DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
2. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Application 18/903,722
U.S. patent 12,106,572 B2
1: A real-time crowd measurement and management system comprising:
a data collection module comprising a plurality of data capturing devices installed in a plurality of zones, respectively, the plurality of data capturing devices are configured to continuously capture crowd data of a plurality of crowds in the plurality of zones, wherein each of the plurality of crowds comprises a plurality of people;
an analysis module configured to:
identify a plurality of crowd characteristics from the captured crowd data, wherein the crowd characteristics comprise a crowd density, a crowd flow, and a crowd mood;
analyse the captured crowd data to determine one or more selected from patterns and
changes in mood of at least one of the plurality of crowds, including measuring the relationship between crowd density, crowd flow, and crowd mood in order to increase predictability of crowd behaviour;
predict, based on the analysis, crowd information comprising at least one of one or more emergent crowd characteristics,
an emergent crowd behaviour of one or more of the plurality of crowds; and
a display module configured to display the predicted crowd information along with at least one of an alert and at least one indicator in real-time.
Regarding claim 2: The real-time crowd measurement and management system of claim 1, wherein the analysis module is configured to suggest one or more actions for managing the plurality of crowds based on the crowd information.
5. The real-time crowd measurement and management system of claim 1, wherein the at least one indicator comprises a colour indicator for denoting the crowd information comprising crowd mood in real-time.
20. A non-transitory computer-readable storage medium measuring and managing crowd in real-time, when executed by a computing device, cause the computing device to, in real time: continuously capture crowd data of a plurality of crowds in a plurality of zones, wherein each of the plurality of crowds comprising a plurality of people; identify a plurality of crowd characteristics from the captured crowd data, wherein the crowd characteristics comprises a crowd density, a crowd flow, and a crowd mood;
analyse the captured crowd data to determine one or more selected from patterns and changes in mood of at least one of the plurality of crowds, including measuring the relationship between crowd density, crowd flow, and crowd mood in order to increase predictability of crowd behaviour; predict, based on the analysis, crowd information comprising at least one of one or more emergent crowd characteristics, an emergent crowd behaviour of one or more of the plurality of crowds; and display the predicted crowd information along with at least one of an alert and at least one indicator in real-time.
1. A real-time crowd measurement and management system comprising: a data collection module comprising a plurality of data capturing devices installed in a plurality of zones, respectively, the plurality of data capturing devices are configured to continuously capture crowd data of a plurality of crowds in the plurality of zones, wherein each of the plurality of crowds comprising a plurality of people; an analysis module configured to: identify a plurality of crowd characteristics from the captured crowd data, wherein the crowd characteristics comprising at least one of a crowd density, a crowd flow, and a crowd mood; analyse the captured crowd data to determine one or more patterns and changes in mood of the plurality of crowds; and predict crowd information comprising at least one of one or more emergent crowd characteristics, an emergent crowd behaviour of the plurality of crowds, and one or more issues based on the analysis in real-time; and suggest one or more actions for managing the plurality of crowds based on the crowd information; and a display module configured to display the predicted crowd information along with at least one of an alert and at least one indicator in real-time, wherein the crowd characteristics comprising one or more quantitative crowd characteristics and one or more qualitative crowd characteristics, wherein the one or more quantitative crowd characteristics comprises a number of people in density (ppsqm i.e. people per square metre), the crowd movement of people (ppmpm i.e. people per metre per minute) i.e. speed and direction of people movement(s), and a mood score from negative, negative-neutral, neutral, neutral-positive to positive, further wherein the mood score comprises categories comprising unknown, neutral, happy, sad, surprise, disgust, worried, fear, anger and a level comprising low, medium, high.
3. The real-time crowd measurement and management system of claim 1, wherein the analysis module is further configured to analyse the crowd data to create a context for the analysis of crowd behaviour.
2. The real-time crowd measurement and management system of claim 1, wherein the analysis module is further configured to: analyse the crowd data to create a context for the analysis of crowd behaviour; and examine and measure a relationship between two quantitative metrics and one qualitative assessment to increase predictability of crowd information comprising the crowd behaviour.
4. The real-time crowd measurement and management system of claim 2, wherein the display module is further configured to:
display the one or more actions; and
display at least one of the crowd information, the one or more actions, and the one or more suggestions, as a graphical representation.
3. The real-time crowd measurement and management system of claim 2, wherein the display module is further configured to: display the one or more actions; and display at least one of the crowd information, the one or more actions and the one or more suggestions as a graphical representation.
6. The real-time crowd measurement and management system of claim 1, wherein the analysis module is further configured to:
predict the one or more patterns and changes in mood of the plurality of crowds based on predictive Bayesian network;
measure the crowd characteristics by using algorithmic analysis and neural networks; measure a rate of change in at least one of the crowd density, flow and mood of the plurality of crowds in the zones, a trend and a rate of the trend in the crowd density, flow and mood; and
indicate the one or more issues comprising possible crowd congestion and crowd crush risks based on the rate of change in at least one of crowd density, crowd flow and crowd mood.
4. The real-time crowd measurement and management system of claim 2, wherein the analysis module is further configured to: predict the one or more patterns and changes in mood of the plurality of crowds based on predictive Bayesian network; measure the crowd characteristics by using artificial intelligence and convolutional neural networks (CNN); measure a speed of change in the crowd density, flow and mood of the plurality of crowds in the zones, a trend and a rate of the trend in the crowd density, flow and mood; and indicate the one or more issues comprising possible crowd congestion and crowd crush risks based on the speed of change in the crowd density, crowd flow and crowd mood.
7. The real-time crowd measurement and management system of claim 1, wherein the data collection module is further configured to receive the crowd data from a plurality of observers present in the plurality of zones via a computing device and a network.
5. The real-time crowd measurement and management system of claim 1, wherein the data collection module is further configured to receive the crowd data from a plurality of observers present in the plurality of zones via a computing device and a network.
11. The real-time crowd measurement and management system of claim 1, wherein the data
capturing devices are configured not to record any one or more selected from facial features and
personal information of the plurality of people in the plurality of crowds.
8. The real-time crowd measurement and management system of claim 1, wherein the data capturing devices are configured not to record any facial features and personal information of the plurality of people in the plurality of crowds.
12. A method for measuring and managing crowd in real-time, the method comprising:
continuously capturing, by a plurality of data capturing devices of a data collection module,
crowd data of a plurality of crowds present in a plurality of zones, wherein each of the plurality of
crowds comprising a plurality of people;
identifying, by an analysis module, a plurality of crowd characteristics from the captured
crowd data, wherein the crowd characteristics comprises a crowd density, a crowd flow, and a
crowd mood;
analysing, by the analysis module, the captured crowd data to determine one or more selected
from patterns and changes in mood of at least one of the plurality of crowds including measuring
the relationship between crowd density, crowd flow, and crowd mood in order to increase
predictability of crowd behaviour;
predicting, based on the analysis, by the analysis module, crowd information comprising at
least one of
one or more emergent crowd characteristics,
an emergent crowd behaviour of one or more the plurality of crowds; and
displaying, by a display module, the predicted crowd information along with at least one of
an alert and at least one indicator in real-time.
13. The method of claim 12, wherein the method further comprises suggesting, by the analysis
module, one or more actions for managing the plurality of crowds based on the crowd
information.
9. A method for measuring and managing crowd in real-time, the method comprising: continuously capturing, by a plurality of data capturing devices of a data collection module, crowd data of a plurality of crowds present in a plurality of zones, wherein each of the plurality of crowds comprising a plurality of people; identifying, by an analysis module, a plurality of crowd characteristics from the captured crowd data, wherein the crowd characteristics comprising at least one of a crowd density, a crowd flow, and a crowd mood; analysing, by the analysis module, the captured crowd data to determine one or more patterns and changes in mood of the plurality of crowds; predicting, by the analysis module, crowd information comprising at least one of one or more emergent crowd characteristics, an emergent crowd behaviour of the plurality of crowds, and one or more issues based on the analysis in real-time; suggesting, by the analysis module, one or more actions based on the crowd information; and displaying, by a display module, the predicted crowd information along with at least one of an alert and at least one indicator in real-time, wherein the crowd characteristics comprising one or more quantitative crowd characteristics and one or more qualitative crowd characteristics, wherein the one or more quantitative crowd characteristics comprises a number of people in density (ppsqm i.e. people per square metre), the crowd movement of people (ppmpm i.e. people per metre per minute) i.e. speed and direction of people movement(s), and a mood score from negative, negative-neutral, neutral, neutral-positive to positive.
14. The method of claim 12, further comprising:
predicting, by the analysis module, the one or more patterns and changes in mood of the
plurality of crowds based on predictive Bayesian network;
measuring, by the analysis module, the crowd characteristics by using neural networks; measuring, by the analysis module, a rate of change in the crowd density, flow and mood of the plurality of crowds in the zones, a trend and a rate of the trend in the crowd density, flow and mood; and predicting, by the analysis module, the one or more issues comprising possible crowd congestion and crowd crush risks based on the rate of change in the crowd density, crowd flow and crowd mood.
12. The method of claim 10 further comprising: predicting, by the analysis module, the one or more patterns and changes in mood of the plurality of crowds based on predictive Bayesian network; measuring, by the analysis module, the crowd characteristics by using artificial intelligence and convolutional neural networks (CNN); measuring, by the analysis module, a speed of change in the crowd density, flow and mood of the plurality of crowds in the zones, a trend and a rate of the trend in the crowd density, flow and mood; and predicting, by the analysis module, the one or more issues comprising possible crowd congestion and crowd crush risks based on the speed of change in the crowd density, crowd flow and crowd mood.
16. The method of claim 12, further comprising receiving, by the data collection module, the
crowd data from a plurality of observers present in the plurality of zones via a computing device
and a network.
13. The method of claim 9 further comprising receiving, by the data collection module, the crowd data from a plurality of observers present in the plurality of zones via a computing device and a network.
17. The method of claim 12, further comprising continually improving, by a machine learning
module, an accuracy and predictive capability of the real-time crowd measurement and
management system.
14. The method of claim 9 further comprising continually improving, by a machine learning module, an accuracy and predictive capability of the real-time crowd measurement and management system.
19. The method of claim 12, wherein one or more selected from facial features and personal information of the plurality of people in the plurality of crowds are not recorded or retained while capturing the crowd data of the plurality of crowds.
15. The method of claim 9, wherein facial features and personal information of the plurality of people in the plurality of crowds are not recorded or retained while capturing the crowd data of the plurality of crowds.
Claims 1, 2, 5, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No.12,106,572 B2.
Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of U.S. Patent No.12,106,572 B2.
Claim 4 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 3 of U.S. Patent No.12,106,572 B2.
Claim 6 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4 of U.S. Patent No.12,106,572 B2.
Claim 7 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of U.S. Patent No.12,106,572 B2.
Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No.12,106,572 B2.
Claims 12 and 13 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 9 of U.S. Patent No.12,106,572 B2.
Claim 14 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 of U.S. Patent No.12,106,572 B2.
Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13 of U.S. Patent No.12,106,572 B2.
Claim 17 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 14 of U.S. Patent No.12,106,572 B2.
Claim 19 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of U.S. Patent No.12,106,572 B2.
Although the claims at issue are not identical, they are not patentably distinct from each other because the scope of the claims of this instant invention are encompassed by the patented claims.
Claim Interpretation
3. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
4. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
5. Claim limitations “a data collection module comprising….,” “an analysis module configured to….,” “a display module configured to….,” and “ a machine learning module configured to…” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder coupled with functional language without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claims 1-11 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 102
6. 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hua et al. (U.S. patent pub. 2009/0222388 A1 will be further referred to as Hua).
Regarding claim 1: Hua discloses a real-time crowd measurement and management system (abstract and paragraph 0002) comprising:
a data collection module comprising a plurality of data capturing devices installed in a plurality of zones, respectively, the plurality of data capturing devices are configured to continuously capture crowd data of a plurality of crowds in the plurality of zones, wherein each of the plurality of crowds comprises a plurality of people (fig. 1a elements 170 and 180 and paragraph 0042);
an analysis module configured to:
identify a plurality of crowd characteristics from the captured crowd data, wherein the crowd characteristics comprise a crowd density, a crowd flow, and a crowd mood (paragraphs 0042 and 0048);
analyse the captured crowd data to determine one or more selected from patterns and changes in mood of at least one of the plurality of crowds, including measuring the relationship between crowd density, crowd flow, and crowd mood in order to increase predictability of crowd behaviour (paragraph 0042);
predict, based on the analysis, crowd information comprising at least one of one or more emergent crowd characteristics, an emergent crowd behaviour of one or more of the plurality of crowds (paragraphs 0042 and 0045); and
a display module configured to display the predicted crowd information along with at least one of an alert and at least one indicator in real-time (paragraph 0016).
Regarding claim 2: The real-time crowd measurement and management system of claim 1, wherein the analysis module is configured to suggest one or more actions for managing the plurality of crowds based on the crowd information (paragraphs 0090 and 0101-0102, a response is generate based on crowd queue length, speed, wait time, etc. The response can be monitor notices and human alerts=suggestions/actions).
Regarding claim 3:The real-time crowd measurement and management system of claim 1, wherein the analysis module is further configured to analyse the crowd data to create a context for the analysis of crowd behaviour (paragraphs 0090 and 0101-0102, the monitor notices are read as context).
Regarding claim 4: The real-time crowd measurement and management system of claim 2, wherein the display module is further configured to:
display the one or more actions; and display at least one of the crowd information, the one or more actions, and the one or more suggestions, as a graphical representation (paragraph 0016, 0044, 0076, and 0101-0102).
Regarding claim 5: The real-time crowd measurement and management system of claim 1, wherein the at least one indicator comprises a colour indicator for denoting the crowd information comprising crowd mood in real-time (paragraph 0090, wherein based on crowd mood a response is generated which can be activating a visual alarm and/or changing the resolution, i.e. results in the shading being changed. This is read as a colour indicator denoting the mood).
Regarding claim 6: The real-time crowd measurement and management system of claim 1, wherein the analysis module is further configured to:
predict the one or more patterns and changes in mood of the plurality of crowds based on predictive Bayesian network (paragraph 0013, 0052-0053, 0070, and 0093-0094;
measure the crowd characteristics by using algorithmic analysis and neural networks (paragraph 0014);
measure a rate of change in at least one of the crowd density, flow and mood of the plurality of crowds in the zones, a trend and a rate of the trend in the crowd density, flow and mood (paragraphs 0011-020 and 0042-0048); and
indicate the one or more issues comprising possible crowd congestion and crowd crush risks based on the rate of change in at least one of crowd density, crowd flow and crowd mood (paragraphs 0011-020 and 0042-0048).
Regarding claim 7: The real-time crowd measurement and management system of claim 1, wherein the data collection module is further configured to receive the crowd data from a plurality of observers present in the plurality of zones via a computing device and a network (paragraph 0042, observers=video camera, audio sensor, and sensors/detectors).
Regarding claim 8: The real-time crowd measurement and management system of claim 1, wherein the real-time crowd measurement and management system:
further comprises a machine learning module configured to continually improve an accuracy and predictive capability of the real-time crowd measurement and management system (paragraph 0014); and
is present in a cloud network (fig. 11 and paragraph 0102).
Regarding claim 9: The real-time crowd measurement and management system of claim 1, wherein for each of the plurality of people, the analysis module is further configured to distinguish between one or more selected from facial features and head movements that are not related to a mood of the plurality of people of the crowds (paragraphs 0014, 0016, and 0054-0057).
Regarding claim 10: The real-time crowd measurement and management system of claim 1, wherein the analysis module uses neural networks for one or more selected from pattern recognition and crowd mood prediction (paragraph 0014).
Regarding claim 11. The real-time crowd measurement and management system of claim 1, wherein the data capturing devices are configured not to record any one or more selected from facial features and personal information of the plurality of people in the plurality of crowds (paragraph 0042, the audio sensor does not record images/faces nor any personal information).
Regarding claim 12: See claim 1.
Regarding claim 13: See claim 2.
Regarding claim 14: See claim 6.
Regarding claim 15: See claim 5.
Regarding claim 16: See claim 7.
Regarding claim 17: See claim 8.
Regarding claim 18: See claim 9.
Regarding claim 19: See claim 11.
Regarding claim 20: See claim 1.
Contact Information
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND BHATNAGAR whose telephone number is (571)272-7416. The examiner can normally be reached on M-F 7:30am-4:00pm.
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/ANAND P BHATNAGAR/
Primary Examiner, Art Unit 2668
July 10, 2026
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