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 .
Applicant’s submission of a Response
Applicant’s submission of a response was received on 07/28/2026. Presently, claims 9-19 are pending.
Response to Arguments
Applicant's arguments filed 07/28/2026 have been fully considered but they are not persuasive. Claims have overcome each and every objection and 112(b) rejection previously set forth in the Office Action mailed 04/29/2026. Applicant’s representative asserts that the amended claims limitations are not met. However, in light of the amendments to the claims, new rejection(s) under 35 U.S.C. 103 have been presented, as discussed in detail below.
In regards to rejections under 35 U.S.C. §101, applicant asserts the following: “Claim 16 has been amended to claim a system and all of the abstract ideas identified in the Office Action have been removed from claim 16.” (Page 6 of Remarks)
Regarding point (1), the examiner respectfully disagrees.
In response to the arguments above, the abstract ideas are still present in claim 16. (See 101 rejection below).
In regards to rejections under 35 U.S.C. §102(a)(1), applicant asserts the following: “Applicants note that neither the attention weights nor the embedding matrix M satisfy both requirements of 1) representing a degree to which an ability to perform an exercise predicts an ability to perform another exercise and 2) being independent of the student. While the attention weights provide a "relevance of each of the previous interactions with the current exercise", the attention weights are not independent of the student. This is because the Keys and Values that form the attention weights are shown to be dependent on the student's performance on the exercises. In particular, equation 2 of Pandey indicates that the Key and the value is dependent on M and equation 1 indicates that M is a matrix of Msi values. On the preceding page of Pandey, each Msi is said to be formed by converting a sequence y to a sequence s and applying the sequence s to an embedding matrix M. Each y in the sequence y is said to be a sum of a value e representing an exercise performed by the student and a value r representing the students response. Since r is dependent on the student, every value computed from r is dependent on the student. This includes every y, the sequence s, matrix elements Msi, matrix M, the Key and the Value and the attention weights themselves. Thus, the attention weights are not independent of the student. Further, there is no way to separate the attention weights from the student. Thus, it is not possible to produce a relevance value from the attention weights that is not dependent on the student.” (Pages 7-8 of Remarks).
Regarding point (2), the examiner respectfully disagrees.
In response to the arguments above, the claim language is very broad and only requires “each relation value is independent of the student”. The model disclosed by Pandey meets these limitations because the model of Pandey defines N as the total number of students and E as the total number of exercises (Page 2 – Table 1: Notations). However, Pandey indexes each interaction according to yt = et + rt x E, where Et is the exercise sequence and rt is the correctness of the response to the exercises (Page 2 – Below Table 1: Notations). Thus, the interactions is indexed by the exercise and answer correctness, without using the student N as an indexing parameter. Accordingly, the same exercise-answer interaction selects the same embedding regardless of the student. Therefore, someone of ordinary skill in the art will come to the conclusion that the resulting relation value is independent of the student, which means that these relation values are independent of the student. (See 102 Rejection below)
In regards to rejections under 35 U.S.C. §103, applicant asserts the following: “Applicant respectfully submits that Krizhevsky doesn't show independent parallel feedforward networks and independent parallel prediction layers. Instead, Krizhevsky shows parallel convolution networks where some of the layers within the networks are linked. Because of this linking, none of the convolution networks are not independent of each other. As such, Applicant respectfully submits that the combination of Pandey and Krizhevsky does not show or suggest a plurality of neural networks operating independently in parallel as found in claim 9.” (Page 9 of Remarks)
Regarding point (3), the examiner notes that Krizhevsky is not relied upon to teach or disclose this new limitation.
In response to the arguments above, the office relies on newly found prior art John E. Mixter (US 20200034691 A1) to show neural networks operating independently in parallel. (See 103 rejection below).
Regarding claim 9, since they recite similar features to claim 16, the rejection is maintained as present below.
Applicant’s representative argues that since the prior art does not disclose or suggest the suggested features of claim 9 or 16 and so, dependent claims are patentable. However, in light of the remarks and standing rejection below, the examiner asserts the prior art of record teaches all the elements as claimed and these elements satisfy all structural, functional, operational, and spatial limitations currently in the claims. Therefore, the standing rejections are proper and maintained.
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 9-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims are directed to at least one of abstract idea groupings, according to the 2019 Revised Patent Subject Matter Guidelines (Mathematical Concepts, Mental Processes and/or Certain Methods of Organizing Human Activity). Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
More specifically, regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a system and/or process, which is are statutory categories of invention.
Step 2A-1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims are analyzed to determine whether it is directed to a judicial exception.
Independent claim 1 recites the following, with the abstract ideas highlighted in bold, including an indication as to the abstract idea grouping(s) to which the indicated limitations belong to, according to the 2019 Revised Patent Subject Matter Guidelines. Independent claims 9 and 16, having substantially similar features, were also analyzed and to which the following conclusion is also applicable:
A system for selecting a next exercise to present to a student from a plurality of candidate next exercises, the system comprising: for each of a plurality of candidate next exercises, a separate respective neural network, wherein each neural network is configured to accept a plurality of relation values of each relation value in the plurality of relation values being associated with a respective exercise in a set of exercises, wherein each relation value represents a degree to which an ability to perform the respective exercise in the set of exercises predicts an ability to perform the candidate next exercise of the neural network and wherein each relation value is independent of the student.
The limitations in claim 16 (as well as claim(s) 9) recites an abstract idea included in the groupings of mental processes connected to technology only through application thereof using generic computing elements (e.g., neural network, processors etc.) and/or insignificant extra-solution activity. According to the 2019 Revised Patent Subject Matter Guidelines:
Mental Processes include concepts performed in the human mind (including an observation, evaluation, judgement, opinion);
Certain Methods of Organizing Human Activity include:
1. Fundamental Economic Principles or Practices (including hedging (i.e., wagering), insurance, mitigating risk);
2. Commercial or Legal Interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations);
3. Managing Personal Behavior or Relationships or Interactions Between People (e.g. social activities, teaching, and following rules or instructions). The interaction encompasses both activity of a single person (for example a person following a set of instructions) and activity that involves multiple people (such as a commercial or legal interaction). Thus, some interactions between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within this grouping;
Specifically, the instant claims include functions/limitations, as highlighted in the independent claim above, that constitute at least:
C. Following rules and/or instructions, such as including the functions related to the playing of a game, which is an abstract idea included in the grouping of Managing Personal Behavior or Relationships or Interactions Between People. These sets of rules are interpreted as at least certain methods of organized human activity insomuch as the claim limitations are directed to performing or following the set of rules or instructions concerning a game while only generically connected to interaction with a computer utilizing non-special purpose generic computing elements and/or insignificant extra-solution activity, as set forth in the claims.
D. Concepts performed in the human mind (e.g., “accepting a plurality of relation values of each relation value in the plurality of relation values being associated with a respective exercise in a set of exercises, a degree to which an ability to perform the respective exercise in the of exercises predicts an ability to perform the candidate next exercise, associating with a respective candidate next exercise of a plurality of candidate next exercises”), which is an abstract idea included in the grouping of Mental Processes. These limitations are interpreted as at least Mental Processes insomuch as the claim limitations are directed to performing the concepts in the human mind, while only generically connected to interaction with a computer utilizing non-special purpose generic computing elements and/or insignificant extra-solution activity as set forth in the claims.
Regarding dependent claims 10-15 and 17-19:
Each claim is dependent either directly or indirectly from the independent claim identified above and includes all the limitations of said independent claim. Therefore, each dependent claim recites the same abstract idea as identified above. Each of the dependent claim further describes additional aspects of the abstract idea, i.e., additional aspects to the Mental Processes. For example, some dependent claims merely provide additional Mental Processes to be performed and/or additional insignificant extra-solution activity, without anything more significant to establish eligibility under 35 U.S.C. 101.
Step 2A-2 of the 2019 Revised Patent Subject Matter Eligibility Guidance
The second prong of step 2a is the consideration if the claim limitations are directed to a practical application.
Limitations that are indicative of integration into a practical application:
-Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
-Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
-Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
-Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
-Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
-Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
-Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
-Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Claims 9-19 clearly do not improve the functioning of a computer or neural network, as they only incorporate generic computing elements/generic neural network elements, do not effect a particular treatment, and do not transform or reduce a particular article to a different state or thing. Similarly, there is no improvement to a technical field. In addition the claims do not apply the judicial exception with, or by use of a particular machine. The claims do not apply or use the judicial exception in a meaningful way. The claimed invention does not suggest improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05 (a)).
This judicial exception is not integrated into a practical application because the claimed invention merely applies the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform the abstract idea (MPEP 2106.05 (f)) and/or generally links the use of the judicial exception to a particular technology or field of use (MPEP 2106.05 (h)). The claimed computer components are recited at a level of generality and are merely invoked as tool to perform the abstract idea. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea.
For the reasons as discussed above, the claim limitations are not integrated to a practical application.
Step 2b of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because no element or combination of elements is sufficient to ensure any claim of the present application as a whole amounts to significantly more than one or more judicial exceptions, as described above. For example, the recitations of utilization of “processor, neural networks”, etc. used to apply the abstract idea merely implements the abstract idea at a low level of generality and fail to impose meaningful limitations to impart patent-eligibility. These elements and the mere processing of data using these elements do not set forth significantly more than the abstract idea itself applied on general purpose computing devices. The recited generic elements are a mere means to implement the abstract idea. Thus, they cannot provide the “inventive concept” necessary for patent-eligibility. “[I]f a patent’s recitation of a computer amounts to a mere instruction to ‘implement]’ an abstract idea ‘on ... a computer,’... that addition cannot impart patent eligibility.” Alice, 134 S. Ct. at 2358 (quoting Mayo, 132 S. Ct. at 1301). As such, the significantly more required to overcome the 35 U.S.C. 101 hurdle and transform the claimed subject matter into a patent-eligible abstract idea is lacking. Accordingly, the claims are not patent-eligible.
Further, the claims would require structure that is beyond generic, such as structure that can be interpreted analogous to a general-purpose structure and general-purpose computing elements in that they represent well-understood, routine, conventional elements that do not add significantly more to the claims. See Alice Corp. v. CLS Bank International, 134 S. Ct. at 2358-59. The elements of a neural networks are well known to electronically implement a predictions for an interaction by GERVAIS et al. (US 20220188640 A1; hereinafter Gervais). Gervais discloses that a conventional neural network is used through a computing program and processing unit to make predictions or a likelihood (¶ 0060), which can be used in the present invention for the next exercise. See Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
The dependent claims do not add “significantly more” for at least the same reasons as directed to their respective independent claims, at least based on the position, as discussed above, that each of the dependent claims merely provide additional limitations to further expand the abstract idea of the independent claims, without adding anything which would establish eligibility under 35 U.S.C. 101.
Consequently, consideration of each and every element of each and every claim, both individually and as an ordered combination, leads to the conclusion that the claims are not patent-eligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 16 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shalini Pandey et al. (A Self-Attentive model for Knowledge Tracing; hereinafter Pandey).
Regarding claim 16, Pandey discloses a system for selecting a next exercise to present to a student from a plurality of candidate next exercises (predicting next interaction; Section 1 - ¶ 1), the system comprising: for each of a plurality of candidate next exercises, a separate respective neural network (neural network; abstract), wherein each neural network is configured to accept a plurality of relation values of each relation value in the plurality of relation values being associated with a respective exercise in a set of exercises (model receives the student's previous interactions and it will predict whether a student will be able to answer the next exercise, which comes from a relation value; Page 2 - Section 2), wherein each relation value represents a degree to which an ability to perform the respective exercise in the set of exercises predicts an ability to perform the candidate next exercise of the neural network (two exercises which are relevant to each other tend to have high attention weights as the performance on one of them impacts the performance of the other, which means that a higher attention weight indicates a stronger relationship between performance on the previous exercise and the performance on the predicted exercise; abstract and Page 5) and wherein each relation value is independent of the student (Pandey defines N as the total number of students and E as the total number of exercises in Page 2 – Table 1: Notations and then Pandey indexes each interaction according to yt = et + rt x E, where Et is the exercise sequence and rt is the correctness of the response to the exercises in Page 2 – Below Table 1: Notations; thus, the interactions are indexed by the exercise and answer correctness, without using the student N as an indexing parameter, which means this is independent of the student).
Regarding claim 17, Pandey discloses wherein each neural network comprises self-attention (self-attention based approach; Section 1 - ¶ 3) such that the neural network generates an attention weight for each exercise in the set of exercises (assigning weights to the previously known exercises; Section 1 - ¶ 3).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 9-15 are rejected under 35 U.S.C. 103 as being unpatentable over Pandey in view of John E. Mixter (US 20200034691 A1; hereinafter Mixter.
Regarding claim 9, Pandey discloses a system comprising a collection of processors implementing: a plurality of neural networks (plurality of neural networks; Section 2 - Layer normalization), each neural network in the plurality of neural networks associated with a respective candidate next exercise of a plurality of candidate next exercises (KCs include exercises with previous interactions and predictions for new ones; Page 1 - Section 1 - ¶ 1), each neural network of the plurality of neural networks comprising: a feedforward network (Feedforward layer and in this type of model data moves through the layers as input and output values; Section 2 – Feedforward Layer); and a prediction layer (Prediction Layer; Section 2 – Prediction Layer); and a selection layer connected to each of the plurality of neural networks (Section 2 - Self Prediction layer).
Pandey does disclose parallelism being applied to neural networks but does not explicitly disclose the plurality of neural networks operating independently in parallel. However Mixter focuses neural networks being independent so that they can be trained and run in parallel, which relates to Pandey because they both apply parallelism to their neural networks. Mixter teaches the plurality of neural networks operating independently in parallel (all independent class level artificial neural networks can be trained and executed in parallel in ¶8 and we can see multiple layers executed in parallel in Fig 1 and ¶12).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pandey to implement the teachings of Mixter for the benefit of optimizing all the layers. If layers work in parallel, the system can generate a bigger amount of predictions for the next exercise or interaction, which also allows the system to handle large amounts of data without slowing down.
Regarding claim 10, Pandey discloses wherein for each feedforward network a respective plurality of input values provided to the feedforward network (Feedforward layer and in this type of model data moves through the layers as input and output values; Section 2 – Feedforward Layer).
Pandey does not explicitly disclose values provided to the feedforward network to be generated in parallel by a respective second plurality of neural networks operating in parallel. However, Mixter teaches how data is processed through multiple layers of a neural network in parallel (all independent class level artificial neural networks can be trained and executed in parallel in ¶8 and we can see multiple layers executed in parallel in Fig 1 and ¶12).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pandey to implement the teachings of Mixter for the benefit of optimizing all the layers. If layers work in parallel, the system can generate a bigger amount of predictions for the next exercise or interaction, which also allows the system to handle large amounts of data without slowing down.
Regarding claim 11, Pandey discloses a self-attention layer generating self-attention weights for each past exercise performed by the student (Section 2 - Self attention layer); a self-attention weight adjustment layer that applies a respective self-attention weight adjustment to each self-attention weight to produce a plurality of adjusted self- attention weights (applying weights; Section 2 - Self attention layer), each respective self-attention weight adjustment being based on: a relation between the past exercise performed by the student that is associated with the self-attention weight and the respective candidate next exercise associated with the neural network (relevance of previous interactions with current exercise; Section 2 - Self attention layer); and a time since the past exercise performed by the student that is associated with the self- attention weight was performed by the student (weights estimated at each timestamp; Section 1 - Fig a); and an output layer that generates an input value for the respective feedforward network (Feedforward layer and in this type of model data moves through the layers as input and output values; Section 2 – Feedforward Layer) based on the plurality of adjusted self-attention weights (Feedforward layer gets their inputs from the outputs of the self-attention layer that has the attention weights used for exercises; Section 2 – Self-Attention Layer and Feedforward Layer).
Pandey does not explicitly disclose wherein each respective second plurality of neural networks operating in parallel to produce the plurality of input values to the feedforward network. However, Mixter teaches how data, such as input values, is processed through multiple layers of a plurality of neural networks in parallel (all independent class level artificial neural networks can be trained and executed in parallel in ¶8 and we can see multiple layers executed in parallel in Fig 1 and ¶12).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pandey to implement the teachings of Mixter for the benefit of optimizing all the layers. If layers work in parallel, the system can generate a bigger amount of predictions for the next exercise or interaction, which also allows the system to handle large amounts of data without slowing down.
Regarding claim 12, Pandey discloses wherein the relation comprises a semantic similarity between the past exercise performed by the student and the respective candidate next exercise associated with the neural network (questions related to each other means there is a semantic similarity and when using matrices, key and value the model learns the correlation between the exercises; Section 1 - ¶3).
Regarding claim 13, Pandey discloses wherein the semantic similarity is determined from an embedding of the past exercise performed by the student and an embedding of the respective candidate next exercise associated with the neural network (embedded exercise matrix corresponding to an exercise; Section 2 - Embedding layer - last ¶) and wherein the self- attention layer utilizes the embedding of the past exercise performed by the student (questions related to each other means there is a semantic similarity and when using matrices, key and value the model learns the correlation between the exercises; Section 1 - ¶3 and Section 2 – Self Attention Layer).
Regarding claim 14, Pandey discloses wherein the relation comprises a value representing a degree to which an ability to perform the past exercise performed by the student predicts the ability to perform the respective candidate next exercise associated with the neural network (predicting the result; Section 2 - Self attention layer - last ¶).
Regarding claim 15, Pandey discloses wherein the relation further comprises a value representing a degree to which an ability to perform the past exercise performed by the student predicts the ability to perform the respective candidate next exercise associated with the neural network (predicting the result; Section 2 - Self attention layer - last ¶).
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Pandey in view of Ghosh et al. (Context-Aware Attentive Knowledge Tracing; hereinafter Ghosh).
Regarding claim 18, Pandey does not explicitly disclose wherein each neural network is configured to accept: for each exercise in the set of exercises, a respective time period since the student performed the exercise.
However, Ghosh teaches wherein each neural network is configured to accept: for each exercise in the set of exercises, a respective time period since the student performed the exercise (monotonic Attention Mechanism that uses context-aware measure to characterize the time distance between questions a learner has responded to in the past; Page 2 – 1.1 Contributions).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pandey to implement the teachings of Ghosh because a monotonic attention mechanism using a context-aware approach adjusts the distance measure according to how related a past exercise/question is to the current one. This means that it recognizes that the most recent interactions are the truest reflection of the student’s current performance or capability.
Regarding claim 19, Pandey does not explicitly disclose wherein each neural network is configured to provide a respective altered attention weight for each exercise in the set of exercises, each altered attention weight being based on the respective time period since the student performed the exercise associated with the altered attention weight.
However, Ghosh teaches wherein each neural network is configured to provide a respective altered attention weight for each exercise in the set of exercises, each altered attention weight being based on the respective time period since the student performed the exercise associated with the altered attention weight (the attention weights for the current question on a past question also depends on the relative number of time steps between them; Page 4 – 3.2 The Monotonic Attention Mechanism).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pandey to implement the teachings of Ghosh because a monotonic attention mechanism using a context-aware approach adjusts the distance measure according to how related a past exercise/question is to the current one. This means that it recognizes that the most recent interactions are the truest reflection of the student’s current performance or capability.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSE ANGELES whose telephone number is (703)756-5338. The examiner can normally be reached Mon-Thu 8am-5pm.
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, Dmitry Suhol can be reached at (571) 272-4430. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSE ANGELES/Examiner, Art Unit 3715
/Jay Trent Liddle/ Primary Examiner, Art Unit 3715
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