Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is in response to the Applicant REM filed on 01/28/2026.
3. Claims 1-20 are pending in the present application. claims 1, 15 and 20 are independent claims.
4. This action is made Final.
Double Patenting
5. Upon an indication of allowability of the present application, and after a prima facie case against the allowed claims has been presented, Applicant will address the double patenting rejections. The Double patenting rejection remained.
Claim Rejections – 35 USC § 101
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The limitation of claims 1, 15 and 20 “determine interests of the user independent of any other user's interests using a trained classifier, the user's independently-determined interests being retained in an interest store independent from any other user's independently-determined interests; determine a plurality of contacts of the user; and determine each contact’s interaction frequency with the user; selecting, at least one contact from the plurality of contacts based on the interaction frequency determined for each of the plurality of contacts, each selected contact having a higher interaction frequency with the user than unselected contacts of the plurality of contacts; determining, an interest shared by the user and the at least one selected contact, determined to have a higher interaction frequency with the user than each unselected contact, the determining comprising comparing the user's interest store comprising the independently-determined interests of the user with each selected contact's interest store comprising the selected contact's independently-determined interests to identify the shared interest; and automatically providing, via the computing device, by the online service in accordance with the determined shared interest, a digital recommendation directed to the user and the at least one selected contact determined to have a higher interaction frequency with the user than each unselected contact; and updating, via the computing device, a relationship data store of the online service to include information indicating that the user and the at least one selected contact determined to have a higher interaction frequency with the user than each unselected contact are related in accordance with the determined shared interest.;" are directed to judicial exception of mental processes. That is, other than claim 20 reciting “A computing device comprising: a processor; and a non-transitory storage medium,” nothing in the claim element precludes the step from practically being performed in the mind. For example, one can mentally generate a timeline and/or track the user interactions and determine identifying information for different users. Determine an interest shared by the user and other contact determined to have a higher interaction frequency based on digital messages shared between users, provide a digital recommendation for example (chat group learning session) and finally updating the social graph (relationship data store) of the user.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim 20 only recites additional elements – using “a computing device of an online service” in claim 1, “online service” in claim 15 “a processor” in claim 20 to perform the steps of claims 1, 15 and 20 respectively. The processor is recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions amount to no more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
The additional limitation of " obtaining, at a computing device, of an online service, a plurality of digital messages of a user; using, via the computing device, the plurality of digital messages of the user to” is recited at a high-level of generality performing generic computer functions such that it amounts no more than mere instructions to apply the exception using a generic computer. Further, the additional limitations of” obtaining, at a computing device, a plurality of digital messages of a user; using, via the computing device, the plurality of digital messages of the user to" are insignificant extra solution activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “a computing device of an online service” in claim 1, “online service” in claim 15 “a processor” in claim 20 amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims 1, 15 and 20 are not patent eligible.
The dependent claims merely incorporate additional elements that narrow the abstract idea without yielding an improvement to any technical field, the computer itself, or limitations beyond merely linking the idea to a particular technological environment.
Claims 2 and 16, recite: automatically maintaining, via the computing device, a relationship data structure to include information indicating a relationship between the user and the at least one selected contact, the relationship identifying the shared interest between the user and the at least one selected contact (falls within the “Mental Processes” grouping of abstract ideas).
Claims 3 and 17, recite: generating, via the computing device, a machine-trained model using a machine-learning algorithm and a training data set; and identifying, via the computing device, the interests of the user using the machine-trained model (Mere Instructions to “Apply” an Exception (MPEP 2106.05(f)) and are insignificant extra solution activity).
Claims 4 and 18, recite: obtaining, via the computing device, a plurality of probabilities corresponding to a plurality of interests from the machine-trained model; and identifying, via the computing device, the interests of the user using a probability threshold, wherein each interest of the user has a corresponding probability that satisfies the probability threshold (Mere Instructions to “Apply” an Exception (MPEP 2106.05(f)) and are insignificant extra solution activity).
Claim 5, recites: the digital messages comprise different types of digital messages (Mere Instructions to “Apply” an Exception (MPEP 2106.05(f))).
Claim 6, recites: the digital messages comprise a same type of digital message (Mere Instructions to “Apply” an Exception (MPEP 2106.05(f))).
Claim 7, recites: the digital messages are obtained from a number of different messaging services (Mere Instructions to “Apply” an Exception (MPEP 2106.05(f))).
Claim 8, recites: the digital messages are obtained from one messaging service (Mere Instructions to “Apply” an Exception (MPEP 2106.05(f))).
Claim 9, recites: determining a contact's interaction frequency with the user comprising using a number of the plurality of digital messages that indicate the contact (falls within the “Mental Processes” grouping of abstract ideas).
Claim 10, recites: determining a contact's interaction frequency with the user comprising using a number of the plurality of digital messages sent by the user that indicate the contact (falls within the “Mental Processes” grouping of abstract ideas).
Claim 11, recites: determining a contact's interaction frequency with the user comprising using a number of the plurality of digital messages received by the user that indicate the contact (falls within the “Mental Processes” grouping of abstract ideas).
Claim 12, recites: determining a contact's interaction frequency with the user comprising using a number of the plurality of digital messages received and opened by the user that indicated the contact (falls within the “Mental Processes” grouping of abstract ideas).
Claim 13, recites: determining a contact's interaction frequency with the user comprising using a number of the plurality of digital messages received and responded to by the user that indicated the contact (falls within the “Mental Processes” grouping of abstract ideas).
Claims 14 and 19, recite: generating, via the computing device, a hierarchical interest matrix comprising the interests of the user identified using the plurality of digital messages (falls within the “Mental Processes” grouping of abstract ideas).
Claims 3, 10 and 17: executing a Structured Query Language (SQL) query on a database view associated with the enhanced data table (Mere Instructions to “Apply” an Exception (MPEP 2106.05(f))).
Claims 4, 11 and 18: selecting the set of changes having a replication timestamp between the high watermark and a timestamp of the oldest transaction in the set of open transactions (Mere Instructions to “Apply” an Exception (MPEP 2106.05(f))).
Claims 5, 12 and 19: determining the set of open transaction is a null set; and selecting the set of changes having a replication timestamp between the high watermark and infinity (falls within the “Mental Processes” grouping of abstract ideas).
Claims 6, 13 and 20: determining the set of changes made to the source system that are relevant to the consumer at the target system based on a database view that utilizes the high watermark and the set of open transactions (falls within the “Mental Processes” grouping of abstract ideas and Mere Instructions to “Apply” an Exception (MPEP 2106.05(f))).
Claims 7, 14: determining the set of changes made to the source system that are relevant to the consumer at the target system based on a consumer identifier (ID) associated with the consumer (falls within the “Mental Processes” grouping of abstract ideas).
Claim Rejections - 35 USC § 112
8. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL. —The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
9. Claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
10. Regarding Claims 1, 15 and 20, the claim recites the limitation “determine interests of the user independent of any other user's interests using a trained classifier, the user's independently-determined interests being retained in an interest store independent from any other user's independently-determined interests” and “comparing the user's interest store comprising the independently-determined interests of the user with each selected contact's interest store comprising the selected contact's independently-determined interests to identify the shared interest”. The specification does not explicitly describe that the interests of the user is determined independent of any other user’s interests. The specification ([0046-0049], [0051-0052], [0091-0093]) describes determining shared common interests between users. Examiner couldn’t find any such languages in the specification.
Claim Rejections - 35 USC § 112
11. 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.
12. Claims 1-20 are NOT rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
13. Claims 1, 15 and 20 recite “determine interests of the user independent of any other user's interests using a trained classifier, the user's independently-determined interests being retained in an interest store independent from any other user's independently-determined interests” and “comparing the user's interest store comprising the independently-determined interests of the user with each selected contact's interest store comprising the selected contact's independently-determined interests to identify the shared interest”. One skilled in the art could interpret the scope of this claim but the specification does not describe that the interests of the user is determined independent of any other user’s interests. However, this still renders the claims definite.
Examiner Note
14. The Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Claim Rejections - 35 USC § 103
15. 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.
16. 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) A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
17. Claims 1, 2 and 5-16 and 19-20 are rejected under 35 U.S.C.103 as being unpatentable over Higgins et al (US 20140214895 A1) in view of Murdoch et al (US 20140122595 A1).
18. Regarding claim 1, Higgins teaches A method comprising:
obtaining, at a computing device, by an online service a plurality of digital messages of a user ([0051], “the machine learning algorithms takes the following data as input: email message frequencies between two specific users”, [0055], “(3) calculate a frequency of interactions metric for a user and a contact through an analysis of, for example, the following data sets: the user's emails, text messages, phone call records, social network data, ..., public data showing common speaking engagements by the user and the contact, data showing common conference attendance by the user and the contact), (4) calculate a change in frequency of interactions by performing a derivative on the above metrics, and (5) calculate a strength of connection metric based on the number of similar traits between two contacts through an analysis of, for example, the following: emails showing the two contacts have common friends, social network data, ... common interests through an analysis of topics discussed with both contacts via email and other messages” see also [0104]);
using, via the computing device, the plurality of digital messages of the user to:
determine interests of the user ([0009], ”a server in communication with client systems stores data describing queries to which those client systems are interested in responding, Local client systems store preferences describing queries to which the client is interested in responding, provide a user interface to configure those preferences, and logic to communicate those preferences to the server.”, [0015], “a client system for responding to queries based on private data stores private user data and permissions regarding users whose queries may be processed using this private user data.”, [0031], “For instance, User A may be interested in information about, insight into, or a connection to Company X to make a sale to Company X.”, [0034], [0051], [0055], “search for common interests through an analysis of topics discussed with both contacts via email and other messages”, [0058], “For instance, a user may be interested in receiving price quotes on an intended product purchase, but does not want to share any personal or demographic information.”, [0066-0067], [0095], [0097], [0102], [0115], “user provide feedback on relevance and his interest on the specific content items he sees”) independent of any other user’s interests using a trained classifier, the user's independently-determined interests being retained in an interest store independent from any other user's independently-determined interests ([0013], “stored personal data”, [0031], “social networking information”, [0034], “The query 112a encapsulates the curated information of interest to the user”, Fig 2, step 200, [0044], “The first step 200 is to index and analyze private user data.. (1) structured user data. (2) each record loaded. (3) a routine that indexes data based on its type is run for each record, (4) based on the record type, the indexing routine follows rules to extract features from the data by parsing text, performing image analysis, and doing statistics as applicable, (5) the extracted features are then organized in a database for efficient retrieval, (6) the data is analyzed to find patterns, hidden connections between the records, and perform knowledge discovery.”, “the output of the indexing and analyzing is an aggregated database for the user of all loaded data that includes contacts with relations between the contacts (with their strengths of connections), and relations between contacts and other data types.”, Fig 3, [0050], [0113], “The system has applied machine learning or other data analysis techniques on the user private data to determine user interest”);
determine a plurality of contacts of the user ([0044], “the output of the indexing and analyzing is an aggregated database for the user of all loaded data that includes contacts (disambiguated) with relations between the contacts (with their strengths of connections), and relations between contacts and other data types.”, [0052], “a user's network of contacts and may result in a weighted adjacency matrix, which can be depicted as a network of nodes with weighted connections 300a, 300b, 300c.”, [0053], “A weighted combination of multiple metrics may be used in most cases to generate a meta-network of the user's contacts. This meta-network may then be used to select the most appropriate recipients for the query.”, [0055], “calculate a strength of connection metric based on the number of similar traits between two contacts through an analysis of, for example, the following: emails showing the two contacts have common friends, social network data, demographic data from user entered tags or learning algorithms, contact list data giving address and phone number information to determine if the contacts live nearby one another, search for common interests through an analysis of topics discussed with both contacts via email and other messages, club memberships, events attendance, public donations, analysis of family holiday letters, memberships on boards of companies, social network data showing support by the contacts for particular sporting teams, movies watched, shopping histories, family analyses, professions, voting histories and other data on political preferences, private life events such as previous divorces and lost family members, immigration status, and languages spoken.”, [0095], “the system analyzes the second user's personal data to find contacts of the second user who are share interests, communication patterns, etc.”); and
determine each contact’s interaction frequency with the user (Fig 3 (see 300a, 300b, or 300c.), [0051], “increasing frequency associated with stronger connection, and decreasing frequency associated with weaker connection”, [0052], “each metric can be applied to a user's network of contacts and may result in a weighted adjacency matrix, which can be depicted as a network of nodes with weighted connections 300a, 300b, 300c. If the user is searching for advice on good movies to watch, those users who have more similar movie-watching history may be weighted most highly. If the user is looking for information on a particular person, such as a good gift idea, the weighting may reflect the user's strength of connection to the target person.”, [0053], “frequency of email interactions may be an appropriate metric when deciding whom to ask for movie recommendations, while shared educational or corporate affiliations may be more appropriate for job search inquires. A weighted combination of multiple metrics may be used in most cases to generate a meta-network of the user's contacts. This meta-network may then be used to select the most appropriate recipients for the query. In some embodiments, these recipients may most likely both to be in a position to provide the information or service requested and to make it a priority to respond to the user initiating the query. The result of the analysis may be that User B is the best recipient for the query, which can be then sent to User B's client 102b”, see also [0055-0056], [0055], “calculate a strength of connection metric based on the number of similar traits between two contacts through an analysis of, for example, the following: emails showing the two contacts have common friends, social network data, demographic data from user entered tags or learning algorithms, contact list data giving address and phone number information to determine if the contacts live nearby one another, search for common interests through an analysis of topics discussed with both contacts via email and other messages, club memberships, events attendance, public donations, analysis of family holiday letters, memberships on boards of companies, social network data showing support by the contacts for particular sporting teams, movies watched, shopping histories, family analyses, professions, voting histories and other data on political preferences, private life events such as previous divorces and lost family members, immigration status, and languages spoken.”, [0095], “the system analyzes the second user's personal data to find contacts of the second user who are share interests, communication patterns, etc.”);
selecting, via the computing device, at least one contact from the plurality of contacts based on the interaction frequency determined for each of the plurality of contacts, each selected contact having a higher interaction frequency with the user than unselected contacts of the plurality of contacts (Fig 3 (see 300a, 300b, or 300c.), [0051], “increasing frequency associated with stronger connection, and decreasing frequency associated with weaker connection”, [0052], “each metric can be applied to a user's network of contacts and may result in a weighted adjacency matrix, which can be depicted as a network of nodes with weighted connections 300a, 300b, 300c. If the user is searching for advice on good movies to watch, those users who have more similar movie-watching history may be weighted most highly. If the user is looking for information on a particular person, such as a good gift idea, the weighting may reflect the user's strength of connection to the target person.”, [0053], “frequency of email interactions may be an appropriate metric when deciding whom to ask for movie recommendations, while shared educational or corporate affiliations may be more appropriate for job search inquires. A weighted combination of multiple metrics may be used in most cases to generate a meta-network of the user's contacts. This meta-network may then be used to select the most appropriate recipients for the query. In some embodiments, these recipients may most likely both to be in a position to provide the information or service requested and to make it a priority to respond to the user initiating the query. The result of the analysis may be that User B is the best recipient for the query, which can be then sent to User B's client 102b”, see also [0055-0056]);
determining, via the computing device, an interest shared by the user and the at least one selected contact determined to have a higher interaction frequency with the user than each unselected contact, the determining comprising comparing the user's interest store comprising the independently-determined interests of the user with each selected contact's interest store comprising the selected contact's independently-determined interests to identify the shared interest (Fig 3, [0050-0055], shared interest between the user and contact see also examples 1-17, [0095]).
Higgins implicitly teaches automatically providing, via the computing device, by the online service in accordance with the determined shared interest, a digital recommendation directed to the user and the at least one selected contact determined to have a higher interaction frequency with the user than each unselected contact (see [0106], “example 10, Corporate Knowledge Discovery”, [0103], “example 7, Collaborate with a Team”).
However Murdoch explicitly teaches automatically providing, via the computing device, by the online service in accordance with the determined shared interest, a digital recommendation directed to the user and the at least one selected contact determined to have a higher interaction frequency with the user than each unselected contact(Abstract, “A system, method and computer program is provided for creating content and dynamically updating content is provided based on meta data extracted from semantic analysis of content and of electronic social interactions among users (interaction frequency between users) and between users and content.”, [0008], “the intelligent collaborative content management utility being operable to (c) capture and analyze (i) feedback from users regarding the digital content items, and (ii) interactions between users (interaction frequency between users) regarding the digital content items, and (d) based on the analysis of (b), dynamically create, assemble, modify, promote, or demote content, so as to generate collaborative digital content.”, [0010-0011], “monitoring social media interactions between users (interaction frequency between users) relevant to the digital content items”, [0019], “the analysis utility is operable to monitor learning activities of users, and based on such learning activities identify shared learning attributes amongst two or more users, and based on such shared learning attributes the analysis utility being operable to suggest that the two or more users form a study group (a digital recommendation directed to the user and the at least one selected contact), using the social media utilities.”, Fig 7, [0031], “extracting meta data from interactions between users in connection with the social media utility.”, [0155]- [0157], “Based on the teaching objectives for example the analysis utility may suggest possible matches for smaller groups of students to work on projects, participate in smaller mediate chat sessions and so on.”, Fig 6, [0170], [0187]- [0188], claim 12); and
updating, via the computing device, a relationship data store of the online service to include information indicating that the user and the at least one selected contact determined to have a higher interaction frequency with the user than each unselected contact are related in accordance with the determined the shared interest ([0157], “Meta Data graphs (a relationship data store) may be generated and then are overlapped to generate graphs where node strengths indicate how strongly users are related at a Data level. Relevant social relationships then further strengthen or weaken specific Nodes and Edges based upon past Interactions and the context Interactions.”, [0170], “The participants' relationship graphs (a relationship data store) are also updated and stored for future use in generation of presentation elements or other information objects, including for use by the analysis utility.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept suggested in Murdoch’s system into Higgins’s and by incorporating Murdoch into Higgins because both systems are related to recommendation engines would provide an E-learning platform that enables one or more student users to access teaching content that is in part user defined, from a variety of network-connected devices.
19. Regarding claim 2, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches automatically maintaining, via the computing device, a relationship data structure to include information indicating a relationship between the user and the at least one selected contact, the relationship identifying the shared interest between the user and the at least one selected contact ([0011], “analyzing the private data to create structured data”, Fig 1, [0031], “creates multiple structures which reveal different types of relationships (variable strength, context, dynamics) between User A and the entities (104a, 106a, 108a, 110a)”, [0049], Fig 3, [0051], [0052], “a weighted adjacency matrix, which can be depicted as a network of nodes with weighted connections 300a, 300b, 300c.” [0053], “a meta-network of the user's contacts” [0056]).
20. Regarding claim 5, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches the digital messages comprise different types of digital messages ([0051], [0055]).
21. Regarding claim 6, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches the digital messages comprise a same type of digital message ([0051], [0055]).
22. Regarding claim 7, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches the digital messages are obtained from a number of different messaging services ([0031], [0051], “the machine learning algorithms takes the following data as input: email message frequencies between two specific users”, [0109] User A has a friend User F who uses a number of communication channels (email, text messaging, social networking sites, discussion boards, etc.).).
23. Regarding claim 8, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches the digital messages are obtained from one messaging service ([0031], [0051], “the machine learning algorithms takes the following data as input: email message frequencies between two specific users”, [0109] User A has a friend User F who uses a number of communication channels (email, text messaging, social networking sites, discussion boards, etc.).).
24. Regarding claim 9, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches determining a contact’s interaction frequency with the user comprising using a number of the plurality of digital messages that indicate the contact ([0051], “A third metric is the number of traits or characteristics shared between individuals... number of shared contacts or keywords between two users... the total number of emails between two users over the past 1 week or the total number over the past month.”, [0055], [0059], “The user may also choose to use the ID only once, or a number of times.”).
25. Regarding claim 10, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches determining a contact’s interaction frequency with the user comprising using a number of the plurality of digital messages sent by the user that indicate the contact ([0051], “A third metric is the number of traits or characteristics shared between individuals... number of shared contacts or keywords between two users... the total number of emails between two users over the past 1 week or the total number over the past month.”, [0055], [0059], “The user may also choose to use the ID only once, or a number of times.”).
26. Regarding claim 11, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches determining a contact’s interaction frequency with the user comprising using a number of the plurality of digital messages received by the user that indicate the contact (Fig 3 (see 300a, 300b, or 300c.), [0051], “increasing frequency associated with stronger connection, and decreasing frequency associated with weaker connection”, [0052], “each metric can be applied to a user's network of contacts and may result in a weighted adjacency matrix, which can be depicted as a network of nodes with weighted connections 300a, 300b, 300c.”, [0053], “frequency of email interactions may be an appropriate metric when deciding whom to ask for movie recommendations, while shared educational or corporate affiliations may be more appropriate for job search inquires. A weighted combination of multiple metrics may be used in most cases to generate a meta-network of the user's contacts. This meta-network may then be used to select the most appropriate recipients for the query. In some embodiments, these recipients may most likely both to be in a position to provide the information or service requested and to make it a priority to respond to the user initiating the query. The result of the analysis may be that User B is the best recipient for the query, which can be then sent to User B's client 102b”, see also [0055-0056]).
27. Regarding claim 12, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches determining a contact’s interaction frequency with the user comprising using a number of the plurality of digital messages received and opened by the user that indicated the contact (Fig 3 (see 300a, 300b, or 300c.), [0051], “increasing frequency associated with stronger connection, and decreasing frequency associated with weaker connection”, [0052], “each metric can be applied to a user's network of contacts and may result in a weighted adjacency matrix, which can be depicted as a network of nodes with weighted connections 300a, 300b, 300c.”, [0053], “frequency of email interactions may be an appropriate metric when deciding whom to ask for movie recommendations, while shared educational or corporate affiliations may be more appropriate for job search inquires. A weighted combination of multiple metrics may be used in most cases to generate a meta-network of the user's contacts. This meta-network may then be used to select the most appropriate recipients for the query. In some embodiments, these recipients may most likely both to be in a position to provide the information or service requested and to make it a priority to respond to the user initiating the query. The result of the analysis may be that User B is the best recipient for the query, which can be then sent to User B's client 102b”, see also [0055-0056]).
28. Regarding claim 13, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches determining a contact’s interaction frequency with the user comprising using a number of the plurality of digital messages received and responded to by the user that indicated the contact (Fig 3 (see 300a, 300b, or 300c.), [0051], “increasing frequency associated with stronger connection, and decreasing frequency associated with weaker connection”, [0052], “each metric can be applied to a user's network of contacts and may result in a weighted adjacency matrix, which can be depicted as a network of nodes with weighted connections 300a, 300b, 300c.”, [0053], “frequency of email interactions may be an appropriate metric when deciding whom to ask for movie recommendations, while shared educational or corporate affiliations may be more appropriate for job search inquires. A weighted combination of multiple metrics may be used in most cases to generate a meta-network of the user's contacts. This meta-network may then be used to select the most appropriate recipients for the query. In some embodiments, these recipients may most likely both to be in a position to provide the information or service requested and to make it a priority to respond to the user initiating the query. The result of the analysis may be that User B is the best recipient for the query, which can be then sent to User B's client 102b”, see also [0055-0056]).
29. Regarding claim 14, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins teaches generating, via the computing device, a hierarchical interest matrix comprising the interests of the user identified using the plurality of digital messages ([0051], “a weighted adjacency matrix”).
30. Regarding claims 15 and 16, those claims recite a non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a processor associated with a computing device perform the method of claims 1 and 2 respectively and are rejected under the same rationale.
31. Regarding claim 20, this claim recites a system performs the method of claim 1 and is rejected under the same rationale.
32. Claims 3, 4, 17 and 18 are rejected under 35 U.S.C.103 as being unpatentable over Higgins et al (US 20140214895 A1) in view of Murdoch et al (US 20140122595 A1) as claimed in claim 1 above and further in view of Yan et al (US 20180060749 A1).
33. Regarding claim 3, Higgins and Murdoch teach the invention as claimed in claim 1 above and further Higgins implicitly teaches generating, via the computing device, a machine-trained model using a machine-learning algorithm and a training data set; and identifying, via the computing device, the interests of the user using the machine-trained model ([0044], [0048], “Other machine learning and data mining techniques can be used to conduct the search through the indexed and analyzed local data.”, [0049]), “learning algorithm”, [0051-0052], “The client 102a uses machine learning algorithms to calculate the "strength of connection" between individuals in User A's private network based on one or more distance metrics.”[0055]- [0056], [0078], [0113], “Features extracted through machine learning or other data analysis techniques on his private data provides the insight that User A is most likely interested in "iron ore" as it relates to investment activities, and in the South East Asia region of the world. The system has applied machine learning or other data analysis techniques to map public sources to determine that particular mailing lists, blogs, and websites are highly reliable sources for knowledge on topics such as "iron ore."”).
However, Yan explicitly teaches generating, via the computing device, a machine-trained model using a machine-learning algorithm and a training data set; and identifying, via the computing device, the interests of the user using the machine-trained model ([0041], “using machine learning model-based probability threshold to determine user interest”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of teachings suggested in Yan’s system into Higgins and Murdoch combined system and by incorporating Yan into Higgins and Murdoch combined system because all systems are related to recommendation engines would provide content targeting and generation using machine learning.
34. Regarding claim 4, Higgins and Murdoch and Yan teach the invention as claimed in claim 3 above and further Yan further implicitly teaches identifying the interests of the user using the machine-trained model further comprising: obtaining, via the computing device, a plurality of probabilities corresponding to a plurality of interests from the machine-trained model; and identifying, via the computing device, the interests of the user using a probability threshold, wherein each interest of the user has a corresponding probability that satisfies the probability threshold ([0041], “using machine learning model-based probability threshold to determine user interest”).
35. Regarding claims 17 and 18, those claims recite a non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a processor associated with a computing device perform the method of claims 3 and 4 respectively and are rejected under the same rationale.
Respond to Amendments and Arguments
36. Upon an indication of allowability of the present application, and after a prima facie case against the allowed claims has been presented, Applicant will address the double patenting rejections.
37. Applicant’s 35 U.S.C. § 101 arguments on claims 1-20 have been fully considered but they are not persuasive. Claims are directed to judicial exception of mental processes. nothing in the claim element precludes the step from practically being performed in the mind.
For example, one can mentally generate a timeline and/or track the user interactions and determine identifying information for different users. Determine an interest shared by the user and other contact determined to have a higher interaction frequency based on digital messages shared between users, provide a digital recommendation for example (chat group learning session) and finally updating the social graph (relationship data store) of the user.
38. Applicant’s 35 U.S.C. § 112 arguments on claims 1-20 have been fully considered but they are not persuasive. The portions of the present application cited by applicant didn’t explicitly support “determine interests of the user independent of any other user's interests using a trained classifier, the user's independently-determined interests being retained in an interest store independent from any other user's independently-determined interests” limitation.
39. Applicant's arguments under 35 USC § 103 received on 01/28/2026 have been fully considered but they are not persuasive. Applicant respectfully submits that Higgins, including the portions cited by the Examiner, fail to disclose, teach or suggest at least the above-identified claim elements.
First, as is conceded by the Examiner, Higgins does not determine the interests of one user. Rather, Higgins describes, at 55, searching for common interests of "two users" through analysis of topics discussed with both users." Common interests of two users is clearly structurally and functionally different from the claimed subject matter, which determines the interest of one user - i.e., the claimed user - independent of any other user's interests and retains the user's independently-determined interests in an interest store independent from any other user's independent-determined interests.
Furthermore, according to Higgins, at 57, its analysis - i.e., search for common interests - is used to direct one user's query to another user. This is clearly functionally and structurally different from the claimed subject matter, which uses the claimed shared interest to provide a digital recommendation to both the claimed user and the claimed selected contact(s) and to update the claimed relationship data store.
Indeed, Higgins' search for common interests of any contacts fails to disclose the claimed subject matter, which identifies the interest of one user independent of any other user's interests, selects contacts of the one user having the highest frequency of interaction with the one user (relative to unselected contacts), and then determines the claimed shared interest shared by the user and the selected contacts of the user by comparing the user's interest store comprising the independently-determined interests of the user with each selected contact's interest store comprising the selected contact's independently-determined interests.
Thus, Higgins' common interest determination must necessarily occur before any users are selected. This is clearly in stark contrast to the claimed subject matter which selects contacts having the highest frequency of interaction with the user (relative to frequency of interaction with the user of unselected contacts) and then, after selecting the contact(s) makes the claimed shared interest determination and updates a relationship data store of the claimed online service to include information indicating that the user and the at least one selected contact determined to have a higher interaction frequency with the user than each unselected contact are related in accordance with the shared interest.
Examiner answers to Applicant arguments under 35 USC § 103:
Referring to the previous Office action, Examiner has cited relevant portions of the references as a means to illustrate the systems as taught by the prior art. As a means of providing further clarification as to what is taught by the references used in the first Office action, Examiner has expanded the teachings for comprehensibility while maintaining the same grounds of rejection of the claims, except as noted above in the section labeled “Status of Claims.” This information is intended to assist in illuminating the teachings of the references while providing evidence that establishes further support for the rejections of the claims.
It is noted that Higgins et al in view of Murdoch et al teach every limitation of claims 1-20.
CONCLUSION
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HICHAM SKHOUN whose telephone number is (571)272-9466. The examiner can normally be reached Normal schedule: Mon-Fri 10am-6:30pm.
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/HICHAM SKHOUN/Primary Examiner, Art Unit 2164