DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to the preliminary claim amendment filed on January 30, 2025 and wherein claims 1-20 canceled and claims 21-40 added.
In virtue of this communication, claims 21-40 are currently pending in this Office Action.
In the response to this office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
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 obviousness-type 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); and 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, 29-35, 39-40 rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-4, 7, 9-14, 19-20 of U.S. Patent No. 12,190,065 B1. Although the conflicting claims 1-4, 7, 9-14, 19-20 of U.S. Patent No. 12,190,065 B1 are not identical, they are not patentably distinct from each other because claims of the instant application above are broader and anticipated by conflicting claims 1-4, 7, 9-14, 19-20 of U.S. Patent No. 12,190,065 B1, for example, the instant claims recites “first data representing first content corresponding to an intended recipient”, compared to “first data representing first content and a user identifier corresponding to an intended recipient of the first content” recited by the conflicting claims, etc. The following is a comparison between claims of the instant application and conflicting claims 1-4, 7, 9-14, 19-20 of U.S. Patent No. 12,190,065 B1 for reference:
Claims 21-25, 29-35, 39-40 in the current application
claims 12,190,065 of Conflicting U.S. Patent No. 1-4, 7, 9-14, 19-20 B1
21. A computer-implemented method, comprising: receiving, by a computing system and from a first content provider, first data representing first content corresponding to an intended recipient; processing the first data to determine whether to cause a device associated with the intended recipient to output the first content, the processing including: using a machine learning (ML) model to classify the first data as corresponding to a first topic of a plurality of topics, and determining that the first topic is represented in second data indicating information corresponding to the first topic; receiving input data representing a user input to the device; determining, by the computing system, response data representing an output to be provided by the device in response the user input, wherein the computing system refrains from including the first content in the response data based at least in part on the first data being classified as corresponding to the first topic and the first topic being represented in the second data; and sending the response data to the device to cause the device to provide the output.
23. The computer-implemented method of claim 21, further comprising: determining a user identifier corresponding to the intended recipient, wherein the second data is associated with the user identifier.
22. The computer-implemented method of claim 21, further comprising: determining a metric representing a trustworthiness of the first content provider, wherein the computing system refrains from including the first content in the response data based at least in part on the metric.
24. The computer-implemented method of claim 21, further comprising: receiving user feedback data associated with the first topic; determining, in the user feedback data, a number of negative user feedback events associated with the first topic; and generating the second data based at least in part on the number of negative user feedback events.
25. The computer-implemented method of claim 21, further comprising: receiving, by the computing system, third data representing second content corresponding to the intended recipient; determining, using the ML model, that the third data corresponds to a second topic; determining the second topic is unrepresented in the second data; and determining the third data is to be presented based at least in part on determining the second topic is unrepresented in the second data.
26. The computer-implemented method of claim 21, wherein receiving the first data comprises receiving supplemental content not directly responsive to the user input.
27. The computer-implemented method of claim 21, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to adult-only content.
28. The computer-implemented method of claim 21, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to the first content provider.
29. The computer-implemented method of claim 21, wherein receiving the input data occurs before receiving the first data.
30. The computer-implemented method of claim 29, wherein receiving the input data comprises receiving data requesting output of notifications associated with a user identifier.
31. A system comprising: at least one processor; and at least one memory comprising instructions that, when executed by the at least one processor, cause the system to: receive, by a computing system and from a first content provider, first data representing first content corresponding to an intended recipient; process the first data to determine whether to cause a device associated with the intended recipient to output the first content, such processing including: using a machine learning (ML) model to classify the first data as corresponding to a first topic of a plurality of topics, and determining that the first topic is represented in second data indicating information corresponding to the first topic; receive input data representing a user input to the device; determine, by the computing system, response data representing an output to be provided by the device in response the user input, wherein the computing system refrains from including the first content in the response data based at least in part on the first data being classified as corresponding to the first topic and the first topic being represented in the second data; and send the response data to the device to cause the device to provide the output.
33. The system of claim 31, wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the system to: determine a user identifier corresponding to the intended recipient, wherein the second data is associated with the user identifier.
32. The system of claim 31, wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the system to: determine a metric representing a trustworthiness of the first content provider, wherein the computing system refrains from including the first content in the response data based at least in part on the metric.
34. The system of claim 31, wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the system to: receive user feedback data associated with the first topic; determine, in the user feedback data, a number of negative user feedback events associated with the first topic; and determine the second data based at least in part on the number of negative user feedback events.
35. The system of claim 31, wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the system to: receive, by the computing system, third data representing second content corresponding to the intended recipient; determine, using the ML model, that the third data corresponds to a second topic; determine the second topic is unrepresented in the second data; and determine the third data is to be presented based at least in part on determining the second topic is unrepresented in the second data.
36. The system of claim 31, wherein the first data comprises supplemental content not directly responsive to the user input.
37. The system of claim 31, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to adult-only content.
38. The system of claim 31, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to the first content provider.
39. The system of claim 31, wherein the input data is received before receipt of the first data.
40. The system of claim 39, wherein the user input requests output of notifications associated with a user identifier.
1. A computer-implemented method, comprising: receiving, by a computing system and from a first content provider via a network, first data representing first content and a user identifier corresponding to an intended recipient of the first content; processing the first data to determine whether to cause a device associated with the user identifier to output the first content, the processing including: using a machine learning (ML) model to classify the first data as corresponding to a first topic of a plurality of topics, and determining that the first topic is represented in second data associated with the user identifier; receiving input data representing a user input to the device; determining, by the computing system, response data representing an output to be provided by the device in response the user input, wherein the computing system refrains from including the first content in the response data based at least in part on the first data being classified as corresponding to the first topic and the first topic being represented in the second data; and sending the response data to the device to cause the device to provide the output.
2. The computer-implemented method of claim 1, further comprising: determining a metric representing a trustworthiness of the first content provider; and determining, based at least in part on the metric, to process the first data to determine whether to cause the device to output the first content.
4. The computer-implemented method of claim 1, further comprising: receiving user feedback data associated with the user identifier; determining, in the user feedback data, a number of negative user feedback events associated with the first topic; and generating the second data based at least in part on the number of negative user feedback events.
3. The computer-implemented method of claim 1, further comprising: receiving, by the computing system and from a second content provider via the network, third data representing second content and the user identifier; processing the third data to determine whether to cause the device to output the second content, the processing including: using the ML model to classify the third data as corresponding to a second topic of the plurality of topics, the second topic being different than the first topic, and determining that the second topic is unrepresented in the second data; and based at least in part on the third data being classified as corresponding to the second topic and the second topic being unrepresented in the second data, including at least a portion of the second content in the response data.
10. The computer-implemented method of claim 9, wherein: the computing system receives the first data after receiving the input data.
9. The computer-implemented method of claim 1, wherein the input data represents a request for the device to output notifications associated with the user identifier.
11. A computing system, comprising: at least one processor; and at least one memory comprising instructions that, when executed by the at least one processor, cause the computing system to: receive, from a first content provider via a network, first data representing first content and a user identifier corresponding to an intended recipient of the first content; process the first data, to determine whether to cause a device associated with the user identifier to output the first content, at least in part by: using a machine learning (ML) model to classify the first data as corresponding to a first topic of a plurality of topics, and determining that the first topic is represented in second data associated with the user identifier; receive input data representing a user input to the device; determine response data representing an output to be provided by the device in response to the user input, wherein the computing system refrains from including the first content in the response data based at least in part on the first data being classified as corresponding to the first topic and the first topic being represented in the second data; and send the response data to the device to cause the device to provide the output.
12. The computing system of claim 11, wherein the at least one memory further comprises additional instructions that, when executed by the at least one processor, further cause the computing system to: determine a metric representing a trustworthiness of the first content provider; and determine, based at least in part on the metric, to process the first data to determine whether to cause the device to output the first content.
14. The computing system of claim 11, wherein the at least one memory further comprises additional instructions that, when executed by the at least one processor, further cause the computing system to: receive user feedback data associated with the user identifier; determine, in the user feedback data, a number of negative user feedback events associated with the first topic; and generate the second data based at least in part on the number of negative user feedback events.
13. The computing system of claim 11, wherein the at least one memory further comprises additional instructions that, when executed by the at least one processor, further cause the computing system to: receive, from a second content provider via the network, third data representing second content and the user identifier; process the third data, to determine whether to cause the device to output the second content, at least in part by: using the ML model to classify the third data as corresponding to a second topic of the plurality of topics, the second topic being different than the first topic, and determining that the second topic is unrepresented in the second data; and based at least in part on the third data being classified as corresponding to the second topic and the second topic being unrepresented in the second data, including at least a portion of the second content in the response data.
19. The computing system of claim 11, wherein the input data represents a request for the device to output notifications associated with the user identifier.
20. The computing system of claim 19, wherein the at least one memory further comprises additional instructions that, when executed by the at least one processor, further cause the computing system to: receive the first data after receiving the input data.
Claims 26-28, 36-38 rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1, 11 of U.S. Patent No. 12,190,065 B1 in view of references Zong et al. (US 20210234816 A1), Ryu (US 20100211551 A1), and Wheeler (US 9262751 B1). The conflicting claims 1, 11 of U.S. Patent No. 12,190,065 B1 does not explicitly teach limitations “wherein receiving the first data comprises receiving supplemental content not directly responsive to the user input” as recited in claims 26, 36, “wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to adult-only content” as recited in claim 27, 37, and “wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to the first content provider” as recited in claims 28, 38. Zong teaches the receiving supplemental content not directly responsive to the user input (the classifying tokens 424 receiving both user message as the user input and generated individual/group models, as supplemental content not directly responsive to the user input or the message 422 in fig. 4), as recited in claims 26, 36, for benefits of achieving an improvement in efficiency of a messaging system (by automatic recognition of input message of whether the message is suitable or non-suitable for outputting or sending, para 24), Ryu (above) teaches wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to adult-only content (filter out adult contents with much higher accuracy by three harmful content blocking steps, abstract) for benefits of improving the text processing performance (by more accurately removing out harmful contents, abstract), and Wheeler (above) teaches the first content provider (spam senders, col 6, ln 3-7) and wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to the first content provider (the content sent by the spam senders is blocked through a spam folder for unwanted email messages, para 6, ln 8-23) for benefits of improving the efficiency in operating the text (by level of complaint upon the volume, col 4, ln 48-58, by dynamically adjusting complaint threshold as the volume, col 5, ln 7-17),
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to applied wherein receiving the first data comprises receiving supplemental content not directly responsive to the user input, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to adult-only content, and wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to the first content provider, as taught by Trim, Ryn, and Wheeler, respectively, to the computer-implemented method, as taught by conflicting claims 1, 11, for the benefits discussed above, respectively. The following is a comparison between claims of the instant application and conflicting claims 1, 11 of U.S. Patent No. 12,190,065 B1 for reference:
Claims 26-28, 36-38 in the current application
claims 12,190,065 of Conflicting U.S. Patent No. 1-4, 7, 9-14, 19-20 B1
21. A computer-implemented method, comprising: receiving, by a computing system and from a first content provider, first data representing first content corresponding to an intended recipient; processing the first data to determine whether to cause a device associated with the intended recipient to output the first content, the processing including: using a machine learning (ML) model to classify the first data as corresponding to a first topic of a plurality of topics, and determining that the first topic is represented in second data indicating information corresponding to the first topic; receiving input data representing a user input to the device; determining, by the computing system, response data representing an output to be provided by the device in response the user input, wherein the computing system refrains from including the first content in the response data based at least in part on the first data being classified as corresponding to the first topic and the first topic being represented in the second data; and sending the response data to the device to cause the device to provide the output.
26. The computer-implemented method of claim 21, wherein receiving the first data comprises receiving supplemental content not directly responsive to the user input.
27. The computer-implemented method of claim 21, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to adult-only content.
28. The computer-implemented method of claim 21, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to the first content provider.
31. A system comprising: at least one processor; and at least one memory comprising instructions that, when executed by the at least one processor, cause the system to: receive, by a computing system and from a first content provider, first data representing first content corresponding to an intended recipient; process the first data to determine whether to cause a device associated with the intended recipient to output the first content, such processing including: using a machine learning (ML) model to classify the first data as corresponding to a first topic of a plurality of topics, and determining that the first topic is represented in second data indicating information corresponding to the first topic; receive input data representing a user input to the device; determine, by the computing system, response data representing an output to be provided by the device in response the user input, wherein the computing system refrains from including the first content in the response data based at least in part on the first data being classified as corresponding to the first topic and the first topic being represented in the second data; and send the response data to the device to cause the device to provide the output.
36. The system of claim 31, wherein the first data comprises supplemental content not directly responsive to the user input.
37. The system of claim 31, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to adult-only content.
38. The system of claim 31, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to the first content provider.
1. A computer-implemented method, comprising: receiving, by a computing system and from a first content provider via a network, first data representing first content and a user identifier corresponding to an intended recipient of the first content; processing the first data to determine whether to cause a device associated with the user identifier to output the first content, the processing including: using a machine learning (ML) model to classify the first data as corresponding to a first topic of a plurality of topics, and determining that the first topic is represented in second data associated with the user identifier; receiving input data representing a user input to the device; determining, by the computing system, response data representing an output to be provided by the device in response the user input, wherein the computing system refrains from including the first content in the response data based at least in part on the first data being classified as corresponding to the first topic and the first topic being represented in the second data; and sending the response data to the device to cause the device to provide the output.
11. A computing system, comprising: at least one processor; and at least one memory comprising instructions that, when executed by the at least one processor, cause the computing system to: receive, from a first content provider via a network, first data representing first content and a user identifier corresponding to an intended recipient of the first content; process the first data, to determine whether to cause a device associated with the user identifier to output the first content, at least in part by: using a machine learning (ML) model to classify the first data as corresponding to a first topic of a plurality of topics, and determining that the first topic is represented in second data associated with the user identifier; receive input data representing a user input to the device; determine response data representing an output to be provided by the device in response to the user input, wherein the computing system refrains from including the first content in the response data based at least in part on the first data being classified as corresponding to the first topic and the first topic being represented in the second data; and send the response data to the device to cause the device to provide the output.
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 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.
Claims 21, 23, 25-31, 33, 35-40 are rejected under 35 U.S.C. 103 as being unpatentable over Bennett (US 20090307220 A1) and in view of reference Zong et al. (US 20210234816 A1, hereinafter Zong).
Claim 21: Bennett teaches a computer-implemented method (title and abstract, ln 1-17, method steps, para 57, by a microprocessor or a DSP, para 40 to implement stored computer instructions, para 41), comprising:
receiving, by a computing system (including an image search server circuitry 407) and from a first content provider (from a plurality of web hosting servers and collected in the image database 429 in fig. 4 or image database 181 in fig. 1), first data representing first content (images with titles or images with characteristic parameters of the search image, para 20 and retrieved from the image database 429) corresponding to an intended recipient (the search string provided by the user, e.g., a user entered “children Arts” in field 281, as the search string and the user is intended recipient, para 9);
processing the first data to determine whether to cause a device associated with the intended recipient to output the first content (whether adult content is included in the retrieved image from the image database, by using an adult content filer module 175, para 22), the processing including:
using a module (digital image correlation module 173 in fig. 1) to classify the first data as corresponding to a first topic of a plurality of topics (characteristic parameters, of the image with the title of the image, para 22), and determining that the first topic is represented in second data indicating information (the adult content provided by the user at step 659) corresponding to the first topic (sample images compared with adult content, para 22);
receiving input data representing a user input to the device (user’s entry of the string words or adult content filtering parameters received from the client device at step 659, para 53);
determining, by the computing system, response data representing an output to be provided by the device in response the user input (for generating a plurality of search result pages in response to user’s image search request discussed above in fig. 7, para 665 and the adult content should be filtered out for the search result pages, para 53), wherein the computing system refrains from including the first content in the response data (by using adult content filtering at step 657 in fig. 7) based at least in part on the first data being classified as corresponding to the first topic (the images selected based on the search strings and search images, and compared with the adult content, para 53) and the first topic being represented in the second data (the adult content to be used above is coming from user’s terminal or client device, para 53); and
sending the response data to the device to cause the device to provide the output (delivering the search result page at 667 and without the adult content defined by the client device or the user).
However, Bennett does not explicitly teach using a machine learning model to perform the disclosed classification of the first data as corresponding to the first topic of the plurality of topics.
Zong teaches an analogous field of endeavor by disclosing a computer-implemented method (title and abstract, ln 1-15, method steps in figs. 5-7 and implemented by a computer system 1012 in fig 10) and wherein the first data is disclosed (messages, para 20) and further teaches processing the first data (via the system 200 in fig. 2) to determine whether to send the first data to a device associated with the user identifier (messages unsuited for sending are automatically identified before being sent over a communications network, para 20, and based on machine learning model being a probabilistic topic model, etc., para 23, and token analysis, para 35-37), the processing including: using a machine learning ML model (a machine learning classification model generated by classifier modeler 204, para 33) to classify the first data as corresponding to a first topic of a plurality of topics (the machine learning classification model executed by token classifier 206 in fig. 2, para 33, and making a suitability-versus-unsuitability determination of the message is based on an association between tokens extracted from the message and topics identified by a topic model generated by classifier modeler 204, para 35-36 and individual topic models corresponding to topics relevant to specific individuals, e.g., views, opinions, objectives, etc., para 52) for benefits of improving the efficiency of the classification of topic of the messages (by using machine learning models without a need of manual compilation for the determination, para 24 and more efficiently to predict an object within an image by using CNN caption generator, para 56 in an optimized resource utilizations, para 78).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the machine learning model to classify the first data as corresponding to the first topic of the plurality of topics, as taught by Zong, to the module utilized to classify the first data as corresponding to the first topic of the plurality of topics in the computer-implemented method, as taught by Bennett, for the benefits discussed above.
Claim 31 has been analyzed and rejected according to claim 21 above and the combination of Bennett and Zong further teaches a system (Bennett, a system in fig. 1, and Zong, a computer system in fig. 10), comprising:
at least one processor (Bennett, processing circuitry 409 is a microprocessor, a DSP, etc., in fig. 4, para 40, and Zong, processor 1016); and
at least one memory (Bennett, local storage 417, and Zong, a memory 1028) comprising instructions (Bennett, storing computer instructions, para 41, and Zong, program modules stored in the memory 1028, para 101) that, when executed by the at least one processor, cause the computing system to perform the functions of claim 21 (Bennett, implemented modules 421, 423, 425, 427, etc., in fig. 4, para 41, and Zong, the application program and the program modules and executed by the computer system 1012 or processor 1016, para 102).
Claim 23: the combination of Bennett and Zong further teaches, according to claim 21 above, the computer-implemented method further comprising:
determining a user identifier (Bennett, the client device used by the user to perform search and navigate, para 6, and sent the entries to the server through the Internet 455 in fig. 4, and thus, the MAC address is inherency and used by the user to send and to receive data through the Internet, and Zong, Internet applied, para 28 and user 102a, 102b, …, 102n in fig. 1 and with the user ID in table, para 41) corresponding to the intended recipient, wherein the second data is associated with the user identifier (Bennett, the adult filter parameters from the client device, and discussed in claim 21 above, and Zong, the topic from the group topic model and/or individual topic model, 216, 218).
Claim 25 has been analyzed and rejected according to claim 21 above and the combination of Bennett and Zong further teaches third data received to represent second content corresponding to the intended recipient; determining, using the ML model, that the third data corresponds to a second topic; determining the second topic is unrepresented in the second data; and determining the third data is to be presented based at least in part on determining the second topic is unrepresented in the second data (discussed in claim 21 above, and after delivering a search result page 667 and the processing is returned back to process the remining search string and search image from client device or waits the user response at B’, para 54).
Claim 26: the combination of Bennett and Zong further teaches, according to claim 21 above, wherein receiving the first data comprises receiving supplemental content not directly responsive to the user input (Bennett, the entry of the search string, such as “beach houses”, para 9, and Zong, the classifying tokens 424 receiving both user message as the user input and generated individual/group models, as supplemental content not directly responsive to the user input or the message 422 in fig. 4).
Claim 27: the combination of Bennett and Zong further teaches, according to claim 21 above, wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to adult-only content (Bennett, filtering out the adult content at step 661 in fig. 7).
Claim 28: the combination of Bennett and Zong further teaches, according to claim 21 above, a first content provider (spam senders, col 6, ln 3-7) and wherein the computing system refrains from including the first content in the response data based at least in part on the first data corresponding to the first content provider (Bennett, filtering out adult content and discussed in claim 21 above, and Zong, the content sent by the spam senders is blocked through a spam folder for unwanted email messages, para 6, ln 8-23 and for benefits of improving the efficiency in operating the text (by level of complaint upon the volume, col 4, ln 48-58, by dynamically adjusting complaint threshold as the volume, col 5, ln 7-17).
Claim 29: the combination of Bennett and Zong further teaches, according to claim 21 above, wherein receiving the input data occurs before receiving the first data (Bennett, receiving the input data occurs anytime, including before receiving the first data, para 53).
Claim 30: the combination of Bennett and Zong further teaches, according to claim 29 above, wherein receiving the input data comprising receiving data requesting output of the notifications associated with a user identifier (Bennett, user requested search result and Zong, user input to token classifier 206 to make the message unsuitable or suitable for sending, para 53).
Claim 33 has been analyzed and rejected according to claims 31, 23 above
Claim 35 has been analyzed and rejected according to claims 31, 25 above
Claim 36 has been analyzed and rejected according to claims 31, 26 above
Claim 37 has been analyzed and rejected according to claims 31, 27 above
Claim 38 has been analyzed and rejected according to claims 31, 28 above
Claim 39 has been analyzed and rejected according to claims 31, 29 above
Claim 40 has been analyzed and rejected according to claims 29, 30 above
Claims 22, 32 are rejected under 35 U.S.C. 103 as being unpatentable over Bennett
(above) and in view of references Zong (above) and Wheeler (US 9262751 B1).
Claim 22: the combination of Bennett and Zong teaches the first content provider (Bennett, discussed in claim 21 above, and Zong, the user’s communication device among the users’ communication devices 104a-104n, as a source of the message, para 30), and refraining from including the first content in the response data (Bennett, discussed in claim 21 above, and Zong, discussed in claim 21 above), according to claim 21 above, except determining a metric representing a trustworthiness of the first content provider and wherein the computing system refrains from including the first content in the response data based at least in part on the metric.
Wheeler teaches an analogous field of endeavor by disclosing a computer-implemented method (title and abstract, ln 1-9 and method steps in figs. 2-6, computer-implemented method, col 11, ln 59-62) and wherein a metric representing a trustworthiness of the first content provided is determined (metrics regarding a message sender are collected with the aid of message header, sender reputation, authentication and/or a content filter, etc., e.g., determine whether IP address of the message sender has been blacklisted, i.e., low trustworthiness if the IP address of the message sender is blacklisted inherently, col 4, ln 17-33) and wherein the computing system refrains from including the first content in the response data based at least in part on the metric (e.g., if IP address of the message sender is blacklisted and the discussion above), to process the first data to determine whether to send the first data to the device (the email receipt provider 70 refuse to accept the email message, col 4, ln 25-33) for benefits of improving secured and safety communication (by feedback back a redact message to the message sender, col 4, ln 39-47, to avoid volumed spams distributed, col 4, ln 48-58).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied determining the metric representing the trustworthiness of the first content provider and wherein the computing system refrains from including the first content in the response data based at least in part on the metric and , as taught by Wheeler, to the first content provider and refraining from including the first content in the response data in the computer-implemented method, as taught by the combination of Bennett and Zong, for the benefits discussed above.
Claim 32 has been analyzed and rejected according to claim 31, 23 above.
Claims 24, 34 are rejected under 35 U.S.C. 103 as being unpatentable over Bennett
(above) and in view of references Zong (above) and Hoctor et al (US 20150312632 A1, hereinafter Hoctor).
Claim 24: the combination of Bennett and Zong further teaches receiving user feedback data associated with the user identifier (Bennett, the adult filtering parameters received from the client device at step 659, and Zong, the objectionable topic is obtained via user input, para 37), according to claim 21 above, except determining, in the user feedback data, a number of negative user feedback events associated with the first topic; and generating the second data based at least in part on the number of negative user feedback events.
Hoctor teaches an analogous field of endeavor by disclosing a compute-implemented method (title and abstract, ln 1-14 and method seps in figs. 6-9) and wherein receiving user feedback data associated with the user identifier (user feedback received after a first service is subscribed by a user, i.e., user’s identifier, e.g., subscribing identification inherently and receiving the information including contents from a third-party data source at step 604, para 89); determining, in the user feedback data, a number of negative user feedback events associated with a topic (negative feedback about the first service including provided content from a third party, para 88; determining whether the words associated with negative feedback occur with a frequency greater than a frequency threshold, or if the words associated with negative feedback occur within a predetermined number of words of the keywords associated with a first service at step 606, para 89) and generating the second data based at least in part on the number of negative user feedback events (at the following step 608, a likelihood that the user will change the first service based on the identified data indicating a negative interest derived from the determination of the frequency greater than the frequency threshold or the number of keywords over a predetermined threshold above, para 91) for benefits of providing best service to the user and improving the services in the future (para 2 and well-known in the field).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied receiving the user feedback data associated with the user identifier; determining, in the user feedback data, the number of negative user feedback events associated with the first topic; and generating the second data based on at least in part on the number of negative user feedback events, as taught by Hoctor, to receiving user feedback data associated with the user identifier in the method, as taught by the combination of Bennett and Zong, for the benefits discussed above.
Claim 34 has been analyzed and rejected according to claim 31, 24 above.
Conclusion
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/LESHUI ZHANG/
Primary Examiner,
Art Unit 2695
Prosecution Insights