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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on September 12th, 2025 has been entered.
Amendments
This action is in response to amendments filed July 29th, 2025, as part of an after-final submission, but not entered at that time. With these amendments, Claims 1, 11, and 20 are amended; Claim 9 and 19 are cancelled: Claims 21 and 22 are new. With the filing of the Request for Continued Examination on September 12th, 2025, the amendments have now been entered, and Claims 1-8, 10-18, and 20-22 are currently pending.
Claim Interpretation
The term a computer readable storage medium has been specially defined by [0042] of the specification to not include transitory signals per se.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8, 10-18, and 20-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent Claims 1, 11, and 20 each recite the limitation outputting an instance of the global machine learning model to each of the multiple client devices in the federated cloud computing environment. However, nowhere in the specification is such an action described. The closest description of such an action may be in [0031], which states “one or more embodiments can include generating and/or providing software implementing the techniques depicted in FIG. 2 as a service in a cloud environment.” However, “providing software in a cloud environment” does not clearly imply outputting a model to a plurality of clients; “as a service” implies that a server performs computation for the clients. The specification only appears to disclose the use of the global model by a server, not downloading any instance of the model to any clients.
Similarly, independent Claims 1, 11, and 20 each recite performing one or more actions based at least in part on the one or more generated column labels and results of the standardizing, but [0031] only states that the automated actions are based on the results of the standardizing, not on the generated column labels. Further, none of the automated actions described in the specification (based at least in part on results of the standardizing) appear to including automatically configuring a global machine learning model. The global machine learning model appears to be the model which performs the standardization and generates the column labels; rather than being configured based on the standardizing and column labels. Again, the closest description of such an action may be in [0031], which states “one or more embodiments can include generating and/or providing software implementing the techniques depicted in FIG. 2 as a service in a cloud environment” but it is not clear whether this step in the disclosure either a) is one of the automated actions of Step 210 or b) is detailed enough to support the entire limitation of automatically configuring a global machine learning model for use across the federated cloud computing environment and which complies with one or more data privacy constraints designated by at least a portion of the multiple client devices.
Further, no privacy constraints designated by at least a portion of the multiple client devices appear to be disclosed in the specification. None of the mentions of privacy “constraints” indicate that the constraints are designated by the clients themselves, merely that the constraints apply to the clients’ data. In particular, [0011] demonstrates that the privacy constraints appear to be a general principle guiding system development, rather than provided by or designated by the clients themselves.
Dependent claims are rejecting for inheriting the lack of written description of a parent claim.
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 1-8, 10-18, and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 1 further recites steps of determining one or more similar data columns across at least a portion of the multiple datasets (a mental process of judgement); generating one or more column labels for the one or more similar data columns (a mental process of judgement); aggregating embedding vectors associated with predicted column names for the one or more similar data columns, generated by at least a portion of the multiple client devices (a mental process of organization of data); and standardizing at least a portion of data within the one or more similar data columns by processing the one or more generated column labels (a mental process of judgement). Thus, the claim recites an abstract idea of determining similar columns across multiple datasets and standardizing the data therein.
The claim does not recite any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
obtaining multiple datasets from multiple client devices, within a federated cloud computing environment, in accordance with one or more data privacy techniques - insignificant extra solution activity of data gathering, see MPEP 2106.05(g)
using at least one federated learning technique to standardize the data within the similar data columns – the mere use of a computer or other machinery as a tool to perform the abstract idea, see MPEP 2106.05(f)(2)
automatically configuring a global machine learning model, for use across the federated cloud computing environment and which complies with one or more data privacy constraints designated by at least a portion of the multiple client devices is merely stating to generate a model to perform the mental process (MPEP 2106.05(f)(2), using a computer or other machinery as a tool) and stating that the model has a certain outcome (MPEP 2106.05(f)(1), reciting the idea of a solution rather than the steps that achieve that solution)
outputting portions of the standardized data and outputting an instance of the global machine learning model to the client devices¸ which is insignificant extra-solution activity of data output or display (MPEP 2106.05(g))
and stating wherein the method is carried out by at least one computing device – the mere use of a computer or other machinery as a tool to perform the abstract idea, see MPEP 2106.05(f)(2);
none of which can integrate the abstract idea into a practical application, for the respective given reasons. Thus, the claim is directed to the abstract idea of determining similar columns across multiple datasets and standardizing the data therein.
Finally, taken alone and in combination, the additional elements cannot provide significantly more than the abstract idea itself because collecting data from client devices and outputting data to the client devices is well understood, routine and conventional (MPEP 2106.05(d), transmitting data over a network); doing so in a private manner is also well-understood, routine, and conventional (Callcut, US PG Pub 202/0311300, as cited by the applicant, [0043], “conventional privacy-enhancing techniques [between] data providers and algorithm developers” denotes that the concept of privacy-preserving federated computation is well-known); the use of a computer or other machinery as a tool to perform an abstract idea cannot provide an inventive concept (MPEP 2106.05(f)(2)); merely stating the idea of a solution cannot provide an inventive concept (MPEP 2106.05(f)(1)); and there is no nexus between the additional elements which can add significantly more than the abstract idea of determining similar columns across multiple datasets and standardizing the data therein. Therefore, the claim is subject-matter ineligible.
Claims 2-5, dependent upon Claim 1, recite only new limitations which are mental processes and can be performed by a human in the mind or using pencil and paper as a memory aid (Claim 2: identifying similar columns using node anchoring techniques; Claim 3: constructing a graph; Claim 4: mapping nodes from one of the graphs to nodes of one other graph; Claim 5: learning feature name standardization using feature label embedding techniques) and no new additional elements, thus no additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself.
Claim 6, dependent upon Claim 5, recites that the mental process of learning feature name standardization includes to predict the same of a column and a feature embedding using vales in the columns (an additional mental process) and is to be performed by training a machine learning model to do so, which is an additional element which neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself (i.e. “apply it” on a computer, see MPEP 2106.05(f)).
Claims 7 and 8, dependent upon Claim 6, recite only new limitations which are mental processes or mathematical processes (Claim 7: using one or more text clustering techniques to generate clusters among the columns wherein the values comprise text; Claim 8: determining cluster labels, deriving word embeddings, generate a single label embedding vector) and no new additional elements, thus no additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself.
Claim 10, dependent upon Claim 1, only recites an additional element which specifies the particular technological environment in which the abstract idea takes place (as a service in a cloud environment) which by MPEP 2106.05(h) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claims 11-18 recite a computer program product to perform precisely the methods of Claims 1-8, which by MPEP 2106.05(f)(2) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea itself, and are thus rejected for the reasons set forth in the rejection of Claims 1-10, respectively. Similarly, Claims 20-22 recite a system comprising: a memory and a processor to perform precisely the methods of Claims 1, 2, and 5, respectively, and are thus also rejected by MPEP 2106.05(f)(2) for the reasons set forth in the rejections of Claims 1, 2, and 5, respectively.
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, 10, 11, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Scannapieco et al., “Privacy Preserving Schema and Data Matching.”
Regarding Claim 1, Scannapieco teaches a computer-implemented method (Scannapieco, pg. 661, 1st column, 3rd paragraph, “We conducted our experiments on a 3.00GHz Pentium 4 Processor”) comprising: obtaining multiple datasets from multiple client devices, within a federated cloud computing environment, in accordance with one or more data privacy techniques (Scannapieco, Abstract, “In many business scenarios, record matching is performed across data sources with the aim of identify common information shared among these sources … we propose a protocol for record matching that preserves privacy both at the data level and at the schema level … if two data sources need to identify their common data … the protocol uses a third party” denotes privacy techniques, a federated cloud computing environment, and multiple datasets obtained); determining one or more similar data columns across at least a portion of the multiple datasets (Scannapieco, pg. 658, 2nd column, last paragraph, “Each party maps its local schema to the global schema … By applying the mapping expressions, each part obtains a matched schema” where “Name, DateofBirth,” etc. are data columns); generating one or more column labels for the one or more similar data columns (Scannapieco, pg. 659, column 1, Protocol 1, “Output: PMatch, PMatch”) wherein generating the one or more column labels comprises: aggregating embedding vectors, associated with predicted column names for the one or more similar data columns, generated by at least a portion of the multiple client devices (Scannapieco, pg. 659, column 1, Protocol 1, & pg. 656, 1st column, 5th paragraph, “Our approach is based on the idea of transforming records of
R
P
and
R
Q
into a metric space while preserving the distances between the record values … The third party W will compare the records in the metric space in order to decide their matching … motivates the use of this embedding technique with respect to our privacy requirements” & pg. 659, 2nd column, 1st paragraph, “the set of matching attributes” denotes predicted column names); standardizing at least a portion of the data within the one or more similar data columns by processing the one or more generated column labels using at least one federated learning technique (Scannapieco, pg. 7, 1st column, 1st paragraph, “In this way, W has the possibility of intersecting the sets of attributes
S
'
P
and
S
'
Q
… the third party exports its global schema … the schema exported by W is the union of all attributes of all global relations” where the matched records and schema are standardized data and where the whole processes of privately matching schemas and records is a federated learning technique); and performing one or more automated actions based at least in part on the one or more generated column labels and results of the standardizing of the at least a portion of data within the one or more similar data columns, wherein performing one or more automated actions comprises: automatically configuring a global machine learning model, for use across the federated cloud computing environment and which complies with one or more data privacy constraints; outputting corresponding portions of the standardized data to the respective ones of the multiple client devices; and associated with the one or more generated column labels; outputting corresponding portions of the standardized data to the respective ones of the multiple client devices; and outputting an instance of the global machine learning model to each of the multiple client devices in the federated cloud computing environment (Scannapieco, pg. 7, 1st column, 1st paragraph, “In this way, W has the possibility of intersecting the sets of attributes
S
'
P
and
S
'
Q
… the third party exports its global schema” where the “global schema” is a global machine learning model used across the federated cloud computing environment and complies with one or more data privacy constraints and is associated with the generated column labels/attributes and is outputted to each of the client devices, where the output includes matching records/standardized data); wherein the method is carried out by at least one computing device (Scannapieco, pg. 661, 1st column, 3rd paragraph, “We conducted our experiments on a 3.00GHz Pentium 4 Processor”).
Regarding Claim 10, Scannapieco teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Scannapieco further teaches wherein software implementing the method is provided as a service in a cloud environment (Scannapieco, Abstract, “The protocol uses a third party” & pg. 655, Fig. 1).
Claim 11 recites a computer program product to perform precisely the method of Claim 1. As Scannapieco performs their method on a computer, in which a computer readable storage medium having program instructions in inherent, Claim 11 is thus rejected for the reasons set forth in the rejection of Claim 1. Similarly, Claims 20 recites a system comprising: a memory and a processor to perform the method of Claim 1 and is thus also rejected for the reasons set forth in the rejection of Claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 2-4, 12-14, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Scannapieco et al., “Privacy Preserving Schema and Data Matching,” in view of Melnick et al, “Similarity Flooding: A Versatile Graph Matching Algorithm and its Application to Schema Matching.”
Regarding Claim 2, Scannapieco teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Scannapieco’s method of determining similar data columns, however, does not comprise identifying one or more similar data columns across at least a portion of the multiple datasets using one or more graph node anchoring techniques. However, Melnick, in the analogous art of schema matching, teaches this limitation (Melnick, pg. 3, Fig. 2 & Tables 1 and 2 & Page 3, Overview of the Approach, “The similarity values indicate how well the corresponding nodes match their counterparts”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to augment Scannapieco by using the column matching methods of Melnick, i.e. using graph node anchoring techniques. The motivation to do so is that Melnick’s flooding schema matching algorithm produces “labor savings” and has good “accuracy” (Melnick, Abstract).
Regarding Claim 3, the Scannapieco/Sato combination of Claim 2 teaches the computer implemented method of Claim 2 (and thus the rejection of Claim 2 is incorporated). The schema matching algorithm of Melnick (already incorporated into the combination) further teaches wherein using one or more graph node anchoring techniques comprises, for each of the multiple datasets, constructing a graph using at least a portion of available sensitive data contained therein (Melnik, Page 2, Overview of the Approach, “… we translate the schemas from their native format into graphs ... “ & Page 3, Figure 2); and wherein in constructing the graph, each node of the graph represents a column in the given dataset (Melnik, Page 1, Introduction, Paragraph 5, “The elements of models represent artifacts like relational tables and columns” & Page 2, Overview of the Approach, Paragraph 2, “The elements of S1 and S2 are tables and columns” & Page 3, Tables 1 and 2); and each edge weight represents a correlation among pairs of columns in the given dataset (Melnik, Page 4, Similarity Flooding Algorithm, Figure 3 & Page 4, Similarity propagation graph, “The weights placed on the edges of the propagation graph indicate how well the similarity of a given map pair propagates to its neighbors and back”).
Regarding Claim 4, the Scannapieco/Sato combination of Claim 3 teaches the computer implemented method of Claim 3 (and thus the rejection of Claim 3 is incorporated). The schema matching algorithm of Melnick (already incorporated into the combination) further teaches wherein using one or more graph node anchoring techniques comprises mapping one or more nodes from one of the constructed graphs to one or more nodes of at least one other of the constructed graphs (Melnik, Page 3, Tables 1 and 2, where the tables show the correspondence between nodes of the two different graphs); by leveraging graph connectivity structure (Melnik, Page 3, Overview of the Approach, “Uses an initial mapping like initialMap [which maintains graph connectivity structure] … over a number of iterations, the initial similarity of any two nodes propagates through the graphs” & Page 3, Overview of the Approach, “… the SF algorithm was able to produce a good mapping … by merely using graph structures”).
Claims 12-14 recite a computer program product to perform precisely the methods of Claims 2-4, respectively. As Scannapieco performs their method on a computer, in which a computer readable storage medium having program instructions in inherent, Claims 12-14 are thus rejected for the reasons set forth in the rejections of Claim 2-4, respectively. Similarly, Claims 21 recites a system comprising: a memory and a processor to perform the method of Claim 2 and is thus also rejected for the reasons set forth in the rejection of Claim 2.
Claims 5-8, 15-18, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Scannapieco, in view of Zhang et al., “Sato: Contextual Semantic Type Detection in Tables” (hereinafter Sato).
Regarding Claim 5, Scannapieco teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Scannapieco’s method of processing the one or more generated column labels, however, does not comprise the recited limitations. Sato, however, in the analogous art of schema matching, teaches wherein processing one or more generated column labels using at least one federated learning technique comprises learning feature name standardization across at least a portion of the multiple datasets (Sato, Page 2, Tables A and B where the tables show creating feature labels & Page 4, Figure 3, where the figure shows the process of column label generation through the given models) by utilizing one or more feature label embedding techniques and based at least in part on the one or more generated column labels (Sato, Page 3, Single-column prediction model, “… we choose Sherlock as our single column prediction model” wherein Sherlock uses word embeddings to characterize features of matched columns (Hulsebos, Sherlock: A Deep Learning Approach to Semantic Type Detection)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to standardize feature names, as does Sato. The motivation to do this is create clear and relevant column headers (Sato, Figure 1, “… to help effectively resolve ambiguities”).
Regarding Claim 6, the Scannapieco/Sato combination of Claim 5 teaches the computer-implemented method of Claim 5 (and thus the rejection of Claim 5 is incorporated). The feature name standardization of Sato (already incorporated into the Scannapieco/Sato combination) further teaches wherein learning feature name standardization across at least a portion of the multiple datasets comprises training at least one machine learning model (Sato, Figure 2 & Page 4, Table intent estimator, “We use an LDA model to estimate a table’s intent as a topic vector”) using values in at least a portion of the one or more similar data columns to predict a name of the given data column and at least one corresponding embedding vector of the given data column (Sato, Figure 1 demonstrates predicting a name & Page 6, Feature extraction, “We use the public Sherlock feature extractors to extract the four groups of base features, CHAR, WORD, PARA, and STAT, generating a feature vector with 1587 dimensions for each column in a table”).
Regarding Claim 7, the Scannapieco/Sato combination of Claim 6 teaches the computer-implemented method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The feature name standardization of Sato (already incorporated into the Scannapieco/Sato combination) further teaches wherein the values comprise text (Sato, Tables A and B & Page 3, Deep learning for single-column prediction, “The column-wise features used in SATO include character … word … [and] paragraph embeddings”) and wherein training the at least one machine learning model comprises using one or more text clustering techniques to generate one or more clusters among the at least a portion of the one or more similar data columns (Sato, Page 4, Topic models, “The main advantage of probabilistic topic models such as LDA over clustering algorithms is that probabilistic models can represent a data point (e.g., document) as a mixture of topics”, implying that it is common practice to use clustering algorithms to identify semantic groupings in text).
Regarding Claim 8, the Scannapieco/Sato combination of Claim 7 teaches the computer-implemented method of Claim 7 (and thus the rejection of Claim 7 is incorporated). The feature name standardization of Sato (already incorporated into the Scannapieco/Sato combination) further teaches determining one or more cluster labels based at least in part on the one or more generated clusters (Page 2, Tables A and B); deriving one or more-word embeddings for each of the one or more cluster labels (Sato, Page 10, Column embeddings, “… we analyze and compare embedding vectors from … Sato … as column embeddings … since the final layer combines input signals to compose semantic representations” & Page 10, Figure 10); and generating a single unified label embedding vector based at least in part on aggregating the derived word embeddings (Sato, Page 3, “… estimates ‘intent’ (a global descriptor) of a table using topic modeling …” & Page 3, “This module first creates a vector representation for the global context of a given table by computing a topic vector from the values of the entire table”).
Claims 15-18 recite a computer program product to perform precisely the methods of Claims 5-8, respectively. As Scannapieco performs their method on a computer, in which a computer readable storage medium having program instructions in inherent, Claims 15-18 are thus rejected for the reasons set forth in the rejections of Claim 5-8, respectively. Similarly, Claims 22 recites a system comprising: a memory and a processor to perform the method of Claim 5 and is thus also rejected for the reasons set forth in the rejection of Claim 5.
Response to Arguments
Applicant’s arguments originally filed July 29th, 2025 (as part of an after-final amendment) have been fully considered, but are not fully persuasive.
Applicant’s arguments regarding the 35 U.S.C. 101 rejections of the previous office action, as being directed towards an abstract idea without significantly more, have been fully considered, but are unpersuasive.
Applicant first asserts (pg. 11 of the response) “that the amended limitations, which require automated actions (e.g. carried out by a centralized server comprising federated learning infrastructure) within a federated cloud computing environment in interaction with multiple client devices within the federated cloud computing environment, precludes an interpretation that the claims are directed to a mental process.” However, applicant’s assertion does nothing to address whether the independent claims recite an abstract idea (which they clearly do, in determining similar data columns, generating one or more column labels for the similar columns, standardizing at least a portion of data). Applicant does not explain how the “federated learning environment” is an additional element which could integrate the abstract idea into a practical application. Applicant further asserts that “configuring a global machine learning model” ingrates the abstract idea into a practical application; however, this limitation merely recites to use a computer or other machinery as a tool to perform the recited mental process, which by MPEP 2106.05(f)(2) cannot do so. Merely training a machine learning model to perform a task remains directed towards the abstract idea of the task, without specific improvements in machine learning itself (see, for example, Desjardins). The statement that the system achieves this results “while also preserving data privacy” is merely claiming the result, and failing to recite any steps which achieve the result of privacy, see MPEP 2106.05(f)(1). Applicant argues (pg. 12, final paragraph) specifically that privacy is improved by the claimed invention, but again the independent claims only recite the privacy as an outcome and “fails to recite details of how a solution to a problem [of privacy] is accomplished,” precisely the situation of MPEP 2106.05(f)(1).
Applicant’s arguments with respect to the 35 U.S.C. 103 rejections of the claims have been fully considered but are moot, as the new grounds of rejection does not rely upon any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Specifically, new reference Scannapieco teaches “standardizing at least a portion of data within the one or more similar data columns by processing the one or more generated column labels using at least one federated learning technique” and “automatically configuring a global machine learning mode, for use across the federated cloud computing environment and which complies with one or more data privacy constraints designated by at least a portion of the multiple client devices, based at least in part on input data from the multiple client devise and associated with the one or more generated column labels” and “outputting corresponding portions of the standardized data to the respective ones of the multiple client devices” and “outputting an instance of the global machine learning model.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN M SMITH whose telephone number is (469)295-9104. The examiner can normally be reached Monday - Friday, 8:00am - 4pm Pacific.
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, Kakali Chaki can be reached at (571) 272-3719. 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.
/BRIAN M SMITH/Primary Examiner, Art Unit 2122
Prosecution Insights