DETAILED ACTION
1. Claims 1-20 are pending in this examination.
Notice of Pre-AIA or AIA Status
2.1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2.2. 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.
Response to Arguments
3.1. Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
3.2. Applicant's arguments with respect to the 101 rejections have been fully considered but they are not persuasive the rejection has been maintained. Please see below:
Claim Rejections - 35 USC § 101
4.1. 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.
4.2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
4.3. Independent claims 1, 11, and 18 are directed to a task-driven privacy-preserving data-sharing, comprising: obtaining,
The claims do not recite additional elements that are sufficient to amount to “significantly more” than the judicial exception because: the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of a computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment.
The claim recites additional element using a computing device, which is conventional and routine. Furthermore, learning the similarity comprises generating, by the computing device, a similarity matrix that quantifies the similarity between the first distilled data and the second distilled data are a mathematical formula; 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.
As per claims 2, 12 and 19; claims are directed to the distilled data, the first distilled data, and the second distilled data respectively comprise latent representations generated using
In particular, the claim recites additional element– a variational autoencoder long short-term memory deep generative model, the latent representations being invariant; which is insignificant extra solution activity; Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
As per claims 3, 13 and 20; claims are directed to provide task-driven privacy-preserving data-sharing of claim 1, wherein learning the similarity comprises: performing,
In particular, the claim recites additional element– by the computing device, a cross-correlation similarity operation using a bilinear attention unit; which is insignificant extra solution activity, Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
As per claim 4, claim is directed to provide task-driven privacy-preserving data-sharing of claim 1, wherein learning the similarity comprises: calculating,
In particular, the claim recites additional element– by the computing device; which is conventional and routine. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
As per claim 5, claim is directed to provide task-driven privacy-preserving data-sharing of claim 4, wherein selecting the one or more data values comprises: selecting,
In particular, the claim recites additional element– by the computing device; which is conventional and routine. Accordingly, this additional element does not integrate the abstract idea into a practical application because It does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
claims 6 and 14; claims are directed to provide task-driven privacy-preserving data-sharing of claim 1, wherein the first distilled data and the second distilled data respectively comprise
In particular, the claim recites additional element– latent representations of multi-variate time series data; which is conventional and routine. Accordingly, this additional element does not integrate the abstract idea into a practical application because It does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
claims 7 and 15; claims are directed to provide task-driven privacy-preserving data-sharing of claim 6, wherein learning the similarity comprises: learning,
In particular, the claim recites additional element– by the computing device; with the multi-variate time series data; which is conventional and routine. Accordingly, this additional element does not integrate the abstract idea into a practical application because It does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
As per claim 8, claim is directed to provide task-driven privacy-preserving data-sharing of claim 1, further comprising: implementing, defined task service based on such contribution data; Thus, this limitation recites a concept that falls into the “mental process group” of abstract ideas. Therefore, the claim recites an abstract idea.
In particular, the claim recites additional element– by the computing device; which is conventional and routine. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
As per claims 9 and 16; claims are directed to provide task-driven privacy-preserving data-sharing of claim 1, further comprising: learning,
In particular, the claim recites additional element– by the computing device; which is conventional and routine. Accordingly, this additional element does not integrate the abstract idea into a practical application because It does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
As per claims 10 and 17; claims are directed to provide task-driven privacy-preserving data-sharing of claim 9, wherein selecting the one or more data values comprises: selecting,
In particular, the claim recites additional element– by the computing device; which is conventional and routine. Accordingly, this additional element does not integrate the abstract idea into a practical application because It does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Claim Rejections - 35 USC § 103
5.1. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
5.2. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application No. 20220300853 to Gonzalez Sanchez et al (“Gonzalez Sanchez”) in view of US Patent Application No. 20210157312 to Cella et al (“Cella”), and in view of US Patent Application No. 20120158953 to Barnes et al (“Barnes”).
As per claims 1, 11 and 18, Gonzalez Sanchez discloses method/device/medium to provide task-driven privacy-preserving data-sharing, comprising; obtaining, by a computing device, distilled data respectively corresponding to a plurality of entities the distilled data being ([0034], each involved entity may apply the PPF to the dataset to transform the dataset into a privacy preserving version of the dataset (“protected dataset”). At step 3, in an embodiment, one or more of these privacy preserving versions of the datasets (“protected datasets”) may be used as input to train an ML/AI model. For example, one or more of the “Protected Leader Dataset,” “Protected Dataset A,” “Protected Dataset B,” . . . “Protected Dataset ZZ” may be used to train the ML/AI model. also see fig. 1 and associated texts);
being generated based on an artificial intelligence (AI) data distillation process ([0042], fig. 3 and associated texts);
learning, by the computing device, a similarity between a first entity of the plurality of entities and at least one second entity of the plurality of entities with respect to a defined task service based on first distilled data of the first entity and second distilled data of each of the at least one second entity, the distilled data comprising the first distilled data and the second distilled data ([0042], an obfuscated version of the original data, is then used to train an ML model on a simple classification task. The accuracy loss was determined with respect to the accuracy of a model trained with the raw data to provide a measure of the utility retained by the data after the transformation. At the same time, the similarity between the reconstructed data and the original data was measured with a few metrics. In the specific plot of FIG. 3, the Structural Similarity Measure Index (SSIM) is used, which ranges from 0 (very dissimilar) to 1 (equal), [0040], All the participants in the process (i.e., involved entities and leader entity(ies)) use the PPF to create private representations of their own raw data. This data could be personal data, sensor data, medical data or any other kind of private, confidential or sensitive data. In any case, after the PPF is applied, a numeric vector representation will be created and will not include any of the original values. also see fig. 3 and associated texts, ([0034], each involved entity may apply the PPF to the dataset to transform the dataset into a privacy preserving version of the dataset (“protected dataset”). At step 3, in an embodiment, one or more of these privacy preserving versions of the datasets (“protected datasets”) may be used as input to train an ML/AI model. For example, one or more of the “Protected Leader Dataset,” “Protected Dataset A,” “Protected Dataset B,” . . . “Protected Dataset ZZ” may be used to train the ML/AI model. also see fig. 1 and associated texts):
selecting, by the computing device, one or more data values from the second distilled data based on the similarity ([0042], The reconstructed data, which is an obfuscated version of the original data, is then used to train an ML model on a simple classification task. The accuracy loss was determined with respect to the accuracy of a model trained with the raw data to provide a measure of the utility retained by the data after the transformation. At the same time, the similarity
between the reconstructed data and the original data was measured with a few metrics. In the specific plot of FIG. 3, the Structural Similarity Measure Index (SSIM) is used, which ranges from 0 (very dissimilar) to 1 (equal). Vanilla refers to the accuracy/similarity obtained with raw data, that has not been processed using a PPF); and
providing, by the computing device, the one or more data values to the first entity for implementation of the defined task service based on the one or more data values ([0039] The leader is the entity or one of the entities that is in charge of the generation of the PPF. To this end, the leader can use data it owns or generate the PPF based on previously generated PPFs. As an example, the leader can train a PCA transformation matrix for its own data. After the PPF is generated, the
leader distributes it to the other entities. [0067], One of the entities (the leader) generates a PPF and shares it with the other entities. [0069] 2. All the entities create private representations of their datasets and share it with the leader. [0070] 3. The leader or another entity uses some or all the transformed data to train an ML model. [0057], sometimes may even provide competing services (e.g., SPOTIFY is hosted in GOOGLE Cloud, but GOOGLE is also running YOUTUBE Music that is a direct competitor). However, the hosted service would be interested to collaborate with the cloud provider to obtain better performance and security).
Gonzalez Sanchez does not explicitly disclose however in the same field of endeavor, Cella discloses distilled data being representative of local data of each of the plurality of entities ([0952], an embodiment of platform 100 may include a local data collection system 102, which may be disposed in an environment 104, such as an industrial environment similar to that shown in FIG. 3, for collecting data from or about the elements of the environment, such as machines, components, systems, sub-systems, ambient conditions, states, workflows, processes, and other elements."… (Para [0025]-"In embodiments, each entity node of the one or more entity nodes includes one or more properties of a respective properties of the respective industrial entity represented by the entity node,);
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 teaching of Gonzalez Sanchez with the teaching of Cella by including the feature of local data, in order for Gonzalez Sanchez’s system for diagnosing problems in a manufacturing environment and/or suggesting ways to improve operations. A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models (Cella, Abstract).
Gonzalez Sanchez and Cella do not explicitly disclose however in the same field of endeavor, Barnes discloses wherein learning the similarity comprises generating, by the computing device, a similarity matrix that quantifies the similarity between the first distilled data and the second distilled data([0151], The third node (e.g., the third computing entity) may receive the obfuscated and/or original data points from the analytics nodes (e.g., the first and second nodes) and may compute a privacy-preserving similarity matrix/similarity score between the data points for the analytics nodes. For instance, the third node may compare the data points between the first and second nodes to determine a similarity score for each comparison. The third node may generate a similarity matrix based on the similarity scores. The third node may then send the similarity matrix to the analytics nodes such as the first and/or the second nodes).
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 teaching of Gonzalez Sanchez with the teaching of Cella/Barnes by including the feature a similarity matrix in order for Gonzalez Sanchez’s system for mitigating the risk of information leaks by obscuring the organization's web activity. Systems and methods are disclosed for determining whether a third party observer could determine that an organization has an intent with respect to subject matter based on the organization's web activity. The determination that there is a risk of information leaks to the third party observer can be completed by analyzing the entropy of web usage information destined for the third party observer's servers. Systems and methods are also disclosed for mitigating the risk of information leaks by obscuring the organization's web activity. The web activity can be obscured by selecting candidate actions that can be used to generate neutralizing web traffic from the organization's network which will obscure an intent the organization has with respect to a particular subject matter. For example, the candidate actions can identify specific queries, links, or actions that the organization can take to neutralize their web activity to a less remarkable point in the search space (Barnes, abstract).
As per claims 2, 12 and 19 the combination of Gonzalez Sanchez, Cella and Barnes discloses the method to provide task-driven privacy-preserving data-sharing of claim 1, wherein the distilled data, the first distilled data, and the second distilled data respectively comprise: latent representations generated using a variational autoencoder long short-term memory deep generative model, the latent representations being invariant to defined features of each of the distilled data, the first distilled data, and the second distilled data (Cella [1660], [3622], [1685]).
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 teaching of Gonzalez Sanchez with the teaching of Cella by including the feature of models, in order for Gonzalez Sanchez’s system for diagnosing problems in a manufacturing environment and/or suggesting ways to improve operations. A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models (Cella, Abstract).
As per claims 3, 13 and 20 the combination of Gonzalez Sanchez, Cella and Barnes discloses the method to provide task-driven privacy-preserving data-sharing of claim |, wherein learning the similarity comprises: performing, by the computing device, a cross-correlation similarity operation using a bilinear attention unit to compare the first entity with catch of the at least one second entity with respect to the defined task service based on the first distilled data and the second distilled data (Cella, [0981], Embodiments of the methods and systems disclosed herein may include use of an analog crosspoint switch for collecting variable groups of vibration input channels. For vibration analysis, it is useful to obtain multiple channels simultaneously from vibration transducers mounted on different parts of a machine (or machines) in multiple directions. By obtaining the readings at the same time, for example, the relative phases of the inputs may be compared for the purpose of diagnosing various mechanical faults. Other types of cross channel analyses such as cross-correlation, transfer functions, Operating Deflection Shape (“ODS”) may also be performed.). The motivation regarding the obviousness of claim 2 is also applied to 3, 13 and 20.
As per claim 4, the combination of Gonzalez Sanchez, Cella and Barnes discloses the method to provide task-driven privacy-preserving data-sharing of claim 1, wherein learning the similarity further comprises: calculating, by the computing device, similarity weights respectively corresponding to pairings of the first entity with each of the at least one second entity with respect to the defined task service based on the first distilled data and the second distilled data, wherein each of the similarity (Gonzalez Sanchez, [0042]-[0043], the Structural Similarity Measure Index (SSIM) is used, which ranges from 0 (very dissimilar) to 1 (equal). Vanilla refers to the accuracy/similarity obtained with raw data, that has not been processed using a PPF, also see [0016]).
Gonzalez Sanchez does not explicitly disclose however in the same field of endeavor, Cella discloses similarity weights Para [1075]-"varying weights applied to given input sources, sensors, data pools and the like, using learning feedback 4012 to promote favorable packages and de-emphasize less favorable packages."; Pair [5499]-"For example, inputs to the regression model may be removed, including single inputs, pairs of inputs, triplets, and the like, to determine whether the absence of inputs createsa material degradation of the success of the model 55052. This may assist with recognition of inputs that are in fact correlated (e.g., are linear combinations of the same underlying data), that are overlapping, or the like."; Para [5501]- "Regression analysis may include estimating, by the machine learning model 55052 relationships between a dependent variable, i.e. an outcome variable, and one or more independent variables, i.e. predictors, covariates, and/or features. Similarity learning may include learning, by the machine learning model 55052, from examples using a similarity function, the similarity function being designed to measure how similar or related two objects are."). The motivation regarding the obviousness of claim 2 is also applied to claim 4.
As per claim 5, the combination of Gonzalez Sanchez, Cella and Barnes discloses the method to provide task-driven privacy-preserving data-sharing of claim 4, wherein selecting the one or more data values comprises: selecting, by the computing device, the one or more data values from the second distilled data of the at least one entity of the at least one second entity based on the similarity (Gonzalez Sanchez, [0042]-[0043], also see [0016]). Gonzalez Sanchez does not explicitly disclose however in the same field of endeavor, Cella discloses similarity weights Para [1075]-"varying weights applied to given input sources, sensors, data pools and the like, using learning feedback 4012 to promote favorable packages and de-emphasize less favorable packages."; Pair [5499]-"For example, inputs to the regression model may be removed, including single inputs, pairs of inputs, triplets, and the like, to determine whether the absence of inputs createsa material degradation of the success of the model 55052. This may assist with recognition of inputs that are in fact correlated (e.g., are linear combinations of the same underlying data), that are overlapping, or the like."; Para [5501]- "Regression analysis may include estimating, by the machine learning model 55052 relationships between a dependent variable, i.e. an outcome variable, and one or more independent variables, i.e. predictors, covariates, and/or features. Similarity learning may include learning, by the machine learning model 55052, from examples using a similarity function, the similarity function being designed to measure how similar or related two objects are."). The motivation regarding the obviousness of claim 2 is also applied to claim 5.
As per claims 6, and 14, the combination of Gonzalez Sanchez, Cella and Barnes discloses the method to provide task-driven privacy-preserving data-sharing of claim 1, wherein the first distilled data and the second distilled data respectively comprise latent representations of multi-variate time series data respectively obtained locally by the first entity and each of the at least one second entity (Cella, [1068]. a time series, [1689]). The motivation regarding the obviousness of claim 2 is also applied to claims 6, and 14.
As per claims 7, and 15, the combination of Gonzalez Sanchez, Cella and Barnes discloses the method to provide task-driven privacy-preserving data-sharing of claim 6, wherein learning the similarity comprises: learning, by the computing device, the similarity between the first entity and each of the at least one second entity, respectively, at one or more time steps with respect to the defined task service, the one or more time steps being associated with the multi-variate time series data (Cella, [1689], [5501]). The motivation regarding the obviousness of claim 2 is also applied to claims 7, and 15.
As per claim 8, the combination of Gonzalez Sanchez, Cella and Barnes discloses the method to provide task-driven privacy-preserving data-sharing of claim 1, further comprising: implementing, by the computing device, a reinforcement learning process based on contribution data respectively contributed by the at least one second entity to the defined task service and performance of the defined task service based on such contribution data (Cella, [5509], [5484]). The motivation regarding the obviousness of claim 2 is also applied to claim 8.
As per claims 9, and 16, the combination of Gonzalez Sanchez, Cella and Barnes discloses the method to provide task-driven privacy-preserving data-sharing of claim 1, further comprising: learning, by the computing device, at least one correlation between contribution data respectively contributed by the at least one entity of the at least one second entity to the defined task service and performance of the defined task service based on such contribution data (Cella, [5197], [5225], [6423]). The motivation regarding the obviousness of claim 2 is also applied to claims 9, and 16.
As per claims 10, and 17, the combination of Gonzalez Sanchez, Cella and Barnes discloses the method to provide task-driven privacy-preserving data-sharing of claim 9, wherein selecting the one or more data values comprises: selecting, by the computing device, at least one data value from at least one of the contribution data or the second distilled data of the at least one entity of the at least one second entity based on the similarity and the at least one correlation (Cella, [1082], [5501]). The motivation regarding the obviousness of claim 2 is also applied to claims 10, and 17.
6.1 The prior art made of record and not relied upon is considered pertinent to applicant's disclosure as the prior art discloses many of the claim features (See PTO-form 892).
6.2. a). US Patent Application No. 20200045546 to Su et al., discloses an example apparatus for visual question answering includes a receiver to receive an input image and a question. The apparatus also includes an encoder to encode the input image and the question into a query representation including visual attention features. The apparatus includes a knowledge spotter to retrieve a knowledge entry from a visual knowledge base pre-built on a set of question-answer pairs. The apparatus further includes a joint embedder to jointly embed the visual attention features and the knowledge entry to generate visual-knowledge features. The apparatus also further includes an answer generator to generate an answer based on the query representation and the visual-knowledge features.
b). US Patent Application No. 20230359769 to Epasto et al., discloses a computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.
Conclusion
7. 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 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 HARUNUR RASHID whose telephone number is (571)270-7195. The examiner can normally be reached 9 AM to 5PM.
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, Eleni A. Shiferaw can be reached at (571) 272-3867. 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.
HARUNUR . RASHID
Primary Examiner
Art Unit 2497
/HARUNUR RASHID/Primary Examiner, Art Unit 2497
Prosecution Insights