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
This action is responsive to the following communication: Non-Provisional Application filed Oct. 8, 2020.
Claims 1-20 are pending in the case. Claims 1, 14 and 15 are independent claims.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claim 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,967,400 and U.S. Patent No. 11,436,246. The subject matter claimed in the instant application is fully disclosed in U.S. Patent No. 11,967,400 and U.S. Patent No. 11,436,246.
Instant Application No. 18/638,784
U.S. Patent No. 11,967,400
1. A method for presenting, on a computing device, a graphical user interface (GUI) of a therapeutic tool, the method comprising:
presenting, in a first screen of the GUI, a design space for a protein for an application, wherein the design space comprises a plurality of protein sequences, wherein:
each protein sequence is associated with a respective plurality of activities pertaining to the application, the plurality of activities comprises one or more biomedical activities, biochemical activities, or some combination thereof, and
the plurality of protein sequences is generated by a machine learning model, wherein the machine learning model uses causal inference to execute at least one of a plurality of alternative scenarios to filter a superset of protein sequences and to generate the plurality of protein sequences in the design space;
presenting, in a second screen of the GUI, a solution space comprising a subset of the plurality of protein sequences, wherein each protein sequence contains the respective
plurality of activities, wherein the second screen comprises: a first portion presenting one or more color-coded clusters representing the subset of the plurality of protein sequences, and a second portion presenting data pertaining to the subset of the plurality of protein sequences represented by the one or more color-coded clusters, wherein the data describes one or more objects associated with the subset of the plurality of protein sequences, and
the one or more objects comprise a candidate drug compound, an activity, an interaction, a drug, a gene, a pathway, a physical descriptor, a characteristic, an interaction, a folding property, a wave property, a stability of modification, or some combination thereof.
2. The method of claim 1, wherein the one or more objects comprise a candidate drug compound, an activity, an interaction, a drug, a gene, a pathway, a physical descriptor, a characteristic, an interaction, a folding property, a wave property, a measure of a stability of a modification, or some combination thereof.
3. The method of claim 2, wherein the one or more color-coded clusters represent, using an energy correlation, each sequence in the subset, and the energy correlation comprises at least one correlation between each position of each protein sequence in the subset and other positions of other protein sequences in the subset.
4. The method of claim 1, wherein the solution space is presented as a topographical map in the GUI, wherein the topographical map comprises a plurality of indications that each represent a level of activity for a protein sequence associated with a given point on the topographical map.
5. The method of claim 1, wherein the design space is generated based on a knowledge graph pertaining to peptides and the design space is presented as a two-dimensional (2D) elevation map, a three-dimensional (3D) shape or an n-dimensional (nD) mathematical representation.
6. The method of claim 1, wherein the solution space is generated within the design space by one or more machine learning models trained to measure, based on the query parameter, a respective level of one or more of the respective plurality of activities of each of the plurality of protein sequences in the subset, wherein the query parameter comprises a sequence parameter.
7. The method of claim 1, further comprising: receiving, using a graphical element of the second screen, a selection of a protein sequence from the subset of the plurality of protein sequences; and presenting, in a third screen of the GUI, a candidate dashboard comprising information pertaining to the protein sequence, wherein the information pertains to a structure of the protein sequence, a correlation heatmap, experimental data, a list of probabilistic scores generated by inference models, external data related to the protein sequence, or some combination thereof.
8. The method of claim 1, further comprising: receiving a selection of a trial configured to be performed by a machine learning model, wherein the machine learning model uses the solution space; and receiving, from an artificial intelligence engine, one or more results of performing the trial, wherein the one or more results: provide a location of a point reached in the solution space after performing a traversal of the solution space defined by the trial, and provide a metric of a machine learning model used by the artificial intelligence engine to perform the trial, wherein the metric pertains to one or more of memory usage, graphic processing unit temperature, power usage, processor usage, or central processing unit temperature.
9. The method of claim 1, further comprising: receiving, from a graphical element of a business intelligence screen of the GUI, a target product profile, wherein the target product profile comprises pharmacology data, pharmacokinetic data, pharmacodynamic data, activity data, manufacturing data, compliance data, clinical trial data, or some combination thereof; receiving, from an artificial intelligence engine, a second subset of the plurality of protein sequences, wherein the second subset of the plurality of protein sequences is selected based on the target product profile; and presenting, in the GUI, the second subset of the plurality of protein sequences.
10. The method of claim 1, further comprising: receiving, in the GUI, one or more parameters pertaining to one or more machine learning models of an artificial intelligence engine, wherein the one or more parameters pertain to one or more constraints for the one or more machine learning models to implement when performing one or more trials using the solution space.
11. The method of claim 1, wherein the therapeutic tool is a peptide therapeutic tool.
12. The method of claim 1, wherein the protein is a peptide.
13. The method of claim 1, wherein the one or more query parameters comprise a plurality of biomedically-related ontology terms, a plurality of non-biomedically-related ontology terms, or some combination thereof.
14. The method of claim 14, wherein the plurality of biomedically-related ontology terms pertains to indications, genes, symptoms, or some combination thereof, and the plurality of non- biomedically-related ontology terms pertain to characteristics, descriptors, or some combination thereof.
15. The method of claim 1, further comprising: receiving, using a graphical element of the second screen, a selection of a protein sequence from the subset of the plurality of protein sequences; and causing the sequence to be analyzed, manufactured, synthesized, or produced.
Instant Application No. 18/638,784
1. A method for presenting, on a computing device, a graphical user interface (GUI) of a therapeutic tool, the method comprising:
presenting, in a first screen of the GUI, a design space for a protein for an application, wherein the design space comprises a plurality of protein sequences, wherein:
each protein sequence is associated with a respective plurality of activities pertaining to the application, the plurality of activities comprises one or more biomedical activities, biochemical activities, or some combination thereof, and
the plurality of protein sequences is generated by a machine learning model, wherein the machine learning model uses causal inference to execute at least one of a plurality of alternative scenarios to filter a superset of protein sequences and to generate the plurality of protein sequences in the design space;
presenting, in a second screen of the GUI, a solution space comprising a subset of the plurality of protein sequences, wherein each protein sequence contains the respective
plurality of activities, wherein the second screen comprises:
a first portion presenting one or more color-coded clusters representing the subset of the plurality of protein sequences, and a second portion presenting data pertaining to the subset of the plurality of protein sequences represented by the one or more color-coded clusters, wherein the data describes one or more objects associated with the subset of the plurality of protein sequences, and
the one or more objects comprise a candidate drug compound, an activity, an interaction, a drug, a gene, a pathway, a physical descriptor, a characteristic, an interaction, a folding property, a wave property, a stability of modification, or some combination thereof.
1. A method for presenting, on a computing device, a graphical user interface (GUI) of a therapeutic tool, the method comprising:
presenting, in a first screen of the GUI, a design space for a protein for an application, wherein the design space comprises a plurality of protein sequences, wherein:
each protein sequence is associated with a respective plurality of activities pertaining to the application, and
the plurality of protein sequences is generated by a machine learning model, wherein the machine learning model uses causal inference to execute at least one of a plurality of alternative scenarios to filter a superset of protein sequences and to generate the plurality of protein sequences in the design space;
receiving via a graphical element, a selection of one or more query parameters of the design space; and
presenting, in a second screen of the GUI, a solution space comprising a subset of the plurality of protein sequences, wherein each protein sequence contains the respective plurality of activities, wherein the second screen comprises: a first portion presenting one or more color-coded clusters representing the subset of the plurality of protein sequences, and a second portion presenting data pertaining to the subset of the plurality of protein sequences represented by the one or more color-coded clusters, wherein the data describes one or more objects associated with the subset of the plurality of protein sequences, and
wherein the subset of the plurality of protein sequences is based on an indication that the protein sequence has not been previously generated by the machine learning model and wherein
the one or more objects comprise a candidate drug compound, an activity, an interaction, a drug, a gene, a pathway, a physical descriptor, a characteristic, an interaction, a folding property, a wave property, a stability of modification, or some combination thereof.
2. The method of claim 1, wherein the one or more objects comprise a candidate drug compound, an activity, an interaction, a drug, a gene, a pathway, a physical descriptor, a characteristic, an interaction, a folding property, a wave property, a measure of a stability of a modification, or some combination thereof.
3. The method of claim 2, wherein the one or more color-coded clusters represent, using an energy correlation, each sequence in the subset, and the energy correlation comprises at least one correlation between each position of each protein sequence in the subset and other positions of other protein sequences in the subset.
4. The method of claim 1, wherein the solution space is presented as a topographical map in the GUI, wherein the topographical map comprises a plurality of indications that each represent a level of activity for a protein sequence associated with a given point on the topographical map.
5. The method of claim 1, wherein the design space is generated based on a knowledge graph pertaining to peptides and the design space is presented as a two-dimensional (2D) elevation map, a three-dimensional (3D) shape or an n-dimensional (nD) mathematical representation.
6. The method of claim 1, wherein the solution space is generated within the design space by one or more machine learning models trained to measure, based on the query parameter, a respective level of one or more of the respective plurality of activities of each of the plurality of protein sequences in the subset, wherein the query parameter comprises a sequence parameter.
7. The method of claim 1, further comprising: receiving, using a graphical element of the second screen, a selection of a protein sequence from the subset of the plurality of protein sequences; and presenting, in a third screen of the GUI, a candidate dashboard comprising information pertaining to the protein sequence, wherein the information pertains to a structure of the protein sequence, a correlation heatmap, experimental data, a list of probabilistic scores generated by inference models, external data related to the protein sequence, or some combination thereof.
8. The method of claim 1, further comprising: receiving a selection of a trial configured to be performed by a machine learning model, wherein the machine learning model uses the solution space; and receiving, from an artificial intelligence engine, one or more results of performing the trial, wherein the one or more results: provide a location of a point reached in the solution space after performing a traversal of the solution space defined by the trial, and provide a metric of a machine learning model used by the artificial intelligence engine to perform the trial, wherein the metric pertains to one or more of memory usage, graphic processing unit temperature, power usage, processor usage, or central processing unit temperature.
9. The method of claim 1, further comprising: receiving, from a graphical element of a business intelligence screen of the GUI, a target product profile, wherein the target product profile comprises pharmacology data, pharmacokinetic data, pharmacodynamic data, activity data, manufacturing data, compliance data, clinical trial data, or some combination thereof; receiving, from an artificial intelligence engine, a second subset of the plurality of protein sequences, wherein the second subset of the plurality of protein sequences is selected based on the target product profile; and presenting, in the GUI, the second subset of the plurality of protein sequences.
10. The method of claim 1, further comprising: receiving, in the GUI, one or more parameters pertaining to one or more machine learning models of an artificial intelligence engine, wherein the one or more parameters pertain to one or more constraints for the one or more machine learning models to implement when performing one or more trials using the solution space.
11. The method of claim 1, wherein the therapeutic tool is a peptide therapeutic tool.
12. The method of claim 1, wherein the protein is a peptide.
13. The method of claim 1, wherein the one or more query parameters comprise a plurality of biomedically-related ontology terms, a plurality of non-biomedically-related ontology terms, or some combination thereof.
14. The method of claim 13, wherein the plurality of biomedically-related ontology terms pertains to indications, genes, symptoms, or some combination thereof, and the plurality of non-biomedically-related ontology terms pertain to characteristics, descriptors, or some combination thereof.
15. The method of claim 1, further comprising: receiving, using a graphical element of the second screen, a selection of a protein sequence from the subset of the plurality of protein sequences; and causing the sequence to be analyzed, manufactured, synthesized, or produced.
U.S. Patent No. 11,436,246
1. A method for presenting, on a computing device, a graphical user interface (GUI) of a therapeutic tool, the method comprising:
presenting, in a first screen of the GUI, a design space for a protein for an application, wherein the design space comprises a plurality of protein sequences, wherein:
each protein sequence is associated with a respective plurality of activities pertaining to the application, the plurality of activities comprises one or more biomedical activities, biochemical activities, or some combination thereof, and
the plurality of protein sequences is generated by a machine learning model, wherein the machine learning model uses causal inference to execute at least one of a plurality of alternative scenarios to filter a superset of protein sequences and to generate the plurality of protein sequences in the design space; receiving, via a graphical element in the first screen, a selection of one or more query parameters of the design space; and
presenting, in a second screen of the GUI, a solution space comprising a subset of the plurality of protein sequences, wherein each protein sequence contains the respective plurality of activities, wherein the subset of the plurality of protein sequences is selected based on the one or more query parameters; receiving, using a graphical element of the second screen, a selection of a protein sequence from the subset of the plurality of protein sequences, wherein the selection is based on an indication that the protein sequence has not been previously generated by the machine learning model; and responsive to the selection of the protein sequence, presenting, in the second screen, additional information pertaining to the protein sequence, wherein the additional information comprises: a candidate drug compound, an interaction, an activity, a drug, a gene, a pathway, or some combination thereof.
2. The method of claim 1, wherein the second screen comprises: a first portion presenting one or more color-coded clusters representing the subset of the plurality of protein sequences, and a second portion presenting data pertaining to the subset of the plurality of protein sequences represented by the one or more color-coded clusters, wherein the data describes one or more objects associated with the subset of the plurality of protein sequences, and
the one or more objects comprise a candidate drug compound, an activity, an interaction, a drug, a gene, a pathway, a physical descriptor, a characteristic, an interaction, a folding property, a wave property, a stability of modification, or some combination thereof.
Claim Objections
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 2 is objected to. Claim 2 fails to specify a further limitation of the subject matter claimed in independent claim 1.
Independent Claim 1 recites “the one or more objects comprise a candidate drug compound, an activity, an interaction, a drug, a gene, a pathway, a physical descriptor, a characteristic, an interaction, a folding property, a wave property, a stability of modification, or some combination thereof.” Dependent Claim 2 recites “the one or more objects comprise a candidate drug compound, an activity, an interaction, a drug, a gene, a pathway, a physical descriptor, a characteristic, an interaction, a folding property, a wave property, a measure of a stability of a modification, or some combination thereof.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Kieu Vu, can be reached on (571) 272-4057. 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://portal.uspto.gov/external/portal. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/PEI YONG WENG/Primary Examiner, Art Unit 2141
Prosecution Insights