DETAILED ACTION
This Office action has been issued in response to amendment filed May 26, 2026.
Claims 1, 2, 19 and 20 have been amended. Claims 1-20 are pending. Applicant’s arguments are carefully and respectfully considered. Accordingly, rejections have been removed where arguments were persuasive, but rejections have been maintained where arguments were not persuasive. Also, a new rejection based on the newly added amendments have been set forth. Accordingly, claims 1-20 are rejected and this action has been made FINAL, as necessitated by amendment.
Response to Arguments
Applicant’s remarks and arguments directed to 35 USC 103 rejection, presented on 05/26/26 have been fully considered but they are moot in view of the new ground of rejection presented in this office action.
Remarks
In claims 1, 6, 7, 12, 19 and 20 recited the limitations of “providing the final output” or “providing the media item”. The phrase “providing” may not provide all the times. As such, the phrase should be avoided or change as configure to. The phrase ‘providing’ made the remaining claim limitations have no patentable weight.
Claims 17 and 18 recited the limitations of “specific handling action”. The claim does not define what constitute “specific handling action”. Any types of data manipulation can be ‘specific handling action”.
Claim Rejections- 35 USC § 103
5. 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 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.
6. 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.
7. Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sklaroff et al. (US 2024/0233882 A1), hereinafter Sklaroff in view of Cao et al. (US 2023/0108579 A1), hereinafter Cao.
As for claim 1, Sklaroff teaches a method performed by a set of one or more computers, the method comprising: receiving a network input (see [004], [0007], machine learning model to receive an input that include network architecture);
processing the network input using a generative neural network to generate an output sequence of output tokens (see [0006], a neural network model, or a support vector machine model, [0025], using a reactivity neural network to generate a network output that defines a predicted reactivity),
wherein each output token is selected from a vocabulary of tokens that includes a plurality of visible tokens and one or more pairs of invisible tokens, each pair of invisible tokens comprising a respective beginning invisible token and a respective end invisible token (see [0028], e.g., receiving a representation of a sequence of tokens, wherein one or more of the tokens are masked tokens, a sequence of tokens generated by replacing each masked token in the sequence of tokens representing the scaffold molecule by one or more non-masked tokens, [0112], e.g., one or more of the training examples in the second set of training examples each comprise a respective sequence of tokens representing natural language text and chemical structure data, [0340], e.g., each sequence of tokens representing a respective output molecule can begin and end with a respective predefined delimiter token);
wherein the generative neural network comprises an auto-regressive neural network that auto-regressively generates tokens from the vocabulary, and wherein generating the output sequence of output tokens comprises: generating, at a first position in the output sequence and by the generative neural network, a beginning invisible token from one of the pairs of invisible tokens conditioned on the network input and output tokens at any positions preceding the first position in the output sequence (see [0004], generate the output based on the received input and on values of the parameters of the model, [0032], for each masked token: processing a network input that comprises each token preceding the masked token in the sequence of tokens representing the scaffold molecule, using a molecular generation neural network, [0096], generative neural network is an autoregressive neural network, [0098], e.g., one or more positions in the sequence, a respective output token at each of one or more preceding positions in the sequence);
generating, at each of one or more….positions following the first position and by the generative neural network, respective visible tokens conditioned on the network input, the output tokens at any positions preceding the first position in the output sequence and the beginning invisible token (see [0032], e.g., the sequence of tokens representing the scaffold molecule starting from a first masked token, [0098], e.g., a respective output token at each of one or more preceding positions in the sequence of output tokens, using the generative neural network);
and generating, at one or more…..positions following the first position and by the generative neural network, an end invisible token from the one of the pairs conditioned on the network input, the output tokens at any positions preceding the first position in the output sequence, the beginning invisible token and the respective visible tokens at the….positions following the first position (see [0032], e.g., starting from a first masked token, comprising, for each masked token: processing a network input that comprises each token preceding the masked token in the sequence of tokens representing the scaffold molecule, generate a score distribution over a set of non-masked tokens; selecting a non-masked token from the set of non-masked tokens in accordance with the score distribution, [0080], e.g., each final molecule is predicted to result from a chemical reaction involving a pair of initial molecules from the set of initial molecules);
processing the output sequence of output tokens to generate a….output sequence, comprising: determining that the output sequence includes a beginning invisible token from one of the pairs followed by an end invisible token from the same pair (see [0032], e.g., starting from a first masked token, comprising, for each masked token: processing a network input that comprises each token preceding the masked token in the sequence of tokens representing the scaffold molecule, generate a score distribution over a set of non-masked tokens; selecting a non-masked token from the set of non-masked tokens in accordance with the score distribution, [0025], generate a network output that defines a predicted reactivity of the first molecule with the second molecule);
and in response, removing, from the output sequence, the beginning invisible token, the end invisible token, and each visible token that is between the beginning invisible token and
the end invisible token in the output sequence; and providing the….output sequence in response
to the network input (see [0032], e.g., starting from a first masked token, comprising, for each masked token: processing a network input that comprises each token preceding the masked token in the sequence of tokens, generate a score distribution over a set of non-masked tokens, [0078], e.g., filtering operation is parametrized by a set of filtering criteria and operates on a set of input molecules to remove any molecules from the set of input molecules that satisfy one or more filtering criteria in the set of filtering criteria, [0082], e.g., output molecules include applying the filtering criteria, characterize one or more properties).
Sklaroff teaches the claimed invention including the limitations of one or more positions; generate an output sequence ([0098]). But does not explicitly teach the limitations of one or more subsequent positions; a final output sequence or the final output sequence. However, in the same field of endeavor Cao teaches the limitations of “one or more subsequent positions; a final output sequence or the final output sequence” (see Cao, [0033], e.g., the output position from a final dual layer of the one or more dual layers in the neural network to generate a respective score distribution over a vocabulary, [0062], e.g., generates the output tokens in the output sequence auto-regressively, one after the other, by, for each token, processing a combined sequence).
Sklaroff and Cao both references teach features that are directed to analogous art and they are from the same field of endeavor, such as manipulating tokenize data, identifying, marking sequence of data, position data in sequence, receiving and processing the tokenize data, using and storing data in a database.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Cao’s teaching to Sklaroff’s system to initialize an entity memory data for each prompt entity in the memory data by processing the data identifying the prompt. Thus enable a user to specify a custom set of important entities, where each important entity has custom associated attributes. A custom set of entities can process only an input sequence without specifically designating important entities for the generation of the output sequence. Also provide dual layer output which improve handing entity mentions in the output sequence ([0008], [0037]).
As for claim 19,
The limitations therein have substantially the same scope as claim 1 because claim 19 is a system claim for implementing those steps of claim 1. Therefore, claim 19 is rejected for at least the same reasons as claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Cao’s teaching to Sklaroff’s system to initialize an entity memory data for each prompt entity in the memory data by processing the data identifying the prompt. Thus enable a user to specify a custom set of important entities, where each important entity has custom associated attributes. A custom set of entities can process only an input sequence without specifically designating important entities for the generation of the output sequence. Also provide dual layer output which improve handing entity mentions in the output sequence ([0008], [0037]).
As for claim 20,
The limitations therein have substantially the same scope as claim 1 because claim 20 is a non-transitory storage media claim for implementing those steps of claim 1. Therefore, claim 20 is rejected for at least the same reasons as claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Cao’s teaching to Sklaroff’s system to initialize an entity memory data for each prompt entity in the memory data by processing the data identifying the prompt. Thus enable a user to specify a custom set of important entities, where each important entity has custom associated attributes. A custom set of entities can process only an input sequence without specifically designating important entities for the generation of the output sequence. Also provide dual layer output which improve handing entity mentions in the output sequence ([0008], [0037]).
As to claim 2, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the output sequence comprises visible tokens that are after the end invisible token in the output sequence and that are generated conditioned on the visible tokens that are between the beginning invisible token and the end invisible token in the output sequence (see Sklaroff, [0004], [0032], [0096]).
As to claim 3, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the visible tokens comprise text tokens that represent text data (see Sklaroff, [0028], [0095]).
As to claim 4, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the visible tokens comprise image tokens that represent image data (see Sklaroff, [0396]; Also see Cao, [0042]).
As to claim 5, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the visible tokens comprise audio tokens that represent audio data (see Sklaroff, [0028], [0396]).
As to claim 6, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein: the network input is received from a user device, the set of one or more computers are remote from the user device, and providing the final output sequence in response to the network input comprises providing the final output sequence to the user device (see Sklaroff, [0004], [0007]; Also see Cao, [0033]).
As to claim 7, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein removing, from the output sequence, the beginning invisible token, the end invisible token, and each visible token that is between the beginning invisible token and the end invisible token in the output sequence is performed by the set of one or more computers prior to providing the final output sequence to the user device, such that the tokens between the beginning invisible token and the end invisible token are not transmitted to the user device (see Sklaroff, [0029], [0032]; Also see Cao, [0033]).
As to claim 8, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein: the network input is received as input from a user device, the set of one or more computers includes only the user device, and providing the final output sequence in response to the network input comprises providing the final output sequence for presentation on the user device (see Sklaroff, [0004], [0005]; Also see, Cao, [0033]).
As to claim 9, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein: the set of one or more computers includes a server remote from a user device and the user device, the server performs the processing of the network input using the generative neural network to generate the output sequence and transmits the output sequence to the user device, and the user device performs the processing of the output sequence to generate the final output sequence by removing the beginning invisible token, the end invisible token, and each visible token that is between the beginning invisible token and the end invisible token (see Sklaroff, [0004], [0007], [0032]; Also see Cao, [0033], [0134]).
As to claim 10, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the network input comprises an initial prompt that characterizes a media item to be generated by the generative neural network, and wherein the tokens between the beginning invisible token and end invisible token represent an expanded prompt for generating the media item (see Sklaroff, [0004], [0006], [0032]).
As to claim 11, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the media item is an image, a video, or an audio sample (see Sklaroff, [0004], [0396]).
As to claim 12, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the generative neural network is configured to generate the media item conditioned on the tokens between the beginning invisible token and end invisible token, and wherein the method further comprises: providing the media item in response to the network input (see Sklaroff, [0004], [0032], [0397]).
As to claim 13, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the network input comprises a query, wherein the tokens between the beginning invisible token and end invisible token represent intermediate data for generating a response to the query, and wherein the output sequence further comprises visible tokens that follow the end invisible token that represent the response to the query generated conditioned on the intermediate data (see Sklaroff, [0028], [0032], [0082]).
As to claim 14, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the network input further comprises an image or a video and the query is a query about the image or video (see Sklaroff, [0396]; Also see, Cao, [0133]).
As to claim 15, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the intermediate data is grounding data specifying locations in the image or video of one or more objects (see Sklaroff, [0179]; Also see, Cao, [0132]).
As to claim 16, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the intermediate data is a reasoning output (see Sklaroff, [0025]).
As to claim 17, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the vocabulary of tokens includes a plurality of distinct pairs of invisible tokens, and wherein removing, from the output sequence, the beginning invisible token, the end invisible token, and each visible token that is between the beginning invisible token and the end invisible token in the output sequence is performed according to a specific handling action determined based on an identity of the pair of invisible tokens included in the output sequence (see Sklaroff, [0025], [0032], [0075]).
As to claim 18, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Sklaroff and Cao teaches:
wherein the plurality of distinct pairs of invisible tokens includes a first pair associated with a first handling action and a second pair associated with a second handling action that is different from the first handling action (see Sklaroff, [0075], [0077]-[0078]).
Prior Arts
8. US 2024/0104353 A1 teaches generating an output token sequence from an input token sequence by combining a look ahead tree search, such as a Monte Carlo tree search, with a sequence-to-sequence neural network system. The sequence-to-sequence neural network system has a policy output defining a next token probability distribution, and may include a value neural network providing a value output to evaluate a sequence ([0004]).
US 20230362013 A1 teaches a token include one or more steganographic images and one or more invisible-ink-printed patterns (e.g., infrared printed patterns, near-infrared (NIR) printed patterns, the steganographic images appear substantially the same regardless of whether the patterns become visible (e.g., after being photocopied and printed with typical ink visible to the naked human eye) ([0004]).
WO 2024/200119 teaches token include ID element uniquely identifying the features of the token from token description (page1-2).
Also see, US 20240412042, US 20250021800, US 20250363303, US 12086713, US 20230029590, US 20240403639, US 20250139959, WO2023225335, EP4497113, US 20250218168, WO2025072952A1, these references also read the claim recited limitation. These references are state of the art at the time of the claimed invention.
Conclusion
9. The examiner suggests, in response to this Office action, support being shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application (see 37 C.F.R. § 1.75(d)(1), 37 C.F.R. § 1.83(f)).
10. The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action (see MPEP § 7.96). Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
11. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
12. Any inquiry concerning this communication or earlier communication from the examiner should be directed to Daniel A Kuddus whose telephone number is (571) 270-1722. The examiner can normally be reached on Monday to Thursday 8.00 a.m.-5.30 p.m. The examiner can also be reached on alternate Fridays from 8.00 a.m. to 4.30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Boris Gorney can be reached on (571) 270-5626. The fax phone number for the organization where this application or processing is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from the either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only.
For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANIEL A KUDDUS/ Primary Examiner, Art Unit 2154
6/17/26
Prosecution Insights