DETAILED ACTION
This action is in response to communication filed on 8 August 2024. Claims 5 and 11-12 are amended. Claims 15-18 are added. Claims 6-10 and 13-14 are canceled. Claims 1-5,11-12 and 15-18 are pending in the application and have been considered below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 2 is objected to because of the following informalities:
Claim 2 recites the limitations “a target channel” in the second and the last elements of the claim. It is not obvious if the two limitations are citing the same ‘target channel’ or different ones. In addition, claim 1 recites a “a target channel” in the last element of the claim. As claim 2 is depending on claim 1, it is not clear if any of the ‘target channel’ limitations in claim 2 are referencing the same ‘target channel’ in claim 1 or a different one.
Appropriate correction is required.
Claim 11 is objected to because of the following informalities:
Claim 11 recites the limitations “a computer execution instruction” in the second and the third elements of the claim. It is not obvious if the two limitations are referencing the same ‘computer execution instruction’ or different ones.
Appropriate correction is required.
Claim 15 is objected to because of the following informalities:
Claim 15 recites the limitations “determining … a target channel” in the last element of the claim. As claim 15 is depending on claim 11 which also recites “a target channel”, it is not obvious if claim 15 is referencing the same “target channel” as claim 11 or a different one.
Appropriate correction is required.
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.
Claims 1-2, 11-12, 15 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitations "the application" in the first element of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the user's browsing" in the first element of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the user's operation" in the first element of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the operation parameters" in a few elements of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitations "the application start method" in the third element of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitations "the application" in the first element of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the user's browsing" in the first element of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitations "the application start method" in the preamble of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitations "the application" in the first element of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "the user's browsing" in the first element of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the user's operation" in the first element of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "the operation parameters" in a few elements of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-5 and 15-18 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite as they are dependent on the rejected independent claims 1 and 11, respectively.
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, 5, 11-12, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over YU et al. (US20150334461A1) in view of MILLER et al. (US20100229100A1).
As to claim 1, YU and MILLER teach
an application start method, comprising: in response to a start instruction of a user for the application (see figs. 1-2, par. 0004, wherein a method for dynamically recommending favorite channels or programs, implemented on a computer and a non-transitory computer-readable storage medium, comprises collecting historical data of a user's operations; as taught by YU),
determining a target channel among each channel of the application based on the historical browsing information (see fig. 1, par. 0004, for collecting historical data of a user's operations; classifying the historical data; determining a ranking of channels or programs in each class; querying broadcast information; matching the broadcast information with the ranking of channels or programs; and recommending a favorite channel or program list with a predetermined degree of match; as taught by YU),
and displaying the target channel of the application (see fig. 1, par. 0046, wherein the favorite channel list may be displayed in an electronic screen, or displayed in other manners to facilitate the user's observation; as taught by YU).
YU does not expressly teach obtaining historical browsing information of the application, the historical browsing information comprising information generated during the user's browsing of the application.
In similar field of endeavor, MILLER teaches:
obtaining historical browsing information of the application (see figs. 1-5, par. 0036, wherein load the first browsing history into the first application for use by the first application; as taught by MILLER),
the historical browsing information comprising information generated during the user's browsing of the application (see figs. 1-8, par. 0017, wherein during execution of a first application on a WCD, the recording of a first browsing history of the first application, where the first browsing history contains a first ordered list of data items used by the first instance of the first application. Furthermore, during execution of a second application on the WCD, a second browsing history of the second application may also be recorded, where the second browsing history contains a second ordered list of data items used by the second application. The first and second browsing histories may be represented in a combined fashion as an inter-application browsing history; as taught MILLER).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the YU apparatus to include the teachings of MILLER for obtaining historical browsing information of the application, the historical browsing information comprising information generated during the user's browsing of the application. Such a person would have been motivated to make this combination as it is beneficial for the user to be able to receive recommendations based on the history of the user interaction with the application as there is a great probability that the user would make a selection based on that history (see also MILLER, par. 0019).
As to claim 2, YU and MILLER teach the limitations of claim 1. YU further teaches wherein
the historical browsing information comprise a historical channel operated by the user before the application exited last time and operation information generated during the user's operation of the historical channel (see figs. 1-2, par. 0041, wherein at step S1, channels or programs often viewed by the user are mostly collected to prevent the historical data from being too massive. In one embodiment, what are collected here are channels or programs that are viewed by the user for more than three minute once; as taught by YU);
determining a target channel among each channel of the application based on the historical browsing information comprises: determining, based on operation information of the historical channel, a level of interest of the user in the historical channel (see par. 0042, wherein channels and programs in each class are ranked according to a user's preference degree, for example, in the same time period, a program most frequently watched recently has the highest degree, a channel with the longest accumulated view duration has the second highest degree, and so on so forth, thereby determining the ranking of channels and programs in each time period; as taught by YU);
determining, based on the level of interest, a target channel among the historical channel and a preset recommended channel of the application (see par. 0045, where at step S5, a favorite channel list with a predetermined degree of match is recommended to the user according to the match situations of step S4, and the user chooses to view by pressing a key according to the recommended list; as taught by YU).
As to claim 5, YU and MILLER teach the limitations of claim 2. YU further teaches
wherein the determining a target channel among the historical channel and the preset recommended channel of the application, based on the level of interest comprises: in response to the level of interest being that the user is interested in the historical channel, determining the historical channel as the target channel (see par. 0042, wherein channels and programs in each class are ranked according to a user's preference degree, for example, in the same time period, a program most frequently watched recently has the highest degree, a channel with the longest accumulated view duration has the second highest degree, and so on so forth, thereby determining the ranking of channels and programs in each time period; see also par. 0045, where at step S5, a favorite channel list with a predetermined degree of match is recommended to the user according to the match situations of step S4, and the user chooses to view by pressing a key according to the recommended list; as taught by YU).
in response to the level of interest being that the user is not interested in the historical channel, determining the preset recommended channel of the application as the target channel (see par. 0046, wherein when the broadcast time of the user's most favorite program is not reached currently and the current recommendation is not necessarily the most preferred one for next time period, the current recommendation may be displayed first and the user is reminded when said next time period comes; as taught by YU).
Claim 11 amounts to the device for executing the method of claim 1. Accordingly, claim 11 is rejected for substantially the same reasons as presented above for claim 1 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 12 amounts to the non-transitory computer readable storage medium storing a computer execution instruction that when executed, implements the application start method of claim 1. Accordingly, claim 12 is rejected for substantially the same reasons as presented above for claim 1 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 15 amounts to the device for executing the method of claim 2, respectively. Accordingly, claim 15 is rejected for substantially the same reasons as presented above for claim 2 and based on the references’ disclosure of the necessary supporting hardware and software.
Claim 18 amounts to the device for executing the method of claim 5, respectively. Accordingly, claim 18 is rejected for substantially the same reasons as presented above for claim 5 and based on the references’ disclosure of the necessary supporting hardware and software.
Claims 3-4 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over YU et al. (US20150334461A1) in view of MILLER et al. (US20100229100A1) and further in view of WEI et al. (CN109145210A).
As to claim 3, YU and MILLER teach the limitations of claim 2. YU does not expressly teach wherein the operation information of the historical channel comprises operation parameters; the operation parameters comprise at least one of the following parameters: a stay duration of the historical channel, a quantity of content consumed on the historical channel, a number of clicks on a detail page of the historical channel, a stay duration of the detail page, and a quantity of operations performed on content of the historical channel; determining, based on the operation information of the historical channel, the level of interest of the user in the historical channel comprises: obtaining a preset weight value corresponding to the operation parameters, and determining, based on the operation parameters and the weight value corresponding to the operation parameters, a value of interest of the user in the historical channel; in response to the value of interest being greater than a preset threshold value, determining that the level of interest of the user in the historical channel is that the user is interested in the historical channel; in response to the value of interest being less than or equal to the preset threshold value, determining that the level of interest of the user in the historical channel is that the user is not interested in the historical channel.
In similar field of endeavor, WEI teaches :
wherein the operation information of the historical channel comprises operation parameters; the operation parameters comprise at least one of the following parameters: a stay duration of the historical channel, a quantity of content consumed on the historical channel, a number of clicks on a detail page of the historical channel, a stay duration of the detail page, and a quantity of operations performed on content of the historical channel (see figs. 1-7, page 14, ll. 9-20, wherein in the embodiment of the present application, the information is an example of an article, and may be according to the following formula. updates the αc corresponding to wc Where Hit_ctr represents the average click rate of the recommended articles that are clicked in each channel in the channel distribution in the user portrait wc. Hit_rate represents the proportion of the clicked recommended articles of each channel in the channel distribution in the user portrait wc among the recommended articles. Hit_weight represents a user portrait of each channel. α is the initial value and the initial value of each channel is the same; as taught by WEI);
determining, based on the operation information of the historical channel, the level of interest of the user in the historical channel comprises: obtaining a preset weight value corresponding to the operation parameters (see page 14, ll. 9-20, wherein it can be seen from the above scheme that if the channel corresponding to a certain user portrait has more representations in the historical display, it means that the click rate of the channel is high, and the score should be higher, that is, the weight αc should also be higher. For example, if the "entertainment" and "political" channels in a user's portrait are displayed in the user's history, and the click-through rate is 10%, the user's interest in "entertainment" in the portrait of the cycle. Higher than "politics." Generally speaking, the higher the user's interest rate is, the higher the click rate of “politics” and “entertainment” is, the more the user prefers the “politics” with low interest, so the weight of the “political” channel. Αc should be adjusted higher; see also page 15, ll. 21-22, wherein determining a head interest weight according to the user identification information, where the head interest weight is the largest interest weight in each channel; as taught by WEI),
and determining, based on the operation parameters and the weight value corresponding to the operation parameters, a value of interest of the user in the historical channel; in response to the value of interest being greater than a preset threshold value, determining that the level of interest of the user in the historical channel is that the user is interested in the historical channel (see page 15, ln. 23 – page 16, ln. 15, wherein determining a head interest weight according to the user identification information, where the head interest weight is the largest interest weight in each channel; Determining a target interest popularity type corresponding to the user identification information according to the head interest weight and the correspondence between the interest weight and the interest popularity type. In the embodiment of the present application, the interest popularity type may be divided by the head interest weight, considering that different users have different preferences for the channel type in the recommendation result, for example, when the interest is widely divided, the user may be on the channel. The head interest weights are divided into four categories, wherein the range of head interest weights is (0, 1). The larger the value, the more unique the user's interest, the smaller the user's interest is. The specific division can be: The weight of the head interest (0.00, 0.25) is a broad user base of interest; The head interest weight ∈ [0.25, 0.50) is a broad user base of interest; The head interest weight [0.50, 0.75) is a moderately single user group of interest; The head interest weight ∈ [0.75, 1.00] is a single user group with a heavy interest. Of course, the above four division manners are only examples. In the embodiment of the present application, the number of correspondence between the head interest weight and the interest popularity type is not limited. Taking the above division mode as an example, after determining the interest weight of each channel according to the user identification information, the maximum interest weight, that is, the head interest weight, can be found, thereby determining the user identifier according to the orientation in which the head interest weight falls. The type of target interest broadness corresponding to the information, that is, the user base of interest; as taught by WEI);
in response to the value of interest being less than or equal to the preset threshold value, determining that the level of interest of the user in the historical channel is that the user is not interested in the historical channel (see page 12, ll. 24-26, wherein indicates the number of target channels in the set Ak is j, that is, the first restriction condition means that the number of recommended articles per target channel in a recommendation result does not exceed Nd at most, and the minimum is 0; as taught by WEI).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the YU and MILLER apparatus to include the teachings of WEI wherein the operation information of the historical channel comprises operation parameters; the operation parameters comprise at least one of the following parameters: a stay duration of the historical channel, a quantity of content consumed on the historical channel, a number of clicks on a detail page of the historical channel, a stay duration of the detail page, and a quantity of operations performed on content of the historical channel; determining, based on the operation information of the historical channel, the level of interest of the user in the historical channel comprises: obtaining a preset weight value corresponding to the operation parameters, and determining, based on the operation parameters and the weight value corresponding to the operation parameters, a value of interest of the user in the historical channel; in response to the value of interest being greater than a preset threshold value, determining that the level of interest of the user in the historical channel is that the user is interested in the historical channel; in response to the value of interest being less than or equal to the preset threshold value, determining that the level of interest of the user in the historical channel is that the user is not interested in the historical channel. Such a person would have been motivated to make this combination as the background APP can intelligently recommend information which may be interested to the user by collecting the use behaviors of the user and performing statistical analysis on the use behaviors, taking article recommendation as an example: in general, normal users do not want their channels too far from their interests and too concentrated for a number of articles that are recommended at one time (see WEI, page. 2, ll. 13-30).
As to claim 4, YU and MILLER teach the limitations of claim 2. YU further teaches wherein
the operation information of the historical channel comprises an operation parameter; the operation parameters comprise at least one of the following parameters: a stay duration of the historical channel, a quantity of content consumed on the historical channel, a number of clicks on a detail page on the historical channel, a stay duration of the detail page, and a quantity of operations performed on content of the historical channel (see figs. 1-7, page 14, ll. 9-20, wherein in the embodiment of the present application, the information is an example of an article, and may be according to the following formula. updates the αc corresponding to wc Where Hit_ctr represents the average click rate of the recommended articles that are clicked in each channel in the channel distribution in the user portrait wc. Hit_rate represents the proportion of the clicked recommended articles of each channel in the channel distribution in the user portrait wc among the recommended articles. Hit_weight represents a user portrait of each channel. α is the initial value and the initial value of each channel is the same; as taught by WEI);
determining, based on the operation information of the historical channel, the level of interest of the user in the historical channel comprises: obtaining a preset parameter threshold corresponding to the operation parameters (see page 14, ll. 9-20, wherein it can be seen from the above scheme that if the channel corresponding to a certain user portrait has more representations in the historical display, it means that the click rate of the channel is high, and the score should be higher, that is, the weight αc should also be higher. For example, if the "entertainment" and "political" channels in a user's portrait are displayed in the user's history, and the click-through rate is 10%, the user's interest in "entertainment" in the portrait of the cycle. Higher than "politics." Generally speaking, the higher the user's interest rate is, the higher the click rate of “politics” and “entertainment” is, the more the user prefers the “politics” with low interest, so the weight of the “political” channel. Αc should be adjusted higher; see also page 15, ll. 21-22, wherein determining a head interest weight according to the user identification information, where the head interest weight is the largest interest weight in each channel; as taught by WEI);
in response to there being N operation parameters greater than a parameter threshold corresponding to the operation parameters, determining that the level of interest of the user in the historical channel is that the user is interested in the historical channel (see page 15, ln. 23 – page 16, ln. 15, wherein determining a head interest weight according to the user identification information, where the head interest weight is the largest interest weight in each channel; Determining a target interest popularity type corresponding to the user identification information according to the head interest weight and the correspondence between the interest weight and the interest popularity type. In the embodiment of the present application, the interest popularity type may be divided by the head interest weight, considering that different users have different preferences for the channel type in the recommendation result, for example, when the interest is widely divided, the user may be on the channel. The head interest weights are divided into four categories, wherein the range of head interest weights is (0, 1). The larger the value, the more unique the user's interest, the smaller the user's interest is. The specific division can be: The weight of the head interest (0.00, 0.25) is a broad user base of interest; The head interest weight ∈ [0.25, 0.50) is a broad user base of interest; The head interest weight [0.50, 0.75) is a moderately single user group of interest; The head interest weight ∈ [0.75, 1.00] is a single user group with a heavy interest. Of course, the above four division manners are only examples. In the embodiment of the present application, the number of correspondence between the head interest weight and the interest popularity type is not limited. Taking the above division mode as an example, after determining the interest weight of each channel according to the user identification information, the maximum interest weight, that is, the head interest weight, can be found, thereby determining the user identifier according to the orientation in which the head interest weight falls. The type of target interest broadness corresponding to the information, that is, the user base of interest; as taught by WEI);
otherwise, determining that the level of interest of the user in the historical channel is that the user is not interested in the historical channel; wherein N is a positive integer less than or equal to a number of operational parameters (see page 12, ll. 24-26, wherein indicates the number of target channels in the set Ak is j, that is, the first restriction condition means that the number of recommended articles per target channel in a recommendation result does not exceed Nd at most, and the minimum is 0; as taught by WEI).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the YU and MILLER apparatus to include the teachings of WEI wherein the operation information of the historical channel comprises an operation parameter; the operation parameters comprise at least one of the following parameters: a stay duration of the historical channel, a quantity of content consumed on the historical channel, a number of clicks on a detail page on the historical channel, a stay duration of the detail page, and a quantity of operations performed on content of the historical channel; determining, based on the operation information of the historical channel, the level of interest of the user in the historical channel comprises: obtaining a preset parameter threshold corresponding to the operation parameters; in response to there being N operation parameters greater than a parameter threshold corresponding to the operation parameters, determining that the level of interest of the user in the historical channel is that the user is interested in the historical channel; otherwise, determining that the level of interest of the user in the historical channel is that the user is not interested in the historical channel; wherein N is a positive integer less than or equal to a number of operational parameters. Such a person would have been motivated to make this combination as the background APP can intelligently recommend information which may be interested to the user by collecting the use behaviors of the user and performing statistical analysis on the use behaviors, taking article recommendation as an example: in general, normal users do not want their channels too far from their interests and too concentrated for a number of articles that are recommended at one time (see WEI, page. 2, ll. 13-30).
Claims 16-17 amount to the device for executing the method of claims 3-4, respectively. Accordingly, claims 16-17 are rejected for substantially the same reasons as presented above for claims 3-4 and based on the references’ disclosure of the necessary supporting hardware and software
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Publication Number
Filing Date
Title
US20200402019A1
2019-06-18
Techniques to apply machine learning to schedule events of interest
US8849802B2
2011-09-27
Historical browsing session management
US20200110754A1
2019-07-04
Method and system for generating digital content recommendation
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KOOROSH NEHCHIRI whose telephone number is (408)918-7643. The examiner can normally be reached M-F, 9-5 PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William L. Bashore can be reached on 571-272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KOOROSH NEHCHIRI/Examiner, Art Unit 2174
/WILLIAM L BASHORE/ Supervisory Patent Examiner, Art Unit 2174
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