Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-20 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.
Regarding claim 1:
The claim recites “aggregation result that aggregates the events” should read“ aggregation result that aggregates the plurality of events.”
The claim recites “the platform system”, which lacks antecedent basis in the claim, and should recite “the online platform system.”
The claim recites “receiving an attribute result of performing a first two-party computation” examiner notes that “of” should be “from.”
Regarding claims 1 and 3-4, claim 1 begins by reciting “perform operations comprising” but does not recite performing a homomorphic encryption as part of the operations. This interpretation is further bolstered by the language of inclusion in claims 2 and 5 “the operations comprise” which ties the operations of their respective claim to the “operations” of claim 1. Therefore the examiner is unsure if performing a homomorphic encryption is part of the “operations” of the system.
Regarding claim 1, the claim recites aggregating for a subcut of users, examiner respectfully notes that an empty set is a subset of all sets, therefore the subcut may be an empty subset. Applicant should amend to verify that the subset actually have more than zero elements.
Regarding claim 2:
The claim recites “the online platform”, which lacks antecedent basis in the claim, and should recite “the online platform system.”
The claim recites “totaling a financial amount due the online platform based on the aggregation result.” Examiner is unsure what the applicant intended by “due the online platform.”
Regarding claim 3:
The claim recites “the subcut”, which lacks antecedent basis in the claim, and should recite “the subcut of users.”
Regarding claim 6:
The claim recites “a single bit secret”, examiner respectfully points out a bit is either 0 or 1, examiner is unsure what a single bit secret entails, because if i is being sent as part of S.sup.Adprovider.sub.i it must consist of no other data other than 1 or 0, which then means i can never be more than 1 and could be zero which would render the claim inoperable and performing no actions on 0 users in the subcut.
Furthermore regarding claim 6, the claim recites deriving a number i but then recites that i is the number of users, and an attributed value, examiner is unclear how one variable is being defined 3 times.
Furthermore subsets can be empty, further rendering the claim in operable because no numbers can be derived or attributed, and with zero users, and no aggregation to be performed.
Regarding claim 9:
The claim recites “the platform system”, which lacks antecedent basis in the claim, and should recite “the online platform system.”
Regarding claims 2 and 5 (by nonlimiting example) recites “the operations comprise” but should recite “the operations further comprise” as they are not replacing the previous definitions of what the operations comprise.
Regarding claim 9-10, the claims define “the platform system,” examiner respectfully notes “the platform system” is not defined as part of the system of claims 1-10 and therefore is unsure whether it is part of the system or not.
Examiner respectfully stops here as it would be cause undue hardship to point out all possible antecedent, ambiguous, and grammatical issues within the claims, and their many possible interpretations. Applicant is respectfully requested to clarify antecedent basis for claim elements, and clarify the functional language, apply appropriate limits to the subcut, in order to assist the examiner in applying reasonable interpretations and understanding clearly scope of the claims.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to receiving and aggregating data using a mathematical relationship, formula, or calculation without significantly more.
Are the claims directed to a statutory category? Yes a system, method, and medium.
Are the claims directed to a law of nature, a natural phenomenon, or an abstract idea? Yes, the claims are directed at gathering and manipulating data, and applying a mathematical formula.
The claim(s) recite(s):
Regarding claim 1, A system, comprising: one or more hardware processors; and at least one memory storing instructions that cause the one or more hardware processors to perform operations comprising: (Examiner notes that these
receiving, from an online platform system, an advertisement opportunity data comprising a plurality of advert opportunities, and a plurality of opportunity timestamps; (Examiner respectfully notes that transmitting/receiving information or displaying information is insignificant extra-solution activity and therefore part of the abstract idea. Revised Guidance 55, n.31; see also MPEP § 2106.05(g). Data gathering and display elements are merely insignificant extra-solution activity that do not add significantly more to the abstract idea to render the claimed invention patent eligible. See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) (en bane), aff'd on other grounds, 561 U.S. 593 (2010) ("[T]he involvement of the machine or transformation in the claimed process must not merely be insignificant extra-solution activity"); see also MPEP § 2106.05(g); and see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ( computer receives and sends information over a network).
receiving, from an advertiser system, advertisement event data comprising a plurality of events and a plurality of event timestamps; ; (Examiner respectfully notes that transmitting/receiving information or displaying information is insignificant extra-solution activity. Revised Guidance 55, n.31; see also MPEP § 2106.05(g). Data gathering and display elements are merely insignificant extra-solution activity that do not add significantly more to the abstract idea to render the claimed invention patent eligible. See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) (en bane), aff'd on other grounds, 561 U.S. 593 (2010) ("[T]he involvement of the machine or transformation in the claimed process must not merely be insignificant extra-solution activity"); see also MPEP § 2106.05(g); and see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ( computer receives and sends information over a network).
receiving an attribute (Examiner respectfully notes that transmitting/receiving information or displaying information is insignificant extra-solution activity. Revised Guidance 55, n.31; see also MPEP § 2106.05(g). Data gathering and display elements are merely insignificant extra-solution activity that do not add significantly more to the abstract idea to render the claimed invention patent eligible. See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) (en bane), aff'd on other grounds, 561 U.S. 593 (2010) ("[T]he involvement of the machine or transformation in the claimed process must not merely be insignificant extra-solution activity"); see also MPEP § 2106.05(g); and see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ( computer receives and sends information over a network))
result of performing a first two-party computation between the advertiser system and the platform system to attribute the plurality of events to the plurality of advert opportunities based on comparing opportunity timestamps and event timestamps; (Examiner respectfully notes that the two-party computation is not performed by the claimed system, nor are its elements part of the claimed system)
and deriving an aggregation result that aggregates the events for a subcut of users based on performing a homomorphic encryption, or on performing a non-trusted third party computation. (Aggregating data based on some computation is again merely part of the abstract idea, data collection is an abstract process that we perform mentally everyday. The examiner further points to the “or” in the limitation, meaning homomorphic encryption is not required, and a third party computation can be merely a count of some data which is so broad that it is conceptually abstract. Aggregating based on some computation is abstract in the sense it is a purely mathematical concept, its abstract in that it can be performed by pen and paper and a mental process, and furthermore according to the specification its abstract in its application to a business method
Regarding claim 2, the claim recites that the operations comprise “totaling a financial amount” examiner respectfully points to claim 1 above to the interpretation of aggregating data, in essence a summation which is part of the abstract idea.
Regarding claims 3-8, the claims recite steps part of a mathematical relationship, formula, or computation.
Regarding claim 9, the claim first recites steps communicating messages, which is extra-solution activity and considered to be part of the abstract idea.
Regarding claim 10, the claims defines the type of message, examiner respectfully notes this is extra-solution activity and considered to be part of the abstract idea.
Claims 11-20 inherit the same rejections and interpretation of claims 1-10. Furthermore:
Regarding claims 11-15, the claims are contingent on the “or” limitation in claim 11 “and deriving an aggregation result that aggregates the events for a subcut of users based on performing a homomorphic encryption, or on performing a non-trusted third party computation.”
Therefore if a non-trusted third party computation is elected in claim 11, claims 13-15 will not be elected because they are contingent on homomorphic encryption being elected.
Claims 16-20 inherit the same interpretation as claims 11-15 above.
This judicial exception is not integrated into a practical application because:
Examiner respectfully notes:
It is unclear if the system claim performs the homomorphic encryption, claim 1 recites its based on performing a homomorphic encryption, then defines the encryption steps in later claims, but it is unclear if the claimed system performs the recited steps. For example the same language is used to describe the two-party computation “result of performing a first two-party computation” between two elements NOT part of the claimed system. Therefore it is also unclear if “based on performing” is functional language of the method and system or outside the scope of the system or method. The functional steps recited are receiving and aggregating.
The majority of the claim limitations are directed at receiving data, getting data ready for processing, and applying a mathematical relationship and computations.
The claims are directed at receiving data and organizing data and then the claims branch to homomorphic encryption (claims 3-7) and third party computation (claims 6-8).
Third party computation: Examiner notes this branch requires the computation be performed outside the scope of the claimed system, which if removed would leave generic computer processing, and establishing/performing mathematical relationships and computations.
Homomorphic encryption: Examiner notes that homomorphic encryption with shares, additives, and masks are mathematical relationships that produce inherently built in benefits that are a product of the formula and are therefore part of the abstract idea.
The claims are silent to how much of the functional steps are performed. For example many of the critical steps such as the encryption step, deriving step, adding steps, methods of communication etc are not provided with any form of specificity. The claims largely encompass data selection, data manipulation and formatting, and transmitting and receiving data. The claims are deemed to be an attempt to capture the application of a mathematical computation (homomorphic encryption) or capture all data aggregation using third party computations, resulting in claims that merely receive, send, and organize data. In Data Engine Technologies LLC v. Google LLC (Fed. Cir 2018) The courts determined Claim 12 of the ‘259 patent to be patent eligible because it provided limitations directed at the specific technical solution and concluded that the invention therein was "directed to a specific method for navigating through three-dimensional electronic spreadsheets" rather than an abstract idea. The courts further determined that a broad version of the claim, Claim 1 of the ‘551 patent was patent ineligible and was struck down under 35 U.S.C. 101 as the court determined the claim "generically recites associating each of the cell matrices with a user-settable page identifier and does not recite the specific implementation of a notebook tab interface." And further stated "not limited to the specific technical solution and improvement in electronic spreadsheet functionality that rendered representative claim 12 of the '259 patent eligible . . . [i]nstead, claim 1 . . . covers any means for identifying electronic spreadsheet pages.". For the same reasoning and rationale the examiner is of the opinion that the claim is directed at an abstract idea.
In its analysis, the Federal Circuit enquired whether "the claims are directed to a specific improvement in the capabilities of computing devices, or, instead, 'a process that qualifies as an "abstract idea" for which computers are invoked merely as a tool."' Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., 880 F.3d 1356, 1361-62 (Fed. Cir. 2018) (quoting Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016).
Again, for claims 11-15 and 16-20 based on the election in claims 11 and 16 could render them unelected and not required as part of the method.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The claims lack to add a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional" in the field; or
Furthermore, they simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
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.
Claim(s) 1, 3-5, 9, 11, 13-15, 16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyman (US 20200211058 A1) in view of Knox (US 11334680 B1) in view of Walcott et al. (US 20220405800 A1) in view of Becker et al. (US 20190013950 A1).
Regarding claim 11, Lyman teaches a method comprising: (0012; Method)
receiving, (Examiner notes that if the data is collected from different servers then they must have been received) from an online platform system, (demand side platform 7 see Fig 5) an advertisement opportunity data (DSP log events) comprising a plurality of advert opportunities, (0005-0006; real world impression from being served an ad) and a plurality of opportunity timestamps (0015; comparing the timestamps received for the DSP log events and the Ad server log events; See also 0012-0013; 0003; a demand side platform (“DSP”) processes bids for the impression on behalf of advertisers, and an Ad Server then delivers the winning advertiser's advertisement to the user's device.);
receiving (Examiner notes that if the data is collected from different servers then they must have been received), from an advertiser system, (Ad Server) advertisement event data (Ad server log events) comprising a plurality of events (ad serving events) and a plurality of event timestamps (timestamps for each event log); (0005-0006; real world impression from being served an ad) and a plurality of opportunity timestamps (0015; comparing the timestamps received for the DSP log events and the Ad server log events; See also 0012-0013; 0003; a demand side platform (“DSP”) processes bids for the impression on behalf of advertisers, and an Ad Server then delivers the winning advertiser's advertisement to the user's device.);
receiving an attribute result (0016-0017; pair attributes from comparing) of performing a first computation (comparing) between the advertiser system (Ad server; see ) and the platform system (demand side platform; see mapping above) to attribute (creating pairs) the plurality of events (DSP log events) to the plurality of advert opportunities (Ad server log events) based on comparing opportunity timestamps and event timestamps; (0015; Figs 1-5; The two data sets 4, 6 contain many differing such fields, but the preferred implementation focuses on comparing those that appear to be “like for like,” which may include, for example, the following: (a) the timestamp of the impression event; The events being DSP log events and the Ad server log events. See in Figs 1 and 2 showing events 4 and 6, and 0015 teaches the events 4 and 6; The comparing results in creating a table indicating direct matches of individual fields; 0003; a demand side platform (“DSP”) processes bids for the impression on behalf of advertisers, and an Ad Server then delivers the winning advertiser's advertisement to the user's device.; Examiner points to Figs 1-5 and 0012-0017 as a whole);
Lyman does not explicitly teach the underlined receiving an attribute result of performing a first two-party computation between the advertiser system and the platform system
and deriving an aggregation result that aggregates the events for a subcut of users based on performing a homomorphic encryption, or on performing a non-trusted third party computation.
In an analogous art Knox teaches receiving an attribute result (resulting mappings Fig 4 steps 408 and 410 as well as Col 14 Lines 1-40) of performing a first two-party computation (two party computation) between the advertiser system (advertisement publishers) and the platform system (platforms)(Col 4 Line 60 [Wingdings font/0xE0] Col 5 Line 14: In some embodiments, the mappings can be used to securely perform a lift analysis to measure the effectiveness of an electronic advertising campaign across publishers and platforms without revealing confidential details about each party's data to the other party or third parties.; FIG. 1 illustrates an example system 100 including a secure computation module 102, according to an embodiment of the present technology. The secure computation module 102 can be configured to allow two or more parties to jointly and securely compute functions (or operations) based on their respective datasets without revealing those datasets to one another. For example, in various embodiments, the secure computation module 102 can compute such functions as secure multi-party computations (or secure MPCs).)
and deriving an aggregation result that aggregates the events for a subcut of users (Col 14 Lines 61-67; Each SCID 556, 558, 560, and 562 can be aggregated to determine a set of values 564 that are responsive to the reach and frequency analysis. For example, the set of values 564 can include a count of unique users that were reached and an average reach frequency for the users. Many variations are possible. See Col 13 and 14 as well as Figure 4 and prior mapping which teach the result of lift analysis/secure computation based on datasets collected and vectorized and is performed for specific unique users)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Lyman] to include [a result of a two party computation being aggregated for specific users] as is taught by [Knox].
The suggestion/motivation for doing so is to [improve multi-party computation see Background].
Lyman in view of Knox do not explicitly teach deriving an aggregation result that aggregates the events for a subcut of users based on performing a homomorphic encryption, or on performing a non-trusted third party computation.
In an analogous art Walcott teaches and deriving an aggregation result (aggregate sum) that aggregates the events for a subcut of users (single customer) based on performing a homomorphic encryption, (0059; One skilled in the art will understand that the aggregate sum of the credit allocated should total the conversion event value. In an embodiment, the credit is multiplied with the revenue amount to apply a revenue weighting. In an embodiment, this multiplication shall be done according to the rules of the homomorphic encryption scheme mentioned earlier.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Lyman in view of Knox] to include [a result of aggregation based on a homomorphic encryption] as is taught by [Walcott].
The suggestion/motivation for doing so is to [improve data processing and security in advertisement and business models see Background].
Lyman in view of Knox in view of Walcott do not explicitly teach deriving an aggregation result that aggregates the events for a subcut of users based on o performing a non-trusted third party computation.
In an analogous art Becker teaches deriving an aggregation result (aggregating) that aggregates the events (purchase) for a subcut of users (users who purchased product) (0045; [0045] FIG. 1 depicts a system 100 that implements private stream aggregation (PSA) and a homomorphic signature system to improve the privacy of clients that buy a product from a seller based on advertisements from an influencer and to ensure that the influencer can verify the accuracy of aggregate information about the clients that is received from the seller; 0067; If the verification process accepts the decrypted sum of noisy plaintext data 192, then the untrusted aggregator 170 can rely upon the aggregate information in the decrypted sum of noisy plaintext data, such as the average age and gender distribution of users who bought the product from the seller. [0068] If the untrusted aggregator 170 accepts the sum of noisy plaintext data after successful verification, then the untrusted aggregator 170 uses the decrypted sum of noisy plaintext data in combination with the number of clients that generated the data to identify aggregate statistical information about the users of the clients 104A-104N in one or more categories (block 236). Using the examples described above, the processor 174 in the untrusted aggregator 170 executes the stored program instructions 182 to identify the aggregate statistical information categories including the average age (noisy sum of ages divided by the number of users) and the proportion of female and male users (sum of all numeric gender values divided by the number of users normalized between the two numeric female/male values). The untrusted aggregator 170 determines the number of clients that purchased the product based on the number of unique ciphertext/signature pairs received in fixed-sized batches of communications from a predetermined number of the clients 104A-104N (e.g. 1,000 clients in each batch in one configuration), which enables the untrusted aggregator 170 to determine the number of purchases that the clients 104A-104N made based on the advertising from the influencer I.; See also mapping below for 3rd party computation)
based on performing a homomorphic encryption, (Examiner respectfully this element is part of an “OR” limitation, the alternative element has been elected, and therefore this element is not being elected.)
Or on performing a non-trusted third party computation. (0048; the untrusted aggregator 170 or another third party computing device[0069] FIG. 3, reference 312 depicts the operation of the untrusted aggregator 170 of the influencer I in more detail. As depicted in FIG. 3, the untrusted aggregator 170 performs addition to generate the sums of the ciphertexts c.sub.i and the signatures a, using the aggregation process (Agg). The untrusted aggregator uses the PSA decryption process (Dec using the homomorphic decryption key data sk.sub.A) to decrypt the aggregate ciphertext c.sub.agg that generates the decrypted sum of noisy plaintext data (x.sub.agg) 192.[0070] During the process 200, the untrusted aggregator 170 or a third-party computing device can also verify the accuracy of the aggregate data that is published by the aggregator 150 or the untrusted aggregator 170 (block 240).)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Lyman in view of Knox in view of Walcott] to include [aggregating results based on performing a non-trusted third party computation] as is taught by [Becker].
The suggestion/motivation for doing so is to [improve data processing and security in advertisement and business models see Background].
Regarding claims 13-15, the claims are contingent on the election between “homomorphic encryption” OR “untrusted third party computation” in claim 11, of which the examiner respectfully elects “untrusted third party computation” and since claims 13-15 are contingent on the election of “homomorphic encryption” which was not elected, claims 13-15 are also not being elected.
Regarding claims 16, and 18-20, the claims inherit the rejections of claims 11, and 13-15 above for reciting similar limitations of the form of a media claim. Lyman does not explicitly teach a non-transitory media but Knox teaches Col 24 Lines 42-53.
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Lyman] to include [non-transitory computer readable media] as is taught by [Knox].
The suggestion/motivation for doing so is to [improve multi-party computation see Background].
Regarding claims 1, and 3-5, the claims inherit the same rejections as claims 11, and 13-15 above for reciting similar limitations in the form of a system claim. (0005: system)
Regarding claim 9, Lyman in view of Knox in view of Walcott in view of Becker teach the system of claim 1, and is disclosed above, Lyman does not explicitly teach but Knox further teaches wherein the platform system comprises a messaging system configured to communicate messages between users. (Col 18 Lines 19-35: The social networking system 730 is also capable of linking a variety of entities. Examples of types of nodes include users, non-person entities, content items, web pages, groups, activities, messages, concepts, and any other things that can be represented by an object in the social networking system 730. Col 20 Lines 1-20: Continuing this example, the second user may then send the first user a message within the social networking system 730. The action of sending the message, which may be stored, is another edge between the two nodes in the social graph representing the first user and the second user. Additionally, the message itself may be identified and included in the social graph as another node connected to the nodes representing the first user and the second user.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Lyman] to include [communicating messages between users] as is taught by [Knox].
The suggestion/motivation for doing so is to [improve multi-party systems Background].
Claim(s) 2, 12, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyman (US 20200211058 A1) in view of Knox (US 11334680 B1) in view of Walcott et al. (US 20220405800 A1) in view of Becker et al. (US 20190013950 A1) in view of Lizotte III (US 20130290096 A1).
Regarding claim 12, Lyman in view of Knox in view of Walcott in view of Becker teach the method of claim 11, and is disclosed above, Lyman in view of Knox in view of Walcott in view of Becker do not explicitly teach further comprising totaling a financial amount due the online platform based on the aggregation result.
In an analogous art Lizotte teaches totaling a financial amount due the online platform based on the aggregation result. (0250: The POS terminal may then transmit transaction information such as the captured payment device identification information and a total amount due for the transaction to the processing platform.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Lyman in view of Knox in view of Walcott in view of Becker] to include [totaling a financial amount due to the online platform] as is taught by [Lizotte].
The suggestion/motivation for doing so is to [improve data mobile payment 0002-0005].
Regarding claims 2 the claims inherit the same rejections as claims 12 above for reciting similar limitations in the form of a system claim. (0005: system)
Regarding claims 17, the claims inherit the rejections of claims 12 above for reciting similar limitations of the form of a media claim. Lyman does not explicitly teach a non-transitory media but Knox teaches Col 24 Lines 42-53.
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Lyman] to include [non-transitory computer readable media] as is taught by [Knox].
The suggestion/motivation for doing so is to [improve multi-party computation see Background].
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyman (US 20200211058 A1) in view of Knox (US 11334680 B1) in view of Walcott et al. (US 20220405800 A1) in view of Becker et al. (US 20190013950 A1) in view of Catalano et al. (US 20230214875 A1).
Regarding claim 10, Lyman in view of Knox in view of Walcott in view of Becker teach the system of claim 9, and is disclosed above, Lyman in view of Knox in view of Walcott in view of Becker do not explicitly teach wherein the messages comprise ephemeral messages.
In an analogous art Catalano teaches wherein the messages comprise ephemeral messages. ([0170] FIG. 9 is a schematic diagram illustrating an access-limiting process s 900, in terms of which access to content (e.g., an ephemeral message 902, and associated multimedia payload of data) or a content collection (e.g., an ephemeral message group 904) may be time-limited (e.g., made ephemeral). [0171] An ephemeral message 902 is shown to be associated with a message duration parameter 906, the value of which determines an amount of time that the ephemeral message 902 will be displayed to a receiving user of the ephemeral message 902 by the messaging client 104. In one example, an ephemeral message 902 is viewable by a receiving user for up to a maximum of 10 seconds, depending on the amount of time that the sending user specifies using the message duration parameter 906. [0172] The message duration parameter 906 and the message receiver identifier 424 are shown to be inputs to a message timer 910, which is responsible for determining the amount of time that the ephemeral message 902 is shown to a particular receiving user identified by the message receiver identifier 424. In particular, the ephemeral message 902 will only be shown to the relevant receiving user for a time period determined by the value of the message duration parameter 906. The message timer 910 is shown to provide output to a more generalized ephemeral timer system 202, which is responsible for the overall timing of display of content (e.g., an ephemeral message 902) to a receiving user. [0173] 1. The ephemeral message 902 is shown in FIG. 9 to be included within an ephemeral message group 904 (e.g., a collection of messages in a personal story, or an event story). The ephemeral message group 904 has an associated group duration parameter 908, a value of which determines a time duration for which the ephemeral message group 904 is presented and accessible to users of the messaging system 100. The group duration parameter 908, for example, may be the duration of a music concert, where the ephemeral message group 904 is a collection of content pertaining to that concert. Alternatively, a user (either the owning user or a curator user) may specify the value for the group duration parameter 908 when performing the setup and creation of the ephemeral message group 904.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Lyman in view of Knox in view of Walcott in view of Becker] to include [communicating between users ephemeral messages] as is taught by [Catalano].
The suggestion/motivation for doing so is to [improve message exchange [0003]].
Conclusion
Claims 6-8 are not being rejected under prior art rejections.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sehrawat et al. (US 20220255720 A1): [0148] Each multiplication and addition operation can occur only between compatible ciphertexts. Since the matrices A.sub.0 and A are different, ciphertexts at each level of the circuit are multiplication compatible. Hence, after level 0, the aggregator must perform “gate conversions”, i.e., transforming multiplication compatible ciphertext to addition compatible ciphertext. This can be easily carried out by performing component wise product of the multiplication compatible ciphertext with Â, where ÂA.sub.0=A mod q. Converting addition compatible ciphertext to multiplication compatible cyphertext can be accomplished by computing the component-wise product of the appropriate short matrix. [0149] The set-system custom-character can also provide support for general access structures in which parties do not rely on shared keys to carry out fully homomorphic encryption computations of ciphertext. Instead, general access structures allow for distributing shares of a secret such that any authorized subset of secret holders, as specified by the general access structure, can recompute the secret and therefore decrypt corresponding ciphertext. Such general access structures are monotone (e.g., if a subset of parties A belongs to the general access structure and if A is a subset of another set of parties B, then B also belongs to the general access structure, and hence is also authorized to recompute the secret).
Tanaka et al. (US 20200387616 A1):
[0041] [Encryption Method]
[0042] The present invention uses an encryption method that can carry out, for example, the following arithmetic without decrypting cyphertext. As a measure for implementing such an encryption method, Reference literatures 1 and 2 are known.
[0043] 1. Addition: to generate a cyphertext [a+b] of addition a+b using [a] and [b] as input.
[0044] 2. Multiplication: to generate a cyphertext [ab] of multiplication ab using [a] and [b] as input.
[0045] 3. Sum: to generate a cyphertext [s.sub.a] of sum s.sub.a using [a] as input.
[0046] 4. Sum of products: to generate a cyphertext [s.sub.ab] of sum of products s.sub.ab using [a] and [b] as input. [0047] [Reference literature 1] SHAMIR, Adi. “How to share a secret”, Communications of the ACM, 1979, 22.11: p. 612-613. [0048] [Reference literature 2] GENTRY, Craig, et al. “Fully homomorphic encryption using ideal lattices”, In: STOC. 2009. p. 169-178.
Skvortso et al. (US 20240243900 A1): [0014] In some implementations, the first encrypted data structure has an additively homomorphic encryption. Additionally, the data processing system can calculate the first sum without having to decrypt the encrypted count value of the first register and the encrypted fingerprint value of the first register. [0048] Accordingly, aspects of this technical solution can provide increased security and privacy of data and data counting systems through the use of encrypted probabilistic data structures and a homomorphic encryption (e.g., additively homomorphic encryption) scheme. In many implementations, probabilistic data structures, such as bloom filters, may be generated to determine reach and frequency of device identifiers and attributes in a networking environment. A set of data records (e.g., device identifiers, user identifiers) associated with devices or users in a network may be maintained, and a probabilistic data structure may be generated comprising values that can correspond to counter registers. Hash functions can be used to update the data structures that can be identified, and data records may be hashed to extract index values, count values, frequency values, fingerprints, or other such identifiers to one or more positions in the probabilistic data structure. An aggregated public key comprising a public key may be obtained, and the data structure can be encrypted using the aggregated shared key to generate an encrypted data structure, with the encrypted data structure transmitted to a networked worker computing device. [0074] In some implementations, the encryption key maintainer 130 can maintain, in the memory of the data processing system 105, a private decryption key corresponding to an aggregated public encryption key. The private decryption key and a public decryption key can be generated as an encryption key pair by the encryption key maintainer. For example, the encryption key maintainer 130 can choose a random integer X modulo q to generate a key. This can be the private key (sometimes referred to as the “decryption key” or “the secret key”) used for decryption. Additionally, the public key can be generated as G.sup.X. In some instances, the secret key X be distributed as shares using a secret sharing scheme. Each party (e.g., the data processing systems 105A-N) can compute a “public key share” using its share of the secret key, and the “true” public key can then be computed by combining the secret key shares in the exponent (note that combining shares is a linear operation, which can be done in the exponent using the group operation). Decryption shares can be computed by performing the decryption operation using the secret key shares and combining the result in the same manner as the public key was computed. [0197] In some implementations, the first encrypted data structure has an additively homomorphic encryption. Additionally, the system can calculate the first sum without having to decrypt the encrypted count value of the first register and without having to decrypt the encrypted fingerprint value of the first register. [0199] In some implementations, the second encrypted data structure has an additively homomorphic encryption. Additionally, the system can calculate the second sum without having to decrypt the encrypted count value of the second register and without having to decrypt the encrypted fingerprint value of the second register.
Reagan et al. (US 20240419876 A1): § 3.2.1.1.1 Homomorphic Encryption [0008] Homomorphic encryption works like standard encryption with the added benefit that functions can be computed directly on encrypted data, providing end-to-end confidentiality. HE fits the mold of today's client-cloud service model, requiring that only one party, typically the cloud, be involved in the computation. The drawbacks of HE are limited functionality and the inability to control data usage. Integer (including fixed point). HE schemes (See, e.g., the documents: Z. Brakerski, C. Gentry, and V. Vaikuntanathan, “(leveled) fully homomorphic encryption without bootstrapping,” ACM Transactions on Computation Theory, 2014 (incorporated herein by reference); J. H. Cheon, A. Kim, M. Kim, and Y. Song, “Homomorphic encryption for arithmetic of approximate numbers,” in International conference on the theory and application of cryptology and information security, 2017 (incorporated herein by reference); and J. Fan and F. Vercauteren, “Somewhat practical fully homomorphic encryption,” Cryptology ePrint Arc hive, 2012 (incorporated herein by reference).) only provide functional support for addition and multiplication, limiting what can be computed. Binary schemes exist (e.g., TFHE (See, e.g., Chillotti, N. Gama, M. Georgieva, and M. Izabache ne, “The fast fully homomorphic encryption over the torus,” Journal of Cryptology, 2020 (incorporated herein by reference).)) that, like GCs, can compute arbitrary functions. However, these are far from practical as a single gate can take 75-600 milliseconds to process (See, e.g., the documents: H. Hsiao, V. Lee, B. Reagen, and A. Alaghi, “Homomorphically encrypted computation using stochastic encodings,” arXiv preprint arXiv:2203.02547, 2022 (incorporated herein by reference); and D. Micciancio and Y. Polyakov, “Bootstrapping in fhew-like cryptosystems,” in Proceedings of the 9th on Workshop on Encrypted Computing & Applied Homomorphic Cryptography, 2021, pp. 17-28 (incorporated herein by reference).). Integer HE slowdown is typically on the order of 5-6 orders of magnitude (See, e.g., the documents: A. Feldmann, N. Samardzic, A. Krastev, S. Devadas, R. Dreslinski, K. Eldefrawy, N. Genise, C. Peikert, and D. Sanchez, “F1: A fast and programmable accelerator for fully homomorphic encryption (extended version),” 2021 (incorporated herein by reference).); and B. Reagen, W. Choi, Y. Ko, V. Lee, G.-Y. Wei, H.-H. S. Lee, and D. Brooks, “Cheetah: Optimizing and accelerating homomorphic encryption for private inference,” 2020 (incorporated herein by reference).); most systems research has been focused here (See, e.g., the documents: A. Feldmann, N. Samardzic, A. Krastev, S. Devadas, R. Dreslinski, K. Eldefrawy, N. Genise, C. Peikert, and D. Sanchez, “F1: A fast and programmable accelerator for fully homomorphic encryption (extended version),” 2021 (incorporated herein by reference); S. Kim, J. Kim, M. Kim, W. Jung, M. Rhu, J. Kim, and J. H. Ahn, “Bts: An accelerator for bootstrappable fully homomorphic encryption,” 12 2021 (incorporated herein by reference); B. Reagen, W. Choi, Y. Ko, V. Lee, G.-Y. Wei, H.-H. S. Lee, and D. Brooks, “Cheetah: Optimizing and accelerating homomorphic encryption for private inference,” 2020 (incorporated herein by reference); M. S. Riazi, K. Laine, B. Pelton, and W. Dai, “Heax: An architecture for computing on encrypted data,” in Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, 2020 (incorporated herein by reference); N. Samardzic, A. Feldmann, A. Krastev, N. Manohar, N. Genise, S. Devadas, K. Eldefrawy, C. Peikert, and D. Sanchez, “Craterlake: a hardware accelerator for efficient unbounded computation on encrypted data,” 06 2022, pp. 173-187 (incorporated herein by reference); and S. Sinha Roy, F. Turan, K. Jarvinen, F. Vercauteren, and I. Verbauwhede, “Fpga-based high-performance parallel architecture for homomorphic computing on encrypted data,” in 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 387-398 (incorporated herein by reference).). § 3.2.1.1.2 Secret-Sharing [0009] Secret-sharing (SS) enables secure computation by splitting data into shares. Each party (e.g., a client and server) computes a function on their share and their respective results can be combined to reveal the function output. Since both parties must work together to correctly perform the computation, SS provides control over how data is used in addition to confidentiality. Another benefit of SS is that many of the costly operations can be moved offline. Recent research has shown these protocols work well for private neural inference (See, e.g., the documents: Z. Ghodsi, N. K. Jha, B. Reagen, and S. Garg, “Circa: Stochastic relus for private deep learning,” 2021 (incorporated herein by reference).); N. K. Jha, Z. Ghodsi, S. Garg, and B. Reagen, “Deepreduce: Relu reduction for fast private inference,” 2021 (incorporated herein by reference); and P. Mishra, R. Lehmkuhl, A. Srinivasan, W. Zheng, and R. A. Popa, “Delphi: A cryptographic inference service for neural networks,” in 29th USENIX Security Symposium (USENIX Security 20). USENIX Association, August 2020, pp. 2505-2522 (incorporated herein by reference).). SS, like HE, is limited in that it excels at addition and multiplication, typically relying on other PPCs for non-linear functions. It is common practice to pair SS with GCs in high-performance private neural inference protocols. Another major drawback is that SS requires HE in the offline phase, which introduces high overhead.
Mozaffari et al. (US 20240064016 A1): [0019] The other approach to enforce client data privacy is secure aggregation (sometimes referred to as “sAGR”), where model update aggregation is done using cryptographic techniques such as partially homomorphic encryption or secure multi-party computation. sAGR protects privacy of clients' data from an adversarial aggregation server because the server sees just the encrypted version of clients' model updates. Moreover, this privacy is enforced without compromising global model utility. However, the encrypted model updates themselves provide the perfect cover for a malicious client to poison the global model—the server cannot tell the difference between a honest model update and a poisoned one, since both are encrypted. [0024] In various embodiments, parameter permutation for federated learning utilizes cPIR may rely on homomorphic encryption, though it can be computationally expensive, particularly for large models. However, hyperparameters may be implemented for the federated learning techniques, in some embodiments, that allow for computation/utility trade off hyper-parameters in parameter permutation for federated learning, that enables us to achieve an interesting tradeoff between computational efficiency and model utility. For example, one or more hyperparameters can be specified or changed to adjust the computation burden for a proper utility goal by altering the size and number of shuffling patterns for the parameter permutation for federated learning clients. Such hyperparameters allow various embodiments to provide LDP-federated learning guarantees at low model utility cost. In another example, hyperparameters can create shuffling windows whose size can be reduced to drastically cut computation overheads, but at the cost of reducing model utility due to lower privacy amplification (given a fixed privacy budget). In some embodiments, hyperparameter configurations can be set to provide “light” or “heavy” parameter permutation. For the hyperparameter configuration that provides a light version of parameter permutation for federated learning, where client encryption, and server aggregation need to perform using a limited amount of training time (e.g., 52.2 seconds and 21 minutes respectively), the result of federated learning to train a model that still provides some accurate results (e.g., 32.85% test accuracy) while still providing client data privacy and protection against poisoning attacks. For a hyperparameter configuration for client encryption and server aggregation with larger time allowances (e.g., 32.1 minutes and 16.4 hours respectively) greater model accuracy can be provided (e.g., 72.38% test accuracy) again while providing client data privacy and protection against poisoning attacks. The choice of hyperparameters allows for techniques for parameter permutation for federated learning to fit within the resources (e.g., time, computing resource utilization, etc.) allotted to performing federated learning. [0043] Generating PIR queries: Now the client encodes the shuffle indices π.sub.u using a PIR protocol. This process comprises two steps, as indicated at local encrypt 146a, 146b, and 146c: first creating a binary mask of the shuffled index, and then encrypting it using the public key of HE that the client received in first step (Algorithm 1, FIG. 4, line 11-12). Generally, a PIR client may access to the j.sup.th record privately from an untrusted PIR server that holds a dataset θ with d records; e.g., the PIR server cannot know that the client requested the j.sup.th record. To do so, the PIR client creates a unit vector (binary mask) {right arrow over (b)}.sub.j; of size d where all the bits are set to zero except the j.sup.th position being set to one: [0066] One threat model may be Honest-but-Curious Aggregator. In this threat model, the aggregation server correctly follows the aggregation algorithm, but may try to learn clients' private information by inspecting the model updates sent by the participants. For creating the PIR queries, Paillier homomorphic encryption may be used (as discussed above). Before starting parameter permutation for federated learning, a key server may be used to generate and distribute the keys for the homomorphic encryption (HE). A key server generates a pair of public and secret homomorphic keys (Pk, Sk), sends them to the clients, and sends only the public key to the server. Either a trusted external key server or a leader client can be responsible for this role. For the leader client, before the training starts, the aggregation server randomly selects a client as the leader. The leader client then generates the keys and distributes them to the clients and the server as above. [0068] Another threat model that can be addressed using parameter permutation for federated learning techniques may be Curious and Colluding Clients. In this threat model, some clients may collude with the FL server to get private information about a victim client by inspecting its model update. For this threat model, thresholded Paillier may be used. In the thresholded Paillier scheme, the secret key is divided to multiple shares, and each share is given to a different client. For this threat model, an external key server may generate the keys and sends (Pk, Sk.sub.i) to each client, and sends the public key to the server. Now each client can partially decrypt an encrypted message, but if less than a threshold, say t, combine their partial decrypted values, they cannot get any information about the real message. On the other hand, if combining ≥t partial decrypted values, the secret can be recovered.
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Abderrahmen Chouat
Examiner
Art Unit 2451
/Chris Parry/Supervisory Patent Examiner, Art Unit 2451