DETAILED ACTION
Status
This communication is in response to the application filed on 9 March 2023. Claims 1-14 are pending and presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit under 35 U.S.C. 119(e) to U.S. Provisional Application No. 63/318,321, filed on 9 March 2022, is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 27 July 2023 was filed after the mailing date of the application on 9 March 2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to methods (claims 1-13) and an apparatus (claim 14), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a computer-implemented method for predicting a diagnosis of a human subject, the method comprising: accessing rating information created by the human subject for a plurality of pictures that are organized into picture categories, wherein the rating information includes positive ratings and negative ratings of the plurality of pictures; determining, by one or more processors, from the rating information, approach ratings and avoidance ratings for the picture categories; computing, by the one or more processors, for the picture categories, approach entropy values, avoidance entropy values, approach standard deviation values, and avoidance standard deviation values, from the approach ratings, the avoidance ratings, the positive ratings, and the negative ratings; generating, by the one or more processors, a value function of the approach entropy values and the avoidance entropy values as a function of the approach ratings and the avoidance ratings; generating, by the one or more processors, a limit function of the approach standard deviation values and the avoidance standard deviation values as a function of the approach ratings and the avoidance ratings; deriving one or more first judgment variables from the value function; deriving one or more second judgment variables from the limit function; applying the one or more first judgment variables and the one or more second judgment variables to a trained machine learning (ML) classifier; and generating, by the trained ML classifier, a diagnostic prediction of a neurological condition for the human subject based on the one or more first judgment variables and the one or more second judgment variables.
Independent claims 12 and 14 are analyzed in a similar manner to claim 1 above since claim 12 is directed to a method encompassing the same activities as at claim 1, except claim 12 is broader in that the rating information need only be “associated with” the human subject (where being “created by” at claim 1 necessarily means it is associated with the human subject), rating “evaluation items” (as opposed to “pictures” at claim 1, the evaluation items encompassing pictures per dependent claim 13), the deriving of a judgment variable need only be “from the value function or the limit function” (rather than claim 1 indicating a variable from each function), and prediction merely being a “diagnostic prediction” (rather than a “diagnostic prediction of a neurological condition” at claim 1). Claim 14 is directed to “a apparatus comprising: one or more memories storing rating information … and one or more processors coupled to the one or more memories, the one or more processors configured to” perform the same or similar activities as at claim 12.
The dependent claims (claims 2-11 and 13) appear to be encompassed by the abstract idea of the independent claims since they merely indicate the neurological condition being cognitive decline or a history of depression (claim 2), using a random forest classifier or a gaussian mixture model (claim 3), what the derived judgment variables may be (including a list of variables) (at claims 4-5 and 9), generating the value function by applying a curve fitting tool to a plot of computed values (claims 6-7), deriving a third judgment variable from a generated tradeoff function between the approach and avoidance entropy values, and applying that variable also with the trained ML model (claim 8), the pictures being presented through a rating task running on a device (claim 10), the picture categories including one of sports, disasters, cute animals, aggressive animals, nature, or food (claim 11), and/or the evaluation items being a picture, sound, or video (at claim 13).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of making a diagnostic prediction based on computations derived from a person’s response to a stimulus such as pictures; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter:
Mathematical concepts (e.g., relationships, formulas, equations, and/or calculations) as based on performing mathematical calculations (see MPEP § 2106.04(a)(2)(I)(C)) including generating functions and computing values to apply to a model; and/or
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) since at least the observations involved in accessing information, determining, computing, generating, deriving, and applying steps so as to generate an evaluation, judgment, or opinion regarding the indicated diagnostic prediction could apparently be performed in the human mind and/or with the use of pen/pencil and paper.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are the method being computer-implemented with the activities being performed by one or more processors (at claims 1 and 12) and a apparatus comprising: one or more memories … and one or more processors coupled to the one or more memories, the one or more processors configured to perform the same or similar activities (at claim 14). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. Applicant page 44, line 24 to page 45, line 4 (as submitted, ¶ 0196 as published) indicates “Suitable computer systems include personal computers (PCs), workstations, servers, laptops, tablets, palm computers, smart phones, electronic readers, and other portable computing devices, etc.” for performing the activities – i.e., a generic or general purpose computer (see also at least p. 45, line 18 to p. 46, line 12 as submitted ( 0199-0200 as published) as indicating a generic or general purpose computer).
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 103
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 of this title, 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 1-2, 4-10, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Breiter (U.S. Patent Application Publication No. 2012/0296701, hereinafter Breiter ‘701) in view of Publicover et al. (U.S. Patent Application Publication No. 2012/0293773, hereinafter Publicover) .
Claim 1: Breiter ‘701 discloses a computer-implemented method for predicting a diagnosis of a human subject, the method comprising:
accessing rating information created by the human subject for a plurality of pictures that are organized into picture categories, wherein the rating information includes positive ratings and negative ratings of the plurality of pictures (see Breiter ‘701 at least at, e.g., ¶¶ 0012, “The procedure may present the individuals with a plurality of evaluation items that belong to predetermined categories. The procedure may collect approach and avoid response data from the individuals to the evaluation items that are presented to the individuals. The approach and avoid response data may be collected through the use of various techniques, such as keypresses on a keyboard, alternating keypresses, swiping a touchscreen, button holds on a touchscreen, etc. The collected approach and response data may be processed to produce one or more of the trade-off plot, the value function plot, and the saturation plot for the individual”, 0078, “the keypress data manipulation engine 206 accesses the response data records 600 stored at the keypress data store 208, and processes the information stored in those records 600 to generate relative preference data”; citation hereafter by number only) ;
determining, by one or more processors, from the rating information, approach ratings and avoidance ratings for the picture categories (0029, “the inventor evaluated whether splitting ratings of preference into explicit measures of approach and avoidance (while viewing beautiful and average faces, or distinct categories of facial expression, or distinct categories of physical activity, or food”, 0064, “This response data may be partitioned as "avoidance" if it is below a mean view time for the group of participants, or as "approach" if it is above the mean view time. Alternately, the view time or exposure time response data may be used to produce a positive value function plot and saturation plot alone from analyses”, 0073, “The data collector component 211 of the keypress procedure application 204 captures and stores the response data, which may include the total time that each evaluation item is maintained, e.g., viewed for photographs or video clips, by the participant, the number of approach keypresses and the number of avoid keypresses”;
computing, by the one or more processors, for the picture categories, approach entropy values, avoidance entropy values, approach standard deviation values, and avoidance standard deviation values, from the approach ratings, the avoidance ratings, the positive ratings, and the negative ratings (0080, “the keypress data manipulation engine 206 computes, for each participant, an approach Shannon entropy value (H+) and an avoid Shannon entropy value (H-) for each marketing option”, 0117, “the keypress data manipulation engine 206 also may compute an approach standard deviation value for each marketing option per participant, as indicated at block 710, and an avoid standard deviation value for each marketing option per participant, as indicated at block 712”);
generating, by the one or more processors, a value function of the approach entropy values and the avoidance entropy values as a function of the approach ratings and the avoidance ratings (0080 and 0086 indicating the approach value function, 0091 and 0097 indicating the avoidance value function);
generating, by the one or more processors, a limit function of the approach standard deviation values and the avoidance standard deviation values as a function of the approach ratings and the avoidance ratings (0187, “FIG. 12 is an illustration of a saturation plot 1200 for the relative preference data generated by a single participant. The Saturation plot 1200 has an x-axis 1202 and a y-axis 1204 that intersect at origin 1205. The x-axis 1202 represents mean keypresses with the positive side of the x-axis 1202 representing mean approach keypresses, and the negative side of the x-axis 1202 representing mean avoid keypresses. The y-axis 1204 represents the standard deviation, with the positive side of the y-axis 1204 representing standard deviation for approach, and the negative side of the y-axis 1204 representing standard deviation for avoid”, see Fig. 12);
deriving one or more first judgment variables from the value function (0102-0104);
deriving one or more second judgment variables from the limit function (0189, “The distance a value pair 1206a-d is away from the x-axis, i.e., the magnitude of the standard deviation, indicates how difficult the decision was for the participant to either approach or avoid the respective marketing option”);
applying the one or more first judgment variables and the one or more second judgment variables to a trained machine learning (ML) classifier (0208, indicating/describing using a prediction engine, 0209, “Outcomes generated by the prediction engine 2208 may be analyzed by the error measure and learning engine 2206. The results of such analysis may be used to modify, e.g., refine, the operations of the relative preference engine 2202 and/or the classification engine 2204”); and
Breiter ‘701, however, does not appear to explicitly disclose generating, by the trained ML classifier, a diagnostic prediction of a neurological condition for the human subject based on the one or more first judgment variables and the one or more second judgment variables. Where Breiter ‘701 discloses determining marketing options (at 0044, 0076, 0078), but also that “some participants may show a significant restriction in the range or dispersion of their preferences across the trade-off plot. Such a restriction in their trade-off plot may have diagnostic significance for psychiatric illness, such as addiction” (at 0202), it is not specific regarding prediction of a neurological condition. Publicover, though, teaches “A low-cost, unobtrusive, portable platform that may repeatedly measure responses and reaction times has a wide range of applications…. [such as] diagnosing post-traumatic stress disorder, …, [and] acquiring foundational clinical data to assess neurological or cognitive disorders” (Publicover at 0019), where “[l]ooking toward versus looking away from an object or image may, for example, be used to assess whether the wearer is attracted by a scene or whether there is avoidance of particular types of visual images” (Publicover at 0073). Therefore, the Examiner understands and finds that to predict a neurologic condition based on response to an image is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide a low-cost, unobtrusive, portable platform to more easily collect data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the rating predictions of Breiter ‘701 with the neurological or cognitive disorder information of Publicover in order to predict a neurologic condition based on response to an image so as to provide a low-cost, unobtrusive, portable platform to more easily collect data.
The rationale for combining in this manner is that to predict a neurologic condition based on response to an image is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide a low-cost, unobtrusive, portable platform to more easily collect data as explained above.
Claim 2: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 1 wherein the neurological condition for which the diagnostic prediction is generated is cognitive decline or a history of depression (Publicover at 0019, “cognitive disorders” indicating a decline in cognitive function or speed).
Claim 4: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 1 wherein the one or more first judgment variables include one or more of:
a risk aversion value based on (i) a ratio of a second derivative of the value function to a first derivative of the value function and (ii) a predetermined quantity of the approach ratings (Breiter ‘701 at 0240, “a measure of "risk aversion" from the value function computed as the second derivative of the value function, divided by the first derivative, midway along its graphical extent”);
a loss resilience value based on (i) an absolute value of the ratio of the second derivative of the value function to the first derivative of the value function and (ii) a predetermined quantity of the avoidance ratings (Breiter ‘701 at 0232, “slope, S-, of the avoidance curve 1114 near the origin 1105, e.g., near points 1106g and 1106e, may be computed. These slope values, may be included as preference feature values, as can their absolute ratio, as given by:” | S-/S+ | );
a loss aversion value based on an absolute value of a ratio of a linear regression slope of a logarithm of the avoidance ratings versus a logarithm of the avoidance entropy values to a linear regression slope of a logarithm of the approach ratings versus a logarithm of the approach entropy values (Breiter ‘701 at 0168, “For all participants, it may be important to assess the difference in slopes between the approach and avoidance sections of the value function plot, to determine how "loss averse" a participant or a subgroup of participants is regarding the marketing options or experimental conditions tested. The extent of loss aversion may segregate subgroups of participants”);
an ante value based on a positive offset of the approach ratings when the approach entropy values is zero; and
an insurance value based on a negative offset of the avoidance ratings when the avoidance entropy values is zero.
Claim 5: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 1 wherein the one or more second judgment variables include one or more of:
a peak positive risk value based on a given value of the approach standard deviation values when a derivative of the approach standard deviation values to a derivative of the approach ratings is zero;
a peak negative risk value based on a given value of the avoidance standard deviation values when a derivative of the avoidance standard deviation values to a derivative of the avoidance ratings is zero;
a reward tipping point being a given value of the approach ratings when the derivative of the approach standard deviation values to the derivative of the approach ratings is zero;
an aversion tipping point being a given value of the avoidance ratings when the derivative of the avoidance standard deviation to the derivative of the avoidance ratings is zero;
a total reward risk value based an area under the limit function for the approach ratings and the approach standard deviation values (Breiter ‘701 at 0110); and
a total aversion risk based on an area under the limit function for the avoidance ratings and the avoidance standard deviation values (Breiter ‘701 at 0110).
Claim 6: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 1 wherein the generating the value function includes applying a curve fitting tool to a plot of the approach entropy values and the avoidance entropy values versus the approach ratings and the avoidance ratings (Breiter ‘701 at 0187, Fig. 12).
Claim 7: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 1 wherein the generating the limit function includes applying a curve fitting tool to a plot of the approach standard deviation values and the avoidance standard deviation values versus the approach ratings and the avoidance ratings (Breiter ‘701 at 0187, Fig. 12).
Claim 8: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 1 further comprising:
generating a tradeoff function between the approach entropy values and the avoidance entropy values (Breiter ‘701 at 0154, Fig. 13); and
deriving one or more third judgment variables from the tradeoff function, wherein the applying further includes applying the one or more third judgment variables from the tradeoff function to the trained ML model and the prediction is further based on the third judgment variables (Breiter ‘701 at 0154, 0163, 185, Figs. 13, 17, and 21).
Claim 9: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 8 wherein the one or more third judgment variables include one or more of:
a reward-aversion tradeoff value based on a mean of polar angles of points on a plot of the tradeoff function (Breiter ‘701 at 0150, 153, Fig. 10);
a tradeoff range value based on a standard deviation of the polar angles of the points on the plot of the tradeoff function (Breiter ‘701 at 0150, 153, Fig. 10);
a reward-aversion consistency based on an average Euclidian distance of the points on the plot of the tradeoff function to an origin of the plot; and
a consistency range value based on a standard deviation of radial distances of the points on the plot of the tradeoff function to the origin of the plot (Breiter ‘701 at 0150, 0152, Figs. 8 and 9).
Claim 10: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 1 wherein the pictures are presented to the human subject through a rating task running on a device (Breiter ‘701 at 0012, “The procedure may present the individuals with a plurality of evaluation items that belong to predetermined categories…. The approach and avoid response data may be collected through the use of various techniques, such as keypresses on a keyboard, alternating keypresses, swiping a touchscreen, button holds on a touchscreen, etc.”).
Claims 12-14 are rejected on the same basis as claim 1 above since Breiter ‘701 discloses the broader method of claim 12 - claims 12 and 14 being broader in that the rating information need only be “associated with” the human subject (where being “created by” at claim 1 necessarily means it is associated with the human subject), rating “evaluation items” (as opposed to “pictures” at claim 1, the evaluation items encompassing pictures per dependent claim 13), the deriving of a judgment variable need only be “from the value function or the limit function” (rather than claim 1 indicating a variable from each function), and prediction merely being a “diagnostic prediction” (rather than a “diagnostic prediction of a neurological condition” at claim 1). And Breiter ‘701 discloses a apparatus comprising: one or more memories … and one or more processors coupled to the one or more memories, the one or more processors configured to perform the activities of claims 12 and 1 (see Breiter ‘701 at 0039-0041).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Breiter ‘701 in view of Publicover in further view of Claussen et al. (U.S. Patent Application Publication No. 2020/0356898, hereinafter Claussen) .
Claim 3: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 1 but does not appear to explicitly disclose wherein trained ML classifier is a random forest classifier or a gaussian mixture model. Claussen, though teaches to “use one or more techniques to train the classifier or machine learning model. For example the offline training node 319 may use … a multivariate gaussian mixture model (MGMM)…. [where] MGMM may be used to process the input to the classifier or machine learning model. For example, MGMM may break the input sensor data into segments or patches, fit the input to a model, and use the fitted model as the input to the classifier or machine learning model. Other models may include deep neural network classifiers, random forest classifiers, or temporal models such as recursive neural networks or hidden markov models. Once trained, the trained classifier or machine learning model may be transferred to the machine learning model storage” (Claussen at 0052). Therefore, the Examiner understands and finds that to use either a gaussian mixture model or a random forest is applying a known technique to a known device, method, or product ready for improvement to yield predictable results because they are each one of various common and readily accepted techniques for training a classifier or machine learning model.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the rating predictions and models of Breiter ‘701 in view of Publicover with the model types of Claussen in order to use either a gaussian mixture model or a random forest because they are each one of various common and readily accepted techniques for training a classifier or machine learning model.
The rationale for combining in this manner is that to use either a gaussian mixture model or a random forest is applying a known technique to a known device, method, or product ready for improvement to yield predictable results because they are each one of various common and readily accepted techniques for training a classifier or machine learning model as explained above.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Breiter ‘701 in view of Publicover in further view of Miura et al. (U.S. Patent Application Publication No. 2008/0260264, hereinafter Miura) .
Claim 11: Breiter ‘701 in view of Publicover discloses the computer-implemented method of claim 1 but does not appear to explicitly disclose wherein the picture categories include one or more of sports, disasters, cute animals, aggressive animals, nature, and food. Miura, though, teaches “a method for generating aesthetic characters, in which an arbitrary image pattern is selected from a pattern database in which various patterns are stored, and … a system which includes: means for selecting an arbitrary image pattern from a pattern database in which various image patterns are stored” (Miura at 0013), where the classification or categories of images includes those related to nature, plants, animals, humans, art and culture, food, religion, sport, games, weather, disaster, etc. (Miura at 0077). Therefore, the Examiner understands and finds that to use picture categories that include one or more of sports, disasters, cute animals, aggressive animals, nature, and food is applying a known technique to a known device, method, or product ready for improvement to yield predictable results because they are broad categories into which most or many pictures could generally fit.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the rating predictions and models of Breiter ‘701 in view of Publicover with the image categories of Miura in order to use picture categories that include one or more of sports, disasters, cute animals, aggressive animals, nature, and food because they are broad categories into which most or many pictures could generally fit.
The rationale for combining in this manner is that to use picture categories that include one or more of sports, disasters, cute animals, aggressive animals, nature, and food is applying a known technique to a known device, method, or product ready for improvement to yield predictable results because they are broad categories into which most or many pictures could generally fit as explained above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Raz et al. (U.S. Patent Application Publication No. 2021/0183519, hereinafter Raz) indicates “Utilizing a mobile device operated by a clinician responsible for screening patients for eligibility to receive the complex therapy, the patient is instructed to describe a recent dream they have had, or to react to an image or photograph known to elicit a negative emotional response, and this response is recorded, pre-processed, and transmitted via the mobile device to a remote server for analysis” (Raz at 0182).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT D GARTLAND whose telephone number is (571)270-5501. The examiner can normally be reached M-F 8:30 AM - 5 PM.
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685
Prosecution Insights