DETAILED ACTION
1. Applicant's response, filed 15 August 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
3. Claims 3-4, 11-12, and 16-17 are cancelled.
Claims 1-2, 5-10, 13-15, and 18 are currently pending and under examination herein.
Claims 1-2, 5-10, 13-15, and 18 are rejected.
Claim 9 is objected to.
Priority
4. The instant application does not claim the benefit of priority to any earlier filed applications. Therefore, the effective filing date of claims 1-2, 5-10, 13-15, and 18 is 1 December 2021.
Claim Objections
5. The objection to claim 9 is withdrawn in view of the claim amendments filed 15 August 2025.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
6. Claims 5-7 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. This rejection is newly recited and necessitated by claim amendment.
Claims 5-7 recite “the computer-implemented method of claim 14” which lacks antecedent basis because claim 14 recites a non-transitory computer readable medium rather than a computer-implemented method. It is unclear if applicant intended to delete the “4” from the claims as the strike-through is not visible. It is recommended to utilize double brackets [[]] around the number form to indicate its deletion if the claims are intended to depend from claim 1. For examination purposes, it is interpreted that the claims are intended to depend from claim 1.
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.
7. Claims 1-2, 5-10, 13-15, and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Any newly recited portions herein are necessitated by claim amendment.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1, 9 and 14 recite extracting target proteins (
S
e
0
) in the PPI network for each entity e as a target field of each entity e, where e includes i, j, k from the drug-protein associations and the cell line-protein associations; extending the target proteins along edges in the PPI network to form a radiant field (
S
e
k
) of each entity, wherein the radiant field captures a local interaction between proteins that are not directly targeted by each entity e; determining an interaction field (
S
e
h
) for each entity e … that relates to the union of their respective target field and radiant fields; feeding the interaction field into an aggregation layer interatively to obtain a representation of each entity e …; and determining a probability of the synergistic effect based on a therapy score and a toxicity score relating to the interaction fields.
Claims 2, 10 and 15 recite determining the contribution of each protein to the synergistic effect, wherein the each protein is targeted by or related to any of i, j, and k.
Claims 5, 13, and 18 recite wherein determining the therapy score and the toxicity score includes calculating the inner product of the representations of i,j,k to measure the similarity between each of i,j,k.
Claim 6 recites wherein a transformation matrix is applied to the therapy score to determine the probability of the synergistic effect.
Claim 7 recites wherein a weighted inner product is applied to determine the therapy score.
Claim 8 recites wherein maximum pooling is implemented to determine the synergistic effect.
The limitations for extracting target proteins, extending alone the edges, determining an interaction field for a protein-protein interaction (PPI) network, or converting interaction fields to a latent representation are evaluations or judgements that can be made by making mental observations of the network or circling features on the network that could be performed with pen and paper, which fall under the “mental processes” grouping of abstract ideas. The limitations for determining a probability, determining contributions, and determining scores using inner products, a transformation matrix or maximum pooling all describe verbal equivalents for mathematical calculations that are performed, which fall under the “mathematical concepts” grouping of abstract ideas. Furthermore, calculations like determining a probability or inner product can easily be performed with pen and paper, and therefore also fall under the “mental processes” grouping of abstract ideas. While claims 1-18 recite performing these abstract steps with a computer, a processor, and/or a computer readable medium, there are no additional limitations that indicate that these components require anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-2, 5-10, 13-15, and 18 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to effect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or insignificant extra-solution activity. Specifically, the claims recite the following additional elements:
Claim 1 recites providing a protein-protein interaction (PPI) network, drug-protein associations and cell line-protein associations and executing the abstract idea with a computer and a graph convolutional neural network.
Claim 9 recites at least one input device configured to access a protein-protein interaction (PPI) network, drug-protein associations and cell line-protein associations; and at least one processor and a graph convolutional neural network to carry out the abstract idea.
Claim 14 recites providing a protein-protein interaction (PPI) network, drug-protein associations and cell line-protein associations, storing the abstract idea steps on a non-transitory computer-readable medium, and carrying out the abstract idea with a processor and graph convolutional neural network.
There are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Furthermore, the recitation of using a graph convolutional network for determining an interaction field and a representation of each entity provides no limitations on how the graph convolutional network functions. Therefore, this limitation also equates to mere instructions to implement the abstract idea on a generic computer. The limitations for obtaining data or an input device for accessing data equate to mere data gathering activity because they merely provide data that is utilized as input for the recited abstract idea. Therefore, these limitations are insignificant extra-solution activity that does not integrate the recited judicial exception into a practical application. As such, claims 1-2, 5-10, 13-15, and 18 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite the following additional elements:
Claim 1 recites providing a protein-protein interaction (PPI) network, drug-protein associations and cell line-protein associations and executing the abstract idea with a computer and a graph convolutional neural network.
Claim 9 recites at least one input device configured to access a protein-protein interaction (PPI) network, drug-protein associations and cell line-protein associations; and at least one processor and a graph convolutional neural network to carry out the abstract idea.
Claim 14 recites providing a protein-protein interaction (PPI) network, drug-protein associations and cell line-protein associations, storing the abstract idea steps on a non-transitory computer-readable medium, and carrying out the abstract idea with a processor and graph convolutional neural network.
Limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Furthermore, the recitation of using a graph convolutional network for determining an interaction field and a representation of each entity provides no limitations on how the graph convolutional network functions. Therefore, this limitation also equates to mere instructions to implement the abstract idea on a generic computer. The limitations for obtaining data or an input device for accessing data are conventional activities. Specifically, the courts have identified steps of receiving data over a network or storing and retrieving information in memory as conventional computer functions in Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-2, 5-10, 13-15, and 18 are not patent eligible.
Response to Arguments
Applicant's arguments filed 15 August 2025 have been fully considered but they are not persuasive.
8. Applicant asserts that the amended claim 1 recites an improved GCN that identifies synergistic drug combination for specific disease and that the improved GCN outperforms other methods and therefore recites a practical application (pg. 10, last paragraph to pg. 12, para. 2 of Applicant’s Remarks). Applicant further asserts that the same argument applies to the other pending claims (pg. 12, para. 3 of Applicant’s Remarks). This argument is not persuasive.
MPEP 2106.05(a) states:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification.
While the Applicant points to data in the specification and states that the GCN is improved, it is not clear what steps or components described in the instant specification where required to achieve the data reflected by the row of “present invention” that Applicant asserts provides evidence of the improvement. For example, the instant specification includes information on the representations of the aggregation layer, the final representation of the entity, how to compute the similarities that are not all reflected in the instant claims. Furthermore, it is unclear if some of these properties are properties of the GCN itself or calculations that are performed outside of the GCN. Also, Fig. 6 of the instant application also includes another GCN type model in the data and it is unclear how the claimed GCN is distinct from the GCN in the table. Therefore, it is unclear if the asserted improvement to technology is commensurate in scope with the claimed invention and it is unclear that the asserted improvement is in the GCN itself or not. For such an argument to be persuasive, Applicant will need to provide evidence of how the asserted improvement is commensurate in scope and how the improvement is realized or provided in the additional elements recited in the claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
9. The rejection of claims 1-3, 9-11, and 14-16 under 35 U.S.C. 103 as being unpatentable over Huang et al. (PLOS Computaitonal Biology 2013 9(3): e1002998; pgs. 1-9) in view of Jiang et al. (Computational and Structural Biotechnology Journal 2020, vol. 18, pgs. 427-438) is withdrawn in view of the claim amendments filed 15 August 2025.
10. The rejection of claims 4, 6, 12, and 17 under 35 U.S.C. 103 as being unpatentable over Huang et al. and Jiang et al. as applied to claims 1, 9, and 14 above, and further in view of Cheng et al. (Nature Communications 2019, 10:1197, pgs. 1-11) is withdrawn in view of the cancellation of these claims in the claim amendments filed 15 August 2025.
11. The rejection of claim 8 under 35 U.S.C. 103 as being unpatentable over Huang et al. and Jiang et al. as applied to claim 1 above, and further in view of Grattarola et al. (“Understanding Pooling in Graph Neural Networks, 11 Oct 2021, arXiv preprint; pgs. 1-21) is withdrawn in view of the claim amendments filed 15 August 2025.
12. Claims 1-2, 6, 9-10, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (PLOS Computaitonal Biology 2013 9(3): e1002998; pgs. 1-9; previously cited) in view of Jiang et al. (Computational and Structural Biotechnology Journal 2020, vol. 18, pgs. 427-438; previously cited) and Cheng et al. (Nature Communications 2019, 10:1197, pgs. 1-11; previously cited). This rejection is newly recited and necessitated by claim amendment.
With respect to claims 1-2, 9-10, and 14-15, Huang et al. discloses a method for predicting drug-drug interactions (DDIs) that utilizes an S-score (abstract). Huang et al. discloses that Dis occur if drugs have an antagonistic, additive, synergistic, or indirect pharmacologic effect on one another (pg. 1, col. 1, para. 1). Huang et al. discloses obtaining drug-target association data and mapping it onto a protein-protein interaction (PPI) network (pg. 2, col. 2, para. 2; Fig. 2A). Huang et al. then discloses defining a target-centered system for each drug, which includes drug target and their first step neighboring proteins in the PPI network (pg. 2, col. 2, para. 2; Fig. 2C). Huang et al. then discloses determining a system connection score to describe the connection between two target centered systems in the PPI (pg. 2, col. 2, para. 2; Fig. 2D). Huang et al. then discloses that the S-score is included into a Bayesian probabilistic model to predict the likelihood of drug-drug interactions (pg. 4, col. 1, para. 3). Huang et al. also discloses that the code for calculating the S-score is available online (pg. 8, col. 2, para. 3), which indicates that this method is intended to be computer-implemented.
Huang et al. is silent to obtaining cell line-protein associations, determining an interaction field by a graph convolutional neural network, feeding the interaction field into an aggregation layer iteratively to obtain a representation of each entity e by the graph convolutional neural network, and determining the probability of the synergistic effect is based on determining a therapy score and a toxicity score relating to the interaction fields in claims 1, 9, and 14; and wherein a transformation matrix is applied to the therapy score to determine the probability of the synergistic effect in claim 6. However, these limitations were known in the art at the time of the effective filing date of the invention, as taught by Jiang et al. and Cheng et al.
Regarding claims 1-2, 9-10, and 14-15, Jiang et al. discloses a graph convolutional network (GCN) model to predict synergistic drug combinations in particular cancer cell lines (abstract). Jiang et al. discloses that the developed TGCN model was cell-line specific and was based on drug-drug synergy data from 39 cell lines, a drug target interaction network, and a protein-protein interaction network (pg. 428, col. 2, paras. 2-5). Jiang et al. discloses that the method includes constructing the DDS subnetwork and using this subnetwork to map to the embedding space that is then used to predict the drug-drug synergy (Fig. 1; pg. 428, col. 2, para. 6 to pg. 431, col. 1, para. 6). Jiang et al. then discloses that the traine dmdeol can be used to predict synergy scores, that can be treated as probabilities (pg. 432, col. 1, last para. to col. 2, para. 1). Jiang et al. also teaches that the method is performed using software designed for running fast parallel computing (pg. 432, col. 1, para. 3). Jiang et al. discloses that the initial DDS subnetwork graph is fed into a multi-layer propagation process that utilizes a propagation rule that after k layers of the feature vector generates the embedding vector (pg. 429, col. 2, paras. 2-3).
Concerning claims 1-2, 9-10, and 14-15, Cheng et al. discloses a network-based model for predicting the efficacy of drug combinations for specific diseases (abstract). Cheng et al. discloses a method that measures network proximity of drug-target modules using a separation method that looks at distances between the targets of each drug and the distance between the target module pairs (pg. 2, col. 2, para. 1; Fig. 1). Cheng et al. further discloses defining drug pairs based not only on their overlap of the modules but also the separation between the modules (Figs. 1 and 2; pg. 3, col. 2, para. 1 to pg. 5, col. 2, para. 2). Based on Figs. 3 and 4 of the instant specification, it appears that the claimed therapy score is equivalent to evaluating the overlap between modules in Cheng et al. and the claimed toxicity score is equivalent to evaluating the separation between the modules in Cheng et al.
Pertaining to claim 6, Jiang et al. discloses that the synergy score is calculated using a transformation of the embedding vector (pg. 430, col. 2, para. 2).
An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Jiang et al. discloses that their model allows for synergistic predictions with an accuracy greater than 0.80 (pg. 432, col. 2, para. 1). Furthermore, Jiang et al. discloses that their GCN approach achieved the best performance in comparison to 5 other prediction methods (pg. 433, col. 1, para. 3). Additionally, Jiang et al. discloses the ability to develop cell line specific predictions (pg. 428, col. 2, para. 2). Therefore, one of ordinary skill in the art would have been motivated to utilize the cell line specific GCN prediction method taught by Jiang et al. in the DDI interaction prediction method taught by Huang et al, in order to improve the prediction and identification of synergistic drug combinations and allow for cell line specific predictions. Furthermore, one of ordinary skill in the art would predict that the GCN method taught by Jiang et al. could be readily added to the method of Huang et al. with a reasonable expectation of success because both methods pertain to the prediction of drug-drug interactions, including synergistic interactions, utilizing PPI network and drug-protein information.
Cheng et al. discloses that the utiliziation of the separation measure is able to discriminate FDA-approved pairwise combinations and outperforms other approaches whereas measures like the z-score, which appears to be equivalent to the S-score of Huang et al, are not able to discriminate (pg. 2, col. 2, para. 1 to pg. 3, col. 1, para. 1). Therefore, one of ordinary skill in the art would have been motivated to add the separation measurement method taught by Cheng et al. in the synergism prediction method taught by Huang et al. and Jiang et al., in order to improve the identification of synergistic drug combinations, as taught by Cheng et al. Furthermore, one of ordinary skill in the art would predict that the method taught by Cheng et al. could be readily added to the method of Huang et al. and Jiang et al. with a reasonable expectation of success because the methods pertain to the prediction of drug-drug interactions, including synergistic interactions, utilizing PPI network and drug-protein information. The invention is therefore prima facie obvious.
13. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Huang et al., Jiang et al., and Cheng et al. as applied to claim 1 above, and further in view of Grattarola et al. (“Understanding Pooling in Graph Neural Networks, 11 Oct 2021, arXiv preprint; pgs. 1-21; previously cited). This rejection is newly recited and necessitated by claim amendment.
The limitations of claims 1 have been taught above by Huang et al., Jiang et al. and Cheng et al.
Huang et al., Jiang et al. and Cheng et al. are silent to wherein maximum pooling is implemented to determine the synergistic effect. However, this limitation was known in the art at the time of the effective filing date of the invention, as taught by Grattarola et al.
As to claim 8, Grattarola et al. discloses that graph neural networks are often built by alternating transformation layers with pooling layers (pg. 1, para. 1). Grattarola et al. further discloses that there are numerous pooling methods, that include pooling methods that look for maximums (Table 1).
An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Grattarola et al. discloses that pooling layers, including max pooling layers are commonly used in graph neural network training to reduce the number of nodes and thus reduce graph size (abstract; pg. 1, para. 1). Therefore, one of ordinary skill in the art would have been motivated to include pooling layers in graph neural networks as taught by Grattarola et al. in the GCN taught by Huang et al., Jiang et al. and Cheng et al., in order to reduce the graph size, as taught by Grattarola et al. Furthermore, one of ordinary skill in the art would predict that the method taught by Grattarola et al. could be readily added to the method of Huang et al., Jiang et al. and Cheng et al. with a reasonable expectation of success because both Grattarola and Jiang et al. utilize graph neural networks. The invention is therefore prima facie obvious.
Response to Arguments
Applicant's arguments filed 15 August 2025 have been fully considered but they are not persuasive.
14. Applicant asserts that the claimed invention calculates the therapy score by three methods: weighted inner product, max polling, and transformation matrix based on the similarity between the final representations of drug pairs and cell lines and does not rely on the distance between a drug and a disease in the network like in Cheng (pg. 13, para. 6 to pg. 14, para. 1 of Applicant’s Remarks). Applicant further asserts that the toxicity score is computed as the inner product of the final representation of two drugs, which is different then Cheng (pg. 14, para. 1 of Applicant’s Remarks). This argument is not persuasive.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., calculating a therapy score by three methods: weighted inner product, max polling, and transformation matrix and calculating the toxicity score as the inner product of the final representation) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
15. Applicant asserts that a person of ordinary skill in the art would not apply the quantification scheme of NP into the GCN model of Jiang because they are irrelevant and it would change the operation of the GCN model (pg. 14, para. 2 of Applicant’s Remarks). This argument is not persuasive.
The instant claims do not recite that the determination of the therapy score and toxicity score is carried out with the GCN. Rather, the instant claims only recite determining a probability of the synergistic effect based on a therapy score and a toxicity score relating to the interaction fields. There is no indication that the determination of the toxicity score and therapy score is related to the GCN as claimed. The above rejection does not propose to change the GCN with the model taught by Cheng et al. Rather, the above rejection merely adds the toxicity score and therapy score calculation method to the method of Huang and Jiang. Applicant is relying on features of the invention that are not claimed.
16. Applicant further asserts that NP performs the worst among the machine learning models listed in Figure 6 and thus the use of the improved GCN provides an unexpected result (pg. 14, para. 3 of Applicant’s Remarks).
As discussed above, the instant claims do not recite that the determination of the therapy score and toxicity score is carried out with the GCN. Therefore, Applicant’s assertion that the claims provide unexpected results are not commensurate in scope with the claims. Applicant’s description of the invention in the remarks is narrower than the instant claims. Applicant is reminded that the showing of unexpected results must be commensurate in scope with the claims (see MPEP 716.02(d)).
Conclusion
17. No claims are allowed.
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.
E-mail Communications Authorization
18. Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
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/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685