DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Disposition of Claims
Claims 1-25 are pending in the instant application. Claims 9 and 15-20 have been cancelled. No claims have been newly added. Claims 1, 2, 4-6, 14, 21, and 22 has been amended. The rejection of the pending claims is hereby made final.
Response to Remarks
The examiner has considered Applicant’s arguments pertaining to the rejection of the pending claims under 35 USC 112(b). Applicant’s arguments and amendments have been considered by the examiner and are found to be persuasive. The rejection of the pending claims under 35 USC 112 (b) is hereby withdrawn.
The examiner has considered Applicant’s arguments pertaining to the rejection of the pending claims under 35 USC 101. Applicant’s arguments and amendments have been considered by the examiner and are found to be persuasive. The rejection of the pending claims under 35 USC 101 is hereby withdrawn.
The examiner has considered Applicant’s arguments and amendments pertaining to the rejection of the pending claims under 35 USC 103 in view of the applied prior art of record, but finds said arguments to be moot in view of the grounds of rejection presented below.
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.
Claims 1-8, 10-14, and 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US 2023/0143933) in view of Lee et al (US 2024/0160898).
Regarding claims 1 and 21, the prior art discloses a method and a system comprising a memory to store data and instructions; and a processor operable to communicate with the memory, wherein the processor is operable to: comprising: preprocessing, by a neural network, first party data of a first party (see at least paragraph [0134] to Wang et al, wherein by a first server of a secure multi-party computation (MPC) system and from an application on a user device, a request for a digital component (402). For example, computing system MPC1 of the MPC cluster 130 can receive a request for one or more digital components from client device 110 for a particular digital component slot); and preprocessing the first party data and the second party data enables data exchange between the first party and the second party for the secure computation while maintaining a privacy of the first party data and the second party data (see at least paragraph [0135] to Wang et al, wherein he sub-requests can include a secret share for each of the computing systems MPC1 and MPC2 of a digital component request based on sensitive information, and a digital component request based on non-sensitive information for the SSP 170.); and outputting the secure computation).
Wang et al does not appear to explicitly disclose the use of a neural network; preprocessing, by a neural network, second party data of a second party; performing a secure computation of the first party data and the second party data using a secure forward pass gadget API that executes the secure computation and a secure backward pass API that executes the secure computation on an output of a forward pass of the neural network, wherein the secure forward pass API and the secure backward pass API enable privacy-preserving floating point operations.
However, Lee et al discloses a method of using FPGA for AI inference software stack acceleration, further comprising the use of a neural network; preprocessing, by a neural network, second party data of a second party; performing a secure computation of the first party data and the second party data using a secure forward pass gadget API that executes the secure computation and a secure backward pass API that executes the secure computation on an output of a forward pass of the neural network, wherein the secure forward pass API and the secure backward pass API enable privacy-preserving floating point operation (see at least paragraph [0024] to Lee et al, wherein the neural network models comprise of activation nodes, connection between nodes and weight parameter related to said connection. The unquantized weight parameters are usually in floating-point value which requires a larger number of bits to represent said value. The quantization is meant to convert the neural network model with weight parameters of floating-point values, to weight parameters of full integer values, which in turn requires a lower number of bits to represent said value. Quantization can also be done on the inputs, biases, activations, etc. For example, the quantization can be done using the TensorFlow Lite converter, which converts the TensorFlow neural network model to TensorFlow Lite model. If the neural network model is trained with a different training framework such as Pytorch (not Tensorflow), Tensorflow Lite converter can also be used to perform the quantization. There are Python function/API to facilitate the conversion between different saved model format from various training frameworks. Quantization can be done post-training or via quantization aware training. Post-training quantization refers to a process of performing quantization on a trained neural network model. Quantization aware training emulates inference-time quantization, which models quantization errors in forward and backward passes. During quantization aware training, forward propagation is based on integers (which is low precision behavior) while back propagation is based on floating point).
The examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The examiner submits that the combination of the teaching of the system and method for using secure multi-party computation to improve content selection process integrity, as disclosed by Wang et al and the system and method of securing sensitive information, as taught by Lee et al, in order to for a more secure form of sharing data between two parties that ensures the privacy of sensitive information, could have been readily and easily implemented, with a reasonable expectation of success. As such, the aforementioned combination is found to be obvious to try, given the state of the art at the time of filing.
Regarding claim 2, the prior art discloses the method of claim 1, wherein the secure forward pass API performs, using a forward pass of the neural network, secure mathematical operations on the first party data and the second party data (see at least paragraphs [0095]-[0097] to Wang et al).
Regarding claim 3, the prior art discloses the method of claim 2, wherein the secure mathematical operations include one or more of matrix addition, matrix multiplication, or a comparison (see at least paragraph [0095] to Wang et al, wherein where Π denotes the multiplication of multiple items).
Regarding claim 4, the prior art discloses the method of claim 1, wherein the secure forward pass API performs the secure two party computation of a thirty two bit single precision floating point operations on the first party data and the second party data (see at least paragraph [0097] to Wang et al, wherein the false positive rate of the cuckoo filter is controlled by the length (i.e. number of bits) of the entries in the cuckoo filter, k, and can affect the candidacy of the selection value).
Regarding claim 5, the prior art discloses the method of claim 1, wherein the secure performs a secure two party computation of a thirty two bit single precision floating point operations (see at least paragraph [0025] to Lee et al). The initial training may be performed in an offline stage).
Regarding claims 6 and 22, the prior art discloses the method and system of claims 1 and 21, wherein the secure backward pass API updates the neural network to optimize towards a reward (see at least paragraph [0025] to Lee et al). The initial training may be performed in an offline stage).
Regarding claim 7, the prior art discloses the method of claim 1, wherein the first party data is private data of the first party and the second party data is private data of the second party (see at least paragraph [0071] to Wang et al, wherein the application 112 encrypts the digital component requests based on sensitive information because if MPC1 can access the request parameters intended for MPC2, MPC1 can reconstruct the cuckoo filter table A and recover the user group identifiers, and thus breach the privacy of the user of the application 112).
Regarding claim 8, the prior art discloses the method of claim 1, wherein preprocessing the first party data and the second party data makes the first party data and the second party data compatible for the secure computation (see at least paragraph [0063] to Wang et al, wherein the computing systems MPC1 and MPC2 can compute the secret shares for the result of the selection processes using secure MPC techniques with one or more roundtrips between the computing systems).
Regarding claims 10 and 24, the prior art discloses the method and system of claims 1 and 21, wherein the first party and the second party are in a supply chain optimizing towards individual rewards and the method further comprises: adjusting the supply chain in response to the secure computation (see at least paragraph [0064] to Wang et al, wherein once the secret share computation is completed, MPC1 and MPC2 can each return one of the shares of the selection result to the client device. The client device can then reconstruct the selection result in cleartext using the two secret shares).
Regarding claim 11, the prior art discloses the method of claim 10, wherein the supply chain is a food supply chain (see at least paragraph [0025] to Wang et al, wherein when a user visits a particular website and interacts with a particular item presented on the website or adds an item to a virtual cart, the user can be assigned to a group of users who have visited the same website or other websites that are contextually similar or are interested in the same item. To illustrate, if the user of the client device 110 searches for shoes and visits multiple webpages of different shoe manufacturers, the user can be assigned to the user group “shoes,” which can include identifiers for all users who have visited websites related to shoes). The examiner submits that the disclosure of Wang et al teaches wherein users may be grouped with other users based off of their interaction with a merchant website. The examiner submits that food merchants and associated websites would be included within the disclosure of the applied prior art, and therefore the limitation “food supply chain” is considered to be anticipated by the language of the disclosure, as it is implicitly disclosed within the teaching of Wang et al.
Regarding claim 12, the prior art discloses the method of claim 10, wherein the supply chain is one of an energy market, a cloud supply chain, communication networks, or a media supply chain (see at least paragraph [0038] to Wang et al, wherein a DSP 150 can interact with multiple supply-side platforms SSPs on behalf of digital component providers 160 to provide digital components for presentation with the resources and/or applications of multiple different publishers 140).
Regarding claims 13 and 23, the prior art discloses the method and system of claims 1 and 21, further comprising: making a decision dependent on the first party data and the second party data in response to the secure computation (see at least paragraph [0075] to Wang et al, wherein JIT selection values are conditional, depending on, for example: (1) whether the user is a member of a particular user group).
Regarding claim 14, the prior art discloses the method of claim 1, further comprising: a plurality of additional parties in a supply chain (see at least paragraph [0137] to Wang et al, wherein the two or more different servers include a supply- side platform server that automates the process of distributing digital components and communicates with one or more content publishers); performing, using the secure forward pass API and the secure backward pass API in the neural network, the secure computation of data of the plurality of additional parties (see at least paragraph [0074] to Wang et al, wherein the request can be parsed into sub-requests and parses the request into the three sub-requests so that the sub-requests can be forwarded to separate recipients); and outputting the secure computation (see at least paragraph [0070] to Wang et al, wherein The application 112 can then transmit a digital component request that includes a corresponding secret shares to the corresponding computation server of the MPC system).
Regarding claim 25, the prior art discloses the device of claim 24, wherein the first party has a first goal for the action and the second party has a second goal for the action (see at least paragraph [0039] to Wang et al, wherein groups, e.g., user interest groups, cohorts of similar users, or other group types involving similar user data, when the users visit particular resources or perform particular actions at the resource (e.g., interact with a particular item presented on a web page or add the item to a virtual cart).
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The examiner has considered all references listed on the Notice of References Cited, PTO-892.
The examiner has considered all references cited on the Information Disclosure Statement submitted by Applicant, PTO-1449.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd A Obeid can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TALIA F CRAWLEY/Primary Examiner, Art Unit 3627
