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 Office Action is in response to the application filed on 06/23/2025.
Claims 1-19 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 06/23/2025 and 10/16/2025 has been considered (see form-1449, MPEP 609).
Drawings
The drawings filed on 06/23/2025 are objected because the images are unclear and blurry.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1-11 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Brogle et al. (US Patent 10,013,467, hereinafter Brogle), in view of De Sousa Webber (US PGPUB 2017/0053025, hereinafter Webber).
As per as claim 1, Brogle discloses:
A computer-implemented method for generating an artificial environment within a memory of a computer, in which coded identifiers that relate to a particular target and which are described in at least one document are extracted and analyzed (Brogle, e.g., [col. 6, lines 48-56], “…coded representations of the representational data (e.g. chemical identifiers, nucleotide sequences, textual fingerprint data, or a hybrid of the foregoing). The method comprises generating a virtual n-dimensional manifold within a memory of a computer using a manifold-generator module which comprises code executing in a processor and placing…” and [claim 1]), the method comprising:
submitting, in electronic form, a search to at least one document database for documents describing the target using a defined search strategy (Brogle, e.g., [claim1], “…submitting, in electronic form, a search to at least one document database for documents describing the subject matter using a defined search strategy…”);
extrapolating, to a first array within the memory of the computer, at least one coded identifier described in at least one document returned from the search, the extrapolating step using an extraction module comprising code executing in a processor (Brogle, e.g., [claim 1], “… extrapolating, to a first array within the memory of the computer, at least one chemical identifier described in at least one document returned from the search, the extrapolating step using an extraction module comprising code executing in a processor…”);
transforming each coded identifier in the first array into a respective coded form having a range of values using a conversion module comprising code executing in the processor (Brogle, e.g., [claim 1], “…transforming each chemical identifier in the first array into a respective coded form having a range of values using a conversion module comprising code executing in the processor…”);
populating the respective coded identifier into a second array within the memory of the computer (Brogle, e.g., [claim 1], “…populating the respective coded forms into a second array within the memory of the computer…”);
generating a virtual n-dimensional array of nodes configured to encompass the range of values in the second array using a node array generator module comprising code executing in the processor, wherein each node of the virtual n-dimensional array having an associated weight vector value based on the range of values in the second array (Brogle, e.g., [claim 1], “…generating a virtual n-dimensional array of nodes configured to encompass the range of values in the second array using a node array generator module comprising code executing in the processor, each node of the virtual n-dimensional array having an associated weight vector value based on the range of values in the second array…”);
placing each coded form in the second array into a node of the virtual n-dimensional array according to an unsupervised learning algorithm using a placement module comprising code executing in the processor to effect a placement (Brogle, e.g., [claim 1], “… placing each coded form in the second array into a node of the virtual n-dimensional array according to an unsupervised learning algorithm using a placement module comprising code executing in the processor to effect a placement; and outputting a visual representation of the virtual n-dimensional array…”);
calculating a distance metric for a plurality of pairs of coded forms placed within the virtual n- dimensional array (Brogle, e.g., [col. 15, lines 45-57], “… placing each coded form in the second array into a node of the virtual n-dimensional array according to an unsupervised learning algorithm using a placement module comprising code executing in the processor to effect a placement; and outputting a visual representation of the virtual n-dimensional array…”);
generating at least one node and at least one edge file corresponding to those plurality of pairs of coded forms that have a distance metric below a pre-determined threshold (Brogle, e.g., [col. 13, lines 45-60], “…the biologic identifiers are plotted to a first n-dimensional array and the chemical identifiers are plotted to a second n-dimensional array. In this configuration, an indication of the relationship between the biological identifier component of the hybrid structure mapped to the first n-dimensional map and the chemical identifier component of the hybrid structure mapped to the second n-dimensional map array can be maintained…” and [col. 21, lines 5-14], (hydrogen bonding patterns) ( edge file)); and
To make records clearer regarding to features of “plurality of pairs of coded forms that have a distance metric below a pre-determined threshold” and “using the at least one node and at least one edge file, generating a network diagram of the plurality of pairs of coded forms that have a distance metric below a pre-determined threshold”.
However Webber, in an analogous art, discloses “plurality of pairs of coded forms that have a distance metric below a pre-determined threshold” (Webber, e.g., [0093-0095], “…identification of the second data item and an identification of a level of semantic similarity between the first data item and the second data item, based on the determined distance... enumeration of data items that satisfy a requirement for similarity (e.g., having a distance between the data items that falls below a predetermined threshold)…”, “using the at least one node and at least one edge file, generating a network diagram of the plurality of pairs of coded forms that have a distance metric below a pre-determined threshold” (Webber, e.g., [0094-0095], [0114-0115], “…received data item with each of a plurality of SDRs in the SDR database 120 as described above and return an enumeration of data items that satisfy a requirement for similarity (e.g., having a distance between the data items that falls below a predetermined threshold)…”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Webber and Brogle to predicting where an unseen data document would be clustered to, or analyzing the cluster structure of the used data document collection (Webber, e.g., [0003]).
As per as claim 2, the combination of Webber and Brogle disclose:
The method of claim 1, further comprising outputting a visual representation of the network diagram of coded forms (Brogle, e.g., [col. 2-3, lines 50-20], “… coded representations of the representational data (e.g. chemical identifiers, nucleotide sequences, textual fingerprint data, or a hybrid of the foregoing)….” And [col. 7, lines 15-60]).
As per as claim 3, the combination of Webber and Brogle disclose:
The method of claim 1 wherein the submitting step further comprises: receiving from the at least one document database, a plurality of documents, and filtering the plurality of documents using a filtering criterion to obtain a filtered subset of documents (Brogle, e.g., [col. 9, lines 36-50]), “…using a filtering module 312 which can comprise code executing in the processor 102 in order to perform a filtering step 212. The filtering module 312 in one particular embodiment configures the processor to only access those source documents which include in their discussion a particular sub-set of the biological target of interest. In FIG. 2A, the filtering module 312 can configure the processor to execute a filtering step in which the results of a database query are filtered to identify those documents which relate to sodium channel inhibitors or other small molecule compounds. In FIG. 2B the filtering module 312 can configure the processor to identify those documents relating to an immune-mediated inflammatory disease….”).
As per as claim 4, the combination of Webber and Brogle disclose:
The method of claim 1, wherein the filtering criteria is an ailment (Brogle, e.g., [col. 9, lines 36-50]) (the examiner asserts, filtering a document relating inflammatory disease which is equivalent to filtering criteria is an ailment).
As per as claim 5, the combination of Webber and Brogle disclose:
The method of claim 1, wherein the extrapolating step further comprises configuring one or more processors by the extraction module to parse text found within the at least one document returned from the search to identify coded identifiers based on at least one pre-determined schema (Brogle, e.g., [col.9-10, lines 60-7]).
As per as claim 6, the combination of Webber and Brogle disclose:
The method of claim 4, wherein the pre-determined schema used to evaluate the parsed text includes subscript and superscript text components (Brogle, e.g., [col.9-10, lines 60-7]).
As per as claim 7, the combination of Webber and Brogle disclose:
The method of claim 1, wherein the extrapolating step further comprises configuring one or more processors by the extraction module to parse text found within the at least one document returned from the search to identify coded identifiers using one or more natural language extraction and association algorithms (Brogle, e.g., [col. 10-11, lines 20-19]).
As per as claim 8, the combination of Webber and Brogle disclose:
The method of claim 1, wherein the extrapolating step further includes configuring one or more processors by the extraction module to parse contents of the at least one document using one or more image recognition algorithms to identify a structural formula present in the at least one document (Brogle, e.g., [col. 11, lines 55-65], “…utilizes image recognition sub-modules to obtain chemical names or formulae from a given structural formula, such as a skeletal formula…”).
As per as claim 9, the combination of Webber and Brogle disclose:
The method of claim 8, further comprising comparing the identified structural formula against a database of known structural formula and associated compound names, and identifying the associated compound name for the identified structural formula (Brogle, e.g., [col. 4, lines 32-40] and [col. 11, lines 55-60]).
As per as claim 10, the combination of Webber and Brogle disclose:
The method of claim 1, wherein the extraction step further comprises identifying duplicate coded identifiers (Webber, e.g., [0218]).
As per as claim 11, the combination of Webber and Brogle disclose:
The method of claim 1, wherein transforming each coded identifier in the first array includes: (i) converting an identified chemical entity into a simplified molecular input line entry (Brogle, e.g., [col. 9, lines 25-35], [col. 10, lines 20-35], [col. 11, lines 55-65]), and (ii) converting the simplified molecular input line entry corresponding to the identified chemical entity into a vector form (Brogle, e.g., [col. 3, lines 30-35], [col. 4, lines 5-15], “… convert each instance of representational data found in the accessed documents into a high-dimensional coded form…”).
As per as claim 13, the combination of Webber and Brogle disclose:
The method of claim 1, wherein the coded identifiers are one of chemical formulae, nucleotide sequences and protein sequences (Brogle, e.g., [col. 11, lines 54-65], “… polypeptides, nucleotide sequences, or any fragments…”).
Claim 14 is essentially the same as claim 1 except that it set forth the claimed invention as a system rather a method, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claim 1.
As per as claim 15, the combination of Webber and Brogle disclose:
The system of claim 14, wherein the one or more processors are configured to output a visual representation of the network diagram of coded forms to at least one display device (Brogle, e.g., [col. 2-3, lines 50-20], “… coded representations of the representational data (e.g. chemical identifiers, nucleotide sequences, textual fingerprint data, or a hybrid of the foregoing)….” And [col. 7, lines 15-60]).
As per as claim 16, the combination of Webber and Brogle disclose:
The method of claim 14, wherein the one or more processor is configured to:
identify, using a text parsing algorithm, the terms contained in each document (Brogle, e.g., [col. 10, lines 1-8], “…perform a text parsing function that identifies candidates for extraction with reference to a rules base…”);
compare each of the identified terms to a library of terms corresponding to the subject matter target (Brogle, e.g., [col. 10, lines 20-60], [col. 11, lines 54-60]);
assign values to each of the identified terms on each document (Brogle, e.g., [col. 10, lines 30-42]); and
generate a numeric representation of the subject matter based on the assigned values (Brogle, e.g., [col. 11, lines 40-49] and [col. 12-13, lines 63-15]).
As per as claim 17, the combination of Webber and Brogle disclose:
The method of claim 14, wherein generating a numeric representation of each document includes discarding all terms that have a numerical representation below a pre-determined threshold Webber, e.g., [0094-0095], [0114-0115], “…received data item with each of a plurality of SDRs in the SDR database 120 as described above and return an enumeration of data items that satisfy a requirement for similarity (e.g., having a distance between the data items that falls below a predetermined threshold)…”).
Claims 18-19 contain essentially the same subject matter as claims 1-2 and therefore are rejected under the same rationale.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Brogle et al. (US Patent 10,013,467, hereinafter Brogle), in view of De Sousa Webber (US PGPUB 2017/0053025, hereinafter Webber) and further in view of Kawasaki Yuji Mizobuchi (US PGPUB 2022/0374648, hereinafter Mizobuchi) .
As per as claim 12, the combination of Webber and Brogle disclose:
The method of claim 1, wherein calculating the distance metric includes calculating for each pair of vectors, a one-way pairing function.
The combination of Webber and Brogle disclose calculating the distance metric (Brogle, e.g., [col. 10, lines 30-40], “…weight is determined by calculating the frequency or uniformity of occurrence of each term in the document or within a collection of documents, or in a larger corpus of text, by assigning weighted values to each term within the document, depending on the frequency of that term or token within the corpus or collection of corpuses selected…”), but Webber and Brogle do not explicitly disclose “calculating for each pair of vectors, a one-way pairing function”.
However Mizobuchi, in an analogous art, discloses “calculating for each pair of vectors, a one-way pairing function” (Mizobuchi, e.g., [abstract], [0005], “…calculating a first embedded vector for each of clusters obtained by clustering samples included in training data, by inputting the samples that represent the clusters to a first distance metric model; performing training of a second distance metric model from the first distance metric model, based on labels set in pairs of the samples included in the training data; calculating a second embedded vector for each of the clusters…” and [0064-0065], “... function that outputs a pair of clusters… based on the embedded vectors output by a distance metric…”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Mizobuchi, Webber and Brogle to learning support function calculates the amount of change in the distance between clusters between before relearning and in the relearning process, wherein a pair of clusters in which the amount of change calculated between before relearning and in the relearning process in this manner is less than a predetermined threshold value (Mizobuchi, e.g., [0068-0070]).
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to extracting from the source documents chemical identifiers and converting those chemical identifiers into coded forms.
a. Brown et al. (US PGPUB 2022/0223232, hereinafter Brown); “Systems And Methods For Evaluating Biological Data Using And Applying A virtual Landscape” discloses “generating an n-dimensional map using the results of a query for compounds enumerated within a collection of documents describing a particular biological target of interest and a curated set of sequences”.
Brown also teaches populating an n-dimensional manifold, such as an n-dimensional node array, with coded representations of the representational data (e.g. chemical identifiers, nucleotide or protein sequences, textual fingerprint data, or a hybrid of the foregoing) [0011].
Brown further teaches predicting new representational data that will occupy the manifold, such as a particular node of the array when placed within the array. In an example where the representational data are chemical identifiers, the further steps include comparing at least one chemical feature [0015].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUAN A PHAM whose telephone number is (571)270-3173. The examiner can normally be reached M-F 7:45 AM - 6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUAN A PHAM/Primary Examiner, Art Unit 2163
