under the new combination of references as indicated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
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TITLE / DESCRIPTION
US 11922470 B2
Null; Bradley William
Title: Impact-based Strength and Weakness Determination
Description: Described herein are techniques for managing the online reputation of an entity such as a business and/or an individual. In various embodiments, the techniques described herein include techniques for determining insights from feedback data about entities to perform reputation management processing, such as virality cause determination (e.g., risk management), impact-based strength and weakness determination, and reputation score calibration
US 20200356900 A1
Briançon; Alain Charles
Title: PREDICTIVE, MACHINE-LEARNING, LOCALE-AWARE COMPUTER MODELS SUITABLE FOR LOCATION- AND TRAJECTORY-AWARE TRAINING SETS
Description: The present disclosure relates generally to predictive computer models and, more specifically, to predictive, machine-learning, time-series computer models suitable for sparse training sets.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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, Cesar Paula can be reached on (571) 272-4128. 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.
/Zelalem Shalu/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145