DETAILED ACTION
This action is in response to communication filed on 11/20/2024
Claims 21-40 are pending.
Claims 1-20 have been cancelled.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/5/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21, 25, 30 and 35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 5, 14, 20, of U.S. Patent No. 10,938,950. Although the claims at issue are not identical, they are not patentably distinct from each other because system with privacy/precision tiers, resource value, blockchain combination is predictable. Further, a comparison between the claims of the instant application and the claims of the patent reveals the patented claims include almost all elements of the instant application. Thus, the claim invention of the instant application is anticipated by the patented invention, thus without a terminal disclaimer, the species claims preclude issuance of the generic application. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claims 21, 30, 35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 11-14, 17-20 of U.S. Patent No. 11,323,544. Although the claims at issue are not identical, they are not patentably distinct from each other because system with privacy/precision tiers, resource value, blockchain combination is predictable. Further, a comparison between the claims of the instant application and the claims of the patent reveals the patented claims include almost all elements of the instant application. Thus, the claim invention of the instant application is anticipated by the patented invention, thus without a terminal disclaimer, the species claims preclude issuance of the generic application. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claims 21, 25, 30 and 35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 11-18 of U.S. Patent No. 11,683,397. Although the claims at issue are not identical, they are not patentably distinct from each other because system with privacy/precision tiers, resource value, blockchain combination is predictable. Further, a comparison between the claims of the instant application and the claims of the patent reveals the patented claims include almost all elements of the instant application. Thus, the claim invention of the instant application is anticipated by the patented invention, thus without a terminal disclaimer, the species claims preclude issuance of the generic application. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claim 21, 24, 30, 35 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 6, 11, 14, 16, 19 of U.S. Patent No. 12,088,687. Although the claims at issue are not identical, they are not patentably distinct from each other because system with privacy/precision tiers, resource value, blockchain combination is predictable. Further, a comparison between the claims of the instant application and the claims of the patent reveals the patented claims include almost all elements of the instant application. Thus, the claim invention of the instant application is anticipated by the patented invention, thus without a terminal disclaimer, the species claims preclude issuance of the generic application. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993).
Claim Objections
Claim 30 is objected to because of the following informalities: On line 6 of the claim, the element “the data aggregation computer process” should be --the data aggregation computer processor--. Appropriate correction is required.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 21-24, 26-32, 34-36, 38, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Freudiger et al. (US 2016/0323102) in view of Kailash et al. (US 2016/0048558).
Regarding claim 21, Freudiger discloses a method of analyzing and aggregating data, comprising:
receiving, via a processor, the data from a plurality of data sources (Freudiger describes collecting data from a plurality of sources; see [0035] a Data Owner or Insight Provider receives from a Data Seeker or Insight Seeker a request for insights (step 31). The request and the subsequent information enhancing is executed in a privacy-preserving manner. The Data Seeker formulates a list of a plurality of secondary objects);
aggregating, via the processor, the data (Freudiger describes creating a list of insights from computing the collected data; see [0035] at least one association relationship between the primary object and a plurality of associated objects is computed to obtain a list of insights (step 35)); and
calculating, via the processor, a resource value for the subset of the aggregated data (Freudiger describes value is calculated based on the aggregated data; see [0057] charges can be based on the number of queries, amount of insights shared, cost of the content, and other factors. Payments may be processed before, during and after the data sharing).
However, the prior art does not explicitly disclose selecting, via the processor, a subset of the aggregated data based on a data request, wherein the data request is indicative of a set of data with a level of specificity above a threshold level of specificity.
Kailash in the field of the same endeavor discloses techniques for optimizing query processing in database by maintaining aggregates at multiple grain sizes (fine and coarse), splitting queries into aligned coarse-grain core ranges and unaligned fine-grain edges, and combining results through addition or subtraction with an exclusion threshold to minimize reads. In particular, Kailash teaches the following:
selecting, via the processor, a subset of the aggregated data based on a data request, wherein the data request is indicative of a set of data with a level of specificity above a threshold level of specificity (Kailash describes a query range indicates the level of specificity (e.g., a sub-day range for fine grain precision) and the exclusion threshold determines when to select and adjust misalignment exceeds threshold, the system shifts to finer granularity to achieve the requested specificity; see [0030] the exclusion threshold (exThres) determines for what amount of un-alignment in the query range, exclusion should be preferred over normal inclusion (addition) method. For example, an aggregate with grain size of a day, the exclusion threshold should be half a day but the actual value depends on the next immediate finer grained aggregate. For example, in this case of the next immediate aggregates grain size was hour then the value would be 12 hours or if the next immediate grain size was minute then the value would be 720 minutes).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to modify the prior art with the teaching of Kailash to incorporate techniques for optimizing query processing in database. One would be motivated to combine the prior art with Kailash to enhance the efficiency of privacy-preserving insight sharing in big data system by incorporating optimized query processing using multigrain aggregates, thereby reducing computation time and resource usage for handling large-scale aggregated dataset while maintaining controlled privacy levels.
Regarding claim 22, Freudiger-Kailash discloses the method of claim 21, wherein the subset of the aggregated data comprises proprietary data (Freudiger [0020] Data Owner (DO) can provide targeted recommendations through a privacy-preserving insight sharing system usable by a Data Seeker (DS) that wants to purchase recommendations from Data Owner).
Regarding claim 23, Freudiger-Kailash discloses the method of claim 22, wherein calculating, via the processor, the resource value for the subset of the aggregated data comprises:
determining a price negotiated between a respective data source of the plurality of data sources and a data consumer associated with the data request (Freudiger [0057] payments are transferred from Data Seeker to Data Owner (step 51). Typically, charges can be based on the number of queries, amount of insights shared, cost of the content, and other factors. Payments may be processed before, during and after the data sharing); and
setting the price of the proprietary data as the resource value (Freudiger [0058] the sharing of insights between the Data Owner and Data Seeker can be made more advantageous for either or both parties through a controlled, graded, or targeted search or release. The controlled, graded, or targeted search or release of insights can be achieved by appropriately selecting a threshold λ).
Regarding claim 24, Freudiger-Kailash discloses the method of claim 21, comprising transmitting, via the processor, the subset of the aggregated data to a data consumer associated with the data request (Freudiger [0035] the modified list of the insights is transmitted to the requesting party (step 38)).
Regarding claim 26, Freudiger-Kailash discloses the method of claim 21, wherein calculating, via the processor, the resource value for the subset of the aggregated data comprises:
determining compensation provided to a respective data source of the plurality of data sources; and calculating the resource value based on the compensation (Freudiger [0057] payments are transferred from Data Seeker to Data Owner (step 51). Typically, charges can be based on the number of queries, amount of insights shared, cost of the content, and other factors. Payments may be processed before, during and after the data sharing).
Regarding claim 27, Freudiger-Kailash discloses the method of claim 21, wherein the subset of the aggregated data comprises business data, financial data, personal data, marketing data, social media data, performance data, proprietary data, technical data, or any combination thereof (see Freudiger [0024] the association relationship is valuable for manufacturers, merchants and consumers. Armed with the knowledge, manufacturers may better plan production lines, merchants can target advertisements to a more receptive audience and optimize inventory, and consumer may be relieved from unwanted advertisement bombardment).
Regarding claim 28, Freudiger-Kailash discloses the method of claim 21, comprising: normalizing, via the processor, the aggregated data based on analytics; and providing, via the processor, the normalized aggregated data to a machine learning model for training (Freudiger [0032] Data Owner gathers data and learns insights into the relationships between a primary good x.sub.p and a group of associated goods expressed as x.sub.1, x.sub.2, . . . , x.sub.n, using a machine-learning algorithm).
Regarding claim 29, Freudiger-Kailash discloses the method of claim 28, wherein calculating, via the processor, the resource value comprises: providing the subset of the aggregated data to the machine learning model; and calculating the resource value based on an output of the machine learning model (see Freudiger [0034] the Data Owner may compute a recommendation based on the input of the Data Seeker, such as a primary merchandise, good, object, service, or event. The Data Owner also selects how many insights are shared with the Data Seeker, for example using a threshold. The threshold can also be restricted based on the input from the Data Seeker. Finally, insights, in the form of associations, are shared with the Data Seeker using machine-learning algorithm).
Regarding claim 30, Freudiger-Kailash discloses a system, comprising: an aggregation platform data store adapted to:
receive data from a plurality of data sources (Freudiger describes collecting data from a plurality of sources; see [0035] a Data Owner or Insight Provider receives from a Data Seeker or Insight Seeker a request for insights (step 31). The request and the subsequent information enhancing is executed in a privacy-preserving manner. The Data Seeker formulates a list of a plurality of secondary objects); and
aggregate the data (Freudiger describes creating a list of insights from computing the collected data; see [0035] at least one association relationship between the primary object and a plurality of associated objects is computed to obtain a list of insights (step 35)); and
a data aggregation computer processor coupled to the aggregation platform data store, the data aggregation computer process adapted to:
identify a subset of the aggregated data based on a data request (Freudiger describes the data owner receives a data request in the form of an “input/query” from the data seeker and in direct response, identifies/computes the specific subset of relevant insights from its aggregated dataset; see [0051] the Data Owner transmits the modified query result rep.sub.1={H(x.sub.1).sup.d.Math.r, H(x.sub.2).sup.d.Math.r, . . . , H(x.sub.n).sup.d.Math.r} to the Data Seeker (step 44));
determine a privacy tier associated with the subset of the data (Freudiger [0012] The extent of sharing can be limited by selecting some insights to share using a predefined privacy threshold depending how much private information a Data Owner is willing to share or Data Seeker is willing to pay for),
wherein the privacy tier is associated with a level of specificity associated with identifying a particular data source (Kailash describes a query range indicates the level of specificity (e.g., a sub-day range for fine grain precision) and the exclusion threshold determines when to select and adjust misalignment exceeds threshold, the system shifts to finer granularity to achieve the requested specificity; see [0030] the exclusion threshold (exThres) determines for what amount of un-alignment in the query range, exclusion should be preferred over normal inclusion (addition) method. For example, an aggregate with grain size of a day, the exclusion threshold should be half a day but the actual value depends on the next immediate finer grained aggregate. For example, in this case of the next immediate aggregates grain size was hour then the value would be 12 hours or if the next immediate grain size was minute then the value would be 720 minutes. Rationale to combine is similar to claim 21); and
calculate a resource value for the subset of the aggregated data based on the privacy tier (Freudiger describes value is calculated based on the aggregated data; see [0057] charges can be based on the number of queries, amount of insights shared, cost of the content, and other factors. Payments may be processed before, during and after the data sharing).
Regarding claim 31, Freudiger-Kailash discloses the system of claim 30, wherein the data aggregation computer processor is adapted to:
normalize the aggregated data via an analytic engine; and provide the normalized aggregated data to a machine learning model for pre-training, wherein the machine learning model is adapted to generate an output based on the aggregated data (Freudiger [0032] Data Owner gathers data and learns insights into the relationships between a primary good x.sub.p and a group of associated goods expressed as x.sub.1, x.sub.2, . . . , x.sub.n, using a machine-learning algorithm).
Regarding claim 32, Freudiger-Kailash discloses the system of claim 31, wherein the data aggregation computer processor is adapted to implement the machine learning model to calculate the resource value for the subset of the aggregated data based on the generated output (see Freudiger [0034] the Data Owner may compute a recommendation based on the input of the Data Seeker, such as a primary merchandise, good, object, service, or event. The Data Owner also selects how many insights are shared with the Data Seeker, for example using a threshold. The threshold can also be restricted based on the input from the Data Seeker. Finally, insights, in the form of associations, are shared with the Data Seeker using machine-learning algorithm).
Regarding claim 34, Freudiger-Kailash discloses the system of claim 30, wherein the data aggregation computer processor is adapted to transmit the subset of the aggregated data with at least one of: a per use license, a limited use license, a sell-out license, and a sub-license right (Freudiger [0033] Data Owner may prefer to share data with a Data Seeker only on a need-to-know or pay-to-view basis, or in a limited, controlled, and targeted manner).
Regarding claim 35, Freudiger-Kailash discloses a system, comprising: an aggregation platform data store adapted to store data from a plurality of data sources; and a data aggregation computer processor coupled to the aggregation platform data store, wherein the data aggregation computer processor is adapted to:
access the aggregation platform data store to identify a subset of data based on a data request (Freudiger describes accesses the data store to compute and identify insights (subset) based on a request; see [0035]; a Data Owner or Insight Provider receives from a Data Seeker or Insight Seeker a request for insights (step 31). At least one association relationship between the primary object and a plurality of associated objects is computed to obtain a list of insights (step 35);
determine a precision tier associated with the subset of the data, wherein the precision tier is associated with a level of detail of the data from the plurality of data sources, and (Kailash describes determining grain sizes (precision tiers) for subsets, where finer grains provide higher detail; see [0015] the coarse grain aggregates 14 deal with a larger dimension than the fine grain aggregate 12. For example, with time as the dimension, the coarse grain aggregates 14 can be in days and the fine grain aggregates 12 can be in hours. Rationale to combine is similar to claim 21);
wherein the precision tier is associated with a plurality of data items collected over a period of time (Kailash describes precision tiers (grain size) associate with multiple data items aggregated over time periods (e.g., hour/day); see [0026-0027] time is the most common dimension that can be divided into range and is very popular in event logs and streaming data. The grain size of these aggregates could vary from small variation on the value of the dimension to a large variation. For example, time-based aggregates could have the aggregate grain size as hour, day or month); and
calculate a resource value for the subset of the data based on the precision tier (see Freudiger [0034] upon a request from a Data Seeker for sharing the insights, the Data Owner may monetize data by sharing the insights in a privacy-preserving way for a fee. To do so, the Data Owner may compute a recommendation based on the input of the Data Seeker, such as a primary merchandise, good, object, service, or event. The Data Owner also selects how many insights are shared with the Data Seeker, for example using a threshold. The threshold can also be restricted based on the input from the Data Seeker. Finally, insights, in the form of associations, are shared with the Data Seeker).
Regarding claim 36, Freudiger-Kailash discloses the system of claim 35, wherein the data aggregation computer processor is adapted to: transmit at least a portion of the resource value to at least one data source of the plurality of data sources associated with the subset of the data (Freudiger [0057] payments are transferred from Data Seeker to Data Owner (step 51). Typically, charges can be based on the number of queries, amount of insights shared, cost of the content, and other factors. Payments may be processed before, during and after the data sharing).
Regarding claim 38, Freudiger-Kailash discloses the system of claim 35, wherein the subset of the data is associated with a precision identifier identifying the level of detail (Kailash describes determining grain sizes (precision tiers) for subsets, where finer grains provide higher detail; see [0015] the coarse grain aggregates 14 deal with a larger dimension than the fine grain aggregate 12. For example, with time as the dimension, the coarse grain aggregates 14 can be in days and the fine grain aggregates 12 can be in hours).
Regarding claim 40, Freudiger-Kailash discloses the system of claim 35, wherein the data aggregation computer processor is adapted to:
provide the data request to a machine learning model to calculate the resource value associated with the data request (Freudiger [0047] the OPRF scheme can be applied to allow Data Owner sharing insight with Data Seeker for compensation); and
calculate the resource value based on an output of the machine learning model and the precision tier (Kailash describes precision tiers (grain size) associate with multiple data items aggregated over time periods (e.g., hour/day); see [0026-0027] time is the most common dimension that can be divided into range and is very popular in event logs and streaming data. The grain size of these aggregates could vary from small variation on the value of the dimension to a large variation. For example, time-based aggregates could have the aggregate grain size as hour, day or month).
Claims 25, 33, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Freudiger et al. (US 2016/0323102) in view of Kailash et al. (US 2016/0048558) in view of Fay et al. (US 2016/0292672).
Regarding claim 25, Freudiger-Kailash discloses the invention substantially, however the prior art does not explicitly disclose the method of claim 24, comprising recording, via the processor, information associated with the data request onto a distributed ledger comprising blockchain technology in response to transmitting the subset of the aggregated data
Fay in the field of the same endeavor discloses techniques for blockchain-based asset exchange. In particular, Fay teaches the following:
comprising recording, via the processor, information associated with the data request onto a distributed ledger comprising blockchain technology in response to transmitting the subset of the aggregated data (Fay [0003] the blockchain is a data structure that stores a list of transactions and can be thought of as a distributed electronic ledger that records transactions between source identifier(s) and destination identifier(s)).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to modify the prior art with the teaching of Fay to incorporate techniques for blockchain-based asset exchange. One would be motivated to combine Fay with the prior art to integrate blockchain-based transaction monitoring and digital currency payments into the privacy-preserving data aggregation system, thereby enhancing secure, auditable, and monetized data exchange.
Regarding claim 33, Freudiger-Kailash discloses the system of claim 30, wherein the resource value comprises digital currency (Fay [0003] blockchain technology (sometimes simply referred to as blockchain) is a relatively new technology that has been used in digital currency implementations. It is described in a 2008 article by Satoshi Nakamoto, called “Bitcoin: A Peer-to-Peer Electronic Cash System,” the entire contents of which are hereby incorporated by reference. Rationale to combine is similar to claim 25).
Regarding claim 37, Freudiger-Kailash discloses the system of claim 35, wherein the at least a portion of the resource value comprises a digital currency (Fay [0003] blockchain technology (sometimes simply referred to as blockchain) is a relatively new technology that has been used in digital currency implementations. It is described in a 2008 article by Satoshi Nakamoto, called “Bitcoin: A Peer-to-Peer Electronic Cash System,” the entire contents of which are hereby incorporated by reference. Rationale to combine is similar to claim 25).
Claim 39 is rejected under 35 U.S.C. 103 as being unpatentable over Freudiger et al. (US 2016/0323102) in view of Kailash et al. (US 2016/0048558) in view of Tran et al. (US 2005/0210008).
Regarding claim 39, Freudiger-Kailash discloses the invention substantially, however the prior art does not explicitly disclose the system of claim 35, wherein the data aggregation computer processor is adapted to adjust the resource value for the subset of the data based on identifying a copyright corresponding to a piece of data within the subset of the data.
Tran in the field of the same endeavor discloses techniques for network-based patent document analysis. In particular, Tran teaches the following:
wherein the data aggregation computer processor is adapted to adjust the resource value for the subset of the data based on identifying a copyright corresponding to a piece of data within the subset of the data (see Tran [0222] the portal also provides access to a bid, auction and sale system wherein the computer system establishes a virtual showroom which displays the IPs offered for sale and certain other information, such as the offeror's minimum opening bid price and bid cycle data which enables the potential purchaser or customer to view the IP asset, view rating information regarding the IP asset and place a bid or a number of bids to purchase the IP asset).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to modify the prior art with the teaching of Tran to incorporate techniques for network-based patent document analysis. One would be motivated to combine Tran with the prior art to integrate intellectual property analysis and valuation techniques into the privacy-preserving data aggregation system, thereby enabling dynamic resource value adjustment for copyrighted data subsets while maintaining efficient query processing and controlled specificity levels.
Conclusion
For the reason above, claims 21-40 have been rejected and remain pending.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIMMY H TRAN whose telephone number is (571)270-5638. The examiner can normally be reached Monday-Friday 9am-5pm PST.
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JIMMY H TRAN
Primary Examiner
Art Unit 2451
/JIMMY H TRAN/Primary Examiner, Art Unit 2451