DETAILED ACTION
This Non-Final Office Action is in response to Applicant's amendments and arguments and request for continued examination filed on December 11, 2025. Applicant has amended claims 1, 8 and 15. Currently, claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/11/25 has been entered.
Response to Amendments
The 35 U.S.C. 112 rejections of claims 1-20 are withdrawn in light of applicant’s amendments to claims 1, 8 and 15.
The 35 U.S.C. 103 rejections of claims 1-20 are maintained in light of applicant’s amendments to claims 1, 8 and 15.
Response to Arguments
Applicant remarks submitted on 12/11/25 have been considered and are not persuasive. Applicant argues on p. 2 of the remarks that the 103 rejections are improper. Examiner disagrees. Applicant argues on p. 2 of the remarks that Rahmat does not teach access to the underlying consumer data or consumer accessible data. Applicant argues that Rahmat discloses generating and transmitting synthetic or fake data that explicitly does not contain actual data values which contracts that the device “processes the consumer data locally by itself to generate a response that includes one or more of the one or more elements of consumer data.” Examiner disagrees and notes that Rahmat at para [0022] and [0026] shows the data that the actual data that is collected from the OT network and that the collected data is copied and then processed so as to be privacy protected. Moreover, para [0032] shows this collected data is used to train the neural network which generates the synthetic output data. This can be considered “processes the consumer data locally by itself to generate a response that includes one or more of the one or more elements of consumer data.” given broadest reasonable interpretation. Therefore, the 103 rejections are maintained.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-9, 12-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mashima et al. (US 2016/0055349 A1) (hereinafter Mashima) in view of Ortiz et al. (US 2020/0014691 A1) (hereinafter Ortiz) in view of Rahmat et al. (US 2021/0385196 A1) (hereinafter Rahmat)
Claims 1, 8 and 15:
Mashima, as shown, discloses the following limitations of claims 1, 8 and 15:
A server (and corresponding method and non-transitory readable medium – see para [0027], showing equivalent computing functionality) comprising: a processor; and one or more memory units storing computer-executable instructions, which when executed by the processor (see para [0027], “The customer device 104 may include a computing device that includes a processor, memory, and network communication capabilities. For example, the customer device 104 may include a laptop computer, a desktop computer, a tablet computer, a mobile telephone, a personal digital assistant (PDA), a smartphone, a mobile e-mail device, a portable game player, a portable music player, a television with one or more processors embedded therein or coupled thereto, or other electronic device capable of accessing the network 122.”), cause the server to:
receive from a remote system in networked communication with the server, a query for one or more elements of consumer data (see para [0004], "According to an aspect of an embodiment, a method of customer data management may include communicating to a third party service provider (third party) an anonymous customer identifier (customer ID) that is uniquely associated with a customer. The method may include receiving from the third party a customer data query that references the customer using the customer ID and requests customer data. The method may include determining whether an access control policy allows disclosure of the customer data requested in the customer data query. In response to the access control policy allowing disclosure of the requested customer data, the method may include accessing the requested customer data and communicating the requested customer data to the third party. In response to the access control policy prohibiting disclosure of the requested customer data, the method may include denying the customer data query." And Figs 1A, 1B);
after receiving the query, communicate to a plurality of consumer devices that are in networked communication with the server, a message that specifies the query and that indicates that one or more elements of consumer data accessible to respective consumer devices of the plurality and that are associated with the query should be communicated to the remote system from the respective consumer devices to the remote system via a communication path that does not include the server (see para [0020], "The resource supply system 100 depicted in FIG. 1A may include the utility 108, the third party 120, the site 128, a public repository (hereinafter “repository”) 124, and the customer 102. The utility 108 may be associated with a utility server 110, the customer 102 may be associated with the site 128 and a customer device 104, and the third party 120 may be associated with a third party server 114. The term “associated with,” when used herein to refer to a relationship between an entity (e.g., the third party 120 and the utility 108) and a server (e.g., the third party server 114 or the utility server 110) or between the customer 102 and the site 128 and the customer device 104, may indicate that the entity and/or the customer 102 owns or otherwise controls, directly or indirectly, the server (110 or 114) or the site 128 and the customer device 104. For example, the third party server 114 may be controlled by the third party 120 and the site 128 and the customer device 104 may be controlled by the customer 102. Data and information communicated from the server or the site 128 and the customer device 104 may be attributed to the entity associated therewith. Additionally, data and information communicated to the server or the site 128 and the customer device 104 may be intended for the entity associated therewith." showing that the customer or third party can control the server and communicate directly to the customer device or third party shows not including a server would have been obvious to one of ordinary skill in the art and see para [0015]-[0017]),
wherein after receiving the message, each consumer device of the plurality retrieves one or more of the one or more elements of consumer data associated with the query from one or more data sources locally (see para [0015], "The utility may determine whether a policy associated with the customer allows disclosure of customer data requested in the customer data query." where it would be obvious to one of ordinary skill in the art that a policy that allows also shows no allowing which is equivalent to inaccessible and see para [0020], "The resource supply system 100 depicted in FIG. 1A may include the utility 108, the third party 120, the site 128, a public repository (hereinafter “repository”) 124, and the customer 102. The utility 108 may be associated with a utility server 110, the customer 102 may be associated with the site 128 and a customer device 104, and the third party 120 may be associated with a third party server 114. The term “associated with,” when used herein to refer to a relationship between an entity (e.g., the third party 120 and the utility 108) and a server (e.g., the third party server 114 or the utility server 110) or between the customer 102 and the site 128 and the customer device 104, may indicate that the entity and/or the customer 102 owns or otherwise controls, directly or indirectly, the server (110 or 114) or the site 128 and the customer device 104. For example, the third party server 114 may be controlled by the third party 120 and the site 128 and the customer device 104 may be controlled by the customer 102. Data and information communicated from the server or the site 128 and the customer device 104 may be attributed to the entity associated therewith. Additionally, data and information communicated to the server or the site 128 and the customer device 104 may be intended for the entity associated therewith.")… and communicates to the remote system, one or more of the elements of consumer data associated with the query that the consumer device is permitted to communicate such that respective identities of the one or more data sources are unknown to the remote system and the server (see para [0014], "An example embodiment includes a method of customer data management. The method may be implemented in data analytics outsourcing. For instance, a utility may implement the method or an entity associated with the utility may implement the method to provide customer data that may be used to forecast energy curtailment potential of a customer. In the method, the third party and the utility may refer to a customer using an anonymous customer identifier (customer ID). The customer ID may be uniquely associated with the customer and may not include any information from which the third party is able to ascertain an identity of the customer. The customer ID may be shared between the utility and the third party." and see para [0016], "In response to the policy allowing disclosure of the requested customer data, the requested customer data may be accessed and communicated to the third party." and see para [0017], "the requested customer data may include basic customer data, which may include data locally stored or controlled by the utility and/or data defined by the policy as basic customer data." and see para [0024], "The repository 124 may include any storage device or storage server that may be capable of communication via the network 122. The repository 124 may include memory and a processor. The repository 124 may host or otherwise store external customer data. Generally, external customer data may include data or information pertaining to the customer 102 that is not stored or directly controlled by the utility 108. The external customer data may be accessible at the repository 124 by providing some basic customer data to the repository 124. For example, the external customer data may include a square footage of the site 128. The utility 108 may provide to the repository 124 an address, which may be basic customer data controlled by and/or stored at the utility server 110 and/or defined by a policy to be basic customer data, to access the square footage."), and
receive, from each consumer device permitted to communicate one or more of the one or more elements of consumer data were communicated to the remote system (see para [0037], "the policy engine 112 may access external customer data from the repository 124. For example, the policy engine 112 may access the basic customer data from the customer database 106 and provide it to the repository 124. In response, the repository 124 may supply to the policy engine 112 the external customer data. In some embodiments, the third party 120 may provide instructions that provide direction to the repository 124 and/or basic customer data involved in obtaining the external customer data. The policy engine 112 may communicate the requested customer data, which may include basic customer data and/or external customer data, to the third party 120 and/or the third party server 114. In response to the policy prohibiting disclosure of the requested customer data, the policy engine 112 may deny the query.").
Mashima, however, does not specifically disclose the remote system consolidates aggregates the one or more of the one or more elements of consumer data and associates the consolidated and aggregated elements of consumer data with the query. In analogous art, Ortiz discloses the following limitations:
wherein after one or more of the one or more elements of consumer data are received from each consumer device, the remote system consolidates aggregates the one or more of the one or more elements of consumer data and associates the consolidated and aggregated elements of consumer data with the query (see para [0101], "FIG. 15 is another example timeline for an example use case, according to some embodiments. A consumer “Kelly Smith” may store and grant permissions to her transaction data on system 100. System 100 may recognize shopping patterns in her shopping data, defines a monthly loyalty value, and offers the value to Kelly. Kelly may opt-into sharing her data in aggregated form (e.g. anonymized and grouped with other consumers' data), which may be offered to retailers. Retailers may pay a fee in order to access the aggregated consumer data. In return, Kelly may receive a reward, such as a loyalty program points, for agreeing to share her data in aggregated form.")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Ortiz with Mashima because providing aggregated consumer data proves more useful information for vendors for commercial purposes (see Ortiz, para para [0013]-[0015]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for distributing consumer information as taught by Ortiz in the method of customer data management of Mashima, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Mashima and Ortiz do not specifically disclose processes the consumer data locally by itself to generate a response that includes one or more of the one or more elements of consumer data such that the remote system receives only the processed response and does not obtain access to the underlying consumer data source or consumer accessible data. In analogous art, Rahmat discloses the following limitations:
processes the consumer data locally by itself to generate a response that includes one or more of the one or more elements of consumer data such that the remote system receives only the processed response and does not obtain access to the underlying consumer data source or consumer accessible data (see para [0026], "In some examples, the neural network module 236 includes or accesses a generative adversarial network (GAN) that can learn attributes related to raw data collected from the OT network 104, so as to generate sanitized data. For example, the data collection application 234 can collect raw data from the unidirectional network interface 206 and provide the raw data to the neural network module 236. The neural network module 236 can learn the distribution of the collected raw data. Based on learning the data distributions associated with raw data, the neural network module 236 can generate a data sample that has a similar distribution to given raw data. Such a data sample can define sanitized data that corresponds to raw data. By way of example, the sanitized data can be sent to the receiver machine 204 from the sender machine 202, and the receiver machine 204 can transmit the sanitized data to the IT network 102, for instance to the SIEM system 116 for analysis. Thus, in such a configuration, the data that leaves the DCU 106 is different than the actual data that is collected from the OT network 104. It is recognized herein that, because the actual raw data is not transmitted to the receiver machine 204 or outside the DCU 106, privacy protections are enhanced, such that various data owners or customers associated with OT networks may have greater confidence in sharing their data for combined analysis at various systems, for instance the SIEM system 116. Further, more data that is shared and analyzed, for instance at the SIEM system 116, can enhance anomaly detection capabilities, among other capabilities that are based on analyzing data." and see para [0022], [0032])
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Rahmat with Mashima and Ortiz because generating a response that limits access to some of the data enhances the security system by protecting the privacy of data from others (see Rahmat, para [0001]-[0002]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for preserving privacy of a device as taught by Rahmat in the Mashima and Ortiz combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 2, 9 and 16:
Further, Mashima discloses the following limitations:
wherein one or more consumer devices receive one or more of the one or more elements of the consumer data from one or more data sources that are remote from the consumer device (see para [0054], "when the customer data query 142 includes a request for the external customer data 150, the third party server 114 may communicate instructions 178 to the policy engine 112. Additionally or alternatively, the evaluation module 168 may communicate the instructions request 180 to the third party server 114. The instructions 178 may include directions that may be implemented by the policy engine 112 to access the external customer data 150 from the repository 124. For example, the instructions 178 may include an indication of the external customer data 150 that is requested, query data items used when querying the repository 124, a location such as a uniform resource locator (URL) address of the repository 124, a query template, other query parameters, or some combination thereof." and see para [0060], " the evaluation module 168 may include the basic data module 164 and the external data module 166. The basic data module 164 may be configured to manage requests for basic customer data 146. The basic customer data 146 may include any data or information stored in a database such as the customer database 106.")
Claims 5, 12 and 18:
Further, Mashima discloses the following limitations:
verify and either approve or deny the query (see para [0037], "In some embodiments, the third party 120 may provide instructions that provide direction to the repository 124 and/or basic customer data involved in obtaining the external customer data. The policy engine 112 may communicate the requested customer data, which may include basic customer data and/or external customer data, to the third party 120 and/or the third party server 114. In response to the policy prohibiting disclosure of the requested customer data, the policy engine 112 may deny the query." and see para [0056], "The authentication of the third party server 114 may be an initial step in ensuring that the customer data 144 is not inadvertently communicated to an unauthorized third party. If the third party server 114 fails the authentication, the evaluation module 168 may deny the query.)
Claims 6-7, 13-14, 19-20:
Mashima does not explicitly disclose wherein the consumer data includes one or more of social media information or personal cloud drive storage that the plurality of consumers chose to make available when processing the query. In analogous art, Ortiz discloses the following limitations:
wherein the consumer data includes one or more of social media information or personal cloud drive storage that one or more consumers associated with the one or more consumer devices chose to make available when processing the query (see para [0065], "Consumer information may also include financial data, transactional data and social network data.")
wherein the computer-executable instructions, when executed by. the processor, further cause the server to: provide a reward to one or more consumers associated with the consumer devices that communicated one or more of the one or more elements of consumer data to the remote system the reward comprising one or more of: a monetary reward, a consumer goods discount, a services discount, or a digital dividend (see para [0097], "In addition, the consumer may specify types of rewards and incentives for data sharing, such as research labs, community data feed, commercial data feed for monetary incentive and market place offers for personalized products or services.")
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for distributing consumer information as taught by Ortiz in the method of customer data management of Mashima, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 3-4, 10-11, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mashima, Ortiz and Rahmat, as applied above, and further in view of Gottemukkala et al. (US 2017/0140405 A) (hereinafter Gottemukkala).
Claims 3-4, 10-11, 17:
Mashima, Ortiz and Rahmat do not specifically disclose composing, by the data insights server, the query, wherein the query includes selected fields for building a plurality of questions for the query. In analogous art, Gottemukkala discloses the following limitations:
compose the query, wherein the query includes selected fields for building a plurality of questions for the query (see para [0059]-[0060], "] Market intelligence requests and questions can be formulated and applied against instantiations of one or more model types (e.g., 310, 315, 320, 326, 327, 328, etc.) included in global market model 305. “Queries” can represent general categories of questions that can be asked relating to markets modeled by global market model 305 and its constituent models. For instance, a particular question, query term, or other request for market intelligence from the global market model 305 can be matched to business logic of one or more query modules (e.g., 335-398) adapted to generate responses to particular categories of market-related questions from the structure and content of the global market model 305 and its constituent models. A natural language query or question, inputs received at a query-building wizard, or other inputs received by a global market intelligence engine and/or query engine from a user or client system can be translated into a machine-usable, structured argument (e.g., including parameters corresponding to or named in the natural language question) for processing using the logic of one or more query modules. For instance, a natural language query or question can be translated into a structured query argument with parameters including one or more filter expressions and, in some cases, one or more particular model types or model type instances, based on the natural language inputs. One or more query modules 335-398 can be identified that include logic that would be responsive to the interpreted natural language query or question. The identified query modules 335-398 can then be used to process the constructed arguments and utilize corresponding business logic to identify relevant models of the global market model and return an answer, or query response, to the input based on an analysis of, filtering of, or identification of market intelligence provided through the one or more identified models in global market model 305. A library of query modules can be provided through business logic that can be applied against the global market model to return responses to particular queries. For instance, in some examples, business logic can be provided that can be applied against a global market model to return query responses, based on particular identified query parameters, to categories of questions handled by query modules in a library of query modules. Structured expressions can be developed from received queries that are adapted to be accepted and processed as inputs by the respective query modules. For instance, a query module can accept the identification of a particular model type or model type instance as well as other parameters and conditions included in a filter expression corresponding to the query. Table 2 provides a listing of examples of query modules and corresponding expression parameters that can be applied against the global market model to return responses to corresponding queries:")
wherein the query includes one or more fields filled in with the one or more elements of consumer data (see para [0045], " model instances of the global market model (e.g., of multiple model types) can be generated in advance of, or concurrently in connection with a particular task or query performed by components, programs, clients, and other systems interfacing with and using the global market model (such as functionality of queries layer 280. Ideally, a global market model includes data and structure allowing instances of at least portions of global market model to be generated and provided for any organization, market, market region, product category, customer segment, value chain role, or product known to exist within the global marketplace. Tools of the model layer 275 can utilize data collected by the data collection layer 270 to populate instances of particular model types consistent with the attributes, fields, and sub-models of the model type. A model type (also referred to herein, generically, as a “model”) can define the semantics, structure, fields, and attributes of a category of models. Fields can define model attributes (e.g., attribute values), as well as relationships, links, or pointers between multiple models (or, generally, between model types)." and see para [0041])
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Ortiz with Mashima, Ortiz and Rahmat because building a plurality of questions for query can enhance the intelligence gathered by providing more access to underlying data (see Gottemukkala, para [0001]-[0002]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for global market modeling for advanced market intelligence as taught by Gottemukkala in the Mashima, Ortiz and Rahmat combination, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kirkpatrick (US 2002/0059258 A1), a system that retrieves data on a date delimited and/or periodic basis from an Internet search engine and displays predefined interest trends associated with the data and where the Internet search engine preferably includes a database containing a plurality of search terms associated with a plurality of web page addresses, and/or a database containing a plurality of previously issued search queries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUJAY KONERU whose telephone number is (571)270-3409. The examiner can normally be reached M-F, 8:30 AM to 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached on 571- 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SUJAY KONERU/
Primary Examiner, Art Unit 3624