DETAILED ACTION
Comments
The present application is being examined under the pre-AIA first to invent provisions. 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.
Claims 1-20 are pending and examined in the instant Office action.
Information Disclosure Statement
The IDS of 9/25/2023 has been considered.
Claim Objection
Claim 1 is objected to because of the following informalities:
In line 9 of claim 1, the phrase “corresponding the candidate embedding”should read “corresponding to the candidate embedding”.
Appropriate correction is required.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon without significantly more. Claims 1-16 are drawn to methods, and claims 17-20 are drawn to systems comprising computers.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
The independent claims recite the mental step of obtaining a mathematical model for embedding text and molecule representations.
The independent claims recite the mental step of receiving a natural language search query.
The independent claims recite the mental step of computing an embedding search query.
The independent claims recite the mental step of identifying, using a distance metric, a candidate embedding in the embedding space that is in proximity to the embedding of the search query.
The independent claims recite the mental step of obtaining a candidate molecule corresponding to the candidate embedding.
The independent claims recite the mental step of generating a representation of the candidate molecule in response to the search query.
Independent claim 11 recites the mental step of tokenizing the text string.
Independent claim 11 recites the mental step of mapping the candidate embedding to text of a candidate molecular property.
Claims 2, 12, and 18 recite the mental step of processing the text description with a sentence embedding model.
Claims 3, 13, and 19 recite the mental step of requiring the embedding to be a transformer text embedding.
Claims 4, 14, and 20 recite the mental step of requiring the candidate embedding to be representative of a SELFIES or SMILES string of a molecule.
Claim 5 recites the mental steps of identifying, using the distance metric, a second candidate embedding in the embedding space that is in proximity to the embedding of the candidate molecule, obtaining a property description corresponding to the second candidate embedding, and generating a depiction of the property description.
Claims 6 and 15 recite the mathematical limitation of cosine similarity.
Claim 7 recites the mental step of the representation of the candidate molecule comprising a graphical depiction of a structure of the molecule.
Claims 8 and 19 recite the mathematical limitation of a transformer-based model.
Claims 9 and 16 recite the mental step of the model comprising being trained using negative descriptions of the molecules.
Claim 10 recites the mathematical limitation of the model comprising being trained using a pseudo-normal negative loss function.
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claim(s) 1-20 recite(s) an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1 : YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment.
There are no limitations that indicate that the claimed analysis engine or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-20 is/are directed to an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 2 : NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional limitations to indicate that the claimed analysis engine requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The limitation for treating the tumor cells equate to mere instructions to apply the judicial exception in a generic way because the treating step is so generically recited. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B : No). As such, claims 1-20 is/are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
35 U.S.C. 103 Rejection #1:
Claims 1-5, 7-9, and 17-20 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Song et al. [CN 112164426 A; on IDS]. An English machine translation of Song et al. is cited in the following rejection statement.
Claim 1 is drawn to a computer-implemented method of querying for molecules. The method comprises obtaining a mathematical model for embedding text and molecule representations in an embedding space. The method comprises receiving a natural language query, wherein the search query includes a text description of a chemical property of a molecule. The method comprises computing, using the mathematical model, am embedding of the search query. The method comprises identifying, using a distance metric, a candidate embedding in the embedding space that is in proximity to the embedding of the search query. The method comprises obtaining a candidate molecule corresponding to the candidate embedding. The method comprises generating, for presentation at a user interface, a representation of the candidate molecule in response to the search query.
Claim 17 is drawn to similar subject matter as claim 1, except claim 17 is drawn to a system with computers.
The document of Song et al. studies drug small molecule target activity prediction based on TextCNN [title]. The abstract of Song et al. teaches that the molecular data is preprocessed to be trained and tested to obtain a natural language and coded drug text data set. The abstract of Song et al. suggests receiving a natural language query by importing the medicine text data in a training set and vectorizing the coded natural language in the medicine text data set at an embedding layer. The fourth paragraph from the bottom of page 4 of Song et al. teaches using distance metrics to obtain text and representation of a selected candidate molecule.
With regard to claims 2-3, 8, 12-13, and 18-19, the second full paragraph on page 5 of Song et al. teaches analyzing and performing transform operations on input texts of sentences.
With regard to claims 4, 7, 14, and 20, the last paragraph on page 2 of Song et al. teaches analyzing chemical data using SMILES. SMILES suggests a graphical interpretation of the structure of the molecule.
With regard to claim 5, the sixth paragraph on page 3 of Song et al. and fourth paragraph from the bottom of page 4 of Song et al. teach using a plurality of distance metrics and embeddings to select a plurality of drugs and determine the drug activities (i.e. a property description of the drug).
With regard to claim 9, the true negative and false negative descriptors on page 6 Song et al. comprise negative descriptions of the molecules.
Song et al. does not use all of the exact terminology of the claims and suggests some of the recited limitations (e.g. query texts).
It would have been obvious to someone of ordinary skill in the art at the time of the filing date of the instant application to interpret the teachings and suggestions of Song et al. to comprise the equivalent terminology and limitations of the instantly rejected claims because Song et al. comprises alternative mathematical limitations that perform the equivalent mathematical functions of the instantly rejected claims [abstract of Song et al.].
35 U.S.C. 103 Rejection #2:
Claim 10 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Song et al. as applied to claims 1-5, 7-9, and 17-20 above, in further view of Li et al. [CN 112786108 A; on IDS]. An English machine translation of Li et al. is cited in the following rejection statement.
Claim 10 is further limiting wherein the mathematical model comprises a model trained using a pseudo-normal negative loss function.
Song et al. makes obvious selecting molecules based on queries, as discussed above.
Song et al. does not teach using a pseudo-normal loss function to train the model.
The document of Li et al. studies a molecular understanding model training method [title]. Paragraph 2 on page 6 of Li et al. teaches training the model using a negative log-likelihood (NLL) function.
It would have been obvious to someone of ordinary skill in the art at the time of the filing date of the instant application to modify the selection of molecules based on distance metrics and queries of Song et al. by use of the NLL function of Li et al. wherein the motivation would have been that Li et al. adds additional mathematical tools to facilitate the analysis of molecules [paragraph 2 of page 6 of Li et al.].
35 U.S.C. 103 Rejection #3:
Claim 6 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Song et al. as applied to claims 1-5, 7-9, and 17-20 above, in further view of Sanh et al. [arXiv:1910.01108v4, 1 March 2020; on IDS].
Claim 6 is further limiting wherein the distance metric comprises cosine similarity.
Song et al. makes obvious selecting molecules based on queries, as discussed above.
Song et al. does not teach using cosine similarity as a distance metric.
The document of Sanh et al. studies a language processing technique (DistilBERT) that using cosine similarity as the distance metric [title and abstract].
It would have been obvious to someone of ordinary skill in the art at the time of the filing date of the instant application to modify the selection of molecules based on distance metrics and queries of Song et al. by use of the cosine similarity distance metric of Sanh et al. wherein the motivation would have been that Sanh et al. adds additional mathematical tools to facilitate the analysis of molecules [abstract of Sanh et al.].
35 U.S.C. 103 Rejection #4:
Claims 11-14 and 16 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Song et al. as applied to claims 1-5, 7-9, and 17-20 above, in further view of Song et al. [arXiv:2004.09297v2, 2 November 2020; on IDS]. The second document of Song et al. is referred to as Song et al. #2 throughout the remainder of this Office action.
Claim 11 is drawn to similar subject matter as claim 1, except claim 11 has the additional limitations of using tokenized string and mapping molecule text to properties.
Song et al. makes obvious selecting molecules based on queries, as discussed above. The sixth paragraph on page 3 of Song et al. and fourth paragraph from the bottom of page 4 of Song et al. teach using a plurality of distance metrics and embeddings to select a plurality of drugs and determine the drug activities (i.e. a property description of the drug).
With regard to claims 12-13, the second full paragraph on page 5 of Song et al. teaches analyzing and performing transform operations on input texts of sentences.
With regard to claim 14, the last paragraph on page 2 of Song et al. teaches analyzing chemical data using SMILES.
With regard to claim 16 the true negative and false negative descriptors on page 6 Song et al. comprise negative descriptions of the molecules.
Song et al. does not teach using tokenized string data.
The document of Song et al. #2 studies masked and permuted pre-training for language understanding [title]. The abstract of Song et al. #2 teaches use of tokenized strings of data.
It would have been obvious to someone of ordinary skill in the art at the time of the filing date of the instant application to modify the selection of molecules based on distance metrics and queries of Song et al. by use of the tokenized strings of text data of Song et al. #2 wherein the motivation would have been that Song et al. #2 adds additional machine learning tool to facilitate the analysis of text data [abstract of Song et al. #2]. Even though the text of Song et al. #2 is drawn to movie database texts, the technique of the tokenization of text strings is a robust technique that can generally be applied to any string of text, including the text strings representing molecules of Song et al.
35 U.S.C. 103 Rejection #5:
Claim 15 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Song et al. in view of Song et al. #2 as applied to claims 1-5, 7-9, 11-14, and 16-20 above, in further view of Sanh et al. [arXiv:1910.01108v4, 1 March 2020; on IDS].
Claim 15 is further limiting wherein the distance metric comprises cosine similarity.
Song et al. and Song et al. #2 make obvious selecting molecules based on queries, as discussed above.
Song et al. and Song et al. #2 do not teach using cosine similarity as a distance metric.
The document of Sanh et al. studies a language processing technique (DistilBERT) that using cosine similarity as the distance metric [title and abstract].
It would have been obvious to someone of ordinary skill in the art at the time of the filing date of the instant application to modify the selection of molecules based on distance metrics and queries of Song et al. and Song et al. #2 by use of the cosine similarity distance metric of Sanh et al. wherein the motivation would have been that Sanh et al. adds additional mathematical tools to facilitate the analysis of molecules [abstract of Sanh et al.].
Related Prior Art
The document of Chen et al. [CN 113836930 A; on IDS] studies Chinese dangerous chemical entity recognition [title]. An English machine translation of this document is cited. Chen et al. teaches the use of a BERT-BiLSTM-self orientation CRF neural network model to analyze textual data associated with Chinese dangerous chemicals. The mathematical algorithm on page 3 of Chen et al. teaches the mathematical foundation of the machine learning technique of Chen et al.
E-mail Communications Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
No claim is allowed.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Russell Negin, whose telephone number is (571) 272-1083. This Examiner can normally be reached from Monday through Thursday from 8 am to 3 pm and variable hours on Fridays.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Larry Riggs, Supervisory Patent Examiner, can be reached at (571) 270-3062.
/RUSSELL S NEGIN/
Primary Examiner, Art Unit 1686 24 July 2026
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