DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Herein, “the previous Office action” refers to the Non-Final Rejection filed on 11/19/2025.
Amendments Received
Amendments to the claims were received on 4/3/2026.
Priority
As detailed on the Filing Receipt filed 6/21/2021, the instant application claims priority to as early as 5/31/2018. At this point in prosecution, all claims are accorded the earliest claimed priority date.
Claim Status
Claims 17-20 and 22-23 are canceled.
Claims 1-16, 21 and 24-30 are pending.
Claims 2, 6-7, 9, 11-13 and 15 stand withdrawn pursuant to 37 CFR 1.142(b) as being directed to a nonelected invention, there being no currently allowable generic or linking claim. Election without traverse was made in the reply filed 8/12/2024.
Claims 1, 3-5, 8, 10, 14, 16, 21 and 24-31 are under examination.
Withdrawn Objections/Rejections
The rejection of claims 1, 3-5, 8, 10, 14, 16, 21 and 24-30 under 35 USC § 101 is hereby withdrawn in view of Applicant’s amendment of the claims and persuasive argument that the amended claims involve the expression of a designed de novo protein by a host cell, reflecting a particular transformation of the host cell to a different state or thing (pg. 9, para. 1).
The rejections of claims 1, 3-5, 8, 16, 24, 27 under 35 USC 103, as being unpatentable over Fleishman and/or in view of Xu, are hereby withdrawn in view of Applicant’s persuasive argument that these references do not teach a graphical representation with directed edges as claimed (pg. 10, para. 6 – pg. 11, para. 2). See ‘Response to Arguments – Claim Rejections Under 35 USC 103’ section for further details.
Response to Arguments - Claim Rejections Under 35 USC § 103
In the remarks filed 4/3/2026, Applicant traverses the rejection under 35 USC § 103 and presents supportive arguments.
Applicant alleges that neither Fleishman nor Xu disclose or suggest decomposing a target structure into a plurality of structural motifs as currently claimed, i.e., by generating a graph with nodes representing residues and edges representing coupling between the residues, and generating a graph with nodes representing residues and directed edges representing residue-backbone influences (pg. 10, para. 5).
As detailed in the rejection, Xu discloses generation of a graph with nodes representing residues and edges representing coupling between the residues (Xu at pg. 258, r. column). Xu also teaches embodiments wherein edges represent residue-backbone influences (Xu at pg. 259, l. column). However, Xu does not discuss edge direction and accordingly does not teach a graph with nodes representing residues and directed edges representing residue-backbone influences. Applicant’s argument is therefore found persuasive, and the previous rejections of the claims under 35 USC § 103 (as being unpatentable over Fleishman and/or in view of Xu) have been withdrawn.
Applicant’s amendment of the claims (filed 4/3/2026) to incorporate new limitations necessitated further search of the prior art. A reference discovered in the course of further search, Wang et al 2007 (newly cited on attached form PTO-892), is considered to remedy the noted deficiency and is newly applied in combination with Fleishman and Xu. See rejection for further details.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 USC §§ 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 USC § 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 USC § 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 USC § 102(b)(2)(C) for any potential 35 USC § 102(a)(2) prior art against the later invention.
Claims 1, 3-5, 8, 10, 14, 16, 21 and 24-31 are rejected under 35 USC § 103 as being unpatentable over Fleishman et al (US 2017/0206308; published 7/20/2017; previously cited), in view of Xu et al (Proc 2005 IEEE Comp Syst Bioinf Conf, pp. 247-256, IEEE Xplore; published 9/6/2005; previously cited) and Wang et al (J Mol Biol 373: 503-519; published 8/2/2007). The new grounds of rejection presented herein were necessitated by Applicant’s presentation of new claims via amendment (filed 4/3/2026).
Claim 1 recites a method for in silico design and expression of an amino acid sequence, comprising steps of: decomposing a target structure into structural motifs using a graphical representation of coupled residues and/or residue-backbone influences of the structure; identifying structural matches for each motif in a database; calculating at least one non-local energetic contribution to a sequence-structure relationship; generating at least one de novo candidate amino acid sequence that is foldable into the target structure or a binding partner of the target structure; and expressing a foldable de novo protein, comprising the candidate amino acid sequence, in a host cell.
The claim further specifies that decomposing the target structure into the plurality of structural motifs comprises: generating a first graph G with nodes representing residues and edges between the nodes representing coupling between the residues represented by the nodes; generating a second graph B, different from the first graph G, with nodes representing residues and directed edges representing residue-backbone influences; and identifying the plurality of structural motifs using the first graph G and/or the second graph B.
The claim also specifies that expressing the foldable de novo protein comprises introducing a nucleic acid sequence encoding the at least one candidate amino acid sequence into the host cell, and recovering the expressed de novo protein.
With respect to claim 1, Fleishman discloses a method of constructing a library of amino acid sequences having a common structural fold, and designing an amino acid sequence having a desired affinity to a molecular surface of interest therefrom, comprising steps of: decomposing a source structure into a plurality of structurally homologous segments, i.e., structural motifs (paras. 0026-27); designing libraries of segments with common structural motifs, i.e., structural databases (paras. 0036 and 0043); matching segments to a molecular surface of interest to design a plurality of structure-surface complexes each having an assigned matching score (paras. 0044, 0168 and 0180); and selecting amino-acid sequences that exhibit desired affinity to the molecular surface of interest based on a combined matching score (paras. 0046 and 0189).
Fleishman further discloses optimizing energy criteria by minimizing polynomial functions, based on weights and constraints pertaining to, e.g., backbone dihedral angles and atomic coordinates, via structural refinement operations including amino acid side-chain packing (para. 0136). Fleishman also discloses weighting energy functions according to a position-specific scoring matrix (para. 0140) that encodes sequence-structure rules (para. 0475), and describes evaluation of side-chain packing efficiency by iteratively calculating an energy score, using the Rosetta all-atom energy function, for each rotational state of each residue that makes an appreciable contribution to binding (para. 0300). In this way, Fleishman discloses deducing values for non-local energetic contributions to a sequence-structure relationship.
Fleishman additionally discloses selection of amino acid sequences based on assessment of fold stability (paras. 0063-64), providing for design of an amino acid sequence that is able to fold into a stable 3D structure exhibiting a desired affinity to the molecular surface of interest (para. 0241). Fleishman also discloses expressing a selected amino acid in an expression system (paras. 0049 and 0208).
Fleishman does not disclose decomposing a target structure into a plurality of structural motifs using a graphical representation as claimed.
Xu discusses an algorithmic approach to protein structural prediction that models a protein structure as a geometric neighborhood graph, with vertices (i.e., nodes) representing a linear series of most-conserved segments (i.e., structural motifs comprising residues) into which the primary structure of a template is parsed and edges representing interactions (i.e., coupling) between residues inter-residue interactions, and teaches formulation and efficient solution of structural prediction problems in terms of decomposing the graph, assigning optimal labels (i.e., sequence alignment positions) to graph vertices and thus minimizing an associated energy function that parameterizes sequence-template alignment (pg. 247, Abstract; pg. 248, r. column).
Xu also discusses embodiment as a ‘residue interaction graph’ for predicting sidechain conformation, wherein vertices represent a linear series of residues into which a backbone structure is parsed, the labels represent possible side-chain conformations for each residue, and the energy function parameterizes side-chain packing (pg. 249, l. column). In a residue-interaction graph as disclosed, edges represent residue-backbone influences.
Additionally, Xu teaches that their graph-based approaches exhibit equal predictive accuracy to, and are generally more computationally efficient than, analogous linear programming approaches to predicting protein structure (pg. 247, Abstract; pg. 253, l. column).
In this way, Xu advantageously teaches: decomposing a target structure into a plurality of structural motifs using a graphical representation of coupled residues of the target structure and/or residue-backbone influences of the target structure; generating a graph with nodes representing residues and edges between the nodes representing coupling between the residues represented by the nodes; and generating a graph with nodes representing residues and edges representing residue-backbone influences; and identifying a plurality of structural motifs using each type of graph.
However, Xu does not specifically disclose a graph with nodes representing residues and directed edges representing residue-backbone influences. Xu does not discuss edge direction.
Wang discusses a method for computational protein docking that incorporates backbone flexibility (pg. 503, Abstract), and teaches structural modeling of a protein-protein complex (i.e., of each constituent protein structure) as a directed fold tree graph with vertices representing residues and edges representing peptide-bond connections between residues (pg. 504, r. column). Wang teaches applicability of their methods to different structural modeling tasks, including protein folding and structural prediction (pg. 505, l. column).
Wang teaches that the fold tree representation can be rendered as three-dimensional coordinates (i.e., a 3D structural model) by selecting and assigning a root vertex (e.g., the N terminus of a constituent protein) to an arbitrary location and traversing the edges of the graph in order, using torsion angles and/or rigid body transformations to build the terminal vertex of each edge given the coordinates of the initial vertex for that edge (pg. 505, l. column). This property is enabled by the directed nature of the fold tree edges.
With respect to claim 3, Fleishman discloses optimizing spatial and/or energy criteria by minimizing functions pertaining to backbone atomic positions (para. 0136), and exemplifies calculating the root mean squared deviation of aligned backbone positions (paras. 0084, 0116, and 0158). In other words, optimizing non-local energetic contributions of backbones in spatial but not sequence proximity.
With respect to claim 4, Fleishman discloses that conformations are characterized by key conserved residue identities that maintain the backbone (para. 0014). Fleishman exemplifies a conserved motif including two cysteine residues that form an intra-chain disulfide bond (para. 0126), i.e., a non-local energetic contribution from a pair of coupled residues within a structural motif.
With respect to claim 5, Fleishman discloses evaluating rotameric states of contributive residues in each match and excluding rotamers that are predicted to form steric clashes (para. 0300). Steric hindrance is a local energetic contribution.
With respect to claim 8, Fleishman discloses sampling backbone conformations and sequence information from natural protein folds for which a set of three dimensional atomic coordinates, i.e., tertiary structure, is available (para. 0098).
Claim 10 recites a method for in silico design of an amino acid sequence, comprising steps of: decomposing a target structure into structural motifs; identifying matches for each motif in a database; sequentially calculating energetic contributions, for single design positions within the plurality of motifs, according to a hierarchy; generating at least one candidate amino acid sequence that is foldable into the target structure or a binding partner of the target structure; and expressing a foldable protein, comprising the candidate amino acid sequence, in an expression system.
The claim further specifies that decomposing the target structure into the plurality of structural motifs comprises: generating a first graph G with nodes representing residues and edges between the nodes representing coupling between the residues represented by the nodes; generating a second graph B, different from the first graph G, with nodes representing residues and directed edges representing residue-backbone influences; and identifying the plurality of structural motifs using the first graph G and/or the second graph B.
The claim further requires that the hierarchy comprises: first, at least one local energetic contribution for backbone ϕ (phi) and ψ (psi) dihedral angles; second, at least one local energetic contribution for backbone ω (omega) dihedral angles; and subsequently, one or more of: at least one non-local energetic contribution from a backbone in spatial but not sequence proximity, and at least one non-local energetic contribution from a pair of coupled residues.
The claim also specifies that expressing the foldable de novo protein comprises introducing a nucleic acid sequence encoding the at least one candidate amino acid sequence into the host cell, and recovering the expressed de novo protein.
With respect to claim 10, Fleishman discloses a method of constructing a library of amino acid sequences having a common structural fold, and designing an amino acid sequence having a desired affinity to a molecular surface of interest therefrom, comprising steps of: decomposing a source structure into a plurality of structurally homologous segments, i.e., structural motifs (paras. 0026-27); designing libraries of segments with common structural motifs, i.e., structural databases (paras. 0036 and 0043); matching segments to a molecular surface of interest to design a plurality of structure-surface complexes each having an assigned matching score (paras. 0044, 0168 and 0180); and selecting amino-acid sequences that exhibit desired affinity to the molecular surface of interest based on a combined matching score (paras. 0046 and 0189).
Fleishman further discloses iteratively optimizing portions (‘split segments’) of the structural motifs according to geometrical, spatial and/or energy criteria by minimizing polynomial functions, based on weights and constraints pertaining to, e.g., backbone dihedral angles and/or atomic positions, and describes particular weight fitting procedures including rigid body orientation, modulation of dihedral angles, amino acid side-chain packing and change of amino acids (para. 0136). Fleishman exemplifies performance of optimization via cyclic coordinated descent using the default all-atom energy function from the Rosetta software suite (para. 0136).
Fleishman further discloses weighting energy functions according to a position-specific scoring matrix (para. 0140), and iteratively calculating an energy score, using the Rosetta all-atom energy function, for each rotational state of each residue that makes an appreciable contribution to binding (para. 0300). In this way, Fleishman discloses deducing energetic contributions for each design position.
Fleishman further discloses that dihedral angles can be modulated based on structural constraints and include ϕ, ψ and ω dihedral angle values for each of the residues (paras. 0324 and 0351). Fleishman thus discloses evaluation of local energetic contributions for backbone ϕ, ψ and ω dihedral angles.
Fleishman further discloses calculating the root mean squared deviation (RMSD) between aligned backbone positions (paras. 0084 and 0158), and notes that antibody stability is influenced by contributions of the entire backbone (para. 0095). Fleishman thus discloses evaluating non-local energetic contributions of backbones in spatial but not sequence proximity.
Fleishman further discloses that conformations are characterized by key conserved residue identities that maintain the backbone (para. 0014), and discloses calculating the RMSD between interfacing residues (para. 0459). Fleishman exemplifies a conserved structural motif including two cysteine residues that form an intra-chain disulfide bond (para. 0126). Fleishman thus discloses evaluation of energetic contributions from pairs of coupled residues.
Fleishman further discloses selection of amino acid sequences based on assessment of fold stability (paras. 0063-64), providing for design of an amino acid sequence that is able to fold into a stable 3D structure exhibiting a desired affinity to the molecular surface of interest (para. 0241). Fleishman also discloses expressing a selected amino acid in an expression system (paras. 0049 and 0208).
Fleishman thus discloses deduction of each of the recited energetic contributions, but does not describe deduction according to a hierarchy as claimed (i.e., in the claimed order of first, second, and subsequent to said second).
However, predictable variations of known elements are obvious. The deductions of Fleishman are performed in a computing environment, and sequential execution of computational steps is well-understood, routine and conventional in the field of computing. The claimed ordering amounts to mere arrangement of known elements.
Thus, the ordering of energetic contributions is not considered to patentably distinguish the instant claims from the teachings of Fleishman. See In re Burhans, 154 F.2d 690 (CCPA 1946), wherein the court ruled that selection of any particular performance order of known process steps is prima facie obvious in the absence of new or unexpected results.
Fleishman does not disclose decomposing a target structure into a plurality of structural motifs using a graphical representation as claimed.
Xu discusses an algorithmic approach to protein structural prediction that models a protein structure as a geometric neighborhood graph, with vertices (i.e., nodes) representing a linear series of most-conserved segments (i.e., structural motifs comprising residues) into which the primary structure of a template is parsed and edges representing interactions (i.e., coupling) between residues inter-residue interactions, and teaches formulation and efficient solution of structural prediction problems in terms of decomposing the graph, assigning optimal labels (i.e., sequence alignment positions) to graph vertices and thus minimizing an associated energy function that parameterizes sequence-template alignment (pg. 247, Abstract; pg. 248, r. column).
Xu also discusses embodiment as a ‘residue interaction graph’ for predicting sidechain conformation, wherein vertices represent a linear series of residues into which a backbone structure is parsed, the labels represent possible side-chain conformations for each residue, and the energy function parameterizes side-chain packing (pg. 249, l. column). In a residue-interaction graph as disclosed, edges represent residue-backbone influences.
Additionally, Xu teaches that their graph-based approaches exhibit equal predictive accuracy to, and are generally more computationally efficient than, analogous linear programming approaches to predicting protein structure (pg. 247, Abstract; pg. 253, l. column).
In this way, Xu advantageously teaches: decomposing a target structure into a plurality of structural motifs using a graphical representation of coupled residues of the target structure and/or residue-backbone influences of the target structure; generating a graph with nodes representing residues and edges between the nodes representing coupling between the residues represented by the nodes; and generating a graph with nodes representing residues and edges representing residue-backbone influences; and identifying a plurality of structural motifs using each type of graph.
However, Xu does not specifically disclose a graph with nodes representing residues and directed edges representing residue-backbone influences. Xu does not discuss edge direction.
Wang discusses a method for computational protein docking that incorporates backbone flexibility (pg. 503, Abstract), and teaches structural modeling of a protein-protein complex (i.e., of each constituent protein structure) as a directed fold tree graph with vertices representing residues and edges representing peptide-bond connections between residues (pg. 504, r. column). Wang teaches applicability of their methods to different structural modeling tasks, including protein folding and structural prediction (pg. 505, l. column).
Wang teaches that the fold tree representation can be rendered as three-dimensional coordinates (i.e., a 3D structural model) by selecting and assigning a root vertex (e.g., the N terminus of a constituent protein) to an arbitrary location and traversing the edges of the graph in order, using torsion angles and/or rigid body transformations to build the terminal vertex of each edge given the coordinates of the initial vertex for that edge (pg. 505, l. column). This property is enabled by the directed nature of the fold tree edges.
With respect to claim 14, Fleishman discloses sampling backbone conformations and sequence information from natural protein folds for which a set of three dimensional atomic coordinates, i.e., tertiary structure, is available (para. 0098).
With respect to claim 16, Fleishman discloses software implementation of numerous method functions (e.g., paras. 0275, 0279, 0283, 0289-90, 0314) and saving data files to a disk, i.e., a non-transitory computer-readable storage medium (para. 0312). The limitations of the method of claim 1 are made obvious by the combined teachings of Fleishman and Xu as described above.
With respect to claim 21, Fleishman discloses software implementation of numerous method functions (e.g., paras. 0275, 0279, 0283, 0289-90, 0314) and saving data files to a disk, i.e., a non-transitory computer-readable storage medium (para. 0312). The limitations of the method of claim 10 are made obvious by the teachings of Fleishman as described above.
With respect to claim 24, Fleishman exemplifies execution of method functions on a standard CPU, i.e., a processor (paras. 0310 and 0433). The limitations of the method of claim 1 are made obvious by the combined teachings of Fleishman and Xu as described above.
With respect to claim 25, Fleishman exemplifies execution of method functions on a standard CPU, i.e., a processor (paras. 0310 and 0433). The limitations of the method of claim 10 are made obvious by the teachings of Fleishman as described above.
With respect to claim 26, Fleishman discloses sampling backbone conformations and sequence information from natural protein folds for which a set of three dimensional atomic coordinates, i.e., tertiary structure, is available (para. 0098). Fleishman further describes design according to natural antibody-protein complexes, i.e., quaternary structures (para. 0459). The limitations of the method of claim 1 are made obvious by the combined teachings of Fleishman and Xu as described above.
With respect to claim 27, Fleishman discloses selection of amino acid sequences based on assessment of fold stability (paras. 0063-64), providing for design of an amino acid sequence that is able to fold into a stable 3D structure exhibiting a desired affinity to the molecular surface of interest (para. 0241).
With respect to claim 28, Fleishman discloses sampling backbone conformations and sequence information from natural protein folds for which a set of three dimensional atomic coordinates, i.e., tertiary structure, is available (para. 0098). Fleishman further describes design according to natural antibody-protein complexes, i.e., quaternary structures (para. 0459). The limitations of the method of claim 10 are made obvious by the teachings of Fleishman as described above.
With respect to claim 29, Fleishman discloses designing amino acid sequences, based on source structures having a common structural fold and amino-acid sequence length identical to that of a target protein (paras. 0026 and 0068), by performing fold stability scoring and selecting a sequence based on the fold stability scoring (para. 0064). Thus, all designed sequences are foldable into a target structure. The limitations of the method of claim 10 are made obvious by the teachings of Fleishman as described above.
With respect to claim 30, Fleishman discloses weighting energy functions according to generated position-specific scoring matrices (paras. 0140 and 0307), i.e., sets of values for energetic contributions organized into tables.
With respect to claim 31, Xu describes the function of the described tree decomposition algorithms as partitioning a graph, representing a protein structure, into subgraphs (pg. 247, r. column – pg. 248, l. column; pg. 250, r. column – pg. 251, l. column). Xu also formulates a problem of label assignment, wherein each possible sequence position for a given core can be treated as a possible label assignment to a vertex in the graph (pg. 249, r. column). In other words, graph cores are labeled (i.e., structural motifs are identified) using derived subgraphs.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented decomposition of the protein structure using a graphical representation, as taught by Xu, to enhance the protein design method of Fleishman, because Xu teaches that computational approaches to protein structural prediction using a graphical representation achieve equal predictive accuracy and generally greater computationally efficiency than analogous linear programming approaches (pg. 247, Abstract; pg. 253, l. column). Said practitioner would have had a reasonable expectation of success because Fleishman and Xu both discuss algorithmic methods of protein structural prediction.
An invention would have been obvious to one of ordinary skill in the art if some teaching in the prior art would have led that person to combine prior art reference teachings to arrive at the claimed invention. Before the effective filing date of the claimed invention, said practitioner would have implemented edge direction, as taught by Wang, to enhance the protein design method of Fleishman in view of Xu, because Fleishman discloses sampling backbone conformations based on 3D atomic coordinates (para. 0098) and Xu teaches graph-based decomposition (pg. 247, Abstract) while Wang teaches that graphical representation with nodes and directed edges allows for conversion to 3D coordinates via graph traversal (pg. 247, Abstract; pg. 253, l. column).
In this way, Wang teaches that implementation of directed edges allows for conversion between protein representations that are utilized by the methods of Fleishman and Xu. Said practitioner would have had a reasonable expectation of success because Fleishman, Xu and Wang all discuss algorithmic methods of protein structural prediction. Additionally, Xu and Wang both particularly discuss protein structural prediction using similar graphical representations.
In this way the disclosure of Fleishman, in view of Xu and Wang, makes obvious the limitations of claims 1, 3-5, 8, 10, 14, 16, 21 and 24-31. Thus, the claimed invention is prima facie obvious.
Conclusion
At this point in prosecution, no claim is 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Theodore C. Striegel whose telephone number is (571)272-1860. The examiner can normally be reached Mon-Fri 12pm-8pm ET.
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/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 May 12, 2026
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