DETAILED ACTION
Response to Amendment
The amendment was received 10/6/2025, Claims 1,2,3,4,6,8,9 & 10,11,12,13,14,15,17 & 18,19,20,21,22,23 pending:
Claim(s) 1,2,3,6,9 and 10,11,12,15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1):
Claim(s) 4,13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Raichelgauz et al. (US 2014/0082211 A1):
Claim(s) 8,17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Hinterberg et al. (US 2022/0349904 A1):
Claim(s) 14 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of HASHIMOTO (US 2022/0401166 A1):
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Kim et al. (US 2006/0204058 A1):
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Tonochi (US 2013/0080367 A1):
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Kaye et al. (US 2007/0008523 A1):
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of FAULRING et al. (US 2022/0183230 A1):
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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 1,2,3,4,6,8,9 & 10,11,12,13,14,15,17 & 18,19,20,21,22,23 not rejected under 35 U.S.C. 101 because the claimed invention is directed to improving the functioning of a machine-learning computer in view of applicant’s disclosure not without significantly more (stream-lined analysis):
18. A system for semi-supervised learning via different modalities, the system comprises:
one or more memory units configured to:
store training sensed information units of a first modality that are associated with a certain pattern;
store multimodality information units that are untagged; wherein a multimodality information unit comprises a first modality portion and a second modality portion;
a training set processor that is configured to:
search for certain pattern related multimodality information units, wherein a certain pattern related multimodality information unit comprises a first modality portion that is related to the certain pattern;
cluster the second portions of the certain pattern related multimodality information units to provide second portion clusters;
generate certain pattern identifiers based on the second portion clusters; and
wherein the system is further configured to respond to the generating of the certain pattern identifiers by performing at least one out of storing the certain pattern identifiers, transmitting the certain pattern identifiers, and generating notifications to be sent once a signature of a query sensed information unit of the second modality comprises the certain pattern identifier.
BACKGROIND
[001] Machine learning1 technologies today work in a supervised manner, which require large and accurately tagged training sets.
[002] In many cases there is no tagged training set for a certain modality.
[003] There is a growing need to provide a solution for performing an adequate training of a machine learning process at an absence of a tagged training set.
Response to Arguments
Specification Objections
Applicant’s arguments, see remarks, pg. 8, filed 10/6/2025, with respect to the specification objection have been fully considered and are persuasive. The objection of the specification has been withdrawn.
Claim Objections
Applicant’s arguments, see remarks, page 8, filed 10/6/2025, with respect to the objection of claim 4 have been fully considered and are persuasive. The objection of claim 4 has been withdrawn.
Claim Rejections – 35 USC 101
Applicant’s arguments, see remarks, pages 8-10, filed 10/6/2025, with respect to 35 USC 101 have been fully considered and are persuasive. The 35 USC 101 rejection of the claims has been withdrawn in the Office action of 4/11/2025, starting page 21.:
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Claim Rejections – 35 USC 102
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “integrated multimodality units”, remarks, page 11, last para) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “second portions extracted”, remarks, page 12, 1st para) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “clusters formed”, remarks, page 12, 1st para) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant's arguments filed 10/6/2025 have been fully considered but they are not persuasive. Applicants state in page 12 of the remarks that Oono does not teach Markush alternative (C). In response since Oonno (US 2019/0018933 A1) teaches Markush alternative (A) in the Office action of 4/11/2025, pages 14,30, Oono teaches the Markush element A,B,C, via MPEP:
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Claim Rejections - 35 USC § 103
Re claims 4,13:
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e.,
“generating concept structures…from multimodal information units having distinct first and second modality portions”, remarks, page 12, 4th para) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant's arguments filed 10/6/2025 have been fully considered but they are not persuasive. Applicants state that Oono does not teach “generating a certain pattern concept structure” of claim 4. This difference has been discussed in the 35 USC 103 rejection of claim 4 in the Office action of 4/11/2025, page 42:
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In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., applicant’s remarks, page 13, 4h para, last S: “processing multimodal units with distinct first and second modality portions”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Re claims 8,17
Applicants that Hinterberg (US 2022/0349904 A1) is not prior art in the 35 USC 103 rejection of claim 4. The examiner respectfully disagrees since Hiterberg’s filing date of Sep. 2, 2020 is prior to applicant’s filing date of 2022-01-17, Jan 17, 2022:
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In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., applicant’s remarks, page 14,1st para, 2nd S: “processing multimodal information units where one modality is specifically acceleration or velocity data that is concurrently sensed with another modality portion”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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.
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.
Claim(s) 1,2,3,6,9 and 10,11,12,15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1):
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Re 1. A method for semi-supervised learning2 (or “fashion” [0024] 3rd S) via different (or “multiple” [0021] 2nd S) modalities, implemented by a training set processor34 (comprising a training-set computer performing high-speed processing via “training data set” “output”5 via U.S. Pat. App. No. 62/262,337: [0008] 5th S), the (training set processor implemented semi-supervised, different modalities learning) method comprises:
obtaining[[, by the training set processor ,]]6 a training sensed information7 units (comprised by “data modalities” “trained” “generative models” [0021] 2nd S) of a first modality (understood given multiple modalities) that are associated with a certain (“methylation”8 [0021] 2nd S) pattern;
obtaining[[, by the training set processor,]] multimodality information units (comprised by “data modalities” “trained” “generative models” [0021] 2nd S) that are untagged (or “unlabeled” [0080] 1st S); wherein a multimodality (unlabeled) information unit comprises a first (“partial” series) modality portion ([0025]:7th S) and a second (“partial”-series) modality portion ([0025]: 7th S) that were concurrently sensed;
Searching[[, by the training set processor,]] (via “multimodal…queries” [0105] searching for CH4 last S & searching “a nearest…representation”—fig. 6: “Multimodal representation”-- leading to the methylation-CH4-formula patten: [0138] last two Ss) for certain (CH4) pattern related (data-trained) multimodality information units, wherein a certain (methylation-CH4) pattern related (data-trained) multimodality information unit comprises (as a contributive factor for training the generative model) a first (“partial” [0021] 2nd S) modality portion that is related (by incorporating “multiple data modalities” [0021] 2nd S) to the certain (methylation-CH4) pattern;
Clustering [[, by the training set processor,]] the second portions (via “cluster two or more sets of data” [0134] affecting “components of genetic information” [0134] last S) of the certain (CH4) pattern related (data-trained) multimodality information units to provide second (sequence) portion (data-set) clusters (of “partial…sequences” [0021] 2nd S);
Generating[[, by the training set processor,]] (via U.S. Pat. App. No. 62/262,337: the arrows in fig. 14) certain pattern identifiers9 (via U.S. Pat. App. No. 62/262,337: fig. 14: “AC” & “BC” resulting in fig. 14: “identification of significant transformations”) based on the second portion (data-set) clusters (fig. 14: “clustering”, twice); and
responding (U.S. Pat. App. No. 62/262,337: figs. 7.8: “Yes”: “No”) to the generating of the certain pattern identifiers (“AC” & “BC”); wherein the (Y/N) responding comprises at least one out of storing the certain pattern identifiers (to archivally “output”10 fingerprint “representations”: U.S. Pat. App. No. 62/262,337: [0105] last S) in one or more memory (“data bit”, U.S. Pat. App. No. 62/262,337: [0037]) units , transmitting (or transferring) the certain pattern identifiers (to “output”11 fingerprint “representations”: U.S. Pat. App. No. 62/262,337: [0105] last S) via a communication unit (or a communicating CPU, for example, via “information…transmission… devices1213”: 62/262,337:[0038] last S), and generating notifications to be sent once14 a signature of a query sensed information unit of the second modality comprises the certain pattern identifier (:
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Oono does not teach “that were concurrently sensed”.
Sramek teaches:
that were concurrently sensed (via “simultaneous or concurrent use of different sensor types” “multi-modal sensor fusion” [0318] 1st S).
Since Oono teaches a display (fig. 4: “DISPLAY”) and “image modalities”:
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, one of skill in the art of multi-modes can make Oono’s (fig. 2 and DISPLAY) be as Sramek’s (fig. 49B) predictably recognizing the change “providing a simplified display (wherein data from a plurality of sensing modalities can be displayed together in a unified way), augmented reality, or the like.”, Sreamek [0318] 3rd S:
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Re 2. The method according to claim 1 wherein the generating of the certain (fingerprint) pattern identifiers comprises removing (via U.S. Pat. App. No. 62/262,337: fig. 7: “Reject” fingerprint) from second portion clusters second portions that fail to distinguish (“as the desired results”, U.S. Pat. App. No. 62/262,337: [0170]:2nd S) between the certain pattern and other patterns.
Re 3. The method according to claim 1 wherein the generating of the certain (fingerprint) pattern identifiers comprises maintaining (to “be retained”, U.S. Pat. App. No. 62/262,337: [0104] last S) in second portion clusters (in U.S. Pat. App. No. 62/262,337: fig. 14: “clustering”, twice) second portions that (a) appear at a first probability (via “a probabilistic encoder and a probabilistic decoder”, U.S. Pat. App. No. 62/262,337: [0031] penult S) in certain (methylation or “structure”15, U.S. Pat. App. No. 62/262,337: [0032] last S) pattern related multimodality information units, (b) appears at a second probability in other multimodality information units that differ from the certain pattern related multimodality information units, wherein the first probability exceeds the second probability (given that Markush alternative (A) is taught the Markush element: [A and/or B] is taught).
Re 6. The method according to claim 1 wherein the responding comprises controlling autonomous operations of a robot or a drone
Re 9. The method according to claim 1 wherein the obtaining of the training sensed information units (comprised by “data modalities” “trained” “generative models” [0021] 2nd S) of the first modality that are associated with the certain (methylation or structural) pattern comprises:
obtaining a group of training sensed information units (comprised by “the training data set”, US 20190018933 A1: [0056] 2nd S) of the first modality; and
finding (or querying), out of the group, the training sensed information units (“for information retrieval”, [0105] last, “comprising… methylation patterns, structural information” [0021] 2nd S) of the first modality that are associated with the certain (methylation or structure) pattern.
Claim 10 is rejected similar to claim 1:
10. A non-transitory computer readable medium (US 20190018933 A1: fig. 4: “MAIN MEMORY”) for semi-supervised learning via different modalities, the non-transitory computer readable medium stores instructions [[for]] that, when executed by a training set processor (comprising a training-set computer performing high-speed processing via “training data set” “output”16 via U.S. Pat. App. No. 62/262,337: [0008] 5th S), cause the training set processor to:
obtainfrom one or more memory units a training sensed information units of a first modality that are associated with a certain pattern[[;]] obtaining multimodality information units that are untagged; wherein a multimodality information unit comprises a first modality portion and a second modality portion that were concurrently sensed;
search
cluster
generate
respondin the one or more memory units , transmitting the certain pattern identifiers via a communication unit, and generating notifications to be sent once a signature of a query sensed information unit of the second modality comprises the certain pattern identifier.
Claim 11 is rejected similar to claim 2:
11. The non-transitory computer readable medium according to claim 10 that stores instructions that, when executed by the training set processor, cause the training set procesessor to eby
Claim 12 is rejected similar to claim 3:
12. The non-transitory computer readable medium according to claim 10 that stores instructions that, when executed by the training set processor, cause the training set processor to eiby
Claim 15 is rejected similar to claim 6:
15. The non-transitory computer readable medium according to claim 10 wherein each one of the first modality and the second modality differs from audio.
Claim 18 is rejected similar to claims 1 and 10:
18. A system for semi-supervised learning via different17 modalities18, the system comprises:
one or more memory units configured to:
store training sensed information units of a first modality19 that are
associated with a certain pattern;
store multimodality information units that are untagged; wherein a multimodality information unit comprises a first modality portion20 and a second modality portion21 that were concurrently sensed;
a training set processor that is configured to:
search for certain pattern related multimodality information units, wherein a certain pattern related multimodality information unit comprises a first modality portion22 that is related to the certain pattern;
cluster the second portions23 of the certain pattern related multimodality information units to provide second portion clusters;
generate certain pattern identifiers based on the second portion clusters; and
wherein the system is further configured to respond to the generating of the certain pattern identifiers by performing at least one out of24
(A) storing the certain pattern identifiers,
(B) transmitting the certain pattern identifiers, and
(C) generating notifications to be sent once25 a signature of a query sensed information unit of the second modality26 comprises the certain pattern identifier.
Claim(s) 4,13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Raichelgauz et al. (US 2014/0082211 A1):
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Re 4., Oono of the combination of Oono,SRAMEK teaches The method according to claim 1 comprising generating a certain pattern concept structure based on the training sensed information units (comprised by “data modalities” “trained” “generative models” [0021] 2nd S); and wherein the searching (via “multimodal…queries” [0105] searching for CH4 last S & searching “a nearest…representation”—fig. 6: “Multimodal representation”-- leading to the methylation-CH4-formula patten: [0138] last two Ss) for certain pattern related multimodality information units comprises generating signatures of the multiple multimodality information units; and searching for at least one signature that matches the certain pattern concept structure.
Oono of the combination of Oono,SRAMEK does not teach the difference:
“generating a certain pattern concept structure based on… generating signatures… searching for at least one signature that matches the certain pattern concept structure”.
Raichelgauz teaches the difference of claim 4:
Re 4. The method according to claim 1 comprising generating a certain pattern concept structure (via fig. 1:140: “Concept Generator (CG)”) based on the training sensed information units (comprised by an initial” MMDEs”—Multi-Media Data Elements-- “training set” [0062] 2nd S); and wherein the searching for certain pattern related multimodality information units (“in the search of a desired multimedia data element” [0047] last S) comprises generating signatures (“generated by SG 120” [0047] 6th S:fig. 1:120: “Signature Generator (SG)”) of the multiple multimodality (multimedia) information units (or “elements” [0047] penult S); and searching for at least one signature (“to find all matches between”, [0035] last S, signature databases) that matches the certain pattern concept structure (via fig. 6:S630: “Match SRC with concept structures (CSs)” “to which the SRC has been shown to match” [0058] 3rd S).
Given that Oono of the combination of Oono,SRAMEK suggest a “retrieval” ([0105] last S) query-search capability, one of skill in the art of query-searching can make Oono’s of the combination of Oono,SRAMEK be as Raichelgauz’s predictably recognizing the change is “efficient to store, retrieve and check for matches”, Raichelgauz [0026] 2nd S.
Claim 13 is rejected similar to claim 4:
13. The non-transitory computer readable medium according to claim 10 that stores instructions that, when executed by the training set processor, cause the training set processor to e a certain pattern concept structure based on the training sensed information units; and wherein the searching for certain pattern related multimodality information units comprises generating signatures of the multiple multimodality information units; and searching for at least one signature that matches the certain pattern concept structire.
Claim(s) 8,17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Hinterberg et al. (US 2022/0349904 A1):
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Re 8., Oono of the combination of Oono, SRAMEK teaches The method according to claim 1 wherein at least one of the first (different) modality and the second (different) modality is selected from acceleration and velocity.
Oono of the combination of Oono, SRAMEK does not teach the difference: “from acceleration and velocity”.
Hinterberg teaches the difference:
8. The method according to claim 1 wherein at least one of the first (“imaging” [0122]) modality and the second modality is selected from acceleration and (“pulse wave” [0122]) velocity.
Since Oono of the combination of Oono, SRAMEK suggests at [0025] other image modalities such as non-invasive (x-ray,MR, ultrasound, Ct, etc.), one of skill in the art of modalities can make Oono’s of the combination of Oono, SRAMEK be as Hinterberg’s recognizing the change “selecting a CV treatment to administer to a subject”, Hinterberg [0162] 4th S.
Claim 17 is rejected similar to claim 8:
17. The non-transitory computer readable medium according to claim 10 wherein at least one of the first modality and the second modality is selected from acceleration and velocity.
Claim(s) 14 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of HASHIMOTO (US 2022/0401166 A1):
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Re 14. (Currently Amended), Oono of the combination of Oono, SRAMEK teaches The non-transitory computer readable medium (US 20190018933 A1: fig. 4: “MAIN MEMORY”) according to claim 10 that stores instructions that, when executed by the training set processor (comprising a training-set computer performing high-speed processing via “training data set” “output”27 via U.S. Pat. App. No. 62/262,337: [0008] 5th S), cause the training set processor (comprising a training-set computer performing high-speed processing via “training data set” “output”28 via U.S. Pat. App. No. 62/262,337: [0008] 5th S) to respond by controlling (“in response to receiving the control signal”, SRAMEK [0076] last S) autonomous operations of a robot (or “the robot”, SRAMEK [0258]: fig. 1: the robot) or a drone
Oono of the combination of Oono, SRAMEK does not teach “autonomous operations…or a drone”.
Hashimoto teaches at [0047] the claimed “autonomous operations” via two autonomous operation modes:
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Since Oono of the combination of Oono, SRAMEK teaches a surgical robot, one of skill in the art of surgical robots can make Oono’s of the combination of Oono, SRAMEK be as Hashimoto’s predictably recognizing the change “to provide a surgical system and a controlling method, which enable an efficient surgical operation using a robot.”, Hashimoto [0006].
Claim 23 is rejected like claim 14.
23. (New) The system according to claim 18, wherein the responding comprises controlling autonomous operations of a robot or a drone.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Kim et al. (US 2006/0204058 A1):
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Re 19. (New), Oono of the combination of Oono,SRAMEK teaches The system according to claim 18 wherein the training set processor (comprising a training-set computer performing high-speed processing via “training data set” “output” via U.S. Pat. App. No. 62/262,337: [0008] 5th S) is configured to (output-)generate the certain pattern identifiers (via U.S. Pat. App. No. 62/262,337: fig. 14: “AC” & “BC” resulting in fig. 14: “identification of significant transformations”) while maintaining (via “storage” “data” [0038] last S) in second portion clusters (“of the…members of B”, pg. 55, 1st para, last S) only or mostly second (member) portions (B) that distinguish between the certain (“fingerprint”29 [0005] 3rd S) pattern and other (fingerprint) patterns).
Oono of the combination of Oono,SRAMEK does not teach “only or mostly”30. Kim teaches: (“represent”) only or mostly (“the most general characteristic” [0090] penult S). Since Oono of the combination of Oono,SRAMEK teaches clusters, one of skill in the art of clusters can make Oono’s of the combination of Oono,SRAMEK be as Kim’s predictably recognizing the change such that “recognition performance of the system may be improved as the template is continually updated”, Kim [0083] 1st S.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Tonochi (US 2013/0080367 A1):
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Re 20. (New), Onoo of the combination of Onoo,SRAMEK teaches The system according to claim 18 wherein the training set processor (comprising a training-set computer performing high-speed processing via “training data set” “output” via U.S. Pat. App. No. 62/262,337: [0008] 5th S) is configured to generate the certain pattern identifiers (via U.S. Pat. App. No. 62/262,337: fig. 14: “AC” & “BC” resulting in fig. 14: “identification of significant transformations”) by maintaining (via “storage” “data” [0038] last S) in second portion clusters (“of the…members of B”, pg. 55, 1st para, last S) second (member) portions (B) that
(a) appear at a first (“Normal distribution”, pg. 20, about 3/4ths of the way down) probability (as listed in a list of probability distributions) in certain (fingerprint) pattern related multimodality (“input” [0096] 2nd S) information units,
(b) appears at a second (“Laplace distribution”,pg. 20, 3/4th of the way down) probability (as listed second of the list of probability distributions) in other multimodality (input) information units that differ from the certain (fingerprint) pattern related multimodality (input) information units (represented as the circles in fig. 1),
wherein the first (Normal) probability exceeds the second (Laplace) probability.
Onoo of the combination of Onoo,SRAMEK does not teach “exceeds”31 (the second probability).
Tonouchi teaches the claimed “exceeds” (probability) via “greater” (“probability”) [0083], last S as in a ratio list than 0.05 in said list.
Since Onoo of the combination of Onoo,SRAMEK teaches a normal distribution, one of skill in probability distributions can make Onoo’s of the combination of Onoo,SRAMEK be as Tonouchi’s predictably recognizing the change so that “Noise can be eliminated, and the prediction accuracy …can be enhanced by excluding thus unexpected events.”,Tonouchi [0089], last S.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of Kaye et al. (US 2007/0008523 A1):
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Re 21. (New), Onoo of the combination of Onoo,SRAMEK teaches via U.S. Pat. App. No. 62/262,337 The system according to claim 18 wherein the training set processor (comprising a training-set computer performing high-speed processing via “training data set” “output”32 via U.S. Pat. App. No. 62/262,337: [0008] 5th S) is configured to generate (“of chemical compound representations” [0005] 1st S) a certain (fingerprint) pattern concept (via chemical representations33) structure (“fingerprints” [0089]) based on the training sensed information units (comprised by “data modalities” “trained” “generative models” [0021] 2nd S), to search (“in the representation space”, pg. 41, 2nd S) for34 certain (fingerprint) pattern related multimodality (“input” [0096] 2nd S) information units by generating (“as fingerprints” [0092]) signatures of the multiple multimodality (“input” [0096] 2nd S) information units; and to search (“in the representation space”, pg. 41, 2nd S) for at least one signature that (“result” [0168, 2nd S) matches the certain (fingerprint) pattern (representational) concept structure (fingerprint).
Onoo of the combination of Onoo,SRAMEK does not teach “signatures…at least one signature”.
Kaye teaches:
(“stored spectral” [0015]) signatures…
at least one (“stored spectral” [0015]) signature.
Since Onoo of the combination of Onoo,SRAMEK teaches fingerprints, one of skill in the art of fingerprints (i.e., a “chemical signature” Kaye [0031]) can make Onoo’s of the combination of Onoo,SRAMEK be as Kaye’s predictably recognizing the change “providing superior speed of identification, and higher identification accuracy and repeatability.”, Kaye [0012] 1st S.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oono et al. (US 2019/0018933 A1) with “U.S. Pat. App. No. 62/262,337, which is herein incorporated by reference in its entirety” [0033]: “GENERATIVE MACHINE LEARNING SYSTEMS FOR DRUG DESIGN” in view of SRAMEK et al. (US 2022/0061927 A1) as applied in the rejection of claims 1,2,3,6,9 and 10,11,12,15 and 18 above further in view of FAULRING et al. (US 2022/0183230 A1):
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Re 22. (New), Onoo of the combination of Onoo,SRAMEK teaches The system according to claim 18 wherein at least one of the first modality (understood given multiple modalities) and35 (C) the second modality36 is selected37 from
(D) acceleration and
(E) velocity.
Onoo of the combination of Onoo,SRAMEK does not teach the Markush alternatives D and E.
Faulring teaches acceleration38 via “accelerating” [0152] penult S.
Since Onoo (at US 20190018933 A1: [0149], last two Ss) of the combination of Onoo,SRAMEK teaches a “GPU” one of skill in the art of GPUs can make Onoo’s of the combination of Onoo,SRAMEK be as Faulring, predictably recognizing the change making neural network processing faster “operating on the multi-modal imagery collected”, Faulring [0152] penult S.
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
Ternovskiy et al. (US 10.175,349 B1)
Ternovskiy teaches “To enable use of the sensor 10 in environments in which the particular modality of an incoming signal is unknown, or where a plurality of modalities must be simultaneously sensed, the partition wall 14 must be constructed of a material that facilitates the reflection of each type of modality.” C. 7, ll. 20-25 as the closest to the claimed “concurrently sensed” of claim 1.
Wang et al. (US 2020/0301053 A1)
Wang teaches “[0008] Another object of the present invention is to provide multi-modal optical sensing devices for simultaneous sensing spectral information and one or more of polarization, angle and phase information of the incident light field.” as the closest to the claimed “concurrently sensed” of claim 1.
THIS ACTION IS MADE FINAL. 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 DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DENNIS ROSARIO/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
1 machine learning: a branch of artificial intelligence in which a computer (via the claimed “processor” in claim 18) generates rules (the claimed “generate certain pattern identifiers”) underlying or based on raw data that has been fed (via “store”) into it, wherein rules is defined: a prescribed method or procedure for solving a mathematical (“pattern”) problem, or one constituting part of a computer (“memory”) program, usually expressed in an appropriate formalism, wherein procedure is defined: a way of acting or progressing in a course of action, esp an established method, wherein way is defined: an aspect of something, wherein aspect is defined: a distinct feature or element (the claimed “generate certain pattern identifiers”) in a problem (the claimed “untagged”), situation (the claimed “untagged”), etc (Dictionary.com).
2 The preamble serves to further define claim 1 (e.g., the preamble prepositionally further defines the recognized machine learning in claim 1 as semi-supervised learning machine learning: semi-supervised machine learning)
3 Claim scope of “processor” via applicant’s disclosure: “[00353] However, other modifications, variations and alternatives are also possible.”
4 processor: a person or thing that processes (Dictionary.com)
5 output: The information that a computer produces by processing a specific input, wherein computer is defined: A programmable machine that performs high-speed processing of numbers, as well as of text, graphics, symbols, and sound. (Dictionary.cm)
6 This (these) non-restrictive comma phrase(s) (, by the training set processor,) does not limit claim 1 under the broadest reasonable interpretation
7 information: Computers. A important or useful facts obtained as output from a computer by means of processing input data with a program. B data at any stage of processing (input, output, storage, transmission, etc.). (Dictionary.com)
8 methylation: Chemistry. the process of replacing a hydrogen atom with a methyl group, wherein methyl group is defined: Chemistry. the univalent group CH 3 −, derived from methane, wherein methane is defined: a colourless odourless flammable gas, the simplest alkane and the main constituent of natural gas: used as a fuel. Formula: CH 4 See also marsh gas firedamp, wherein Formula is defined: Chemistry., an expression of the constituents of a compound by symbols and figures, wherein figures is defined: . (Dictionary.com)
9 machine learning is recognized at this point in claim 1, wherein machine learning is defined: a branch of artificial intelligence in which a computer generates rules underlying or based on raw data that has been fed into it (Dictionary.com)
10 output: Computers.
A information in a form suitable for transmission from internal to external units of a computer, or to an outside medium.
B the process of transferring data from internal storage to an external medium, as paper or microfilm, wherein microfilm is defined: a film bearing a miniature photographic copy of printed or other graphic matter, usually of a document, newspaper or book pages, etc., made for a library, archive, or the like.
11 output: Computers.
A information in a form suitable for transmission from internal to external units of a computer, or to an outside medium.
B the process of transferring data from internal storage to an external medium, as paper or microfilm, wherein microfilm is defined: a film bearing a miniature photographic copy of printed or other graphic matter, usually of a document, newspaper or book pages, etc., made for a library, archive, or the like. (Dictionary.com).
12 transmission: the act or process of transmitting, wherein transmit is defined: to communicate, as information or news (Dictionary.com)
13 device: computer hardware that is designed for a specific function, wherein hardware is defined: computing the physical equipment used in a computer system, such as the central processing unit, peripheral devices, and memory (Dictionary.com)
14 Contingent limitation
15 structure: Chemistry. the manner in which atoms in a molecule are joined to each other, especially in organic chemistry where molecular arrangement is represented by a diagram or model, wherein model is defined: a pattern or mode of structure or formation. (Dictionary.com)
16 output: The information that a computer produces by processing a specific input, wherein computer is defined: A programmable machine that performs high-speed processing of numbers, as well as of text, graphics, symbols, and sound. (Dictionary.cm)
17 different: separate; different. (Dictionary.com)
18 35 USC 112(b) antecedent basis check: ok
19 35 USC 112(b) antecedent basis check: X: ok for now
20 35 USC 112(b) antecedent basis check: ok
21 35 USC 112(b) antecedent basis check: ok
22 35 USC 112(b) antecedent basis check: X: ok for now
23 35 USC 112(b) antecedent basis check: ok
24 Markush element follows: A.B and C
25 Contingent limitation
26 35 USC 112(b) antecedent basis check: ok
27 output: The information that a computer produces by processing a specific input, wherein computer is defined: A programmable machine that performs high-speed processing of numbers, as well as of text, graphics, symbols, and sound. (Dictionary.cm)
28 output: The information that a computer produces by processing a specific input, wherein computer is defined: A programmable machine that performs high-speed processing of numbers, as well as of text, graphics, symbols, and sound. (Dictionary.cm)
29 fingerprint: any unique or distinctive pattern that presents unambiguous evidence of a specific person, substance, disease, etc.
30 being adverbial modifiers for “maintaining”: mostly only maintaining
31 exceed: to be greater in degree or quantity than (a person or thing) (Dictionary.com)
32 output: The information that a computer produces by processing a specific input, wherein computer is defined: A programmable machine that performs high-speed processing of numbers, as well as of text, graphics, symbols, and sound. (Dictionary.cm)
33 representation: a mental image or idea so presented; concept. (Dictionary.com)
34 for: with regard or respect to. (Dictionary.com)
35 and: (used to connect (Markush) alternatives) (Dictionary.com)
36 The “the second modality” is part pf Markush element “C”.
37 Second list of Markush alternatives follow: D and E
38 acceleration: the act of accelerating; increase of speed or velocity. (Dictionary.com)