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 .
Priority
Application 18/193,863 filed on 03/31/2023 claim priority to EUROPEAN PATENT OFFICE (EPO) 20200081.6 filed on 10/05/2020.
Status
This office action is a first office action, non-final rejection, based on the merits. Claims 1-12 are pending and have been considered below.
Claim Objections
Claim 5 objected to because of the following informalities:
Claim 5 claims “wherein second energy consumption scenarios.” Examiner believes this is in reference to “a second energy consumption scenario” of claim 1 and should read “the second energy consumption scenario.” However, it could be a different concept intended by the applicant, explanations are requested.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 2 and 6 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.
Regarding claim 2: Claim 2 claims “the data being similar means a deviation of each data of the first time-series of energy consumption data to the data of the second time-series of energy consumption data of less than between 1–40%.” Examiner is uncertain as to the meaning of “less than between 1–40%.” Does it mean the deviation can be less than 40%, 30%, 20%, 10%, or 1% or does it mean that the deviation is less than 1% only? A different concept could be intended by the Applicant, explanations are requested. Claim 2 will be examined based on the merits as best understood.
Regarding claim 6: Claim 6 states “the quality measure (18) is attributed.” Examiner is uncertain as to what (18) is in reference. Additionally, Examiner is uncertain to what the quality measure is attributed. Explanations are requested.
Claim 6 will be examined based on the merits as best understood.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea being underlined.
A method for evaluating an energy efficiency of a second energy consumption scenario of a site, the method comprising the steps of:
obtaining a first energy consumption scenario, which comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario;
obtaining the second energy consumption scenario, which comprises a second time-series of energy consumption data, wherein the second energy consumption scenario has a same or a shorter duration than the first energy consumption scenario;
comparing the second time-series of energy consumption data to the first time-series of energy consumption data;
when the second time-series of energy consumption data is similar to the first time-series of energy consumption data, outputting the quality measure of the first energy consumption scenario; and
controlling the site’s power consumption, based on the quality measure.
Claim 12 is copied below, with the limitations belonging to an abstract idea being underlined.
An artificial neural net (ANN), which is configured to:
in a first learning phase, obtaining a plurality of first energy consumption scenarios, which each comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario;
in a second learning phase, obtaining a plurality of second energy consumption scenarios, which each comprises a second time-series of energy consumption data of at least one device, and a similarity assessment of each second energy consumption scenario to each first energy consumption scenario;
in a third learning phase, analyzing the similarity assessments, by the ANN, in a productive phase, applying, by the ANN, the similarity assessments to a newly obtained second energy consumption scenario; and
when a similarity assessment of the newly obtained second energy consumption scenario is greater than a predefined value, outputting the quality measure for the energy efficiency of the scenario.
Regarding the underlined limitation: comparing the second time-series of energy consumption data to the first time-series of energy consumption data;
when the second time-series of energy consumption data is similar to the first time-series of energy consumption data, is an abstract idea at it is a programming routines and patterns for comparing data, it is an algorithm or program which is a mathematical routine or a set of mental steps.
Regarding the underlined limitation: analyzing the similarity assessments, by the ANN, in a productive phase, applying, by the ANN, the similarity assessments to a newly obtained second energy consumption scenario is an abstract idea at it is a programming routines and patterns for comparing data, it is an algorithm or program which is a mathematical routine or a set of mental steps.
Regarding the learning phases: Learning phases are part of training a neural network and using the broadest reasonable interpretation, training the machine learning algorithm requires specific mathematical calculations and therefore encompasses mathematical concepts (see 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, example 47 claim 2). These steps recited by the claim therefore amount to a series of mental and/or mathematical steps, making these limitations amount to an abstract idea.
In summary, the highlighted steps in the claims above therefore recite an abstract idea at Prong 1 of the 101 analysis.
The additional elements in the claim have been left in normal font. The additional concepts of “obtaining,” “outputting,” and “controlling” equate to routine data gathering and extra solution data activity (see MPEP 2106.05(g)) and are not significantly more to provide an practical application.
The claims do not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claim does not recite a particular machine applying or being used by the abstract idea. The claim does not effect a real-world transformation or reduction of any particular article to a different state or thing. (Manipulating data from one form to another or obtaining a mathematical answer using input data does not qualify as a transformation in the sense of Prong 2.)
The claim does not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea. (This is understood in the sense of the claimed invention from Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing process including a rubber-molding press, a timer, a temperature sensor adjacent the mold cavity, and the steps of closing and opening the press, in which the recited use of a mathematical calculation served to improve that particular technology by providing a better estimate of the time when curing was complete. Here, the claim does not recite carrying out any comparable particular technological process.) In all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the abstract idea itself, rather than integrate the abstract idea into a practical application.
Step 2b of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
Therefore, claims 1 and 12 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claim 2-11 are similarly ineligible. Dependent claims 2-11 further detail the abstract idea but do not help to integrate the claim into a practical application or make it significant more than the abstract idea (which is recited in slightly more detail, but not in enough detail to be considered to narrow the claim to a particular practical application itself). Considering all the limitations individually and in combination, the claimed additional elements do not show any inventive concept to applying algorithms such as improving the performance of a computer or any technology, and do not meaningfully limit the performance of the application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-4 and 6-12 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka et al., hereinafter Matsuoka, U.S. Pub. No. 2014/0058567 A1 in view of Kamel et al., herein after Kamel, U.S. Pub. No. 2018/0299917 A1.
Regarding Independent claim 1 Matsuoka teaches:
“A method for evaluating an energy efficiency of a second energy consumption scenario of a site” (Matsuoka, ¶ 0031).
“the method comprising the steps of: obtaining a first energy consumption scenario, which comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario” (Matsuoka, fig 49, ¶ 0186: Matsuoka teaches “historical or baseline performance is either calculated or retrieved (e.g. from a database with thermostat management system 4606 in Figs. 46A and 46B) (¶ 0186) where “historical performance” discloses “first energy consumption scenario” where the “energy saving performance is measured only by virtue of physical parameters that can be sensed or governed by the thermostat itself” (¶ 0186). Additionally Matsuoka teaches “calculate or retrieve historical energy saving performance using standalone performance metric” (fig 49 step 4912) where “historical energy saving performance” discloses “a quality measure of the first energy consumption scenario.” Moreover, the historical database contains both real time and non real time setpoint entries (fig 42A step 4212) disclosing the “energy consumption data” is “time-series” data and the thermostat controls an HVAC system disclosing “at least one device.”
“obtaining the second energy consumption scenario, which comprises a second time-series of energy consumption data, wherein the second energy consumption scenario has a same or a shorter duration than the first energy consumption scenario”
Matsuoka teaches “calculate current energy saving performance using standalone performance metric (e.g. using information available from the thermostat itself and/or thermostat connected devices” (fig 49 step 4910) and “In step 4914 the current performance is compared with the historical performance” (00186) where “current performance” discloses “second energy consumption scenario.” Additionally, the data from the thermostat is both real-time and non-real-time data (¶ 0095) disclosing “time-series” data is used to calculate a “second energy consumption scenario.” Moreover, the “current energy saving performance” discloses a “quality measure of the first energy consumption scenario.” Moreover, the compared historical data and current data have similar effective times (¶ 0119) where that time may be bi-weekly, semi-weekly, monthly, bi-monthly or seasonal (¶ 0091).)
“comparing the second time-series of energy consumption data to the first time-series of energy consumption data” (Matsuoka teaches “In step 4914 (of fig 49) the current performance is compared with the historical performance” (¶ 0186))
“when the second time-series of energy consumption data is similar to the first time-series of energy consumption data and controlling the site’s power consumption, based on the quality measure” (Matsuoka teaches “if the current performance is not significantly greater (e.g. greater by more than a predetermined threshold percentage, such as 1-5%) than the historical performance” (¶ 0186) disclosing “the second time-series of energy consumption data is similar to the first time-series of energy consumption data” then “one or more strategies for helping or encouraging the use to improve performance is calculated and displayed to the user” (¶ 0186) disclosing “controlling the site’s power consumption, based on the quality measure.”)
While Matsuoka teaches comparing current (second energy consumption scenario) and historical data (first energy consumption scenario) and displaying strategies for improving energy performance Matsuoka does not explicitly teach “outputting the quality measure of the first energy consumption scenario”
Kamel teaches:
“outputting the quality measure of the first energy consumption scenario” (Kamel, fig 9, fig 10, ¶ 0118-¶ 0119: Kamel teaches displaying a “monthly key performance indicator chart” where the chart “indicates the energy performance for a month” where the color of the bar indicates the energy performance where “red indicates a poor score, green indicates a good score, and yellow indicates a mediocre score that could be improved” (¶ 0118). Therefore the combination of Matsuoka and Kamel disclose the limitation “when the second time-series of energy consumption data is similar to the first time-series of energy consumption data, outputting the quality measure of the first energy consumption scenario.”
It would have been obvious for a person of ordinary skill in to art to have modified the system and method for improving energy efficiency of a device as taught by Matsuoka by including outputting the energy performance indicator as disclosed by Kamel in order to improve the user friendly system of Matsuoka. Matsuoka displays strategies to improve energy efficiency of a device and displaying the energy performance indicator would enhance the user’s understanding of why the energy efficiency of the device needs to be improved.
Regarding claim 2 Matsuoka as modified teaches:
“wherein the data being similar means a deviation of each data of the first time-series of energy consumption data to the data of the second time-series of energy consumption data of less than between 1–40%” (Matsuoka, ¶ 0186: Matsuoka teaches “if the current performance is not significantly greater (e.g. greater by more than a predetermined threshold percentage, such as 1-5%) than the historical performance” (¶ 0186) discloses a deviation of less than between 1-40% between the “first time-series of energy consumption data” (historical performance data) and “the data of the second time-series of energy consumption data” (current performance data).
Regarding claim 3 Matsuoka as modified teaches:
“wherein the data being similar means that a trained artificial neural net, ANN, outputs the data of the second time-series of energy consumption data being part of the data of the first time-series of energy consumption data” Matsuoka teaches the compared historical data (first time-series of energy consumption data) and current data (second time-series of energy consumption data) have similar effective times (¶ 0119) where that time may be bi-weekly, semi-weekly, monthly, bi-monthly or seasonal (¶ 0091). A person of ordinary skill in the art would understand that the current data would be part of the historical data until a new time frame, where a new set of current data, would be collected.)
Regarding claim 4 Matsuoka as modified teaches:
“wherein the quality measure comprises an energy consumption class, a quality estimation and/or a measurement result of the energy consumed in this scenario” (Matsuoka, ¶ 0186-¶ 0188: Matsuoka teaches the “energy saving performance” (quality measure) is based on, in some instances, a “stand-alone performance metric (SPM)” (¶ 0186) where “an SPM is based only on the percentage of time that their HVAC system is cycled on (“on-time percentage” or “OTP”), wherein the performance metric is higher (better) when the on-time percentage is lower” (¶ 0187) where on-time percentage discloses “a measurement result of the energy consumed in this scenario.”)
Regarding claim 6 Matsuoka as modified teaches:
“wherein the quality measure (18) is attributed” (Matsuoka, fig 49, ¶ 0186: The specification states “The quality measure may be attributed, for instance, with a comment, a recommendation, a hint, a statement, or the like” (PG PUB ¶ 0036). Matsuoka teaches “one or more strategies for helping or encouraging the user to improve performance is calculated and displayed to the user” (¶ 0186) where the strategies are with respect to the current energy saving performance (quality measure) (see fig. 49) disclosing “the quality measure (18) is attributed.”)
Regarding claim 7 Matsuoka as modified teaches:
“wherein the at least one device comprises a machine driven by electrical, mechanical, chemical, and/or further energy sources” (Matsuoka teaches a device as a HVAC system which provides “a source of heat using electricity or gas” (¶ 0058).)
Regarding claim 8 Matsuoka as modified teaches:
“wherein the first energy consumption scenario and the second energy consumption scenario comprise energy consumption data of at least two devices” (Matsuoka, fig 49, fig. 50, ¶ 00186-¶ 0190: Matsuoka teaches energy efficient behavior “using performance metrics through competition with others” where “the user’s energy-saving performance is measured using an SPM” (stand-alone performance metric) and “current energy performance (second energy consumption scenario) is either calculated or retrieved” (¶ 0190) where “others” form a “competition group” where a “ranking could be calculated in terms of current energy efficiency or some absolute or relative efficiency metric” (¶ 0190). The ranking “could be calculated in terms of most improved when compared with their own historical performance values” (first energy consumption scenario) (¶ 0190). Therefore Matsuoka discloses “the first energy consumption scenario and the second energy consumption scenario comprise energy consumption data of at least two devices” as each competitor would have their own data associated with their individual devices.)
Regarding claim 9 Matsuoka as modified teaches:
“wherein the first energy consumption scenario and the second energy consumption scenario comprise input-data” (Matsuoka, fig. 49, ¶ 0186: Matsuoka teaches “In step 4914 the current performance is compared with the historical performance” (0186) where “current performance” discloses “second energy consumption scenario.” and “historical performance” discloses “first energy consumption scenario.” Because they are “compared,” they must be “input-data.”)
Regarding claim 10 Matsuoka as modified teaches:
“wherein the input-data comprise environment data, schedule data, production cycle data, and/or other data to influence at least one device of a scenario” (Matsuoka, ¶ 0057, ¶ 0187, Matsuoka teaches using “outside weather, such as the outside temperature” when determining the “User’s energy-saving performance” (¶ 0187) disclosing “environment data” as “input-data”).
Regarding claim 11 Matsuoka as modified teaches:
“when the second time-series of input-data is similar to more than one first time-series of input-data, namely to a primary and a secondary time-series of input-data of a primary and a secondary energy consumption scenario, outputting the quality measure of the primary and the secondary energy consumption scenario” (Matsuoka teaches “In decision step 4920 (of fig. 49), if the current performance (primary time-series of input-data) is not significantly greater (e.g. greater by more than a predetermined threshold percentage, such as 1-5%) than the historical performance (the second time-series of input-data), control passes back up to comparison step 4914 (of fig 49)” (¶ 0186) before which in step 4922 of fig 49 strategies for improving user’s current energy savings performance are “calculated and displayed” (fig. 49). After this the comparison (step 4914) is run again between the current performance and the historical performance disclosing a “secondary time-series of input data” as the current performance changes due to the strategies. Additionally, “the amount of energy and/or money saved is calculated and/or displayed to the user” (¶ 0186) disclosing outputting the quality measure of the primary and the secondary energy consumption scenario” as “the amount of energy saved” discloses the “quality measure.”)
Regarding Independent claim 12 Matsuoka teaches:
“An artificial neural net (ANN)” (Matsuoka, ¶ 0054, Matsuoka teaches “the design of thermostat management system 4606 and use of the thermostat management servers 4620 may be scaled to meet these demands on the system and efficiently track and organize the data from these multiple enclosures and thermostats for processing analysis, control and machine-learning purposes” (¶ 0167) where “machine-learning purposes” discloses a training phase or a learning phase. Moreover, Matsuoka teaches using “artificial neural networks” to carry out the “machine learning and mathematical optimization algorithms” (¶ 0054) therefore Matsuoka teaches using an “ANN” to carry out the “first learning phase,” “second learning phase,” and the analysis and application of the “third learning phase.”
“configured to: obtaining a plurality of first energy consumption scenarios, which each comprises a first time-series of energy consumption data of at least one device, and a quality measure of the first energy consumption scenario” (Matsuoka, fig 49, fig 50, ¶ 0186-¶ 0190: Matsuoka teaches “historical or baseline performance is either calculated or retrieved (e.g. from a database with thermostat management system 4606 in Figs. 46A and 46B) (¶ 0186) where “historical performance” discloses “first energy consumption scenario” where the “energy saving performance is measured only by virtue of physical parameters that can be sensed or governed by the thermostat itself” (¶ 0186). Additionally Matsuoka teaches “calculate or retrieve historical energy saving performance using standalone performance metric” (fig 49 step 4912) where “historical energy saving performance” discloses “a quality measure of the first energy consumption scenario.” Moreover, the historical database contains both real time and non real time setpoint entries (fig 42A step 4212) disclosing the “energy consumption data” is “time-series” data and the thermostat controls an HVAC system disclosing “at least one device.” Matsuoka teaches energy efficient behavior “using performance metrics through competition with others” where each of the “others” has a device for which the “historical performance”(first energy consumption scenario) is “either calculated or retrieved” (¶ 0190) thereby disclosing a “plurality of first energy consumption scenarios”
“obtaining a plurality of second energy consumption scenarios, which each comprises a second time-series of energy consumption data of at least one device, and a similarity assessment of each second energy consumption scenario to each first energy consumption scenario” (Matsuoka, fig 49, fig 50, ¶ 0095, ¶ 0186-¶ 0190)Matsuoka teaches “calculate current energy saving performance using standalone performance metric (e.g. using information available from the thermostat itself and/or thermostat connected devices” (fig 49 step 4910) and “In step 4914 the current performance is compared with the historical performance” (00186) where “current performance” discloses “second energy consumption scenario.” Additionally, the data from the thermostat is both real-time and non-real-time data (¶ 0095) disclosing “time-series” data is used to calculate a “second energy consumption scenario.” Moreover, “In step 4914 the current performance is compared with the historical performance” (¶ 0186) where “current performance” discloses “second energy consumption scenario.” and “historical performance” discloses “first energy consumption scenario” and where the comparison discloses a “similarity assessment.” Matsuoka teaches energy efficient behavior “using performance metrics through competition with others” where each of the “others” has a device for which the “current performance”(second energy consumption scenario) is “either calculated or retrieved” (¶ 0190) thereby disclosing a “plurality of second energy consumption scenarios.”)
“analyzing the similarity assessments, in a productive phase, applying the similarity assessments to a newly obtained second energy consumption scenario” (Matsuoka, fig 49, fig 50, ¶ 0186-¶ 0190: Matsuoka teaches “In step 4914 the current performance is compared with the historical performance” (¶ 0186) where “current performance” discloses “second energy consumption scenario.” and “historical performance” discloses “first energy consumption scenario” and where the comparison discloses a “similarity assessment.” If the result of the comparison is not greater than 1-5%, the system enters a loop where strategies for improving the performance, the “current performance,” are displayed. A new step 4914, the comparison, is performed using a different “current performance” thereby disclosing “applying the similarity assessments to a newly obtained second consumption scenario.”
“when a similarity assessment of the newly obtained second energy consumption scenario is greater than a predefined value, outputting the quality measure for the energy efficiency of the scenario” (Matsuoka, fig 49, fig 50, ¶ 0186-¶ 0190: Matsuoka teaches “In decision step 4920 (of fig 49) if the current performance is not significantly greater (e.g. greater by more than a predetermined threshold percentage, (a predefined value) such as 1-5%) than the historical performance, control passes back up to the comparison step 4914” (¶ 0186) therefore if the performance is greater than 1-5% the “amount of energy and/or money saved is calculated and/or displayed to the user” (¶ 0186) disclosing “outputting the quality measure for the energy efficiency of the scenario.”)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka as modified by Kamel as applied to claim 4 above, and further in view of Venkatakrishnan et al., hereinafter Venkatakrishnan, U.S. Pub. 2012/0053740 A1.
Regarding claim 5 Matsuoka as modified does not teach:
“wherein second energy consumption scenarios of essentially the same quality measure are aggregated.”
Venkatakrishnan teaches:
“wherein second energy consumption scenarios of essentially the same quality measure are aggregated.” (Venkatakrishnan, ¶ 0033-¶ 0034: Venkatakrishnan teaches a “thermostat controller is coupled to the central controller” which provides “consumption data (second energy consumption scenarios) to the user via a user interface of a client application” and “individual home data is compiled into an aggregate home data base for multiple homes for a comparison to be made with the consumer’s individual home and other similar type homes” (¶ 0033) where “similar type homes” are based on “total power/energy consumption of the home” and “power/energy consumption of individual energy consuming devices at the home” (¶ 0034) disclosing “essentially the same quality measure.”)
Both Matsuoka and Venkatakrishnan are concerned with improving the energy efficiency of a system therefore it would have been obvious for a person of ordinary skill in to art to have modified the system and method for improving energy efficiency of a device as taught by Matsuoka by including by including the well known method of aggregating data as taught by Venkatakrishnan as aggregating data provides for enhanced identification of trends and patterns in order to provide an “increase efficiency” (Venkatakrishnan, ¶ 0034).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Brickfield et al., U.S. Pub. No. 2007/0255461 A1, teaches using an artificial neural network to compare current data to historic data for improving energy efficiency.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Denise R Karavias whose telephone number is (469)295-9152. The examiner can normally be reached 7:00 - 3:00 M-F.
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, Arleen M. Vazquez can be reached at 571-272-2619. 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.
/DENISE R KARAVIAS/
Examiner, Art Unit 2857
/MICHAEL J DALBO/Primary Examiner, Art Unit 2857