DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Status of Claims
This Office Action is in response to the Applicant’s amendments and remarks filed 28 March 2026. The Applicant has amended claim 8. Claim 1-8 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09 March 2026 is in compliance with the provisions of 37 CFR 1.97, 1.98. Accordingly, the information disclosure statement is being considered by the examiner.
Reply to Applicant’s Remarks
Applicant’s remarks filed 28 March 2026 have been fully considered and are addressed as follows:
Objections to Drawings:
Applicant’s amendment to the Drawings filed 28 March 2026 have overcome the Objections to Drawings previously set forth.
Claim Interpretations under 35 U.S.C. 112:
Applicant’s amendment to the claims filed 28 March 2026 have avoided the claim interpretation under the 35 U.S.C. 112(f) rejections previously set forth.
Claim Rejections under 35 U.S.C. 112:
Applicant’s amendment to the claims filed 28 March 2026 have overcome the 35 U.S.C. 112 rejections previously set forth.
Claim Rejections under 35 U.S.C. 101:
Applicant’s arguments, see Arguments/Remarks, filed 28 March 2026, with regard to the rejections of claims 1-8 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Regarding the Applicant’s argument that “…the claims are directed to a specific technical solution to a recognized technical problem: how to train an autonomous path planning system for tractor-trailer combinations-articulated vehicles whose unique kinematics require specialized path planning…The solution is a path cost function…and use this cost to train an encoder-decoder neural network…But the specific integration of a path cost function, encoder-decoder architecture, and tractor-trailer kinematic model…such that the claims do more than merely state “apply it with a computer””, the Examiner respectfully disagrees.
Claim 1 does not explicitly recite the specific integration of a path cost function, encoder-decoder architecture, and tractor-trailer kinematic model…cited in the argument. The current claim does not recite any additional elements to the limitations that are directed to at lest one abstract idea. When determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. “A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application” (MPEP 2106.04(d)) …“it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology” (MPEP 2106.05(a) II.), Therefore, the claim lacks additional elements add more than insignificant extra-solution activity to the judicial exception and thus is directed to an abstract idea. Therefore, the rejection under 35 U.S.C. 101 is maintained.
Claim 8 recites similar language as claim 1 and the rejection is maintained for similar reasons above.
Dependent claims 2-7 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-7 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Claims Rejections under 35 U.S.C. 102/103:
Applicant’s arguments, see Arguments/Remarks, filed 28 March 2026, with regard to the rejections of claims 1-8 under 35 U.S.C. 102/103 have been fully considered but they are not persuasive.
Regarding the Applicant’s arguments that “Sung does not disclose a neural network that receive lane marking information as input…Abad does not teach a tractor-trailer combination” for claim 1, the Examiner respectfully disagrees.
Sung teaches The concept of neural network-based online path planning is as follows. Based on the training data set representing desired paths of an automated vehicle, the connectivity between neurons of a neural network is updated such that the neural network models the relationship between the condition of a vehicle (input) and its desired path (output). Once the neural network is fully trained offline, it is embedded in an online path planning algorithm and derives a path online given a vehicle’s operational condition (e.g. the current position of the vehicle (Sung section 1:)…neural activity landscape for an environment is presented in Fig. 3. With the activity landscape modeled by a neural network, a vehicle at a position is moved to one of the neighboring positions…distance to an obstacle and bearing angle between the current position of the vehicle and a goal position, are used as inputs of a neural network. The neural network is then trained to convert the input to a heading of the vehicle that guarantees a collision-free movement of the vehicle…section 4.1: a vehicle should find a feasible path between start s and goal g (Section 2.2), i.e. the neural network receiving input data comprising lane marking information for a target lane of a roadway and generating output data comprising a reference path for autonomous movement by the tractor-trailer combination along the target lane. The “lane marking information” is broadly recited, i.e. it could be interpreted as any information related to physical traffic lanes that are managed by smart movable objects. Sung recite at least “desired paths of an automated vehicle”, which is obviously defined by markings, as input to train the neural network. In addition, Abad teaches a tractor-trailer combination (see Fig. 2C). Therefore, the prior art discloses the claim limitations as recited and the prior art and rejections have been maintained. Note: claim 1 recites a tractor-trailer combination but does not further recite any limitations specifically limiting the method to a truck-trailer combination, i.e., lacking “unique kinematics” specific to the combination.
Regarding the Applicant’s argument for claim 2 that Yang is not from the field of autonomous vehicle path planning, the Examiner respectfully disagrees.
It has been held that the determination that a reference is from a non-analogous art is twofold. First, we decide if the reference is within the field of the inventor's endeavor. If it is not, we proceed to determine whether the reference is reasonably pertinent to the particular problem with which the inventor was involved. In re Wood, 202 USPQ 171, 174. In this case, Yang is in the field of radar signal sorting. Radar technology is widely used in assisting autonomous vehicle path plannings, thus, Yang is from a field of relevance. Therefore, the prior art discloses the claim limitations as recited and the prior art and rejections have been maintained.
Regarding the Applicant’s argument that “Oh doesn’t disclose using a kinematic model within a neural network training process…” for claim 5, the Examiner respectfully disagrees.
Claim 5 recites “a path of the trailer is inferred from the path of the tractor based on a kinematic model associated with the tractor-trailer combination”, while neither “a path of the trailer” nor “the path of the tractor” were recited to be related to the neural network. Similarly, the kinematic model was not recited to be “withing a neural network”. Therefore, the Applicant’s argument is moot, and the rejections is maintained.
Regarding the Applicant’s argument that “…Prasad is in the field of chip design…distinct from autonomous vehicle path planning…no proper motivation to combine…”, the Examiner respectfully disagrees.
It has been held that the determination that a reference is from a non-analogous art is twofold. First, we decide if the reference is within the field of the inventor's endeavor. If it is not, we proceed to determine whether the reference is reasonably pertinent to the particular problem with which the inventor was involved. In re Wood, 202 USPQ 171, 174. In this case, Prasad is in the field of applying neural network to obtain the estimate of cost, i.e., both the application and Prasad are in a similar field of using neural network to solve technical problems. In response to Applicant's argument that there is no suggestion to combine the references, the Examiner recognizes that references cannot be arbitrarily combined and that there must be some reason why one skilled in the art would be motivated to make the proposed combination of primary and secondary references. In re Nomiya, 184 USPQ 607 (CCPA 1975). However, there is no requirement that a motivation to make the modification be expressly articulated. The test for combining references is what the combination of disclosures taken as a whole would suggest to one of ordinary skill in the art. In re McLaughlin, 170 USPQ 209 (CCPA 1971). References are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures. In re Bozek, 163 USPQ 545 (CCPA 1969). In this case, both the application and Prasad are in a similar field of using neural network to solve technical problems. Therefore, the prior art discloses the claim limitations as recited and the prior art and rejections have been maintained.
Claim Rejections - 35 USC § 101
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method for training a path planning module of a tractor-trailer combination (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for training a path planning module of a tractor-trailer combination, the path planning module including a neural network, the neural network receiving input data comprising lane marking information for a target lane of a roadway and generating output data comprising a reference path for autonomous movement by the tractor-trailer combination along the target lane, the method comprising:
based on the input data and the output data, determining a cost value associated with the reference path and providing the cost value to the neural network, and
updating the neural network based upon the cost value.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “the neural network receiving input data...and generating output data…”, “based on the input data and output data, determine a cost value…” and “updating the neural network…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement, or at most, evaluating and adjusting weight of cost values and optimize the neural network operation. In addition, the claim also recites at least one mathematical concept, for example, updating the neural network…is considered as a mathematical optimization or an adjusting of the weight parameters. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the claim does not recite any additional limitations beyond the above-noted abstract ideas (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for training a path planning module of a tractor-trailer combination, the path planning module including a neural network, the neural network receiving input data comprising lane marking information for a target lane of a roadway and generating output data comprising a reference path for autonomous movement by the tractor-trailer combination along the target lane, the method comprising:
based on the input data and the output data, determining a cost value associated with the reference path and providing the cost value to the neural network, and
updating the neural network based upon the cost value.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception 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, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the claim does not recite any additional elements or additional limitations. Hence, the claim is not patent eligible.
As per Claim 8.
Claim 8, an apparatus claim (an autonomous driving system), includes limitations analogous to claim 1 a process claim (a method), but adds a path planner module and a path cost function module. The structures of the “modules” are not provided while they functions as generic computer elements, which do not add significantly more to the abstract idea because, they merely amount to implementing the abstract idea on a computer.
Accordingly, claim 8 is rejected under 35 U.S.C. § 101 because the claim is directed to an abstract idea without significantly more.
Dependent claims 2-7 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-7 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1-8 are ineligible under 35 USC §101.
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.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 3-4 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Sung (“On the training of a neural network for online path planning”) in view of Abad (US20230192074).
As to claim 1, Sung teaches a method for training a path planning module of a tractor-trailer combination, the path planning module including a neural network (Sung, section 1, One of the actively applied solution approaches for online path planning is a neural network… applications…neural network to online path planning for automated vehicles), the neural network receiving input data comprising lane marking information for a target lane of a roadway and generating output data comprising a reference path for autonomous movement by the tractor-trailer combination along the target lane (Sung section 1: The concept of neural network-based online path planning is as follows. Based on the training data set representing desired paths of an automated vehicle, the connectivity between neurons of a neural network is updated such that the neural network models the relationship between the condition of a vehicle (input) and its desired path (output). Once the neural network is fully trained offline, it is embedded in an online path planning algorithm and derives a path online given a vehicle’s operational condition (e.g. the current position of the vehicle). Section 2.2: …neural activity landscape for an environment is presented in Fig. 3. With the activity landscape modeled by a neural network, a vehicle at a position is moved to one of the neighboring positions…distance to an obstacle and bearing angle between the current position of the vehicle and a goal position, are used as inputs of a neural network. The neural network is then trained to convert the input to a heading of the vehicle that guarantees a collision-free movement of the vehicle…section 4.1: a vehicle should find a feasible path between start s and goal g), the method comprising:
based on the input data and the output data, determining a cost value associated with the reference path and providing the cost value to the neural network (Sung, section 1: Based on the training data set representing desired paths of an automated vehicle, the connectivity between neurons of a neural network is updated such that the neural network models the relationship between the condition of a vehicle (input) and its desired path (output); section 3: a neural network is trained to model the relationship between input and its desired output by setting proper weights of the neural network. The goodness of the weight is often measured as a loss function, and one of the most used loss functions in the neural network context is the mean-squared error…), and
updating the neural network based upon the cost value (Sung, section 1: Based on the training data set representing desired paths of an automated vehicle, the connectivity between neurons of a neural network is updated such that the neural network models the relationship between the condition of a vehicle (input) and its desired path (output); section 3: … iteratively updates weight W based on the gradient of the loss function with respect to W, to minimize the loss function; section 4.2: The solutions from the algorithms were set as the desired output of a neural network and the weights of the neural network were updated accordingly based on the process explained in Section 3).
Sung does not explicitly teach a tractor-trailer combination.
However, in the same field of endeavor, Abad teaches determining, based on map data, an approaching merge region comprising an on-ramp merging with a road comprising one or more lanes, wherein a truck is traveling on an initial lane of the road according to a navigation plan. The method involves an indication of movement of a vehicle on the on-ramp, wherein the indication of movement is based on data collected by one or more sensors configured to capture sensor data from an environment surrounding the truck. The method involves determining, for the on-ramp and the one or more lanes, respective avoidance scores indicative of a likelihood of an interaction between the truck and the vehicle based on the approaching merge region (Abad, abstract, Fig. 2, Fig. 4 and related text).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sung so as to include a tractor-trailer combination in view of Abad et al. with a reasonable expectation of success. Those having ordinary skill in the art would understand that the tractor-trailer combination of Abad can be navigated to change path using the method of Sung, as required by the claim. One of ordinary skill would have been motivated to combine Sung and Abad because this would have achieved the desirable result of applying the path planning method to different types of vehicles for the benefits of deploying autonomous vehicles in various applications by applying a neural network to provide collision-free paths.
As to claim 3, Sung in view of Abad teaches the method of claim 1.
Sung further teaches wherein the cost value is determined based on a set of criteria including collision avoidance data relative to at least one static obstacle in or near the target lane (Sung section 4. 1: A path is defined as a sequence of points in ℰ and is considered feasible when it does not cause a collision of the vehicle with any obstacles. An obstacle, represented as a circle, does not move… section 4.3.1: The QP is implemented to minimize the distance between the position of a vehicle Pt at time step t and the goal position Pg over the planning time horizon T, subject to avoiding collisions with obstacles).
As to claim 4, Sung in view of Abad teaches the method of claim 1.
Sung further teaches wherein the cost value is determined based on a set of criteria including deviation of a tractor and a trailer of the tractor-trailer combination from the reference path (Sung section 4. 1: A path is defined as a sequence of points in ℰ and is considered feasible when it does not cause a collision of the vehicle with any obstacles. An obstacle, represented as a circle, does not move… section 4.3.1: The QP is implemented to minimize the distance between the position of a vehicle Pt at time step t and the goal position Pg over the planning time horizon T, subject to avoiding collisions with obstacles).
As to claim 7, Sung in view of Abad teaches the method of claim 1.
Sung further teaches wherein the input data comprises randomly generated data without expert driving data (Sung, section 4.1: Fig. 5 illustrates the state of a vehicle, which will be used as input for a neural network; section 4.3.2: The input layer had three neurons based on the setup for the state of a vehicle, ψ = 〈dobs, aobs, ag〉. The neural network had four hidden layers with 50 nodes on each layer; section 5: We generated 10,000 path planning problem instances on a 50 × 50-unit distance-sized map, randomly allocating 10 obstacles and the start and the goal of a vehicle per problem instance. No obstacle overlapped with any other obstacle and its radius had a value between two- and five-unit distances. The problems were then solved by the offline path planning algorithms, separately, resulting in two training data sets…).
As to claim 8, claim 8 an apparatus claim (autonomous driving system) includes limitations analogous to claim 1, a process claim (method). Sung further teaches a path is defined as a sequence of points in ℰ and is considered feasible when it does not cause a collision of the vehicle with any obstacles. An obstacle, represented as a circle, does not move… (Sung section 4. 1)… The QP is implemented to minimize the distance between the position of a vehicle Pt at time step t and the goal position Pg over the planning time horizon T, subject to avoiding collisions with obstacles (Sung section 4.3.1). For the reasons give above with respect to claim 1, claim 8 is also rejected under 35 U.S.C. § 103 as being unpatentable over Sung in combination with Abad.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Sung in view of Abad as applied to claim 1 above, and further in view of Yang (CN113971440).
As to claim 2, Sung in view of Abad teaches the method of claim 1.
Sung does not explicitly teach wherein the neural network comprises an encoder- decoder architecture in which an encoder portion and a decoder portion receives the input data and the decoder portion generates the output data, and updating the neural network comprises updating parameters of the encoder portion and the decoder portion by backpropagating the cost value through the encoder and decoder portions to reduce the cost value.
However, in the same field of endeavor, Yang teaches the deep self-coding network layer comprises a coding layer and a decoding layer, the coding layer reduces the dimension of input time sequence signals into more compact potential characteristic expression vectors, the time sequence signal clustering layer carries out unsupervised clustering analysis on the potential characteristic expression vectors to obtain radar signal sorting results, and the reconstruction loss function and the KL contrast divergence loss function are taken as the total cost function of the model, and reversely updating the network weight parameters and the clustering center by minimizing the total cost function, and performing joint optimization training on the unsupervised sorting model (Yang, abstract). Based on the two aspects, the invention provides an unsupervised radar signal sorting method based on deep clustering, and the method constructs a deep clustering neural network model to identify a radar communication signal modulation format (Yang, page 5, 5th paragraph of the “Detailed Description”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sung so as to include wherein the neural network comprises an encoder- decoder architecture in which an encoder portion and a decoder portion receives the input data and the decoder portion generates the output data, and updating the neural network comprises updating parameters of the encoder portion and the decoder portion by backpropagating the cost value through the encoder and decoder portions to reduce the cost value in view of Yang et al. with a reasonable expectation of success. Those having ordinary skill in the art would understand that the method of Yang can be used in Sung, as required by the claim. One of ordinary skill would have been motivated to combine Sung and Abad because this would have achieved the desirable result of realizing high-efficiency and accurate sorting of the relevant signal/data (Yang, abstract).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Sung in view of Abad as applied to claim 4 above, and further in view of Oh (US20230182730).
As to claim 5, Sung in view of Abad teaches the method of claim 4.
Sung modified by does not teach wherein a path of the trailer is inferred from the path of the tractor based on a kinematic model associated with the tractor-trailer combination.
However, in the same field of endeavor, Oh teaches the processor 130 may calculate a driving path of the trailer part corresponding to each of the one or more driving paths of the tractor part, using a tractor-trailer model (Oh, para 0087-0089).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sung so as to include wherein a path of the trailer is inferred from the path of the tractor based on a kinematic model associated with the tractor-trailer combination in view of Oh et al. with a reasonable expectation of success. Those having ordinary skill in the art would understand that the method of Oh can be used in Sung, as required by the claim. One of ordinary skill would have been motivated to combine Sung and Oh because this is merely combining prior art elements according to known methods to yield predictable results (KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007)).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sung in view of Abad as applied to claim 1 above, and further in view of Prasad (US20170193136).
As to claim 6, Sung in view of Abad teaches the method of claim 1.
Sung modified by Abad does not teach wherein the cost value is represented as a linear combination of a plurality of individual costs.
However, in the same field of endeavor, Prasad teaches a Deep Convolutional Neural Network is trained to map known designs to a set of conventional cost metrics… A composite cost is defined as a linear combination of conventional and a regularization cost (Prasad, para 0087-0089).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sung so as to include wherein the cost value is represented as a linear combination of a plurality of individual costs. in view of Prasad et al. with a reasonable expectation of success. Those having ordinary skill in the art would understand that the method of Prasad can be used in Sung, as required by the claim. One of ordinary skill would have been motivated to combine Sung and Prasad because this is merely combining prior art elements according to known methods to yield predictable results (KSR International Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007)).
Conclusion
THIS ACTION IS MADE FINAL. 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.
Examiner’s Notes
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP §2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as "Applicants believe no new matter has been introduced" may be deemed insufficient.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HONGYE LIANG whose telephone number is (571)272-5410. The examiner can normally be reached on Monday-Friday 9:00am-5:00pm.
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, Rachid Bendidi can be reached on 571-272-4896. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HONGYE LIANG/Primary Examiner, Art Unit 3664
Prosecution Insights