DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims 1, 9 and 8 are directed to the abstract idea of human organizing of activities. The present claims encompass a human performing each of the limitations recited in the independent and dependent claims of lookup table using a data structure. This method can be processed by human using a dictionary. First parse the word and identify the first letter. Second lookup by alphabet. Third find the word using the word. The claim are broadly written. The limitations need to be further defined. Furthermore, there is no real-life application present. The claims do not define an improvement in any particular field of art. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device.
Dependent claims further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known in data management.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-4, 8-9, 11-13, 17-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Henry (US 10,109,275).
Claim 1,
Henry teaches one or more processors comprising: one or more processing units to: provide a representation of one or more first natural language characters as an input into one or more machine learning models to generate a token of a tokenized representation ([Fig. 4C] [col. 8 lines 43-52] processor; recursive neural network LM receives a sequence of hash (token) vectors (tokenized) as input based on the sequence of words);
subsequent to generation of the token, perform a lookup using a data structure that stores a plurality of associations between a plurality of tokens and one or more second natural language characters ([Fig. 6] [col. 10 step 630] [col. 4 lines 4-7] a word hash vector is retrieved from a data store for each word of the sequences of words at each iteration using the recursive neural network LM); and
based at least on performing the lookup, cause presentation of the one or more second natural language characters ([Fig. 6] [col. 10 step 640] recursive neural network LM; at each iteration the model outputs the retrieved word from the hash data store).
Claims 3 and 11-12,
Henry further teaches the one or more processors of claim 1, wherein the association between the token and the one or more second natural language characters is stored using the data structure, the data structure being implemented to include at least one of an index table, a hash table, a lookup table, or a pointer ([Fig. 6] a word hash vector is computed for words of a vocabulary).
Claims 4 and 13,
Henry further teaches the one or more processors of claim 1, wherein the token generated using the one or more machine learning models comprises a condensed representation of the one or more second natural language characters ([Fig. 6] unclear what the meaning of condensed is; Examiner reads the limitation as outputting the second/final natural characters which is taught by Henry in Fig. 6).
Claims 8, 17 and 20,
Henry further teaches the one or more processors of claim 1, wherein the one or more processors is comprised in at least one of: ([Figs. 3a-c] recursive neural network language model).
Claim 9,
A system comprising one or more processing units to: generate, using a language model, a tokenized representation based on an input prompt; perform a lookup using at least one token of the tokenized representation to determine a set of content corresponding to the at least one token; and cause a presentation of a sequence of text corresponding to the tokenized representation, wherein the presentation of a subset of text from the sequence of text that corresponds to the at least one token includes a presentation of the set of content. (Claim 9 contains subject matter similar to claim 1, and thus is rejected under similar rationale; Henry teaches hash-vector representation of input text via a language model; hash vector lookup in hash table to retrieve word; outputs predicted word sequence)
Claim 18,
generating, via one or more machine learning models, a tokenized representation; retrieving, via a data structure, a set of content by mapping a token of the tokenized representation to the set of content; and based at least on the retrieving, causing presentation of a sequence of text corresponding to the tokenized representation, wherein the presentation of a subset of text from the sequence of text that corresponds to the at least one token includes a presentation of the first set of content. (Claim 18 contains subject matter similar to claim 1, and thus is rejected under similar rationale; Henry teaches hash-vector representation of input text via a language model; hash vector lookup in hash table to retrieve word; outputs predicted word sequence)
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.
Claims 2, 5, 7, 10, 14, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Henry (US 10,109,275) and further in view of Woerner et al. (US 2023/0409604).
Claims 2, 10 and 19,
Henry teaches all the limitations in claim 1. The difference between the prior art and the claimed invention is that Henry does not explicitly teach wherein the representation of the one or more first natural language characters provided as input into one or more machine learning models includes a question or command to provide a link, and wherein the one or more second natural language characters that are represented by the token and retrieved via the data structure include the link as a response to the question or command.
Woerner teaches wherein the representation of the one or more first natural language characters provided as input into one or more machine learning models includes a question or command to provide a link, and wherein the one or more second natural language characters that are represented by the token and retrieved via the data structure include the link as a response to the question or command ([0054] the BDM system 102 may retrieve one or more sets of data from these multi-source, multi-format data streams to answer the question. The BDM system 102 may generate a hyperlink that combines the one or more sets of data into combined data and provide the hyperlink to the client device).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Henry with teachings of Woerner by modifying the word hash language model as taught by Henry to include wherein the representation of the one or more first natural language characters provided as input into one or more machine learning models includes a question or command to provide a link, and wherein the one or more second natural language characters that are represented by the token and retrieved via the data structure include the link as a response to the question or command as taught by Woerner for the benefit of providing a link between question and answer ([0054] Woerner).
Claims 5 and 14,
Henry teaches all the limitations in claim 1. The difference between the prior art and the claimed invention is that Henry does not explicitly teach wherein the one or more processing units are further to generate, prior to the receiving of the one or more first natural language characters, the data structure, and wherein the data structure stores a plurality of associations between a plurality of tokens that each represent a respective link.
Woerner teaches wherein the one or more processing units are further to generate, prior to the receiving of the one or more first natural language characters, the data structure, and wherein the data structure stores a plurality of associations between a plurality of tokens that each represent a respective link ([0054] the BDM system 102 may retrieve one or more sets of data from these multi-source, multi-format data streams to answer the question; the BDM system 102 may generate a hyperlink that combines the one or more sets of data into combined data and provide the hyperlink to the client device).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Henry with teachings of Woerner by modifying the word hash language model as taught by Henry to include wherein the one or more processing units are further to generate, prior to the receiving of the one or more first natural language characters, the data structure, and wherein the data structure stores a plurality of associations between a plurality of tokens that each represent a respective link as taught by Woerner for the benefit of providing a link between question and answer ([0054] Woerner).
Claims 7 and 16,
Henry teaches all the limitations in claim 1. The difference between the prior art and the claimed invention is that Henry does not explicitly teach wherein the one or more second natural language characters represented by the token and retrieved from the data structure include at least one of: a link, source code, predefined factual information, or predefined text.
Woerner teaches wherein the one or more second natural language characters represented by the token and retrieved from the data structure include at least one of: a link, source code, predefined factual information, or predefined text ([0054] the BDM system 102 may retrieve one or more sets of data from these multi-source, multi-format data streams to answer the question; the BDM system 102 may generate a hyperlink that combines the one or more sets of data into combined data and provide the hyperlink to the client device).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Henry with teachings of Woerner by modifying the word hash language model as taught by Henry to include wherein the one or more second natural language characters represented by the token and retrieved from the data structure include at least one of: a link, source code, predefined factual information, or predefined text as taught by Woerner for the benefit of providing a link between question and answer ([0054] Woerner).
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Henry (US 10,109,275) and further in view of Jie et al. (CN 116737895).
Claims 6 and 15,
Henry teaches all the limitations in claim 1. The difference between the prior art and the claimed invention is that Henry does not explicitly teach wherein the one or more processing units are further to tune the one or more machine learning models by learning a relationship between a prompt associated with the one or more first natural language characters and the token.
Jie teaches wherein the one or more processing units are further to tune the one or more machine learning models by learning a relationship between a prompt associated with the one or more first natural language characters and the token ([Fig. 5] [Fig. 9C] fine tuning the large LM; the large model can learn to give a text reply according to the content before compression by informing the model of the compression proportion adopted by the sequence through prompt).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Henry with teachings of Jie by modifying the word hash language model as taught by Henry to include wherein the one or more processing units are further to tune the one or more machine learning models by learning a relationship between a prompt associated with the one or more first natural language characters and the token wherein the one or more processing units are further to tune the one or more machine learning models by learning a relationship between a prompt associated with the one or more first natural language characters and the token as taught by Jie for the benefit of improving long sequence processing ability of large model ([Background] Jie).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659