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 .
Application Status
Present office action is in response to amendment filed 05/04/2026. Claims 1, 6, 19 and 20 are amended. Claims 2, 5 and 15 are cancelled. Claims 1, 3-4, 6-14 and 16-21 are currently pending in the application.
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, 3-4, 6-14 and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
In regard to independent claim 1:
Step 1: Statutory Category?
Independent Claim 1 recites “A computer implemented method comprising:”. Independent Claim 1 falls within the “process” category of 35 U.S.C. § 101.
Step 2A – Prong 1: Judicial Exception Recited?
The Independent Claim 1/Revised 2019 Guidance Table below identifies in italics the specific claim limitations found to recite an abstract idea and in bold the additional (non-abstract) claim limitations that are generic computer components.
Independent Claim 1
Revised 2019 Guidance
A computer implemented method comprising:
A process (method) is a statutory
subject matter class. See 35 U.S.C.
§ 101 (“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.”).
[L1] obtaining, by one or more processors, a predefined learning objective identifying a topic for a class session including multiple students;
The “one or more processors” is an additional non-abstract limitation.
“[O]btaning a predefined learning objective identifying a topic for a class session including multiple students” is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
Alternatively, “obtaining a learning objective for a class session” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that a person/educator could obtain information by reading and/or hearing the information. See January 2019 Memorandum, 84 Fed. Reg. at 52. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372–72 (Fed. Cir. 2011) (obtaining transaction data can be performed by a person reading records of transactions from a database…).
[L2] monitoring, by the one or more processors, student interactions with computing devices during the class session to collect student interaction data, the student interactions including content displayed on a student computing device;
The “one or more processors” “computing devices” and “student computing device” are additional non-abstract limitations.
“[M]onitoring student interactions during the class session to collect student interaction data …” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could watch and/or listen to what students are going.
[L3] wherein monitoring student interactions comprises periodically receiving images of content displayed on a screen of the student computing device
The “screen of the student computing device” is an additional non-abstract limitation.
“Periodically receiving images of content displayed on a screen” is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
[L4a] analyzing, by the one or more processors, the collected student interaction data including content viewed by the student using a machine learning model trained to identify relevancy of the content displayed on the student computing device to the topic to identify patterns indicative of engagement or disengagement with the learning objective,
The “one or more processors”, “machine learning model” and “student computing device” are additional non-abstract limitations.
“Analyzing … the collected student interaction data including content viewed by the student… to identify relevancy of the content… ” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that humans (person/educator) have long analyzed information, mentally and/or using pen and paper.
[L4b] wherein the machine learning model applies a topic analysis algorithm to the content accessed by the students to determine a relevance score relative to the predefined learning objective topic,
The “machine learning model” is an additional non-abstract limitation.
“a topic analysis algorithm to the content accessed by the students to determine a relevance score relative to the predefined learning objective topic” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could visually and/or mentally determine a relevance score relative to the predefined learning objective topic.
[L4c] wherein the machine learning model is trained using a supervised learning algorithm, and the training data comprises labeled examples of student interactions that have been annotated as 'engaged' or 'disengaged' based on their correlation with the predefined learning objective topic;
The “machine learning model” is an additional non-abstract limitation.
“[T]he machine learning model is trained using a supervised learning algorithm, and the training data comprises labeled examples of student interactions that have been annotated as 'engaged' or 'disengaged' based on their correlation with the predefined learning objective topic” is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
[L5] determining, by the one or more processors, an engagement status for each student based on the relevance score and a predetermined engagement threshold;
The “one or more processors” is an additional non-abstract limitation.
“Determining… an engagement status for each student based on the relevance score and a predetermined engagement threshold” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could determine an engagement status for each student mentally and/or using pen and paper.
[L6] generating, by the one or more processors, real-time feedback for an educator based on the engagement status or each student, wherein the feedback includes actionable recommendations for interventions to enhance engagement.
The “one or more processors” is an additional non-abstract limitation.
“Generating … real-time feedback for an educator …” is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., data presentation. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
Alternatively, “generating … real-time feedback for an educator …” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could generate feedback verbally and/or in writing.
[L7] in response to the engagement status indicating disengagement relative to the engagement threshold for a student, automatically controlling the student's computing device to alter content displayed on the device.
The “student's computing device” is an additional non-abstract limitation.
“[A]utomatically controlling the student's computing device to alter content displayed on the device” is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., data presentation. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31.
[L8] monitoring, by the one or more processors, student interactions with the student's computing device following the automatic controlling
“Monitoring … student interactions …” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could listen to or see student interactions.
[L9] to determine a post-control engagement score; and
“Determine a post-control engagement score ” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could mentally and/or using pen and paper determine a score.
[L10a] modifying, by the one or more processors, an effectiveness score associated with the alteration of content based on a change between the post-control engagement score and the engagement status prior to the automatic controlling
“Modifying … an effectiveness score associated with the alteration of content based on a change between the post-control engagement score and the engagement status prior to the automatic controlling” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could mentally and/or using pen and paper modify an effectiveness score.
[L10b] wherein the effectiveness score is used to select a subsequent intervention for a different student determined to be disengaged.
“Select[ing] a subsequent intervention for a different student determined to be disengaged” could be performed alternatively as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could mentally and/or using pen and paper modify an effectiveness score.
It is apparent that, other than reciting the “one or more processors”, “student computing device”, “screen of the student computing device” and “machine learning model” additional non-abstract limitations noted in the Independent Claim 1/Revised 2019 Guidance Table above, nothing in the claim precludes the steps from practically being performed by a human as a certain method of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), in the mind, and/or using pen and paper. The mere nominal recitation of the “one or more processors”, “student computing device”, “screen of the student computing device” and “machine learning model” and automation of a manual process does not take the claim out of the certain method of organizing human activity and mental processes groupings. Accordingly, the claim recites an abstract idea under Step 2A: Prong 1.
Step 2A – Prong 2: Integrated into a Practical Application?
The body of the claim, as noted in the Independent Claim 1/Revised 2019 Guidance Table above, recites the additional limitations of the “one or more processors”, “student computing device”, “screen of the student computing device” and “machine learning model”. The originally filed Specification provides supporting exemplary descriptions of generic computer components: at least pages ¶ 79: the machine learning model is trained using a supervised learning algorithm … ; ¶ 80: The supervised learning algorithm may include one or more of a Support Vector Machines (SVM), Decision Trees, Random Forests, Gradient Boosting Machines, or Neural Networks … ; ¶ 81: The topic analysis algorithm may utilize Natural Language Processing (NLP) techniques to extract features from text, including one or more of the following: named entity recognition, pair-of-speech tagging, sentiment analysis, or topic modeling; ¶ 82: The topic modeling may be pe1fonned using Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF)… ; ¶ 83: The machine learning model may include an image recognition component that utilizes Convolutional Neural Networks (CNNs)… ; ¶ 84: the machine learning model applies sequence analysis algorithms to assess patterns in student activity over time, including one or more of the following: Hidden Markov Models (HMMs); Recurrent Neural Networks (RNNs), or Long Short-Term Memory networks (LSTMs)… ; ¶ 85: the machine learning model utilizes anomaly detection techniques lo identify deviations from typical engagement patterns, employing algorithm as such as One--Class SVM or Isolation Forest; ¶ 86: The machine learning model may incorporate clustering techniques to group students based on similarity in engagement patterns, using algorithms such as K-means clustering or hierarchical clustering; ¶ 116: … The software may be executed on a digital signal processor, ASIC, microprocessor, or other type of processor operating on a computer system, such as a personal computer, server or other computer system, turning such computer system into a specifically programmed machine … The lack of details about the “one or more processors”, “student computing device” and “machine learning model” indicates that each of the above-mentioned additional elements is a generic computer component, performing generic (a) function(s). See Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.”). The claim does not recite (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP § 2106.05(a)); (ii) a “particular machine” to apply or use the judicial exception (see MPEP § 2106.05(b)); (iii) a particular transformation of an article to a different thing or state (see MPEP § 2106.05(c)); or (iv) any other meaningful limitation (see MPEP § 2106.05(e)). See 84 Fed. Reg. at 55. The claimed invention merely implements the abstract idea using instructions executed on generic computer components, as shown in bold above, and as supported in the above noted pertinent portions of the Specification. The instant claim merely uses a programmed computer as a tool to perform an abstract idea. See MPEP § 2106.05(f). The additional limitations noted above, [L1] obtaining data (i.e., data gathering), [L3] periodically receiving data (i.e., data gathering), [L4c] “the machine learning model is trained using a supervised learning algorithm” (i.e., data gathering), [L6] generating real-time feedback (i.e., data presentation) and [L7] “automatically controlling the student's computing device to alter content displayed on the device” (i.e., data presentation) reflect the type of extra-solution activity (i.e., activities in addition to the judicial exception) the courts have determined insufficient to transform judicially excepted subject matter into a patent-eligible application when they are claimed in a merely generic manner. See MPEP § 2106.05(g); see, e.g., CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011) (“We have held that mere ‘[data-gathering] step[s] cannot make an otherwise nonstatutory claim statutory.”’ (alterations in original) (quoting In re Grams, 888 F.2d 835, 840 (Fed. Cir. 1989))); see also Elec. Power, 830 F.3d at 1354 (“[W]e have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis.”). The instant claim as a whole merely uses computer instructions to implement the abstract idea on a computer or, alternatively, merely uses a computer as a tool to perform the abstract idea. No additional limitations are recited in the body of the claim. The claim limitations amount to merely indicating a field of use or technological environment (a computer) in which to apply a judicial exception and, as such, cannot integrate the judicial exception into a practical application. See MPEP § 2106.05(h). Hence, as per MPEP §§ 2106.05(a)–(c), (e)–(h), the additional elements in claim 1, namely the “one or more processors”, “student computing device”, “screen of the student computing device” and “machine learning model” do not, either individually or in combination, integrate the abstract idea into a practical application. Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong 2: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amounts to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the published Specification, as noted above (¶¶ 79-86, 116) describes the “one or more processors”, “student computing device”, “screen of the student computing device” and “machine learning model” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional element(s) “in a manner that indicates that the additional element(s) is/are sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional). The generic description of the “one or more processors”, “student computing device”, “screen of the student computing device” and “machine learning model” indicates the steps performed by the additional elements are well-known enough that no further description is required for a skilled artisan to understand the process and that these computer components are all used in a manner that is well-understood, routine, and conventional in the field. In particular, each of the recited [L1] obtaining data (i.e., data gathering), [L3] periodically receiving data (i.e., data gathering), [L4c] “the machine learning model is trained using a supervised learning algorithm” (i.e., data gathering), [L6] generating real-time feedback (i.e., data presentation) and [L7] “automatically controlling the student's computing device to alter content displayed on the device” (i.e., data presentation) is nothing more than well-understood, routine, and conventional activity because these limitations are not distinguished from generic, conventional data gathering and data presentation with a computer. Considered as an ordered combination, the computer components of representative independent claim 1 add nothing that is not already present when the steps are considered separately. The sequence of obtaining, monitoring, analyzing, determin[ing], training, determining, generating and controlling is equally generic and conventional. Hence, the additional element(s) are generic, well-known, and conventional computing elements. The use of the additional element(s) either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claims are patent ineligible. (Step 2B: NO).
In regard to independent Claim 14:
Independent claim 14 is a machine-readable storage device, which falls within the “machine” category of 35 U.S.C. § 101. The machine-readable storage device having instructions for execution by a processor of a machine to cause the processor to perform operations to perform a method, the operations comprising steps similar to those of representative independent Claim 1. As a result, independent claim 14 is rejected similarly to representative independent Claim 1.
In regard to independent Claim 19:
Independent claim 19 is “a device comprising:”, which falls within the “machine” category of 35 U.S.C. § 101. The device comprising: a processor; and a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising steps similar to those of representative independent Claim 1. As a result, independent claim 4 is rejected similarly to representative independent Claim 1.
In regard to the dependent claims:
Dependent claims 3-4, 6-13, 16-18 and 20-21 include all the limitations of respective independent claims 1, 14 and 19 from which they depend and as such recite the same abstract idea(s) noted above for claims 1, 14 and 19. None of the additional claim activities is used in some unconventional manner nor does any produce some unexpected result. An invocation to use known technology in the manner it is intended to be used for its ordinary purpose is both generic and conventional. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 3-4, 6-13, 16-18 and 20-21 integrates the judicial exception into a practical application. While dependent claims 3-4, 6-13, 16-18 and 20-21 may have a narrower scope than the representative claim, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 3-4, 6-13, 16-18 and 20-21 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
Response to Arguments
The Rejection of Claims Under § 101
Applicant first argues that “The claim does not recite presenting information
for a user to consider. It recites the system taking autonomous control over a different device and altering what that device displays, without requiring any action by the student. A human educator cannot mentally or with pencil and paper "automatically controll[e] the student's computing device to alter content displayed on the device."”. Applicant’s arguments have been fully considered but they are unpersuasive as shown below.
It has been established that using generic computing components for data presentation without more (such as identifying a particular tool for presentation) is abstract as an ancillary part of such collection and analysis”). See Elec. Power Grp., 830 F.3d at 1354 (“we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis”).
Applicant then argues that “A teacher changing their own presentation materials is not the same as remotely commanding a student's individual computing device to change its display” and that “The claim recites machine-to-machine control of a remote device, not a teacher modifying their own materials”. Applicant’s arguments are unavailing. The claimed invention uses generic computers and computer components as tools to perform the abstract idea(s). As indicated in Alice, 573 U.S. at 226, “Nearly every computer will include a ‘communications controller’ and [a] ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”). Additionally, as indicated in MPEP 2106.05(d) II, i: The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)).
Applicant further argues “the amended claims now recite steps that no human could perform mentally: periodically receiving images of screens of multiple student computing devices, automatically controlling a remote student device, then monitoring foe result of that control action to determine a post-control engagement score, and modifying an effectiveness score that is then used to select interventions for other students. This is a self-improving automated feedback loop operating across multiple networked devices. It is not a mental process, and it is not a method of organizing human activity”. Applicant’s arguments have been fully considered but they are unpersuasive as shown below.
As noted in the above rejections, there are no particulars to the additional claim elements to establish more than extra-solution activity. As is clear from the Specification’s description, the features recited in the claims do not require any specialized computer hardware or inventive computer components such as a particular machine, invoke any asserted inventive programming, or use other than generic computer components to perform generic functions. See Spec. 79-86, 116; DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”).
Applicant then argues “The claims are integrated into a practical application because they recite a specific technological process: the system captures screen images from student devices, analyzes the content, sends control commands to alter a remote device, measures the outcome of that control action, and updates effectiveness metrics that govern future device--control decisions for other students. This constitutes a specific improvement lo classroom management technology, not a generic computer implementation an abstract concept”. Applicant’s arguments have been fully considered but they are unpersuasive as shown below.
As noted earlier, the additional elements in the claim amounts to no more than mere instructions to apply the exception using generic computer components. Additionally, the Specification that the elements are well understood, routine, and conventional. Using generic computer components does not integrate the abstract idea into a practical application and cannot provide an inventive concept. “Mere automation of manual processes using generic computers does not constitute a patentable improvement in computer technology.” Credit Acceptance Corp. v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017).
In light of the foregoing, the Examiner maintains that each of Applicant’s pending claims 1, 3-4, 6-14 and 16-21 considered as a whole, is directed to a patent-ineligible abstract idea that is not integrated into a practical application, and does not include an inventive concept.
The Rejection of Claims Under § 103
The prior art rejections of the claims are withdrawn in view of Applicant’s amendment and remarks.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is listed in the attached PTO
Form 892 and is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDDY SAINT-VIL whose telephone number is (571)272-9845. The examiner can normally be reached Mon-Fri 6:30 AM -6:00 PM.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
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