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 .
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 1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Representative claim 1 recites:
“1. A computer system comprising:
one or more processors; and
a memory to store a set of instructions,
wherein the one or more processors execute instructions stored in the memory to perform operations comprising:
dividing one or more hierarchical structures representing one or more design interfaces into a plurality of design fragments;
updating an index with the plurality of design fragments and a plurality of embeddings for the plurality of design fragments; and
processing a search query that specifies one or more design elements using the index.”
Claim 28 contains similar subject matter.
The claims contain mental process steps of “dividing one or more hierarchical structures…,” “updating an index…” and “processing a search query.”
The claims contain additional elements to the mental process in the form of “one or more processors” and “a memory.”
This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
The “one or more processors” and “memory” are recited at a high level of generality. They appear to be generic computing hardware elements. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
It is noted that none of the additional elements appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. As such, none of the additional elements appear to integrate the judicial exception into a practical application.
None of the additional elements are sufficient to amount to significantly more than the judicial exception, in part or in whole.
The recitation of generic hardware of the “one or more processors” and “memory” is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
None of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional. As such, none of the additional elements appears to be, in part or in whole, significantly more than the judicial exception.
Dependent claims 2-15 are merely directed towards additional limitations that further define data types or further describe data analyses that will occur. The “models” of claims 2, 5, 8 are recited at a high level of abstraction and appear to simply be data analyses. None of dependent claims 2-15 appear to include additional elements that incorporate the claimed subject matter into a practical application. The dependent claims also do not include additional elements that, in part or in whole, appear to be significantly more than the abstract idea.
Claim 16 recites:
“16. A non-transitory computer-readable medium that stores instructions, executable by one or more processors, to cause the one or more processors to perform operations comprising:
receiving a search query specifying a representation of one or more design elements;
matching one or more embeddings associated with the representation to a set of embeddings for a set of design fragments, wherein each design fragment in the set of design fragments corresponds to a subset of one or more hierarchical structures representing one or more design interfaces;
generating a set of search results using the set of embeddings; and
outputting the set of search results in a response to the search query.”
The claims contain mental process steps of “matching one or more embeddings…” and “generating a set of search results…”.
The claims contain additional elements to the mental process in the form of “a non-transitory computer readable medium,” “receiving a search query,” and “outputting the set of search results.”
This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
The “non-transitory computer readable medium” is recited at a high level of generality. It appears to be generic computing hardware elements. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). Receiving a search query appears to be a data gathering step, and is thus mere pre-solution insignificant activity (see MPEP 2106.05(g). Outputting the result of a data analysis is insignificant post-solution activity (see MPEP 2106.05(g)(3)).
It is noted that none of the additional elements appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. As such, none of the additional elements appear to integrate the judicial exception into a practical application.
None of the additional elements are sufficient to amount to significantly more than the judicial exception, in part or in whole.
The recitation of generic hardware of the “non-transitory computer readable medium” is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). The additional element of “receiving a search query” is merely extra-solution activity data gathering and is well understood, routine, and conventional (see MPEP 2106.05(g)). Outputting results of a data analysis is insignificant extra-solution activity and is well known (see MPEP 2106.05(g)((3).
None of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional. As such, none of the additional elements appears to be, in part or in whole, significantly more than the judicial exception.
Dependent claims 17-27 are merely directed towards additional limitations that further define data types or further describe data analyses that will occur. The “models” of claims 17 and 18 are recited at a high level of abstraction and appear to simply be data analyses. None of dependent claims 2-15 appear to include additional elements that incorporate the claimed subject matter into a practical application. The dependent claims also do not include additional elements that, in part or in whole, appear to be significantly more than the abstract idea.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 9, 14-18, 24, and 28 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ramanasankaran et al. (US Pre-Grant Publication 2024/0345551).
As to claim 1, Ramanasankaran teaches a computer system comprising:
one or more processors (see Ramanasankaran paragraph [0030]); and
a memory to store a set of instructions (see Ramanasankaran paragraph [0030]), wherein the one or more processors execute instructions stored in the memory to perform operations comprising:
dividing one or more hierarchical structures representing one or more design interfaces into a plurality of design fragments (see Ramanasankaran paragraph [0134]. Ramanasankaran contains a knowledge graph of building data. The knowledge graph is a “hierarchical structure.” This knowledge graph may be split into individual nodes and edges or hierarchical groups of nodes or edges. Splitting the data of the knowledge graph is “dividing” a hierarchical structure into fragments. These are “design” interfaces because they represent data elements used in “designing” a building. Alternatively, and in addition, the Examiner notes that the limitation of “design interfaces” and “design fragments” appear to be merely non-functional descriptive of a data type considered in the invention. No values particular to “design” have any effect on any of the claimed steps. Because the data type of “design” does not have any function effect on any claimed step, it is non-functional descriptive material);
updating an index with the plurality of design fragments and a plurality of embeddings for the plurality of design fragments (see Ramanasankaran paragraphs [0134]-[0141], generally. Ramanasankaran splits a graph of nodes into individual nodes or logical groups, or “design fragments.” From this, Ramanasankaran turns these fragments into natural language prompts, as discussed in paragraphs [0135]-[0136]. These natural language prompts may be converted into vectors through an embedding model, as noted in paragraph [0141]. These vectors, or embeddings, may then be indexed in a searchable index, paragraph [0141] and [0144]); and
processing a search query that specifies one or more design elements using the index (see Ramanasankaran paragraphs [0144]-[0146]. The vectors are stored in a similarity search database. The system may receive user prompts, or queries, to apply to the similarity search database to identify the top related information from the stored vectors related to the user’s query, see paragraph [0146]).
As to claim 2, Ramanasankaran teaches the computer system of claim 1, wherein the operations further comprise:
generating the plurality of embeddings using one or more machine learning models (see Ramanasankaran paragraph [0141]. An embedding model may convert the nodes of the hierarchical structure of Ramanasankaran into embedding vectors. The embedding model may be a machine learning model)
As to claim 3, Ramanasankaran teaches the computer system of claim 2, wherein generating the plurality of embeddings comprises:
generating a text-based representation of a design fragment from the plurality of design fragments (see Ramanasankaran paragraph [0135]-[0136] and [0141]); and
converting the text-based representation into an embedding for the design fragment (see Ramanasankaran paragraph [0135]-[0136] and [0141]).
As to claim 4, Ramanasankaran teaches the computer system of claim 3, wherein the text-based representation comprises at least one of a text-based description of the design fragment or a text-based definition of the design fragment (see Ramanasankaran paragraph [0135]-[0136]).
As to claim 5, Ramanasankaran teaches the computer system of claim 2, wherein the one or more machine learning models comprise at least one of a text embedding model, an image embedding model, or a multimodal embedding model (see Ramanasankaran paragraph [0141]).
As to claim 6, Ramanasankaran teaches the computer system of claim 1, wherein dividing the one or more hierarchical structures into the plurality of design fragments comprises generating a design fragment from a node within the one or more hierarchical structures based on one or more attributes associated with the node (see Ramanasankaran paragraphs [0134]-[0135]).
As to claim 7, Ramanasankaran teaches the computer system of claim 6, wherein the one or more attributes comprise at least one of a node type or a dimension (see Ramanasankaran paragraphs [0134]-[0137]).
As to claim 9, Ramanasankaran teaches the computer system of claim 1, wherein processing the search query comprises:
converting a representation of the one or more design elements into one or more embeddings (see Ramanasankaran paragraphs [0134]-[0138] and [0141]);
matching the one or more embeddings to a set of embeddings in the index (see Ramanasankaran paragraphs [0141] and [0144]-[0146]); and
adding a set of design fragments corresponding to the set of embeddings to a set of search results for the search query (see Ramanasankaran paragraphs [0141] and [0144]-[0146]).
As to claim 14, Ramanasankaran teaches the computer system of claim 1, wherein the operations further comprise detecting a change to the one or more hierarchical structures prior to dividing the one or more hierarchical structures into the plurality of design fragments (see Ramanasankaran paragraph [0133])
As to claim 15, Ramanasankaran teaches the computer system of claim 1, wherein the search query specifies the one or more design elements using at least one of an image, a text-based description, a sketch, or at least a portion of a design interface (see Ramanasankaran paragraphs [0141] and [0144]-[0146]).
As to claim 16, Ramanasankaran teaches a non-transitory computer-readable medium that stores instructions, executable by one or more processors (see Ramanasankaran paragraphs [0030] and [0196]), to cause the one or more processors to perform operations comprising:
receiving a search query specifying a representation of one or more design elements (see Ramanasankaran paragraphs [0144]-[0146] and the rejection of claim 1);
matching one or more embeddings associated with the representation to a set of embeddings for a set of design fragments, wherein each design fragment in the set of design fragments corresponds to a subset of one or more hierarchical structures representing one or more design interfaces (see Ramanasankaran paragraphs [0134]-[0141] and [0144]-[0146] and the rejection of claim 1);
generating a set of search results using the set of embeddings (see Ramanasankaran paragraphs [0144]-[0147] and the rejection of claim 1); and
outputting the set of search results in a response to the search query (see Ramanasankaran paragraphs [0144]-[147] and the rejection of claim 1).
As to claim 17, Ramanasankaran teaches the non-transitory computer-readable medium of claim 16, wherein the operations further comprise:
generating the one or more embeddings using one or more machine learning models (see Ramanasankaran paragraph [0141]).
As to claim 18, Ramanasankaran teaches the non-transitory computer-readable medium of claim 17, wherein the one or more machine learning models comprise at least one of a text embedding model, an image embedding model, or a multimodal embedding model (see Ramanasankaran paragraph [0141]).
As to claim 24, Ramanasankaran teaches the non-transitory computer-readable medium of claim 16, wherein generating the set of search results comprises:
retrieving, from an index, the set of design fragments from a set of mappings that include the set of embeddings (see Ramanasankaran paragraph [0141] and [0144]-[0146]); and
adding the set of design fragments to the set of search results (see Ramanasankaran paragraph [0141] and [0144]-[0146]).
As to claim 28, see the rejection of claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran et al. (US Pre-Grant Publication 2024/0345551) in view of Rogynskyy et al. (US Pre-Grant Publication 2019/0361934).
As to claim 8, Ramanasankaran teaches the computer system of claim 6.
Ramanasankaran does not teach wherein the design fragment is generated based on a classification score outputted by a machine learning model from a representation of the node.
Rogynskyy teaches wherein the design fragment is generated based on a classification score outputted by a machine learning model from a representation of the node (see Rogynskyy paragraphs [0709]-[0710]. Rogynskyy shows that nodes me be categorized based on connections between the nodes. A relevancy score may be used to categorize nodes and the relationships between nodes in a hierarchical data structure. As noted in paragraph [0726]-[0727], such analysis may rely on machine learning).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ramanasankaran by the teachings of Rogynskyy because both references are directed towards analysis of hierarchical data structure. Rogynskyy merely provides additional analysis to Ramanasankaran that will help to better parse and understand the relationships between two nodes in a hierarchical data structure, which will improve the depth of analysis of Ramanasankaran.
Claims 10, 12, 19, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran et al. (US Pre-Grant Publication 2024/0345551) in view of Bouhnik et al. (US Pre-Grant Publication 2019/0361934).
As to claim 10, Ramanasankaran teaches the computer system of claim 9.
Ramanasankaran does not teach wherein matching the one or more embeddings to the set of embeddings comprises:
matching the one or more embeddings to a set of clusters of embeddings in the index;
determining a set of representative design fragments associated with the set of clusters of embeddings; and
adding the set of representative design fragments to the set of search results.
Bouhnik teaches:
wherein matching the one or more embeddings to the set of embeddings comprises:
matching the one or more embeddings to a set of clusters of embeddings in the index (see Bouhnik paragraphs [0045] and [0066]. An embedding is formed from a user query. The query embedding may be matched against cluster embeddings to return results of the corresponding cluster);
determining a set of representative design fragments associated with the set of clusters of embeddings (see Bouhnik paragraphs [0045] and [0066]. Result videos from the cluster are returned. These results as “representative design fragments” because they are data fragments representative of the cluster); and
adding the set of representative design fragments to the set of search results (see Bouhnik paragraphs [0045] and [0066]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ramanasankaran by the teachings of Bouhnik because both references are directed towards data analysis to identify matching results. Bouhnik merely provides additional analysis to Ramanasankaran that will help to better analyze and group results for a user, which will improve the usability of the search system of Ramanasankaran.
As to claim 12, Ramanasankaran teaches the computer system of claim 10, wherein matching the one or more embeddings to the set of clusters of embeddings comprises determining that a similarity between the one or more embeddings and a set of representative embeddings for the set of clusters of embeddings meets or exceeds a threshold (see Bouhnik paragraph [0066]).
As to claim 19, Ramanasankaran teaches the non-transitory computer-readable medium of claim 16.
Ramanasankaran does not teach wherein the one or more embeddings are matched to the set of embeddings based on one or more distances computed between the one or more embeddings and the set of embeddings.
Bouhnik teaches wherein the one or more embeddings are matched to the set of embeddings based on one or more distances computed between the one or more embeddings and the set of embeddings (see Bouhnik paragraphs [0064] and [0066]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ramanasankaran by the teachings of Bouhnik because both references are directed towards data analysis to identify matching results. Bouhnik merely provides additional analysis to Ramanasankaran that will help to better analyze and group results for a user, which will improve the usability of the search system of Ramanasankaran.
As to claim 20, Ramanasankaran teaches the non-transitory computer-readable medium of claim 16.
Ramanasankaran does not teach wherein generating the set of search results comprises:
determining that the set of embeddings corresponds to representative embeddings for a set of clusters of embeddings;
determining a set of representative design fragments associated with the set of clusters of embeddings; and
adding the set of representative design fragments to the set of search results.
Bouhnik teaches wherein generating the set of search results comprises:
determining that the set of embeddings corresponds to representative embeddings for a set of clusters of embeddings (see Bouhnik paragraphs [0045] and [0066]);
determining a set of representative design fragments associated with the set of clusters of embeddings (see Bouhnik paragraphs [0045] and [0066]; and
adding the set of representative design fragments to the set of search results (see Bouhnik paragraphs [0045] and [0066].
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ramanasankaran by the teachings of Bouhnik because both references are directed towards data analysis to identify matching results. Bouhnik merely provides additional analysis to Ramanasankaran that will help to better analyze and group results for a user, which will improve the usability of the search system of Ramanasankaran.
As to claim 21, Ramanasankaran as modified by Bouhnik teaches the non-transitory computer-readable medium of claim 20, wherein determining the set of representative design fragments comprises retrieving, from an index, the set of representative design fragments from a set of mappings that include the representative embeddings (see Ramanasankaran paragraphs [0141] and [0144])
As to claim 22, Ramanasankaran as modified by Bouhnik teaches the non-transitory computer-readable medium of claim 21, wherein each of the representative embeddings comprises a centroid of a corresponding cluster in the set of clusters (see Bouhnik paragraph [0066]).
Claims 11 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran et al. (US Pre-Grant Publication 2024/0345551) in view of Bouhnik et al. (US Pre-Grant Publication 2019/0361934), and further in view of Capper et al. (US Pre-Grant Publication 2005/0144158).
As to claim 11, Ramanasankaran as modified teaches the computer system of claim 10.
Ramanasankaran as modified does not teach wherein processing the search query further comprises:
matching a user selection of a design fragment in the set of representative design fragments to a cluster in the set of clusters; and
updating the set of search results with one or more additional design fragments in the cluster.
Capper teaches wherein processing the search query further comprises:
matching a user selection of a design fragment in the set of representative design fragments to a cluster in the set of clusters (see paragraphs [0072]-[0075]. A user may select results in a cluster); and
updating the set of search results with one or more additional design fragments in the cluster (see paragraphs [0072]-[0075]. The user selection of a result from the cluster updates the search results rankings in the cluster with additional results).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ramanasankaran by the teachings of Capper because both references are directed towards data analysis to identify matching results. Capper merely provides additional analysis to Ramanasankaran that will help to better analyze results for a user and aid a user in additional discovery of results.
As to claim 23, Ramanasankaran as modified teaches the non-transitory computer-readable medium of claim 20.
Ramanasankaran as modified does not teach wherein generating the set of search results further comprises:
matching a user selection of a design fragment in the set of representative design fragments to a cluster in the set of clusters; and
updating the set of search results with one or more additional design fragments from the cluster.
Capper wherein generating the set of search results further comprises:
matching a user selection of a design fragment in the set of representative design fragments to a cluster in the set of clusters (see Capper paragraphs [0072]-[0075]); and
updating the set of search results with one or more additional design fragments from the cluster (see Capper paragraphs [0072]-[0075]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ramanasankaran by the teachings of Capper because both references are directed towards data analysis to identify matching results. Capper merely provides additional analysis to Ramanasankaran that will help to better analyze results for a user and aid a user in additional discovery of results.
Claims 13 and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran et al. (US Pre-Grant Publication 2024/0345551) in view of Amacker (US Patent 8,631,029).
As to claim 13, Ramanasankaran teaches the computer system of claim 9.
Ramanasankaran does not teach wherein processing the search query further comprises:
determining an embedding for a design fragment that is selected by a user from the set of search results; and
generating an additional set of search results based on the embedding.
Amacker teaches wherein processing the search query further comprises:
determining an embedding for a design fragment that is selected by a user from the set of search results (see Amacker 5:22-6:15 and Abstract. A user may selected a result. Subsequent results shown to a user will take into account the user selection. It is noted that Bouhnik paragraphs [0045] and [0066]-[0067] teach to return results from a particular cluster); and
generating an additional set of search results based on the embedding (see Amacker 5:22-6:15 and Abstract).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ramanasankaran by the teachings of Amacker because both references are directed towards data analysis to identify matching results. Amacker merely provides additional analysis to Ramanasankaran that will help to better analyze results for a user and aid a user in additional discovery of results.
As to claim 25, Ramanasankaran teaches the non-transitory computer-readable medium of claim 16.
Ramanasankaran does not teach wherein generating the set of search results comprises:
determining an embedding for a design fragment that is selected by a user from the set of search results; and
generating an additional set of search results based on the embedding.
Amacker teaches wherein generating the set of search results comprises:
determining an embedding for a design fragment that is selected by a user from the set of search results (see Amacker 5:22-6:15 and Abstract and Bouhnik paragraphs [0045] and [0066]-[0067] and the rejection of claim 13); and
generating an additional set of search results based on the embedding (see Amacker 5:22-6:15 and Abstract).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ramanasankaran by the teachings of Amacker because both references are directed towards data analysis to identify matching results. Amacker merely provides additional analysis to Ramanasankaran that will help to better analyze results for a user and aid a user in additional discovery of results.
.
As to claim 26, Ramanasankaran teaches the non-transitory computer-readable medium of claim 25, wherein generating the additional set of search results comprises:
matching the embedding to an additional set of embeddings in an index (see Amacker 5:22-6:15 and Abstract. Also see Ramanasankaran paragraph [0141] for matching an embedding search query to an index); and
adding, to the additional set of search results, an additional set of design fragments mapped to the additional set of embeddings within the index (see Amacker 5:22-6:15 and Abstract).
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran et al. (US Pre-Grant Publication 2024/0345551) in view of Amacker (US Patent 8,631,029), and further in view of Bouhnik et al. (US Pre-Grant Publication 2019/0361934).
As to claim 27, Ramanasankaran teaches the non-transitory computer-readable medium of claim 25.
Ramanasankaran does not teach wherein generating the additional set of search results comprises:
matching an aggregation of the one or more embeddings with the embedding to an additional set of embeddings in an index; and
adding, to the additional set of search results, an additional set of design fragments mapped to the additional set of embeddings within the index.
Bouhnik teaches wherein generating the additional set of search results comprises:
matching an aggregation of the one or more embeddings with the embedding to an additional set of embeddings in an index (see Bouhnik paragraphs [0045] and [0066]); and
adding, to the additional set of search results, an additional set of design fragments mapped to the additional set of embeddings within the index (see Bouhnik paragraphs [0045] and [0066]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ramanasankaran by the teachings of Bouhnik because both references are directed towards data analysis to identify matching results. Bouhnik merely provides additional analysis to Ramanasankaran that will help to better analyze and group results for a user, which will improve the usability of the search system of Ramanasankaran.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES D ADAMS whose telephone number is (571)272-3938. The examiner can normally be reached M-F, 9-5:30 EST.
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/CHARLES D ADAMS/ Primary Examiner, Art Unit 2152
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