DETAILED ACTION
Response to Amendment
Claims 1-2, 4-8, 10-12, 14, 16-17, and 19-25 are pending.
Response to Arguments
Applicant’s arguments filed 04/23/2026 have been fully considered.
Applicant argues on page 10 that the Office Action posted 01/15/2026 has not examined the currently pending claims, which were filed in a preliminary amendment on May 1, 2024. Accordingly, Applicant requests issuance of a new non-final Office Action that examines the currently pending claims.
Applicant’s arguments are persuasive. This Office Action is non-final in view of the currently pending claims.
Claim Rejections - 35 USC § 101
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.
Claim 1-2, 4-8, 10-12, 14, 16-17, and 19-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-2, 4-8, 10-12, 14, 16-17, and 19-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite collecting information and determining terminal ratio.
The limitations of collecting information and determining terminal ratio, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitation of collecting information, as drafted, is a well-understood, routine, conventional data collection in the field; the limitation of determining terminal ratio is observation. 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, claims 1-2, 4-8, 10-12, 14, 16-17, and 19-25 recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, independent claim 1 recites two additional elements terminal devices and live room; independent claim 16 recites a computing device, comprising a memory, a processor, and computer instructions; independent claim 17 recites a computer-readable storage medium; these elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Dependent claims 2, 6-7, 11, 19, 21-23 and 25 add the mathematical calculation. Adding one abstract idea to another abstract idea does not render the claim non-abstract. MPEP 2106.05.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 24 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01.
Claim 24 recites “…determining that the current terminal ratio and the historical terminal ratio meet the first;”. The essential element after “the first;” is omitted.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-8, 10-12, 14, 16-17, and 19-25 are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al. (CN104778591A) in view of Wang (WO2019165697A1).
Regarding claim 1, Guo discloses a method comprising ([Abstract] shows a method for identifying abnormal behaviors from collected event data):
collecting statistics on historical information about terminal devices; determining a historical terminal ratio of different types of terminal devices based on the historical information ([page 5 line 26; page 8 lines 51-52] shows many platforms include clients of the Android system and the IOS system, and the probability of an event occurring based on the Android system and the IOS system is 1/2; [page 14 lines 10-12] shows to count the events (historical events) that occurred in a certain period of time);
collecting current information about terminal devices at a current time point; determining a current terminal ratio of different types of terminal devices at the current time point based on the current information ([col 8 lines 24-25] shows the server may calculate the probability of the continuous event to obtain the continuous probability, that is, the probability that the current event occurs as a continuous event; [page 8 lines 63-66] shows assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 (e.g., based on the fact that the historical terminal ratio of Android and the IOS system is 1/2, the current terminal ratio of an Android based event occurring 10 times in a row is very small and calculated to be 1/1024)); and
determining whether abnormal behavior exists based on the historical terminal ratio and the current terminal ratio ([Abstract] shows identifying abnormal behavior; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), abnormal events with probability less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 and it will be rejected by the server (e.g., the probability of an Android based event occurring 10 times in a row is very small and calculated to be 1/1024. Therefore an arbitrary threshold is chosen to reject rare (abnormal) events that occurs with probability less than 1/1000).)
Guo discloses identifying abnormal behaviors ([Abstract]) but fails to teach determining click-farming in a live room; and
collecting statistics on historical information about terminal devices which accessed a livestreaming platform; and
collecting current information about terminal devices accessing a target live room at a current time point; and
determining whether click-farming exists in the target live room.
However, Wang discloses a method for determining click-farming in a live room ([Abstract] and [page 4 lines 54-55] show identifying click farming in a plurality of live broadcast rooms in the live broadcast platform); and
collecting information about terminal devices which accessed a livestreaming platform ([page 6 line 5] shows each user has a unique IP or a fixed device address when logging in to the website or live broadcast platform);
collecting current information about terminal devices which accessed a target live room ([page 4 lines 62-70] shows user A watching the start time M1 of the live room X, the end time M2 and the viewing time M3; the start time and end time of each live room watch, and the watch time for each live room); and
determining whether click-farming exists in the target live room ([Abstract] and [page 4 lines 54-55] show identifying click farming in a plurality of live broadcast rooms in the live broadcast platform.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method in Guo with the teaching of Wang in order to improve the accuracy of identifying a user as a click farming user (Wang; [Abstract]).
Regarding claim 2, Guo-Wang as applied to claim 1 discloses:
collecting statistics on information about terminal devices which accessed the livestreaming platform at each statistical time point in a preset time duration (Wang; [Abstract] and [page 4 lines 54-55] show identifying click farming in a plurality of live broadcast rooms in the live broadcast platform. Guo; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024); and
calculating a historical terminal ratio in each statistical period according to the information at each statistical time point, wherein the preset time duration comprises at least one statistical period, and each statistical period comprises at least one statistical time point (Wang; [Abstract] and [page 4 lines 54-55] show identifying click farming in a plurality of live broadcast rooms in the live broadcast platform. Guo; ([page 14 lines 10-12] shows to count the events (historical events) that occurred in a certain period of time; [page 8 lines 63-64] shows the probability of an event occurring in the Android system and the IOS system is 1/2).
Regarding claim 4, Guo-Wang as applied to claim 2 discloses the determining whether click-farming exists in the target live room based on the historical terminal ratio and the current terminal ratio comprises: determining a target statistical time point in the preset time duration based on the current time point; obtaining a target historical terminal ratio in a target statistical period corresponding to the target statistical time point; determining that click-farming does not exist in the target live room when the current terminal ratio meets the target historical terminal ratio; determining that click-farming exists in the target live room when the current terminal ratio does not meet the target historical terminal ratio (Wang; [Abstract] and [page 4 lines 54-55] show identifying click farming in a plurality of live broadcast rooms in the live broadcast platform. Guo; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 (e.g., current terminal ratio) and it will be rejected by the server (e.g., the probability of an Android based event occurring 10 times in a row is very small or calculated to be 1/1024. Therefore an arbitrary threshold is chosen to reject rare (abnormal) events that occurs with probability less than 1/1000).)
Regarding claim 5, Guo-Wang as applied to claim 2 discloses the collecting statistics on information about terminal devices which accessed the livestreaming platform at each statistical time point in a preset time duration comprises: obtaining information associated with a set of users of the livestreaming platform at each statistical time point in the preset time duration; and determining information of terminal devices used by users in each set of users (Wang; [col 3 line 11] shows acquiring user characteristics of all users; Guo; [Abstract] shows identifying abnormal behavior; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 (e.g., current terminal ratio) and it will be rejected by the server (e.g., the probability of an Android based event occurring 10 times in a row is very small or calculated to be 1/1024. Therefore an arbitrary threshold is chosen to reject rare (abnormal) events that occurs with probability less than 1/1000).)
Regarding claim 6, Guo-Wang as applied to claim 1 discloses:
determining types of live rooms on the livestreaming platform (Wang; [page 4 lines 55-63] shows a plurality of live broadcast rooms; user A watching the start time M1 of live room X, the end time M2 and the viewing time M3);
collecting statistics on historical information about terminal devices which accessed each type of live room; and calculating a historical terminal ratio corresponding to each type of live room based on the historical information corresponding to each type of live room (Guo; [page 14 lines 10-12] shows to count the events (historical events) that occurred in a certain period of time; [page 8 lines 63-64] shows the probability of an event occurring in the Android system and the IOS system is 1/2).
Regarding claim 7, Guo-Wang as applied to claim 6 discloses:
determining a target type of the target live room; and calculating a current terminal percentage of the target live room based on the current information about the terminal devices accessing a target live room (Wang; page 4 lines 55-63] shows a plurality of live broadcast rooms; user A watching the start time M1 of live room X, the end time M2 and the viewing time M3. Guo; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024.)
Regarding claim 8, Guo-Wang as applied to claim 7 discloses:
determining a target historical terminal ratio according to the target type; determining that click-farming does not exist in the target live room when the current terminal percentage meets the target historical terminal ratio; and determining that click-farming exists in the target live room when the current terminal percentage does not meet the target historical terminal ratio (Wang; [Abstract] and [page 4 lines 54-55] show identifying click farming in a plurality of live broadcast rooms in the live broadcast platform. Guo; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 (e.g., current terminal ratio) and it will be rejected by the server (e.g., the probability of an Android based event occurring 10 times in a row is very small or calculated to be 1/1024. Therefore an arbitrary threshold is chosen to reject rare (abnormal) events that occurs with probability less than 1/1000).)
Regarding claim 10, Guo-Wang as applied to claim 1 discloses collecting statistics on attribute information of each type of terminal device; and determining a historical attribute ratio corresponding to each type of terminal device based on the attribute information of each type of terminal device (Wang; [page 4 line 55] shows a plurality of live broadcast rooms. Guo; [page 8 lines 63-66] shows the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio).)
Regarding claim 11, Guo-Wang as applied to claim 10 discloses the collecting current information about terminal devices accessing a target live room, and determining a current terminal ratio based on the current terminal information comprises: collecting the current information about terminal devices accessing the target live room at the current time point; and calculating the current terminal ratio associated with the target live room and a current attribute ratio corresponding to each type of terminal device based on the current information (Wang; [page 4 line 55] shows a plurality of live broadcast rooms. Guo; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024.)
Regarding claim 12, Guo-Wang as applied to claim 11 discloses the determining whether click-farming exists in the target live room based on the historical terminal ratio and the current terminal ratio comprises: determining whether the current terminal ratio and the historical terminal ratio meet a system click-farming determination rule; determining that click-farming exists in the target live room in response to determining that the current terminal ratio and the historical terminal ratio meet the system click-farming determination rule; in response to determining that the current terminal ratio and the historical terminal ratio do not meet the system click-farming determination rule, determining a target type of terminal device, and determining whether a current attribute ratio corresponding to the target type of terminal device and a historical terminal attribute ratio corresponding to the target type of terminal device meet an attribute click-farming determination rule; and determining that click-farming exists in the target live room in response to determining that the current attribute ratio corresponding to the target type of terminal device and the historical attribute ratio corresponding to the target type of terminal device meet the attribute click-farming determination rule; determining that click-farming does not exist in the target live room in response to determining that the current attribute ratio corresponding to the target type of terminal device and the historical attribute ratio corresponding to the target type of terminal device do not meet the attribute click-farming determining rule (Wang; [Abstract] and [page 4 lines 54-55] show identifying click farming in a plurality of live broadcast rooms in the live broadcast platform. Guo; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 (e.g., current terminal ratio) and it will be rejected by the server (e.g., the probability of an Android based event occurring 10 times in a row is very small or calculated to be 1/1024. Therefore an arbitrary threshold is chosen to reject rare (abnormal) events that occurs with probability less than 1/1000).)
Regarding claim 14, Guo-Wang as applied to any one of claim 1 discloses before the collecting current information about terminal devices accessing a target live room, the method further comprises: determining an initial live room on the livestreaming platform, and obtaining a quantity of users in the initial live room; and identifying the initial live room as the target live room in response to determining that the quantity of users exceeds a preset threshold ((Wang; [page 5 lines 9-10] shows it is judged to be not less than, it means that the popularity of the live broadcast room is relatively high, and the number of viewing users in the live broadcast room is also sufficient.)
Regarding claims 16 and 19-21, claims 16 and 19-21 are directed to a computing device. Claims 16 and 19-21 require limitations that are similar to those recited in the method claims 1-2 and 4-6 to carry out the method steps. And since the references of Guo and Wang combined teach the method including limitations required to carry out the method steps, therefore claims 16 and 19-21 would have also been obvious in view of the method disclosed in Guo and Wang combined.
Furthermore, Guo-Wang as combined discloses a memory, a processor, and computer instructions stored in the memory and executable by the processor, wherein the computer instructions upon execution by the processor cause the processor to implement operations (Guo; [page 19 lines 24-25]).
Regarding claims 17 and 25, claims 17 and 25 are directed to a computer-readable storage medium. Claims 17 and 25 require limitations that are similar to those recited in the method claims 1-2 to carry out the method steps. And since the references of Guo and Wang combined teach the method including limitations required to carry out the method steps, therefore claims 17 and 25 would have also been obvious in view of the method disclosed in Guo and Wang combined.
Furthermore, Guo-Wang as combined discloses computer-readable storage medium, storing computer instructions, wherein the computer instructions, when executed by a processor, cause the processor to implement operations (Guo; [page 19 lines 24-25]).
Regarding claim 22, Guo-Wang as applied to claim 21 discloses:
determining a target type of the target live room; calculating a current terminal percentage of the target live room based on the current information about the terminal devices accessing a target live room (Wang; [page 4 line 55-63] shows a plurality of live broadcast rooms; user A watching the start time M1 of the live room X. Guo; [col 8 lines 24-25] shows the server may calculate the probability of the continuous event to obtain the continuous probability, that is, the probability that the current event occurs as a continuous event; [page 8 lines 63-66] shows assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 (e.g., based on the fact that the historical terminal ratio of Android and the system is 1/2, the current terminal ratio of an Android based event occurring 10 times in a row is very small and is calculated to be 1/1024));
determining a target historical terminal ratio according to the target type; determining that click-farming does not exist in the target live room when the current terminal percentage meets the target historical terminal ratio; and determining that click-farming exists in the target live room when the current terminal percentage does not meet the target historical terminal ratio (Wang; [Abstract] and [page 4 lines 54-55] show identifying click farming in a plurality of live broadcast rooms in the live broadcast platform. Guo; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 (e.g., current terminal ratio) and it will be rejected by the server (e.g., the probability of an Android based event occurring 10 times in a row is very small or calculated to be 1/1024. Therefore an arbitrary threshold is chosen to reject rare (abnormal) events that occurs with probability less than 1/1000).)
Regarding claim 23, Guo-Wang as applied to claim 16 discloses the operations further comprising:
collecting statistics on historical attribute information of each type of terminal device accessed which accessed the livestreaming platform; determining a historical attribute ratio corresponding to each type of terminal device based on the historical attribute information of each type of terminal device (Wang; [page 4 line 55] shows a plurality of live broadcast rooms. Guo; [page 8 lines 63-66] shows the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio)); and
calculating a current attribute ratio corresponding to each type of terminal device based on the current information (Guo; [col 8 lines 24-25] shows the server may calculate the probability of the continuous event to obtain the continuous probability, that is, the probability that the current event occurs as a continuous event; [page 8 lines 63-66] shows assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 (e.g., based on the fact that the historical terminal ratio of Android and the system is 1/2, the current terminal ratio of an Android based event occurring 10 times in a row is very small and is calculated to be 1/1024)).
Regarding claim 24, Guo-Wang as applied to claim 23 discloses the determining whether click-farming exists in the target live room based on the historical terminal ratio and the current terminal ratio comprises (Wang; [Abstract] and [page 4 lines 54-55] show identifying click farming in a plurality of live broadcast rooms in the live broadcast platform):
determining whether the current terminal ratio and the historical terminal ratio meet a first rule; determining that click-farming exists in the target live room in response to determining that the current terminal ratio and the historical terminal ratio meet the first; in response to determining that the current terminal ratio and the historical terminal ratio do not meet the first rule, determining a target type of terminal device and determining whether a current attribute ratio corresponding to the target type of terminal device and a historical attribute ratio corresponding to the target type of terminal device meet a second rule (Guo; [col 8 lines 24-25] shows the server may calculate the probability of the continuous event to obtain the continuous probability, that is, the probability that the current event occurs as a continuous event; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 and it will be rejected by the server (e.g., the probability of an Android based event occurring 10 times in a row is very small and calculated to be 1/1024. Therefore an arbitrary threshold is chosen to reject rare (abnormal) events that occurs with probability less than 1/1000)); and
determining that click-farming exists in the target live room in response to determining that the current attribute ratio corresponding to the target type of terminal device and the historical attribute ratio corresponding to the target type of terminal device meet the second rule; determining that click-farming does not exist in the target live room in response to determining that the current attribute ratio corresponding to the target type of terminal device and the historical attribute ratio corresponding to the target type of terminal device do not meet the second (Guo; [col 8 lines 24-25] shows the server may calculate the probability of the continuous event to obtain the continuous probability, that is, the probability that the current event occurs as a continuous event; [page 8 lines 63-66] shows based on the fact that the probability of an event occurring in the Android system and the IOS system is 1/2 (e.g., historical terminal ratio), events less than 1/1000 will be rejected. Assuming that a certain event occurs 10 times in a row based on the Android system, the continuation probability of the 10th consecutive event is 1/1024 and it will be rejected by the server (e.g., the probability of an Android based event occurring 10 times in a row is very small and calculated to be 1/1024. Therefore an arbitrary threshold is chosen to reject rare (abnormal) events that occurs with probability less than 1/1000)).
Conclusion
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/TAN DOAN/Primary Examiner, Art Unit 2445
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