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 .
Detailed Action
The following action is in response to the communication(s) received on 07/31/2026.
As of the claims filed 07/31/2026:
Claims 1 and 11 have been amended.
Claims 2 and 12 have been canceled.
Claims 21 and 22 have been added.
Claims 1, 3-11, and 13-22 are now pending.
Claims 1 and 11 are independent claims.
Response to Arguments
Applicant’s arguments filed 07/31/2026 have been fully considered but are not fully persuasive.
With respect to the rejection under 35 USC § 101:
Applicant asserts that the combination of the limitations solves a technological problem in a transportation system in determining where vehicles should be repositioned (p.1 last ¶). Examiner respectfully submits that the combination improves merely an abstract idea of determining where vehicles should be repositioned instead of a particular architecture involving training a neural network or a particular vehicle repositioning system; although a neural network is recited, the details involving improving a particular architecture of the neural network or the vehicle repositioning system are not positively recited.
Applicant further asserts that the claims involve managing real vehicles and not merely calculations of an abstract result (p.2 ¶2). Examiner respectfully submits that management of "real vehicles operating in real geographical locations" is not positively recited and thus cannot be read into the claims; being "based on the vehicle repositioning policy" as currently recited is too broad and only revealed as a “vehicle repositioning policy”, which does not recite a particular architecture of a vehicle-pool transportation system.
Applicant further asserts that the claimed combination improves the operation of a vehicle-pool transportation system through the conversion of vehicle-repositioning data improving the allocation of the resources of the transportation system (p.2 ¶3-4). As discussed above, these details are currently recited as a “vehicle repositioning policy,” which does not reveal anything about a practical application of the abstract idea in the current invention.
Applicant further asserts that the claimed TM-CNN is not a generic computer, since it is functionally tied to the vehicle-repositioning process (p.2 ¶5). Examiner respectfully submits that, as described above, the way in which a particular architecture of the TM-CNN is being improved is not recited; thus, such improvement cannot be read into the claims other than the TM-CNN being generally linked to the abstract idea of determining the position of vehicles.
Applicant further asserts that the combination of the limitations is tied to a computer-implemented transportation control technique and thus recite significantly more than the identified abstract ideas (p.3 ¶1-3). Examiner respectfully submits that, as discussed above, the additional elements are merely generally linked to the abstract ideas due to the lack of details to either the neural network or the vehicle-repositioning system. Thus, the combination of the limitations as currently recited do not provide significantly more than merely determining a vehicle position, which is an abstract idea.
Applicant further asserts that the vehicle pool supplies operational data in a physical transportation system and thus a practical application of the abstract idea (p.3 last ¶). Examiner respectfully submits that, as discussed above, there are no details regarding the physical transportation system, as “a vehicle repositioning policy” as currently recited is too broad to discern a physical architecture of a transportation system.
Thus, the claims remain ineligible.
With respect to the art rejection under 35 USC § 102:
Applicant asserts that Ke does not teach sensing repositioning paths, storing historical ride sharing data, or using reinforcement learning to generate a trained value function (p.4 last ¶). Examiner respectfully disagrees: “sensing” repositioning paths are not recited in the claims; it merely recites utilizing the received historical OD-demand information. In a similar manner, “storing the historical ride sharing data” is also not explicitly recited in the claims; it merely recites “obtaining” estimated passenger arrival data. Lastly, the argument regarding reinforcement learning is unpersuasive, as Ke does teach training the value function through reinforcement learning (Ke [p.9 last 2 ¶]; [p.11 alg.1; last 2 lines]; see art rejection for the new claims below).
Applicant further asserts that it would not have been obvious to modify Ke to utilize trajectory data as historical training data in the amended claims 1 and 11 (p.5 ¶1). Examiner respectfully submits that, as discussed above, the vehicle-pool trajectory data as amended and as currently recited is further taught by Ke, as the details of geographic proximity, functional similarity, and demand correlation are not positively recited in the current claims.
Applicant further asserts that Ke’s output is a forecast of future OD demand and not a value function trained from specific sequences of vehicle states, locations, movements, and actions (p.5 ¶1b). Examiner respectfully submits that, as discussed above, vehicle states, locations, movements, and actions are not recited in the input function used to train the value function. As currently recited, the OD demand is used to predict the demand of locations, thus a value function using the locations and actions of vehicles.
Applicant further asserts that substituting Ke’s OD demand with vehicle-pool trajectory data would not be obvious for the person having ordinary skill in the art (p.5 ¶2). Examiner respectfully submits that, as discussed above, Ke’s OD demand fully teaches the current recitation of the historical ride sharing data and the vehicle repositioning policy, as the claims are not positively reciting a specific type of data or vehicle reallocation system that suggests against using Ke’s method.
Thus, the claims remain anticipated by Ke.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites A method, thus a process, one of the four statutory categories of patentable subject matter (Step 1). However, Claim 1 further recites:
determining vehicle repositioning data for a vehicle pool, which is an evaluation or judgement that can be performed in the human mind;
obtaining estimated passenger arrival data, wherein the estimated passenger arrival data is obtained by generating the estimated passenger arrival data..., which is an evaluation or judgement that can be performed in the human mind;
determining a vehicle repositioning policy based on the trained value function and the estimated passenger arrival data, which is an evaluation or judgement that can be performed in the human mind;
and determining vehicle repositioning data for a vehicle pool based on the vehicle repositioning policy, which is an evaluation or judgement that can be performed in the human mind.
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2, the claim recites:
wherein the historical ride sharing data includes trajectory data from the vehicle pool, as the performance of an abstract idea on a computer is not more than instructions to 'apply it' on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application;
…using a temporal memory convolutional neural network (TM-CNN), was the performance of an abstract idea on a computer is not more than instructions to 'apply it' on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application;
training a value function using historical ride sharing data, as the performance of an abstract idea on a computer is not more than instructions to 'apply it' on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application;
using a temporal memory convolutional neural network (TM-CNN), as the performance of an abstract idea on a computer is not more than instructions to 'apply it' on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application.
Thus, the claim is directed towards and abstract idea.
Further, the additional element(s), alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more. The combination of these additional elements does not provide an inventive concept; thus, the claim remains ineligible.
Claim 3, dependent on 1, further recite. no additional abstract ideas. However: Under Step 2A Prong 2, the claim recites:
the TM-CNN includes temporal memory (TM) and a convolutional neural network, and wherein the TM includes at least one of long short-term memory and a gated recurrent unit (GRU), as the performance of an abstract idea on a computer is not more than instructions to 'apply it' on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application.
Thus, the claim is directed towards and abstract idea.
Further, the additional element(s), alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more. The combination of these additional elements does not provide an inventive concept; thus, the claim remains ineligible.
Claim 4, dependent on 3, further recites. no additional abstract ideas. However: Under Step 2A Prong 2, the claim recites:
the CNN includes an encoding layer and a decoding layer, and wherein the TM is interposed in an embedding layer between the encoding layer and the decoding layer, as the performance of an abstract idea on a computer is not more than instructions to 'apply it' on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application.
Thus, the claim is directed towards and abstract idea.
Further, the additional element(s), alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more. The combination of these additional elements does not provide an inventive concept; thus, the claim remains ineligible.
Claim 5, dependent on 4, further recite. no additional abstract ideas. However: Under Step 2A Prong 2, the claim recites:
input into the TM-CNN includes two-dimensional (2D) passenger arrival data representing passenger arrival information for locations within two-dimensional space and for a given time or time period, as the performance of an abstract idea on a computer is not more than instructions to 'apply it' on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application.
Thus, the claim is directed towards and abstract idea.
Further, the additional element(s), alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more. The combination of these additional elements does not provide an inventive concept; thus, the claim remains ineligible.
Claim 6, dependent on 1, further recites
the vehicle repositioning policy is determined periodically according to a predetermined time interval, which is merely a detail of an abstract idea (determining a vehicle repositioning policy).
Thus, the claim recites an abstract idea under Step 2A Prong 1. Under Step 2A Prong 2 and Step 2B, the claim recites:
No additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself; thus, the claim remains ineligible.
Claim 7, dependent on 1, further recites
the vehicle repositioning data is for a plurality of vehicles of the vehicle pool, which is merely a detail of an abstract idea (determining vehicle repositioning data).
Thus, the claim recites an abstract idea under Step 2A Prong 1. Under Step 2A Prong 2, the claim recites: no additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself; thus, the claim remains ineligible.
Claim 8, dependent on 1, further recites
the vehicle repositioning policy is determined using an optimization lookahead method that takes into consideration the estimated passenger arrival data, which is an evaluation or judgement that can be performed in the human mind.
Thus, the claim recites an abstract idea under Step 2A Prong 1. Under Step 2A Prong 2 and Step 2B, the claim recites:
No additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself; thus, the claim remains ineligible.
Claim 9, dependent on 8, further recites
the vehicle repositioning policy is determined using an optimization lookahead method that takes into consideration the estimated passenger arrival data, which is merely a detail of an abstract idea (using an optimization lookahead method).
Thus, the claim recites an abstract idea under Step 2A Prong 1. Under Step 2A Prong 2 and Step 2B, the claim recites:
No additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself; thus, the claim remains ineligible.
Claim 10, dependent on 1, further recites
a controllable fraction is determined based on the historical ride sharing data or other historical ride sharing data, and wherein the controllable fraction is used for determining the vehicle repositioning policy, which is an evaluation or judgement that can be performed in the human mind.
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2 and Step 2B, the claim recites: no additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself; thus, the claim remains ineligible.
Claim 21, dependent on 1, further recites
using reinforcement learning to generate the trained value function, which is an evaluation or judgement that can be performed in the human mind.
Thus, the claim recites an abstract idea under Step 2A Prong 1.Under Step 2A Prong 2, the claim recites:
based on the historical ride sharing data comprising the trajectory data from the vehicles of the vehicle pool, which merely specifies the particular field of use or particular technological environment in which the abstract idea is to be performed, which by MPEP 2106.05(h) cannot integrate the abstract idea into a practical application.
Thus, the claim is directed towards and abstract idea.
Further, the additional element(s), alone or in combination, do not provide significantly more than the abstract idea itself, because the particular field of use or particular technological environment (MPEP 2106.05(h)) cannot provide significantly more. The combination of these additional elements does not provide an inventive concept; thus, the claim remains ineligible.
Claims 11-20 and 22 recite A vehicle repositioning system, thus a machine, one of the four statutory categories of patentable subject matter. However, Claims 8-14 recite comprising: at least one processor; memory storing computer instructions; wherein the vehicle repositioning system is configured to use the at least one processor to execute the computer instructions so that when the computer instructions are executed by the at least one processor, the vehicle repositioning system to perform precisely the abstract ideas and additional elements of Claims 1-10 and 21, respectively. Therefore, Step 2A Prong 1 analysis remains the same. As for Step 2A Prong 2 and Step 2B: performance on a computer cannot integrate an abstract idea into a practical application (Step 2A Prong 2) nor provide significantly more than the abstract idea itself (Step 2B) (MPEP 2106.05(f)), Claims 11-20 and 22 are rejected as subject-matter ineligible for reasons set forth in the rejections of Claims 1-10 and 21, respectively.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ke et al., “Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network” (hereinafter Ke).
Regarding Claim 1, Ke teaches:
A method of determining vehicle repositioning data for a vehicle pool, the method comprising: training a value function using historical ride sharing data; (Ke [abstract] With an accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations, and ride-sharing vehicle routing, etc.
[p.9 ¶2] To deal with the multi-graph problem, we design an MGC network by reshaping the architecture of a GCN layer. The MGC acts as the basic component of this spatial-feature encoder. Afterward, we stack multiple MGC layers in the deep learning network to improve the training performance and introduce a residual network to address the issue of gradient explosion, which constitutes the RMGC block. The output of this RMGC-based encoder is generated through multiple layers of RMGC block and flattened to be a one-dimension feature. In what follows we extend the principle and structure of the aforementioned RMGC network in detail
[p.2 ¶2] To address the aforementioned challenge, this paper proposes a novel deep learning framework named Spatial-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC) to simultaneously predict ride-sourcing passenger demand in various OD pairs. First, we construct multiple OD graphs, in which each OD pair is viewed as a node, and the adjacent matrices of nodes are established to represent different aspects of the relationships among OD pairs, such as neighborhood, distance, functional similarity, and historical demand correlations, and so on.) (Note: the OD graphs are constructed using the historical demand correlations, thus corresponding to training a value function using the historical ride sharing data)
obtaining estimated passenger arrival data, wherein the estimated passenger arrival data is obtained by generating the estimated passenger arrival data using a temporal memory convolutional neural network (TM-CNN); (Ke [p.2 ¶2] To address the aforementioned challenge, this paper proposes a novel deep learning framework named Spatial-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC) to simultaneously predict ride-sourcing passenger demand in various OD pairs. First, we construct multiple OD graphs, in which each OD pair is viewed as a node, and the adjacent matrices of nodes are established to represent different aspects of the relationships among OD pairs, such as neighborhood, distance, functional similarity, and historical demand correlations, and so on. Second, to capture both spatial and temporal correlations, we use a residual multi-graph convolutional (RMGC) network to capture the spatial correlations among OD pairs in different time intervals, and a regular long-short term memory (LSTM) network to characterize the temporal correlations of each OD pair itself.) (Note: the RMGC containing the LSTM network corresponds to the TM-CNN; predicting passenger demand corresponds to generating the estimated passenger arrival data)
determining a vehicle repositioning policy based on the trained value function and the estimated passenger arrival data; (Ke [abstract] With an accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations, and ride-sharing vehicle routing, etc.) (Note: vehicle reallocation corresponds to the repositioning policy based on the trained value function)
and determining vehicle repositioning data for a vehicle pool based on the vehicle repositioning policy. (Ke [abstract] With an accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations, and ride-sharing vehicle routing, etc.) (Note: the ride-sharing vehicle routing corresponds to the vehicle repositioning data)
Regarding Claim 2, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Ke further teaches:
The method of claim 1, wherein the historical ride sharing data includes trajectory data from the vehicle pool. (Ke [abstract] With an accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations, and ride-sharing vehicle routing, etc.) (Note: the idle vehicle reallocations correspond to trajectory data from the vehicle pool)
Regarding Claim 3, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Ke further teaches:
The method of claim 1, wherein the TM-CNN includes temporal memory (TM) and a convolutional neural network, and wherein the TM includes at least one of long short-term memory and a gated recurrent unit (GRU). (Ke [Abstract] Secondly, based on the constructed graphs, a residual multi-graph convolutional (RMGC) network is designed to encode the contextual-aware spatial dependencies, and a long-short term memory (LSTM) network is used to encode the temporal dependencies, into a dense vector space.)
Regarding Claim 4, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 3. Ke further teaches:
The method of claim 3, wherein the CNN includes an encoding layer and a decoding layer, and wherein the TM is interposed in an embedding layer between the encoding layer and the decoding layer. (Ke [abstract] a residual multi-graph convolutional (RMGC) network is designed to encode the contextual-aware spatial dependencies, and a long-short term memory (LSTM) network is used to encode the temporal dependencies, into a dense vector space. Finally, we reuse the RMGC networks to decode the compressed vector back to OD graphs and predict the future OD demand.) (Note: the dense vector space is the resulting layer generated from the encoding layer and is between the decoding layer, thus corresponds to the TM interposed between the encoding and decoding layers)
Regarding Claim 5, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 4. Ke further teaches:
The method of claim 4, wherein input into the TM-CNN includes two-dimensional (2D) passenger arrival data representing passenger arrival information for locations within two-dimensional space and for a given time or time period. (Ke [p.13 ¶1] The location map we used is from the Smart Location Database (SLD), a free data product and service provided by the U.S. EPA Smart Growth Program1. The Manhattan area is divided into districts according to administrative zip code, and each of them records the information including area, demographics, employment, and transit. The database of ride-sourcing demand is for-hire vehicle records from January 2018 to April 2019 collected by New York City Taxi & Limousine Commission. Each trip record is associated with attributes including date, time, and taxi zone location ID (same with the ID in the aforementioned location map) of pick-up and drop-off events.) (Note: the database containing the location map and pick-up events corresponds to the two-dimensional passenger arrival data)
Regarding Claim 6, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Ke further teaches:
The method of claim 1, wherein the vehicle repositioning policy is determined periodically according to a predetermined time interval. (Ke [p.6 ¶3] As mentioned above, previous studies usually divide the space of interest into various regular grids, such as squares and hexagons. These segmentations enable the use of standard machine learning algorithms, such as CNNs, but cannot well represent the administrative and functional properties of the regions under consideration. In this paper, we partition the examined city, Manhattan in New York City, into various irregular zones, according to the administrative zip codes as shown in Fig. 2 (a). A day is uniformly divided into several intervals (for example, 24 hours). Our target is to predict the quantity of the order requests in various OD pairs simultaneously in each time interval.)
Regarding Claim 7, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Ke further teaches:
The method of claim 1, wherein the vehicle repositioning data is for a plurality of vehicles of the vehicle pool. (Ke [p.2 ¶1] The major challenges in the operational management of ride-sourcing services are how to address supply-demand imbalance across space and time, and how to satisfy as many passenger requests as possible with a limited vehicle fleet size. To address these issues, prior studies have proposed a series of approaches, including surge pricing in which prices are raised to suppresses passenger demand in peak-hours, idle vehicle reallocations in which idle vehicles are moved from regions with excessive supply to regions with excessive demand) (Note: the vehicle fleet size corresponds to the plurality of vehicles of the vehicle pool)
Regarding Claim 8, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Ke further teaches:
The method of claim 1, wherein the vehicle repositioning policy is determined using an optimization lookahead method that takes into consideration the estimated passenger arrival data. (Ke [p.10 last ¶] To predict the future OD demand, we first introduce an intermediate layer to expand the dimension of vector L to a new vector with a shape of B×N (N is the number of OD pairs), and then we reshape the new vector to a tensor with a shape of B× N× 1, which becomes a valid input format for RMGC. By stacking various RMGC modules, we can finally obtain the estimated demand of all OD pairs on an OD graph…
Let W, b be all the trainable weights and biases in the whole encoder-decoder architecture, we can train the weights and biases by solving the following optimization problem:
PNG
media_image1.png
50
242
media_image1.png
Greyscale
) (Note: the predicted future OD demand corresponds to the estimated passenger arrival data; the optimization problem corresponds to the optimization lookahead method.)
Regarding Claim 9, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 8. Ke further teaches:
The method of claim 8, wherein the optimization lookahead method uses linear programming (LP). (Ke [p.13 last ¶] The decoder uses one RMGC convolutional block and then one RMGC identity block, with the same settings as that in the encoder, which is then followed by an MGC layer that generates the estimated OD demand. All the activations in the hidden layers are Relu, while the activation in the output later is a linear function.) (Note: the linear output corresponds to linear programming)
Regarding Claim 10, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Ke further teaches:
The method of claim 1, wherein a controllable fraction is determined based on the historical ride sharing data or other historical ride sharing data, and wherein the controllable fraction is used for determining the vehicle repositioning policy. (Ke [abstract] With an accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations, and ride-sharing vehicle routing, etc.) (Note: the ride-sharing vehicle routing corresponds to the vehicle repositioning data; the vehicles being routed by the OD demand decisions corresponds to the controllable fraction)
Independent Claim 11 recites A vehicle repositioning system to perform precisely the methods of Claim 1. Thus, Claim 11 is rejected for reasons set forth in Claim 1.
Claims 12-20, dependent on Claim 11, also recite the system configured to perform precisely the methods of Claims 2-10, respectively. Thus, Claims 12-20 are rejected for reasons set forth in Claims 2-10, respectively.
Regarding Claim 21, Ke respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Ke further teaches:
The method of claim 1, wherein the training of the value function comprises using reinforcement learning to generate the trained value function based on the historical ride sharing data comprising the trajectory data from the vehicles of the vehicle pool. (Ke [abstract] With an accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations, and ride-sharing vehicle routing, etc.
[p.9 last 2 ¶] Then, to train the networks in a deep neural network structure without suffering from gradient explosion, now we develop two multi-graph convolutional network based (MGC-based) residual blocks: the identity block and convolutional block. Residual learning (He et al., 2016) is a powerful tool that allows the training of super deep networks, and is widely used in many traditional convolutional neural network structures.
[p.11 alg.1; last 2 lines]
PNG
media_image2.png
737
882
media_image2.png
Greyscale
) (Note: using residual learning (and the minimization loss function) for training the MGC-based residual blocks corresponds to using reinforcement learning)
Claim 22, dependent on Claim 11, also recites the system configured to perform precisely the methods of Claims 21. Thus, Claim 22 is rejected for reasons set forth in Claims 21.
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 JOSEP HAN whose telephone number is (703)756-1346. The examiner can normally be reached Mon-Fri 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached on (571) 272-3719. 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.
/J.H./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
Prosecution Insights