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 . Claims 1-20 have been examined and are pending.
Pertinent Prior Art
Prior art that is considered pertinent to applicant's disclosure but not currently relied upon:
US 20230137378
Pars. 21, 37-40
Private synthetic text and training data, privacy preserving prompt seed generative model
US 20230315899
Pars. 23-28
Kernel density estimate, synthetic data generation
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are “model configured to” in claim 8 and “privacy-preserving client application is configured to” in claim 5.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 an abstract idea without significantly more. Claims 1-20 are directed to one of the eligible categories of subject matter.
With respect to independent claims 1, 6 and 15 the generating, decode, prompt, produce cover performance of the limitations manually or in the mind (mental processes abstract idea) and/or as a mathematical concept. The receiving, obtaining, store, send limitations are recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 3, 10, 13, 14, 18 the calculate, decode, extract, embed, generate cover performance of the limitations manually or in the mind (mental processes abstract idea) and/or as a mathematical concept. The select, obtain, send, provide are recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 4, 5, 7, 11, 12, 16, 19, 20 the extract, embed, generate, prompt/produce, calculate, determine cover performance of the limitations manually and/or in the mind (mental processes abstract idea) and/or as a mathematical concept. No additional elements are recited and so the claims do not provide a practical application and are not considered to be significantly more. The claims are not eligible.
With respect to dependent claims 2, 8, 9, 17 receive, send are recited at a high level of generality and do not add meaningful limitations to the abstract idea. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 and 8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “model configured to” in claim 8 and “privacy-preserving client application is configured to” in claim 5 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-10, 12-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Alda et al., US 20230325776 A1, hereinafter Alda in view of Georgopoulos et al., Pub. No.: US 20220100800 A1, hereinafter Georgopoulos.
As per claim 1, Alda discloses A system, comprising:
at least one processor; and a memory, storing program instructions that when executed by the at least one processor, cause the at least one processor to implement a data management system (fig.’s 1, 5, 8, 9, pars. 44-49), configured to:
receive, from a client via an interface of the data management system, a differentially private density estimation (DP-DE) distribution (pars. 16-18, 45-46, 64-66 disclose a differentialy private probability estimation model and that “suitably scaled Laplace noise is added to the conditional probability distributions providing differential privacy” and par. 59 and 63 make it clear users completely control the generation process of the distributions (i.e. receive, from a client…)), wherein generation of the DP-DE distribution was based on a plurality of vectors that respectively correspond to a different document of a plurality of documents (pars. 20, 44-46, 57-62 disclose generating the distributions from a corpus of documents by extracting candidate attributes from the documents and forming a structured dataset using the extracted information; pars. 87-94 disclose NLP embeddings, word vector representations, n-grams of text, FastText, Bert etc.), and
Alda does not explicitly disclose however Georgopoulos in the related field of endeavor of document analysis discloses wherein a vector of the plurality of vectors comprises a sequence of keywords extracted from a given document of the plurality of documents and embedded into the vector (Georgopoulos, pars. 8, 11, 23, 24 disclose embedding vector sequences based on document term sequences, that phrases may be one or more terms, sentences, that the model may be a sequence to sequence model, etc.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Georgopoulos would have allowed Alda’s synthetic document generation system to use a known technique for representing document derived term sequences as embedding vector sequences and predicting corresponding output term sequences from those vectors. This would have improved Alda’s synthetic document process by using established vector representations for extracted document terms before generating synthetic text.
obtain a particular vector from the DP-DE distribution, wherein the particular vector comprises a sequence of synthetic keywords embedded into the particular vector, and wherein the sequence of synthetic keywords does not violate a data privacy restriction of the client for the plurality of documents (Alda, pars. 18, 46, 68-70 disclose that once the private Bayesian network is built, new values are sampled for all nodes in the graph and formed into a synthetic structured dataset, while no data privacy is compromised because values are artificial and independent of the original dataset, and Georgopoulos, pars. 8, 11 disclose representing document derived term sequences as embedding vector sequences);
decode the particular vector into the sequence of synthetic keywords (Georgopoulos, pars. 8, 11, 23, 24);
prompt a synthetic text generator to produce one or more synthetic documents, wherein the synthetic text generator is seeded with the sequence of synthetic keywords to produce the one or more synthetic documents (Alda, pars. 18, 48, 71-76 disclose using synthetic structured dataset of relevant features to generate CV synthetic document text by combining values with scripted textual prompts and feeding them into a pretrained NLG model such as GPT2; pars. 74-76 disclose that the generated attributes control the NLG generation by acting as starting points for the CV synthetic document generation and by being embedded into prompts presented to the NLG model);
obtain the one or more synthetic documents from the synthetic text generator (see Alda as cited above including pars. 79-80); and
store the one or more synthetic documents or send the one or more synthetic documents to an endpoint (see Alda as cited above including pars. 49, 79-80).
As per claim 6, Alda discloses A method, comprising: performing, by a data management service implemented by one or more computing devices (fig.’s 1, 5, 8, 9, pars. 44-49):
receiving, via an interface of the data management service, a differentially private kernel density estimation (DP-DE) distribution (pars. 16-18, 45-46, 59, 63-66 disclose a differentialy private probability estimation model and that “suitably scaled Laplace noise is added to the conditional probability distributions providing differential privacy”), wherein generation of the DP-DE distribution was based on a plurality of vectors that respectively correspond to a different document of a plurality of documents (pars. 20, 44-46, 57-62 disclose generating the distributions from a corpus of documents by extracting candidate attributes from the documents and forming a structured dataset using the extracted information; pars. 87-94 disclose NLP embeddings, word vector representations, n-grams of text, FastText, Bert etc.), and
Alda does not explicitly disclose however Georgopoulos in the related field of endeavor of document analysis discloses wherein a vector of the plurality of vectors comprises a sequence of keywords extracted from a given document of the plurality of documents and embedded into the vector (Georgopoulos, pars. 8, 11, 23, 24 disclose embedding vector sequences based on document term sequences, that phrases may be one or more terms, sentences, that the model may be a sequence to sequence model, etc.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Georgopoulos would have allowed Alda’s synthetic document generation system to use a known technique for representing document derived term sequences as embedding vector sequences and predicting corresponding output term sequences from those vectors. This would have improved Alda’s synthetic document process by using established vector representations for extracted document terms before generating synthetic text.
obtaining a particular vector from the DP-DE distribution, wherein the particular vector comprises a sequence of synthetic keywords embedded into the particular vector (Alda, pars. 18, 46, 68-70 disclose that once the private Bayesian network is built, new values are sampled for all nodes in the graph and formed into a synthetic structured dataset, while no data privacy is compromised because values are artificial and independent of the original dataset, and Georgopoulos, pars. 8, 11 disclose representing document derived term sequences as embedding vector sequences);
decoding the particular vector into the sequence of synthetic keywords (Georgopoulos, pars. 8, 11, 23, 24); and
sending the sequence of synthetic keywords to an endpoint as a seed for a synthetic text generator to produce one or more synthetic documents (Alda, pars. 18, 48, 71-76 disclose using synthetic structured dataset of relevant features to generate CV synthetic document text by combining values with scripted textual prompts and feeding them into a pretrained NLG model such as GPT2; pars. 74-76 disclose that the generated attributes control the NLG generation by acting as starting points for the CV synthetic document generation and by being embedded into prompts presented to the NLG model).
As per claim 15, it includes the same or similar subject matter as claims 1 and 6 and is therefore likewise rejected.
As per claim 2, Alda as modified discloses The system of claim 1, wherein the data management system is further configured to: receive, via the interface from a client of the data management system, a request to generate the one or more synthetic documents (Alda, pars. 18, 20, 44-49, 59 and 63 make it clear users completely control the generation process).
As per claim 3, Alda as modified discloses The system of claim 1, wherein to obtain a particular vector from the DP-DE distribution, the data management system is further configured to: calculate a score for the particular vector from the DP-DE distribution; and select the particular vector based on the calculated score (Alda, pars. 45-46, 64-70 disclose sampling new values from a private Bayesian network after adding Laplace noise to conditional probability distributions which teaches selecting values based on distribution scores or probabilities. Georgopoulos pars. 8, 11 discloses representing document derived term sequences as embedding vector sequences).
Analogous claims 10 and 18 are likewise rejected.
As per claim 5, Alda as modified discloses The system of claim 1, wherein the data management system is further configured to provide to a remote computing system a privacy-preserving client application, wherein the privacy-preserving client application is configured to: extract sequences of keywords from the plurality of documents (Alda, pars. 44-46 disclose extracting candidate attributes); embed the sequences of keywords into the plurality of vectors that respectively correspond to a different document of the plurality of documents (Georgopoulos, pars. 8, 11); and
generate the DP-DE distribution based on the plurality of vectors (see Alda and Georgopoulos as cited in the rejection of claim 1).
Analogous claim 14 is likewise rejected.
As per claim 7, Alda as modified discloses The method of claim 6, wherein the endpoint comprises the synthetic text generator, and further comprising: prompting the synthetic text generator to produce the one or more synthetic documents, wherein the synthetic text generator is seeded with the sequence of synthetic keywords to produce the one or more synthetic documents (Alda, pars. 18, 48, 71-76 disclose using synthetic structured dataset of relevant features to generate CV synthetic document text by combining values with scripted textual prompts and feeding them into a pretrained NLG model such as GPT2; pars. 74-76 disclose that the generated attributes control the NLG generation by acting as starting points for the CV synthetic document generation and by being embedded into prompts presented to the NLG model).
As per claim 8, Alda as modified discloses The method of claim 7, further comprising: sending the one or more synthetic documents to a remote network of a client of the data management service, wherein the remote network comprises a model configured to be trained using the one or more synthetic documents (Alda, pars. 18, 48, 71-76 disclose using synthetic structured dataset of relevant features to generate CV synthetic document text by combining values with scripted textual prompts and feeding them into a pretrained NLG model such as GPT2; pars. 74-76 disclose that the generated attributes control the NLG generation by acting as starting points for the CV synthetic document generation and by being embedded into prompts presented to the NLG model).
As per claim 9, Alda as modified discloses the method of claim 6, further comprising receiving a request to generate the sequence of synthetic keywords or a request to generate the one or more synthetic documents (Alda, pars. 18, 20, 44-49, 59 and 63 make it clear users completely control the generation process).
As per claim 12, Alda as modified discloses The method of claim 10, wherein selecting the particular vector based on the calculated score comprises: determining that the calculated score for the particular vector is among a group of highest scores calculated for a plurality of vectors from the DP-DE distribution (Alda, pars. 45-46, 68-70 and Georgopoulos, pars. 8, 11). Analogous claim 20 is likewise rejected.
As per claim 13, Alda as modified discloses The method of claim 6, further comprising: obtaining a different vector from the DP-DE distribution, wherein the different vector comprises a sequence of different synthetic keywords embedded into the different vector (Alda, pars. 68-70 and see Georgopoulos as cited in the rejection of claim 6); decoding the different vector into the sequence of different synthetic keywords (Georgopoulos, pars. 8, 11, 23, 24 wherein predicting or outputting term sequences from embedding vector sequences corresponds to decoding as claimed); and sending the sequence of different synthetic keywords to the endpoint as a seed for the synthetic text generator to produce one or more different synthetic documents (Alda, pars. 18, 48, 71-76 disclose using synthetic structured dataset of relevant features to generate CV synthetic document text by combining values with scripted textual prompts and feeding them into a pretrained NLG model such as GPT2; pars. 74-76 disclose that the generated attributes control the NLG generation by acting as starting points for the CV synthetic document generation and by being embedded into prompts presented to the NLG model).
As per claim 16, Alda as modified discloses The one or more non-transitory, computer-readable storage media of claim 15, wherein the endpoint comprises a large language model (LLM) as the synthetic text generator, and wherein the one or more non-transitory, computer-readable storage media store further program instructions that when executed on or across the one or more computing devices, cause the one or more computing devices to further implement: prompting the LLM to produce the one or more synthetic documents, wherein the LLM is seeded with the sequence of synthetic keywords to produce the one or more synthetic documents (Alda, pars. 18, 48, 71-76 disclose using synthetic structured dataset of relevant features to generate CV synthetic document text by combining values with scripted textual prompts and feeding them into a pretrained NLG model such as GPT2; pars. 74-76 disclose that the generated attributes control the NLG generation by acting as starting points for the CV synthetic document generation and by being embedded into prompts presented to the NLG model).
As per claim 17, Alda as modified discloses The one or more non-transitory, computer-readable storage media of claim 15, storing further program instructions that when executed on or across the one or more computing devices, cause the one or more computing devices to further implement receiving a request to generate the sequence of synthetic keywords or a request to generate the one or more synthetic documents (Alda, pars. 18, 20, 44-49, 59 and 63 make it clear users completely control the generation process).
Claims 4, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Alda as modified above and further in view of Nouri et al., Pub. No.: US 20240249019 A1, hereinafter Nouri.
As per claim 4, Alda as modified discloses the system of claim 3. The combination does not disclose but Nouri discloses wherein the DP-DE distribution is a differentially private kernel density estimation, and wherein to calculate the score for the particular vector, the data management system is further configured to: calculate the score for the particular vector based at least on one or more random Gaussian completions associated with the particular vector (Nouri, pars. 87-96, 95 disclose differentially private synthetic dataset generation and Gaussian kernel density based synthetic).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Nouri would have allowed the combined teaching to provide a known kernel density based differential privacy technique for generating privacy preserving synthetic vectors or data points while keeping the density characteristics of the original data. Analogous claims 11 and 19 are likewise rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED HASAN whose telephone number is (571)270-5008. The examiner can normally be reached M-F 8am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571)270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SYED H HASAN/Primary Examiner, Art Unit 2154
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