DETAILED ACTION
This action is in reference to the communication filed on 9 JUN 2026.
Amendments to claims 1, 7, 8, 11, 17, 18, 20 entered and considered.
Claims 1-20 are present and have been examined.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-20 the independent claim(s) 1, 11, 20 recite(s) a method, a system, and a non-transitory computer readable medium, all of which is/are a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1-20, the independent claim(s) (claims 1, 11, 20 ) is/are directed, in part, to
A method comprising: receiving a request to perform an interaction associated with a user;
receiving behavior data associated with the user;
converting the behavior data into one or more groups of feature vectors;
applying the one or more groups of features vectors to one or more machine-learning models to generate a stress signal indicative of one or more confidence scores in an emotional state associated with the user;
based on the requested interaction, identifying an associated stress signal threshold;
determining a stress response based on the stress signal confidence score exceeding the associated stress signal threshold; and
restricting access based in part on the stress response.
These claim elements are considered to be abstract ideas because they are directed to a mental process, in that the claims ensconce concepts performed in the human mind including observation, evaluation, judgment, and opinion functions. Receiving or using behavior of a user to make a determination of a stress associated with a user, and performing an action based on that received and determined conclusion (i.e. stress), are all examples of concepts performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers a concept performed in the human mind, then it/they falls/ fall into the “mental processes” category.
These claims are also directed to certain methods of organizing human activity – managing personal behavior or relationships or interactions – in that the emotional state of a user is determined and then used to determine a restriction or access to a resource.
The claims are further directed to mathematical concepts – i.e. mathematical relationships, formulas, equations, and/or calculations. Converting information into a vector (i.e. a numerical representation), applying the vectors to machine learning, determining the exceeding of a threshold, and determining an action based on a result are all examples of mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, formulas, equations, and/or calculations, then it/they falls/ fall into the “mathematical processes” category. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional elements: Claim 11 recites a system comprising a memory device and a process device, and claim 20 recites a non-transitory computer readable medium storing instructions executed by a processing device. Claims 1, 11, 20 have been amended to recite a user interface.
Examiner finds that the memory/process device (i.e. processor) and the analogous non transitory computer readable medium storing instructions executed by a processing device (i.e. also a memory and a processor) are recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). Examiner finds no improvement to the functioning of the computer or any other technology or technical field in the above identified elements as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e). Examiner further notes that a memory storing information, as well as a processor sending/receiving information are generally found to be analogous to adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g). In the interest of compact prosecution, Examiner notes the user interface, however, also notes that the claim only provides for access to be restricted rather than even used in the claim. An analogous example would be restricting someone from driving after receiving distressing news. Examiner notes that if the interface were to display information, this is generally found to also be insignificant extra solution activity.
Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as: Claim 11 recites a system comprising a memory device and a process device, and claim 20 recites a non-transitory computer readable medium storing instructions executed by a processing device. In the interest of compact prosecution Examiner notes the claims have been amended to reference an interface. As discussed above, the interface itself is merely something from which a user is restricted from accessing, rather than actively recited.
When considered individually, the memory/processing claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[058] In some examples, some of the steps in the flow chart of FIG. 3 are implemented in program code executed by a processor, for example, the processor in a general-purpose computer, mobile device, or server. In some examples, these steps are implemented by a group of processors. In some examples the steps shown in FIG. 3 are performed in a different order or one or more steps may be skipped. Alternatively, in some examples, additional steps not shown in FIG. 3 may be performed.
0086] The computing system 502 executes program code that causes the processor 506 to perform one or more of the operations described above with respect to FIGS. 1-3. The program code includes operations related to, for example, receiving sensor data, processing the data, training machine-learning models, applying the data to the machine-learning models, and determining an action response. The program code may be resident in the memory device 504 or any suitable computer-readable medium and may be executed by the processor 506 or any other suitable processor. In some embodiments, the program code described above, the sensor input from streams 104-108, associated feature vector embeddings 224-228, and behavior prediction data are stored in the memory device 504, as depicted in FIG. 5. Memory 504 may also include a visual data processor code 528.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-10, 12-19:
Dependent claims 2, 12, 4, 14, 8, 18, 9,19 are not directed to any additional abstract ideas than those identified above, however they do recite non abstract elements such as a camera and/or specific sensors, as well as the nominal recitation of an ATM interface. Examiner finds that the camera and sensors, as claimed at best recite the technology being applied to or by the abstract idea, rather than any improvement or meaningful link therein, and therefore do not support a finding of a practical application. Similarly these sensors/camera appear to merely send data, or at best take a picture, which is considered to be insignificant extra solution activity. Examiner also finds similarly to the above discussion of the interface as noted in the independent claims. As such these elements do not confer a finding of significantly more.
Dependent claims 3, 13, 5, 15, 6, 16, 7, 17, are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as the types of information considered in the mental process, as well as additional clarification or description about the mathematical processes. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 4-8, 10, 11, 14-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Szymkowiak et al (US 20100302000 A1, hereinafter Szymkowiak) in view of Harper et al (US 20230032131 A1, hereinafter Harper).
In reference to claim 1, 11, 20:
Szymkowiak teaches: A method comprising: receiving a request to perform an interaction associated with a user (at least [fig 2 and related text] “FIG. 2 is a flowchart depicting an example online processing phase of an example embodiment of a biometric security technique. At block 210, an identity verification request may be received from a user. For one or more embodiments, the identity verification request may comprise one or more fixed length textual elements typed by the user in response to a prompt from a biometric security system. “;
receiving behavior data associated with the user (at least [fig 2 and related text] “At least in part in response to receiving the identity verification request, the request may be analyzed, and at block 220 a plurality of keyboard metrics corresponding with those generated during the offline processing phase may be generated in accordance with, and at least in part in response to, the analyzed request. For simplicity, the keyboard metrics generated during the offline processing phase and the online processing phase may be referred to herein as registered user metrics and test metrics, respectively.”);
converting the behavior data into one or more groups of feature vectors (at least [fig 3 and related text] “As depicted in FIG. 3, a plurality of identifying signatures of a given registered user forms a data cloud within the hyperspace defined by the above-mentioned keystroke metrics. The volume of a given data cloud is at least partially a manifestation of different stress states associated with the user. In the present example, the hyperspace is shown as a three-dimensional space, wherein, for example, the e.sub.1, e.sub.2 and e.sub.3 dimensions respectively represent an "a" to "e" inter-key latency time, an "h" key holding time, and a "t" key holding time. Of course, these are merely example keystroke metrics, and the scope of claimed subject matter is not limited in this respect…. he data cloud for User.sub.3 303 is well separated from that of User.sub.1 301 and User.sub.2 302. However, the data cloud of User.sub.1 301 partially overlaps with that of User.sub.2 302. A test metric TM.sub.1 311 is disposed proximally to the User.sub.3 303 data cloud. Thus, it can be surmised that the User.sub.3 303 and not User.sub.1 301 or User.sub.2 302 made the identity verification request from which the test metric TM.sub.1 311 was generated. Similarly, test metrics TM.sub.2 312 and TM.sub.3 313 are respectively disposed proximally to the non-overlapping regions of the User.sub.1 301 and User.sub.2 302 data clouds.”);
applying the one or more groups of features vectors to one or more machine-learning models to generate a stress signal associated with the user (at least [fig 2 and related text] “Continuing with the present example embodiment, a matching algorithm may be utilized at block 230 to compare the test metrics with the registered user metrics to generate a similarity measure. In an embodiment, the matching algorithm may comprise one or more of a statistical vector comparison method such as a nearest neighbor algorithm, a Bayesian classifier, and an artificial neural network. “ at [022-025] stress information, see [fig 1 and related text] for discussion of training the recognition system);
based on the requested interaction, identifying a stress [level] threshold (at least [045-045] “From these comparisons, it may be determined at block 270 whether the typing pattern expressed in the test keyboard metrics more closely matches that associated with a high or normal stress state of the closest matching registered user. For example, referring to FIG. 3, let User.sub.3 303 have a high valued "t" key holding time, when typing in a highly stressed state. In other words, User.sub.3 303 had a highly-valued e.sub.3 test metric when highly stressed. Because the TM.sub.1 311 test metric is disposed proximal to the highly-valued e.sub.3 periphery of the User.sub.3 303 data cloud, it is likely that User.sub.3 303 was highly stressed when making the relevant identity verification request.”);
determining a stress response based on the stress signal [exceeding a threshold] (at least [fig 2 and related text] “Further, for the present example embodiment, at block 270 the test metrics may be utilized to determine the likely stress state of the registered user on making the identity verification request. In one embodiment, the test keyboard metrics may be compared with one or more stored keyboard metrics associated with a normal stress state of the user. A significant deviation between the typing patterns expressed in the test keyboard metrics and those in the stored keyboard metrics may be an indication that the corresponding identity verification request from which the test keyboard metrics were derived was created under stress or duress… From these comparisons, it may be determined at block 270 whether the typing pattern expressed in the test keyboard metrics more closely matches that associated with a high or normal stress state of the closest matching registered user. For example, referring to FIG. 3, let User.sub.3 303 have a high valued "t" key holding time, when typing in a highly stressed state. In other words, User.sub.3 303 had a highly-valued e.sub.3 test metric when highly stressed. Because the TM.sub.1 311 test metric is disposed proximal to the highly-valued e.sub.3 periphery of the User.sub.3 303 data cloud, it is likely that User.sub.3 303 was highly stressed when making the relevant identity verification request.”);
restricting access to a user interface based in part on the stress response (at least [fig 2 and related text] “Returning once more to FIG. 2, at least in part in response to a determination at block 270 that the registered user was in a normal stress state upon making the identity verification request, access to the controlled resource may be allowed at block 290. However, at least in part in response to a determination at block 270 that the registered user was in a highly stressed state on making the identity verification request, further investigations of the circumstances of the identity verification request may be undertaken at block 280.” i.e. the user at block 280 is not allowed access to the controlled resource as the user in block 290; see also: “Returning to the example process depicted in FIG. 2, at least in part in response to a determination at block 240 that there is no close match between the test metrics and any of the registered user metrics, access to a controlled resource may be refused at block 250.”).
Szymkowiak as cited teaches all of the above limitations, and further discloses determining an associated level of a stress or emotional response (i.e. normal vs. high), but does not specifically disclose a confidence score in an emotional state. Harper however, does teach:
Generate a stress signal indicative of one or more confidence scores in an emotional state associated with the user (at least [0104] “0104] In example embodiments, the trained models use probabilistic models which an allow the determination of a confidence value for one or more emotional and/or mental states. Various probabilistic models can be used to determine a confidence value of the determined output emotional and/or mental state.”);
Identifying an associated stress signal threshold (at least [047-051] “Optionally, the at least one associated confidence values of the at least one mental states exceeds one or more predetermined thresholds... For example, by using a threshold against which the confidence value can be checked, predicted mental state(s) with a confidence value below the threshold can avoid triggering a dynamic prompt to the user to ensure that only higher confidence predictions of mental states are used by the system to prompt the user.”)
Determining a stress response based on the stress signal confidence score exceeding the associated stress signal threshold (at least [048-051] “For example, by using a threshold against which the confidence value can be checked, predicted mental state(s) with a confidence value below the threshold can avoid triggering a dynamic prompt to the user to ensure that only higher confidence predictions of mental states are used by the system to prompt the user… another example, the model may predict symptoms of depression and/or low mood in a user, and the model confidence is above the threshold for the system to take a further action…”). Szymkowiak and Harper are analogous references as both disclose detecting an emotional state of the user for further action. Including a confidence score and threshold as taught by Harper in the stress/emotion detection taught by Szymkowiak would have been obvious to one of ordinary skill in the art, as Harper teaches that a confidence score of a condition allows for more nuanced future actions with regard to the detected emotion or mental state of a user (see 0018). As such, the restriction or access allowed by the determined emotional state as taught by Szymkowiak would be improved by a confidence score taught by Harper, particularly when Harper teaches a variety of actions taken or not taken based on the confidence threshold being exceeded or not.
In reference to claim 4, 14:
Szymkowiak teaches: wherein the behavior data is received from one or more of a keystroke sensor or a biometric sensor configured to collect biometric behavior data (at least [026-027, 032] “. One or more signals indicative of keystroke metrics including temporal information and force information may be stored in the memory. The force information may be determined by measuring the force with which the user depresses individual keys as the user is typing the registration entry. The recorded raw temporal, force, and keystroke information from the registration entry may be referred to herein as primary keyboard entry data.” And at [035] other biometric sensors such as GSR are used).
In reference to claim 5, 15:
Szymkowiak further teaches: wherein the behavior data includes typing speed data, key-press frequency data, or typing error-rate data, and the one or more machine-learning models includes a pattern recognition model trained on typing attributes (at least [032, 036] “As noted above, the user may be prompted to input a registration entry via the keyboard. One or more signals indicative of keystroke metrics including temporal information and force information may be stored in the memory. The force information may be determined by measuring the force with which the user depresses individual keys as the user is typing the registration entry. The recorded raw temporal, force, and keystroke information from the registration entry may be referred to herein as primary keyboard entry data.. Continuing with the example embodiment depicted in FIG. 1, at block 140 a plurality of keystroke metrics may be calculated from the received primary keyboard entry data to calculate a plurality of keystroke metrics. For one or more embodiments, the calculated keyboard metrics may include inter-key latency times. As used herein the term inter-key latency refers to a length of time between releasing one key and pressing the next, which could be negatively valued in the event of an overlap between the depression of successive keys. Also for one or more embodiments, the calculated keystroke metrics may include hold times and/or typing error measurements. “ and at [030, fig 1 and related text, element 150] user typing patterns are learned to create signatures for the user, i.e. trained).
In reference to claim 6, 16:
Szymkowiak further teaches wherein the biometric behavior data includes heart-rate data or blood pressure data, and the one or more machine-learning models includes a biometric model trained on biometric attributes (at least [035] “To measure GSR in an embodiment, one or more electrodes may be attached to the skin of the user to measure the conductivity thereof. Electrical skin conductance is dependent on the activity of sweat glands which, since they are innervated by the autonomic nervous system, is often used as an indicator of sympathetic activity related to emotional processing of stimuli. In particular, the user's skin's conductivity may increase in the event the user becomes stressed…one or more embodiments in accordance with claimed subject matter may detect the induction of a particular stress state from other physiological variables, such as, altered pulse rate, blood pressure, pupil dilation, body temperature and respiration, to name but a few examples.”
In reference to claim 7, 17:
Szymkowiak/Harper teaches all the limitations above. Harper further teaches: wherein the stress signal threshold is further determined based on a location data associated with the request (at least [029-031, 083-085, 088] prompts and/or biometric data used to determine the threshold may be governed or include GPS/location data of the user). Szymkowiak and Harper are analogous references as both disclose a means of determining user emotions for further action. One of ordinary skill would have found the user’s location as taught by Harper to be an obvious inclusion in the biometrics used to determine emotional/stress state common to both references, as a person’s emotional or mental state might vary drastically from work/home/vacation or anywhere in-between.
In reference to claim 8, 18:
Szymkowiak further teaches: wherein the user interface comprises an automated teller machine (at least [025] “. Example computing platforms may include, but are not limited to, desktop computers, notebook computers, portable wireless devices, cellular telephones, personal digital assistants, gaming consoles, consumer media devices such as televisions and digital video devices, ATM machines, and door entry security systems. However, these are merely several examples of a computing platform, and the scope of claimed subject matter is not limited in this respect.”).
In reference to claim 10:
Szymkowiak wherein the one or more machine-learning models includes a visual-based machine-learning model, a pattern recognition model, and a biometric model, wherein each of the one or more machine-learning models outputs to a respective activation function, and the stress signal is determined based on outputs of each respective activation function (at last [020-024, 030, 041, 044] pattern matching comparing the user’s baseline to the detected levels).
Claim(s) 2, 3, 9, 12, 13, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Szymkowiak in view of Harper, further in view of Manjunath et al (US 20220139546 A1, hereinafter Manjunath).
In reference to claim 2, 12:
Szymkowiak/Harper teaches all the limitations above, and further teaches applicability of facial recognition thereby at least applying the presence of a camera, with Harper teaching an imaging device. However, in the interest of compact prosecution Examiner notes that Manjunath further teaches: wherein the behavior data is received from a camera, and the one or more machine-learning models includes a trained, visual-based machine-learning model (at least [0163] “. The personal smart devices are able to be used to perform additional tasks such as scanning/acquiring a dynamic optical mark/code using a camera. Inheritance factors are based on who the user is (e.g., biometrics such as fingerprints, hand scans, vein patterns, iris scans, facial scans, 3D facial scans, heart rhythm, and ear identification, and behavioral information such as voice tenor and patterns, gait, typing style, web page selection/usage).” Szymkowiak and Manjunath are analogous as both references disclose the use of biometrics to authentic and determine the mood/emotional state of a user. One of ordinary skill in the art would have found the presence of the camera as taught by Manjunath to be obvious in the facial recognition of Szymkowiak/Harper in that the reference discloses the use of facial recognition, which is widely known to incorporate a camera of some sort. As such the camera would have been an obvious inclusion.
In reference to claim 3, 13:
Szymkowiak/Harper teaches all the limitations above. Manjunath further teaches: where the behavior data includes one or more of facially recognized behavioral data, gesture-based behavioral data, or environment-based behavior data (at least [163] “. The personal smart devices are able to be used to perform additional tasks such as scanning/acquiring a dynamic optical mark/code using a camera. Inheritance factors are based on who the user is (e.g., biometrics such as fingerprints, hand scans, vein patterns, iris scans, facial scans, 3D facial scans, heart rhythm, and ear identification, and behavioral information such as voice tenor and patterns, gait, typing style, web page selection/usage). Knowledge factors are based on what a user knows (e.g., passwords, relatives' names, favorite image, previous”). The inclusion of the facial recognition as taught by Manjunath would be obvious to include in the invention of Szymkowiak/Harper in that Szymkowiak contemplates facial recognition as well as for the reasons identified above with respect to claims 2, 12, the motivation therein incorporated by reference.
In reference to claim 9, 19:
Szymkowiak/Harper teaches all the limitations above. Examiner notes that Harper teaches thresholding of stress based on location. Manjunath further teaches: wherein the behavior data is received from a location sensor and the stress signal is based in part on location data associated with the user (at least [fig 56 and related text] “The external information is able to include external factors such as diet, medication, weather, noise, stressful versus serene environments, light information, and others… . The external information is able to be acquired by: sensors (e.g., a thermometer), searching for and acquiring information (e.g., search via a search engine to retrieve data), a microphone/camera, and/or any other manner.”). Including location information in the stress determination common to both references would have been obvious to one of ordinary skill in that Manjunath specifically discloses that locations themselves can be stress inducing or not. One of ordinary skill would recognize that work is typically a more stressful location than home or than a leisure destination, and that the airport is often more stressful than the destination itself.
Relevant Prior Art
The following prior art is made of record:
US 20190311260 A1, to Baldwin, discloses modeling behavioral information of a user based on observed behaviors.
Response to Arguments
Applicant’s remarks as filed on 9 JUN 2026 are fully considered.
Applicant’s remarks regarding the 101 rejection begin on 8. Examiner respectfully disagrees with Applicant’s assertion regarding the abstract idea(s). Examiner finds that the analogy to SRI fails, as the current claims do not “detect suspicious activity by using network monitors and analyzing network packets” as was found to be the case in SRI. Instead, these claims discuss determining an emotional state of a user, and restricting a user’s access based on the emotional state. This is more analogous to not letting someone drive after receiving upsetting news. Contrary to Applicant’s remarks on page 9, these claim limitations are not analogous to those in SRI, at the very least because the network activity itself is not actually being monitored, only the user’s emotional state.
Applicant’s further remarks on page 9 are also found unpersuasive, as Examiner does not find any improvements to the interfaces themselves or any other technical elements. The benefits of the invention are not necessarily disputed, however, no improvement to the technical aspects of authentication nor any other technical field is found in the claim language. The emotion based authentication is not completed in a manner that provides for a practical application of the abstract idea(s) identified above, and certainly not analogous to the technical improvements in SRI as no network analysis is actually completed in the claims.
Applicant’s remarks regarding the prior art begin on page 10. Applicant’s remarks on page 11 appear to discuss the newly amended limitations, and are found moot in view of the new grounds of rejection above.
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 KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687
Prosecution Insights