DETAILED ACTION
This action is made FINAL in response to the amendments filed on 10/02/2025.
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 - 4, 13 - 16 and 25 - 32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1, 13, and 29,
Step 2A, Prong One
The claim recites in part:
classifying the input using a neural network having a plurality of stages and implemented by one or more processors
wherein each stage of the plurality of stages classifies the input using a different number of classification clusters;
a first stage of the plurality of stages uses a first number of classification clusters;
a second stage of the plurality of stages uses a second number of classification clusters;
a third stage of the plurality of stages uses a third number of classification clusters;
the second stage precedes the third stage;
the first stage precedes the second stage;
the second number is greater than the first number; and
the third number is greater than the second number;
taking one or more actions based on the classification of the input.
Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving an input for classification;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites a processing system, memory, and processor which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of neural network, stages, and actions amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receiving an input for classification;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The processing system, memory, and processor are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of neural network, stages, and actions amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 3 and 15, the limitations “wherein classifying the output comprises classifying the input at a stage of the plurality of stages based on an inference generated by a prior stage of the plurality of stages” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
As to claims 2 and 14, the limitations “wherein classifying the output comprises classifying the input at a stage of the plurality of stages based on an inference generated by a prior stage of the plurality of stages” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
As to claims 3 and 15, the limitations “the neural network comprises a neural network including segmentation transformers at each stage of the neural network,output of each stage of the neural network other than a final stage of the neural network is aggregated, and the aggregated output is input into a segmentation transformer associated with the final stage of the neural network to generate the classification of the input” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
As to claims 4,16 and 32, the limitations “wherein each stage of the plurality of stages classifies the input using a larger number of classification clusters than a preceding stage of the plurality of stages” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
As to claims 25, 27, and 30 the limitations “wherein taking the one or more actions comprises identifying one or more portions of an image, corresponding to a human body, in which a disease is present” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 26, 28, and 31 the limitations “wherein taking the one or more actions comprises: identifying a direction of travel for a vehicle and applying a steering input to cause the vehicle to travel in the identified direction of travel; accelerating or decelerating the vehicle; or controlling the vehicle to avoid an obstacle or harm to a person in a vicinity of the vehicle” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
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.
Claim(s) 1, 2, 4, 13, 14, 16, and 32 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al (US 2014/0164376)
As to claim1, Yang et al shows and teaches in figure 4 a computer-implemented method of machine learning (paragraph [0038]…various portions of the disclosed systems above and methods below can include or employ of artificial intelligence, machine learning, or knowledge or rule-based components, sub-components, processes, means, methodologies, or mechanisms (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, classifiers . . . ). Such components, inter alia, can automate certain mechanisms or processes performed thereby to make portions of the systems and methods more adaptive as well as efficient and intelligent), comprising:
receiving an input for classification (paragraph [0022]… the cluster component 120 receives, retrieves, or otherwise obtains or acquires unique strings produced by the pre-process component and clusters the strings);
classifying the input using a neural network having a plurality of stages (paragraph [0036]…string clustering workflow 400) and implemented by one or more processors (paragraph [0052]…one or more processors 820), wherein:
each stage of the plurality of stages (paragraph [0036]…first stage 410 ; second stage 420 ; third stage 430) classifies the input using a different number of classification clusters (paragraph [0036]…CStrLen ; SCEditDist ; SCAdjusted);
a first stage (paragraph [0036]…first stage 410) of the plurality of stages uses a first number (paragraph [0036]…CStrLen) of classification clusters;
a second stage (paragraph [0036]… second stage 420) of the plurality of stages uses a second number (paragraph [0036]… SCEditDist) of classification clusters;
a third stage (paragraph [0036]… third stage 430) of the plurality of stages uses a third number (paragraph [0036]… SCAdjusted) of classification clusters;
the second stage precedes the third stage (Figure 4);
the first stage precedes the second stage (Figure 4);
the second number (paragraph [0036]… 6 of SCEditDist) is greater than the first number (paragraph [0036]…4 of CStrLen); and the third number (paragraph [0036]… 8 of SCAdjusted) is greater than the second number; and
taking one or more actions based on the classification of the input (paragraph [0048]…the inference can be probabilistic--that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources).
As to claim 2, Yang et al shows and teaches in figure 4 the method, wherein classifying the output (paragraph [0026]…the final clustering results ; CFinal) comprises classifying the input at a stage of the plurality of stages (paragraph [0036]…first stage 410 ; second stage 420 ; third stage 430) based on an inference generated by a prior stage of the plurality of stages (paragraph [0048]…the inference can be probabilistic--that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources).
As to claim 4, Yang et al shows and teaches in figure 4 the method, wherein each stage of the plurality of stages (paragraph [0036]…first stage 410 ; second stage 420 ; third stage 430) classifies the input using a larger number of classification clusters than a preceding stage of the plurality of stages (paragraph [0036]…4 of CStrLen ; 6 of SCEditDist ; 8 of SCAdjusted);
Claim 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 32 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
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.
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.
Claim(s) 25, 27, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al (US 2014/0164376) in view of Wallack et al (US 2021/0202092).
As to claim 25, Yang et al shows and teaches in figure 4 taking one or more actions based on the classification of the input (paragraph [0048]…the inference can be probabilistic--that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources).
Yang et al fails to explicitly show/teach wherein taking the one or more actions comprises identifying one or more portions of an image, corresponding to a human body, in which a disease is present.
However, Wallack et al teaches taking the one or more actions comprises identifying one or more portions of an image, corresponding to a human body, in which a disease is present (paragraph [0018]… a method for identifying and diagnosing a presence of a disease or a condition in at least one image of a subject, the method including: classifying the image to one or more body regions, labelling and orientating the image to obtain a classified, labeled and oriented sub-image; directing the sub-image to at least one artificial intelligence (AI) processor to obtain an evaluation result, and comparing the evaluation result to a database with evaluation results and matched written templates or at least one dataset cluster to obtain at least one cluster result; measuring the distance between the cluster result and the evaluation result to obtain at least one cluster diagnosis; and assembling the cluster diagnosis to obtain a report thereby identifying and diagnosing the presence of the disease or the condition in the subject).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made, for Yang et al one or more actions comprises identifying one or more portions of an image, corresponding to a human body, in which a disease is present, as in Wallack et al, for the purpose of the cluster diagnosis greatly reduced lengths of time and are useful for cost and time savings.
Claim 27 has similar limitations as claim 25. Therefore, the claim is rejected for the same reasons as above.
Claim 30 has similar limitations as claim 25. Therefore, the claim is rejected for the same reasons as above.
Claim(s) 26, 28, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al (US 2014/0164376) in view of King et al (US 2020/0211394).
As to claim 25, Yang et al shows and teaches in figure 4 taking one or more actions based on the classification of the input (paragraph [0048]…the inference can be probabilistic--that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources).
Yang et al fails to explicitly show/teach wherein taking the one or more actions comprises: identifying a direction of travel for a vehicle and applying a steering input to cause the vehicle to travel in the identified direction of travel; accelerating or decelerating the vehicle; or controlling the vehicle to avoid an obstacle or harm to a person in a vicinity of the vehicle.
However, King et al fails to explicitly show/teach wherein taking the one or more actions (paragraph [0067]…the perceiver 212 may perform data association (e.g., by using probabilistic filters, clustering, nearest point analysis, or the like) to associate sensor data with a track) comprises: identifying a direction of travel for a vehicle (paragraph [0067]…data indicating a direction of motion of the autonomous vehicle 102) and applying a steering input (paragraph [0067]…data indicating a yaw acceleration, and/or data indicating a steering angle and/or steering angle rate of the autonomous vehicle 102) to cause the vehicle to travel in the identified direction of travel; accelerating or decelerating the vehicle (paragraph [0067]…data indicating an acceleration of the autonomous vehicle 102); or controlling the vehicle to avoid an obstacle or harm to a person in a vicinity of the vehicle (paragraph [0031]..the secondary trajectory 118 may comprise complex maneuvers (including steering, accelerations, etc.) to enable the vehicle 102 to move to a side of a road, avoid obstacles, or generally increase an amount of safety for the occupants).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made, for Yang et al one or more actions comprises identifying a direction of travel for a vehicle and applying a steering input to cause the vehicle to travel in the identified direction of travel; accelerating or decelerating the vehicle; or controlling the vehicle to avoid an obstacle or harm to a person in a vicinity of the vehicle, as in King et al, for the purpose of the controlling an autonomous vehicle.
Claim 28 has similar limitations as claim 26. Therefore, the claim is rejected for the same reasons as above.
Claim 31 has similar limitations as claim 26. Therefore, the claim is rejected for the same reasons as above.
Response to Arguments
Applicant's arguments filed 10/02/2025 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
In the present case, the claims are directed to a technical solution to problems arising in the field of machine learning, and more specifically to addressing performance issues relating to the use of neural networks. Specification at para. [0023].
Certain disclosed techniques provide a solution to this problem, namely by using “a neural network having a plurality of stages,” where “[e]ach stage of the plurality of stages generally classifies [an] input using a different number of classification clusters.” Specification at para. [0048]. More specifically, rather than a neural network generating classifications solely with output layers (e.g., final stages), “each stage of the plurality of stages” preceding a “final stage . . . in the neural network may .. . generate an inference using a reduced number of classification clusters relative to a number of classification clusters used by the final stage.” /d. As discussed in the Specification, such neural networks “more accurately generate inferences for an input than neural networks in which the intermediate stages” do not make use of a differing number of classification clusters. /d. at para. [0025]. The claims embody this particular solution described in the Specification and presented above.
The examiner disagrees. The arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the applicant assert, describe, or even suggest the limitation of “generate an inference using a reduced number of classification clusters relative to a number of classification clusters used by the final stage.” As discussed in the Specification, such neural networks ‘more accurately generate inferences for an input than neural networks in which the intermediate stages’ do not make use of a differing number of classification clusters” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language. Without clear support in the claim language the examiner cannot give weight to arguments premised on these alleged limitations. The applicant needs to add limitations to the claims that clearly describe their invention.
The applicant argues:
More specifically, the 2024 AI SME Update explicitly states that “[t]he mental processes grouping is not without limits, and as such, claim limitations that only encompass [technology] in a way that cannot practically be performed in the human mind do not fall within this grouping.” /d. (emphasis added). Here, the Office asserts that “classifying the input using a neural network having a plurality of stages, wherein each stage of the plurality of stages classifies the input using a different number of classification clusters” and “taking one or more actions based on the classification of the input” encompasses “mental processes,” as these elements could be performed “in the human mind or with the aid pencil and paper.” Office Action, p. 2. However, Applicant respectfully submits that even if a human can “classify[]” an input in the mind or with pen and paper, the claims as amended further include the feature “and implemented by one or more processors.” As explained in the 2024 AI SME Update, this element clearly “only encompass[es] [technology] in a way that cannot practically be performed in the human mind.” As a human mind, even with the use of a pencil and paper, cannot make use of or apply “one or more processors,” this element is a computer-specific operation that can only be performed by the claimed technology itself. As the 2024 AI SME Update explains, “claims do not recite a mental process when they contain limitations that cannot practically be performed in the human mind.” 2024 AI SME Update at 58136.
Applicant therefore respectfully submits that the claims as amended are clearly not directed to a mental process and thus do not recite a judicial exception. Withdrawal of this rejection is respectfully requested at Step 2A, Prong 1 of the Supreme Court’s framework.
The examiner disagrees. The claims, even with the recited “on or more processors,” still encompass mental processes at a level of abstraction that humans can perform using ordinary cognitive reasoning. The steps of classifying input in stages, applying different classification clusters, and selecting actions based on those classifications are well-established mental activities, and the claims do not include any specialized hardware or technological improvement that would restrict them to a computer specific implementation. The processor merely performs the same conceptual reasoning a human can perform
The ““on or more processors”” are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The applicant argues:
Applicant submits that the claims are eligible under Step 2A, Prong 2 because various features of the claims, in fact, integrate any alleged abstract idea into a practical application— namely, improving accuracy of neural network inference generation. In particular, Applicant submits that this practical application reflects an improvement to other technology or a technical field (namely the field of machine learning and, more specifically, to classification using neural networks). Specifically, any alleged abstract idea has been integrated into the practical application of outputting inferences from neural networks that incorporate a plurality of stages, each stage configured to classify input using a different number of classification clusters.
Likewise, here, the features of the present claims reflect an improvement to a technology or technical field. For example, the practical application improves the technical field of machine learning (and more particularly, to classification using neural networks having a plurality of stages). More specifically, “[e]ach stage of the plurality of stages generally classifies [an] input using a different number of classification clusters.” Specification at para. [0048]. The stages may perform classification based upon classification clusters corresponding to a varying level of classification granularity corresponding to a varying number of classification clusters at each stage. Certain techniques described therein may allow intermediate stages of neural networks to “identify sensible patterns in [] input” and for “neural networks that more accurately generate inferences.” Id. at paras. [0024] and [0048]. Thus, particular solutions discussed therein improve upon conventional techniques, such as by enabling more efficient use of computational resources and more accurate inferences. The pending claims reflect these improvements. Thus, the pending claims are eligible because the claims as a whole improve machine learning technology and thus integrate the exception into a practical application of outputting inferences from neural networks using a plurality of stages with different numbers of classification clusters and are therefore not “directed to” the judicial exception.
The examiner disagrees. The arguments presented do not demonstrate that the claims integrate the alleged abstract idea into a practical application, nor do they establish that the claims recite an improvement to computer functionality or machine-learning technology. The recited neural network “stages” and varying numbers of “classification clusters” are recited at a high level of generality and are implemented using conventional neural network components performing their routine functions. The claims do not recite any specific technical mechanism that changes the operation of the computer itself or provides a technological solution to a technological problem; rather they merely process data using generic neural-network structures.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
The applicant argues:
In the Office Action, the Examiner argues that the claims do “not include additional elements that are sufficient to amount to significantly more tha[n] the judicial exception.” Office Action, p. 3. However, as discussed with respect to Step 2A, Prong 2 supra, the Specification provides sufficient details to show how the claimed features improve the technical field of machine learning (e.g., classification using neural networks). For example, the Specification describes how “intermediate stages [of neural networks] may have limited abilities to classify data.” Specification at para. [0024]. In particular, “intermediate stages may [] be unable to identify coherent patterns . .., thus adversely affecting the accuracy of inferences generated by the neural network[s].” /d. The improvements that address these issues with using deep neural networks are addressed by particular features of the claims.
The examiner disagrees. The arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the applicant assert, describe, or even suggest the limitation of “intermediate stages [of neural networks] may have limited abilities to classify data.” Specification at para. [0024]. In particular, “intermediate stages may [] be unable to identify coherent patterns …, thus adversely affecting the accuracy of inferences generated by the neural network[s]. ” Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language. Without clear support in the claim language the examiner cannot give weight to arguments premised on these alleged limitations. The applicant needs to add limitations to the claims that clearly describe their invention. The examiner is unsure how the newly added limitations even relate to what the applicant is arguing. In the claims, the applicant needs to clearly identify the intermediate stages and why they are unable to identify coherent patterns. And how that is an improvement.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
Claim Rejections - 35 USC § 102
Applicant’s arguments with respect to claim(s) 1 – 4 and 13 - 16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRANDON S COLE/Primary Examiner, Art Unit 2128
Prosecution Insights