DETAILED ACTION
Status of Claims
This Action is in response to App. 19/482,340 filed 11/07/2025. The preliminary amendment filed 11/07/2025 has been acknowledged. Claims 4-9 are amended. Claims 1-12 are currently pending and have been examined.
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 .
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.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites the steps of acquiring degree of progress and degree of interest, generating a content interest degree based on the acquired information, and outputting the generated information.
The limitation of acquiring information and generating information, 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 “units”. That is, other than reciting “units” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “unit” language, “acquiring” in the context of this claim encompasses a person reading/viewing information regarding end-user behavior such as by watching the end-user while browsing content. Similarly, the limitation of generating conte interest degree information by correlating the acquired information, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of “unit”. For example, but for the “unit” language, “generating” in the context of this claim encompasses the user noting end-user reaction during particular portions of the content (such as smiling at particular posts/units in the feed). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of utilizing “units” to perform the steps and further outputting the information. The “units”, as defined in the originally filed specification, specifically paragraph 0029 and 0068 are composed of generic computer hardware and/or software. The hardware/software are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of acquiring information, determining correlations, and outputting information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Furthermore, although claims further recite outputting the information, this is merely performing well-understood, routine, and conventional function of outputting/displaying processed information. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using units to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The dependent claims are further directed towards the judicial exception without significantly more. The dependent claims provide limitations on additional units which perform additional abstract steps (such as claim 2, 5), further defining the abstract information (such as claim 3-4, 6-9). These are still directed towards the judicial exception as these further define the abstract elements such as further defining the information and additional abstract steps. They are not significantly more as they do not further integrate the judicial exception into a practical application and the additional element amounts to no more than mere instructions to apply the exception using a generic computer component. The dependent claims is not patent eligible.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Although the preamble of the claims indicate that the claims are directed to a machine, and a machine is one of the statutory classes of invention, the claims do not define a machine as the claims do not recite any structural features. The claims currently recite “…unit configure to…” which as defined in paragraph 29 and 68 of the originally filed specification as being accomplished by software. The claim language refers to software and software per se is not a statutory class of invention. For the reasons noted, the claims are rejected because they are not directed to statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6-8, 10, 12 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Yi et al. (US 20140280890 A1) (hereafter Yi).
As per claim 1:
A content interest degree processing device comprising:
a progress degree acquiring unit configured to acquire progress degree information on a degree of progress of content which progresses on the basis of a user's operation; (See Yi ¶0115, “Moving to 1506, user activities including clicking, skipping and abandoning actions are monitored during the detection period for the user cohort or general users. As described before, content bias properties such as position and presentation style may be also extracted and recorded. In one example, each content item d is associated with a position i, which is a non-negative integer denoting the item's rank in the stream. For example, the top item in the stream is in position 1, and the slot immediately below is position 2, etc. Each content item is also presented using one of the predefined presentation styles (formats) indicated by j. For example, the presentation styles may include (1) pure text only, (2) text plus small thumbnail, and (3) text plus big thumbnail.” See also Yi ¶0129, “For example, measurements associated with user's scrolling actions, such as scrolling speed and acceleration, may be considered by the dynamic tipping point determination unit 1720 to determine a dynamic tipping point to predict the user's intent behind the action. For instance, if it is detected that the user suddenly accelerates the scrolling speed when she/he is reading content number 5 and eventually abandons the stream at content number 8, then the tipping point in this case may be set at content number 5. The details of the scoring function will be described later in FIG. 19.” Yi discloses the concept of collecting user controlled progress/movement across content.)
an interest degree acquiring unit configured to acquire a real-time change of a degree of interest of a user to whom the content has been presented in the content; and (See Yi ¶0115, “Upon examining a content item d at position i in presentation style j, a user may perform one of the three following actions: (1) click on d, (2) skip d and start examining the following article, and (3) abandon the session (i.e. click on nothing in the stream). The numbers of occurrence of these three types of events involving item d at position i in presentation style j may be represented as C.sub.dij, S.sub.dij, and A.sub.dij, respectively. It is understood that the clicking, skipping and abandoning actions referred herein are not limited to specific user actions and may include various user actions that may cause the same or similar effect as "clicking," "skipping," and "abandoning" in different applications, user interfaces, operating system and/or user devices. For example, in some user interfaces or applications, hovering over a piece of content or gazing on the content over a certain period of time may have the same effect as clicking on the content, i.e., selecting the content, and thus, are also considered as a clicking action in the present teaching.” Yi discloses collecting and tracking degree of interest in content items during operation.)
a content interest degree information generating unit configured to generate content interest degree information in which the progress degree information acquired by the progress degree acquiring unit and the real-time change of the degree of interest acquired by the interest degree acquiring unit are correlated. (See Yi ¶0116, “At 1508, the monitored data, i.e., event logs from browser-cookies may be consolidated and analyzed to compute probabilities of clicking, skipping and abandoning actions. The probabilities may be calculated as specific numbers, or parameters that need to be estimated. In the above-mentioned example, using the event logs over an extended period of time, which covers a significant user base and content pool, the following probabilities may be computed:” See also Yi ¶0111, “It is understood that the detections may be done over an extended time period for creating event logs for offline analysis or done in near real-time for computing the user engagement scores for a particular piece of content.” Yi discloses determining tracking and determining changes in user interest in content in relation to user progress through content.)
As per claim 2:
The content interest degree processing device according to claim 1, further comprising a content time density information generating unit configured to, when the content is divided into a plurality of units, generate content time density information by correlating a time density indicating a time required for progress of one unit with the content interest degree information. (See Yi ¶0110, “In still another aspect of the present teaching, the method and system define and measure engagement without the use of clicks. In this aspect, user engagement measure is based on the user's dwell time on the content stream when the user scrolls through the content before abandoning the stream or page, i.e., scroll dwell time. This aspect of the present teaching is available for any web-based personalization system, and can do web-scale personalization where most of the users do not login for privacy concern. The method and system in this aspect of the present teaching do not require users to click, thereby imposing no cost on the user.” Yi discloses the concept of generating a time for users to move from one unit to another in a content stream.)
As per claim 3:
The content interest degree processing device according to claim 1, wherein the progress degree information includes information on a real-time change of the degree of progress, and
wherein the content interest degree information generating unit generates the content interest degree information by correlating the real-time change of the degree of progress acquired by the progress degree acquiring unit with the real-time change of the degree of interest acquired by the interest degree acquiring unit. (See Yi ¶0112, “In this embodiment, the user engagement evaluation module 1406 includes a user activity log database 1418, a model building unit 1420, a user engagement score calculation unit 1422, and a content bias property extraction unit 1424. The user activity log database 1418 in this embodiment stores user events, including clicking, skipping, and abandoning actions, detected over the detection period 1414, which covers a significant user base and content pool. The model building unit 1420 is responsible for building a probability model 1426 for average content based on the data from the user activity log database 1418. The details of building the probability model 1426 will be described later in FIG. 15. The user engagement score calculation unit 1422 then receives data of user activities, including clicking, skipping, and abandoning actions, in near real-time and calculates click odds, skip odds, and abandon odds, respectively, based on the probability model 1426.” Yi discloses tracking real time changes in interest of the user.)
As per claim 6:
The content interest degree processing device according to claim 1, wherein, when the content is divided into a plurality of units, the units into which the content is divided are presented to the user one by one, and the progress degree information includes information indicating the degree of progress of each unit based on identification information for identifying each unit. (See Yi ¶0103, “Referring now to FIGS. 23-25, exemplary user activities with respect to a content stream are illustrated. In FIGS. 23-25, content in a content stream is continuously displayed on a web page 2302 for a user via a user device 2300. In FIG. 23, each piece of content 2304, 2306, 2308 may be arranged in an order from top to bottom of the web page 2302. Depending on the screen size and/or display resolution of the user device 2300, a certain number of content items may be displayed on the web page 2302 simultaneously. Each piece of content may be associated with certain properties, such as the position (ranking) in the content stream and the presentation style. For example, the content 2304 has a position 1 as it is the first piece of content in the stream and a presentation style of "pure text"; the content 2306 has a position of 2 and a presentation style of "text plus small thumbnail"; the content 2308 has a position of 3 and a presentation style of "text plus large thumbnail." It is understood the position indicates the sequence of presenting each content item in the stream and thus, may not always increment from top to bottom if the content stream flows in a different direction, e.g., from left to right or bottom to top. It is also understood that the presentation styles are not limited to the above-mentioned examples and may be text, image, video, animation, audio, or any combination thereof. In FIG. 23, due to the limited screen size, the user needs to scroll down or up the content stream in order to view more content. The action of "scrolling" 2310 described in the present teaching may include any user actions/activities that can cause content in a content stream to be continuously presented. Scrolling may be triggered by touch screen gestures or computer mouse motion or a key press and continue without further intervention until a further user action, or be entirely controlled by input devices.” Yi discloses the content to be divided into individual units, wherein user interaction tracks the movement through the individual units of content.)
As per claim 7:
The content interest degree processing device according to claim 1, wherein, when the content is divided into a plurality of units, identification information for identifying each of the units into which the content is divided is provided, and the progress degree information includes information indicating the degree of progress of each unit based on the identification information. (See Yi ¶0104, “Moving to FIG. 24, the user's scrolling action causes new content to be displayed on the display screen, replacing the previous content 2304, 2306, 2308 shown in FIG. 23. The user may find a particular content 2402 interesting and want to explore the content 2402 by applying a "clicking" action on it. The action of "clicking" 2404 described in the present teaching may include any explicit actions/activities that indicate selection of a piece of content in the stream, such as but not limited to, mouse clicking, pressing/touching on touch screen, key pressing, etc. On the other hand, once a content item is clicked, all the un-clicked content items above it in the stream are considered being "skipped." Accordingly, the action of "skipping" may be introduced as an implicit user action/activity with respect to content streams. In FIG. 24, the explicit action of clicking content 2402 brings implicit actions of skipping content 2406, 2408, 2410.” Yi discloses the content to be divided into units and identifying particular units based on progress.)
As per claim 8:
The content interest degree processing device according to claim 1, wherein, when parts of the content are sequentially presented to the user, information included in a presentation range of the content in information included in the content is presented to the user by moving the presentation range in a predetermined direction on the basis of the user's operation, and
wherein the degree of progress is based on the ratio of the information included in the presentation range to the information included in the content.
(See Yi ¶0104, “Moving to FIG. 24, the user's scrolling action causes new content to be displayed on the display screen, replacing the previous content 2304, 2306, 2308 shown in FIG. 23. The user may find a particular content 2402 interesting and want to explore the content 2402 by applying a "clicking" action on it. The action of "clicking" 2404 described in the present teaching may include any explicit actions/activities that indicate selection of a piece of content in the stream, such as but not limited to, mouse clicking, pressing/touching on touch screen, key pressing, etc. On the other hand, once a content item is clicked, all the un-clicked content items above it in the stream are considered being "skipped." Accordingly, the action of "skipping" may be introduced as an implicit user action/activity with respect to content streams. In FIG. 24, the explicit action of clicking content 2402 brings implicit actions of skipping content 2406, 2408, 2410.” Yi discloses the concept of progression through content to include tracking a movement/scrolling down the content.)
As per claim 10:
A terminal device comprising:
a progress degree acquiring unit configured to acquire progress degree information on a degree of progress of content which progresses on the basis of a user's operation; (See Yi ¶0115, “Moving to 1506, user activities including clicking, skipping and abandoning actions are monitored during the detection period for the user cohort or general users. As described before, content bias properties such as position and presentation style may be also extracted and recorded. In one example, each content item d is associated with a position i, which is a non-negative integer denoting the item's rank in the stream. For example, the top item in the stream is in position 1, and the slot immediately below is position 2, etc. Each content item is also presented using one of the predefined presentation styles (formats) indicated by j. For example, the presentation styles may include (1) pure text only, (2) text plus small thumbnail, and (3) text plus big thumbnail.” See also Yi ¶0129, “For example, measurements associated with user's scrolling actions, such as scrolling speed and acceleration, may be considered by the dynamic tipping point determination unit 1720 to determine a dynamic tipping point to predict the user's intent behind the action. For instance, if it is detected that the user suddenly accelerates the scrolling speed when she/he is reading content number 5 and eventually abandons the stream at content number 8, then the tipping point in this case may be set at content number 5. The details of the scoring function will be described later in FIG. 19.” Yi discloses the concept of collecting user controlled progress/movement across content.)
an interest degree acquiring unit configured to acquire a real-time change of a degree of interest of a user to whom the content has been presented in the content; (See Yi ¶0115, “Upon examining a content item d at position i in presentation style j, a user may perform one of the three following actions: (1) click on d, (2) skip d and start examining the following article, and (3) abandon the session (i.e. click on nothing in the stream). The numbers of occurrence of these three types of events involving item d at position i in presentation style j may be represented as C.sub.dij, S.sub.dij, and A.sub.dij, respectively. It is understood that the clicking, skipping and abandoning actions referred herein are not limited to specific user actions and may include various user actions that may cause the same or similar effect as "clicking," "skipping," and "abandoning" in different applications, user interfaces, operating system and/or user devices. For example, in some user interfaces or applications, hovering over a piece of content or gazing on the content over a certain period of time may have the same effect as clicking on the content, i.e., selecting the content, and thus, are also considered as a clicking action in the present teaching.” Yi discloses collecting and tracking degree of interest in content items during operation.)
a content interest degree information generating unit configured to generate content interest degree information in which the progress degree information acquired by the progress degree acquiring unit and the real-time change of the degree of interest acquired by the interest degree acquiring unit are correlated; and (See Yi ¶0116, “At 1508, the monitored data, i.e., event logs from browser-cookies may be consolidated and analyzed to compute probabilities of clicking, skipping and abandoning actions. The probabilities may be calculated as specific numbers, or parameters that need to be estimated. In the above-mentioned example, using the event logs over an extended period of time, which covers a significant user base and content pool, the following probabilities may be computed:” See also Yi ¶0111, “It is understood that the detections may be done over an extended time period for creating event logs for offline analysis or done in near real-time for computing the user engagement scores for a particular piece of content.” Yi discloses determining tracking and determining changes in user interest in content in relation to user progress through content.)
an output unit configured to output the content interest degree information. (See Yi ¶0085, “FIG. 7 depicts an exemplary diagram of the user understanding unit 155, according to an embodiment of the present teaching. In this exemplary construct, the user understanding unit 155 comprises a baseline interest profile generator 710, a user profile generator 720, a user intent/interest estimator 740, a short term interest identifier 750 and a long term interest identifier 760. In operation, the user understanding unit 155 takes various input and generates user profiles 160 as output. Its input includes third party data such as users' information from such third party platforms as well as content such users accessed and expressed interests, concepts covered in such third party data, concepts from the universal interest space (e.g., Wikipedia or content taxonomy), information about users for whom the personalized profiles are to be constructed, as well as information related to the activities of such users. Information from a user for whom a personalized profile is to be generated and updated includes demographics of the user, declared interests of the user, etc. Information related to user events includes the time, day, location at which a user conducted certain activities such as clicking on a content piece, long dwell time on a content piece, forwarding a content piece to a friend, etc.” Yi discloses presenting output regarding determined user interest.)
As per claim 12:
A program causing a computer to perform:
a progress degree acquiring step of acquiring progress degree information on a degree of progress of content which progresses on the basis of a user's operation; (See Yi ¶0115, “Moving to 1506, user activities including clicking, skipping and abandoning actions are monitored during the detection period for the user cohort or general users. As described before, content bias properties such as position and presentation style may be also extracted and recorded. In one example, each content item d is associated with a position i, which is a non-negative integer denoting the item's rank in the stream. For example, the top item in the stream is in position 1, and the slot immediately below is position 2, etc. Each content item is also presented using one of the predefined presentation styles (formats) indicated by j. For example, the presentation styles may include (1) pure text only, (2) text plus small thumbnail, and (3) text plus big thumbnail.” See also Yi ¶0129, “For example, measurements associated with user's scrolling actions, such as scrolling speed and acceleration, may be considered by the dynamic tipping point determination unit 1720 to determine a dynamic tipping point to predict the user's intent behind the action. For instance, if it is detected that the user suddenly accelerates the scrolling speed when she/he is reading content number 5 and eventually abandons the stream at content number 8, then the tipping point in this case may be set at content number 5. The details of the scoring function will be described later in FIG. 19.” Yi discloses the concept of collecting user controlled progress/movement across content.)
an interest degree acquiring step of acquiring a real-time change of a degree of interest of a user to whom the content has been presented in the content; (See Yi ¶0115, “Upon examining a content item d at position i in presentation style j, a user may perform one of the three following actions: (1) click on d, (2) skip d and start examining the following article, and (3) abandon the session (i.e. click on nothing in the stream). The numbers of occurrence of these three types of events involving item d at position i in presentation style j may be represented as C.sub.dij, S.sub.dij, and A.sub.dij, respectively. It is understood that the clicking, skipping and abandoning actions referred herein are not limited to specific user actions and may include various user actions that may cause the same or similar effect as "clicking," "skipping," and "abandoning" in different applications, user interfaces, operating system and/or user devices. For example, in some user interfaces or applications, hovering over a piece of content or gazing on the content over a certain period of time may have the same effect as clicking on the content, i.e., selecting the content, and thus, are also considered as a clicking action in the present teaching.” Yi discloses collecting and tracking degree of interest in content items during operation.)
a content interest degree information generating step of generating content interest degree information in which the progress degree information acquired in the progress degree acquiring step and the real-time change of the degree of interest acquired in the interest degree acquiring step are correlated; and (See Yi ¶0116, “At 1508, the monitored data, i.e., event logs from browser-cookies may be consolidated and analyzed to compute probabilities of clicking, skipping and abandoning actions. The probabilities may be calculated as specific numbers, or parameters that need to be estimated. In the above-mentioned example, using the event logs over an extended period of time, which covers a significant user base and content pool, the following probabilities may be computed:” See also Yi ¶0111, “It is understood that the detections may be done over an extended time period for creating event logs for offline analysis or done in near real-time for computing the user engagement scores for a particular piece of content.” Yi discloses determining tracking and determining changes in user interest in content in relation to user progress through content.)
an output step of outputting the content interest degree information. (See Yi ¶0085, “FIG. 7 depicts an exemplary diagram of the user understanding unit 155, according to an embodiment of the present teaching. In this exemplary construct, the user understanding unit 155 comprises a baseline interest profile generator 710, a user profile generator 720, a user intent/interest estimator 740, a short term interest identifier 750 and a long term interest identifier 760. In operation, the user understanding unit 155 takes various input and generates user profiles 160 as output. Its input includes third party data such as users' information from such third party platforms as well as content such users accessed and expressed interests, concepts covered in such third party data, concepts from the universal interest space (e.g., Wikipedia or content taxonomy), information about users for whom the personalized profiles are to be constructed, as well as information related to the activities of such users. Information from a user for whom a personalized profile is to be generated and updated includes demographics of the user, declared interests of the user, etc. Information related to user events includes the time, day, location at which a user conducted certain activities such as clicking on a content piece, long dwell time on a content piece, forwarding a content piece to a friend, etc.” Yi discloses presenting output regarding determined user interest.)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yi et al. (US 20140280890 A1) (hereafter Yi), in view of Pradeep et al. (US 20110106750 A1) (hereafter Pradeep).
As per claim 4:
Although Yi discloses the above-enclosed invention including the concept of determining interest levels based on physically tracking user interactions such as eye movement, Yi fails to explicitly disclose determining interest based on autonomic neural activity.
However Pradeep as shown, which talks about evaluating media material using neuro-response, teaches the concept of determining interest based on autonomic neural activity.
The content interest degree processing device according to claim 1, wherein the degree of interest is based on a value which is acquired according to autonomic neural activity of the user to whom the content has been presented. (See Pradeep ¶0021, “According to various embodiments, the techniques and mechanisms of the present invention may use a variety of mechanisms such as survey based responses, statistical data, and/or neuro-response measurements such as central nervous system, autonomic nervous system, and effector measurements to improve ratings prediction.” Pradeep teaches the concept of determining user interest in content based on autonomic neural response and activity.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Pradeep with the invention of Yi. As shown, Yi discloses the concept of determining interest of a user including tracking and measuring interactions including biological functions (such as gaze tracking). Pradeep further teaches the concept of determining user interest in content by measuring autonomic responses. Pradeep teaches this concept as part of utilizing a plurality information to determine user response to content including eye tracking (See Pradeep ¶0028). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Pradeep to further utilize additional information to determine user response.
As per claim 11:
Although Yi discloses the above-enclosed invention including the concept of determining interest levels based on physically tracking user interactions such as eye movement, Yi fails to explicitly disclose determining interest based on autonomic neural activity.
However Pradeep as shown, which talks about evaluating media material using neuro-response, teaches the concept of determining interest based on autonomic neural activity.
The terminal device according to claim 10, further comprising an autonomic neural activity acquiring unit configured to acquire a value based on autonomic neural activity of the user to whom the content has been presented, wherein the interest degree acquiring unit acquires the real-time change of the degree of interest on the basis of the value acquired by the autonomic neural activity acquiring unit. (See Pradeep ¶0021, “According to various embodiments, the techniques and mechanisms of the present invention may use a variety of mechanisms such as survey based responses, statistical data, and/or neuro-response measurements such as central nervous system, autonomic nervous system, and effector measurements to improve ratings prediction.” Pradeep teaches the concept of determining user interest in content based on autonomic neural response and activity.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Pradeep with the invention of Yi. As shown, Yi discloses the concept of determining interest of a user including tracking and measuring interactions including biological functions (such as gaze tracking). Pradeep further teaches the concept of determining user interest in content by measuring autonomic responses. Pradeep teaches this concept as part of utilizing a plurality information to determine user response to content including eye tracking (See Pradeep ¶0028). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Pradeep to further utilize additional information to determine user response.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yi et al. (US 20140280890 A1) (hereafter Yi), in view of Matlick et al. (US 20220188699 A1) (hereafter Matlick).
As per claim 5:
Although Yi discloses the above-enclosed invention including the concept of collecting progress information for a user scrolling through content, Yi fails to explicitly disclose normalizing the scrolling information.
However Matlick as shown, which talks about machine learning techniques for fingerprinting web resources and targeting, teaches the concept of normalizing user activities/interaction with content.
The content interest degree processing device according to claim 1, further comprising a normalization unit configured to normalize the degree of progress in a plurality of pieces of the content interest degree information generated by the content interest degree information generating unit when there are a plurality of pieces of content. (See Matlick ¶0200, “CCM 100 or CCM tag 110 in FIG. 14 may adjust the values assigned in operations 1520-1524 based on the type of device 1400 used for viewing the content. For example, the dwell times, scroll depths, and scroll speeds, may vary between smartphone, tablets, laptops and desktop computers. CCM 100 or tag 110 may normalize or scale the engagement metric values so different devices provide similar relative user engagement results.” Matlick teaches the concept of normalizing the interaction information including progress information.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Matlick with the invention of Yi. As shown, Yi discloses the concept of tracking and collecting user interaction information including a user progressing through a feed of content. Matlick further teaches the concept of normalizing the collected information to further account for differences between user device as usage patterns are different for different device types (See Matlick ¶0200). Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to further normalize collected information based on the device type to accurately reflect the information.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yi et al. (US 20140280890 A1) (hereafter Yi), in view of Liu et al. (US 20140244659 A1) (hereafter Liu).
As per claim 9:
Although Yi discloses the above-enclosed invention including the concept of determining user position through content and video content, Yi fails to explicitly disclose tracking progress through a video content relative to the total video content.
However Liu as shown, which talks about analyzing content consumption, teaches the concept of determining interest based on tracking progress in a video content.
The content interest degree processing device according to claim 1, wherein a time required for the user to view a video of the content presented to the user is able to be changed by the user, and
wherein the progress degree information includes information on the degree of progress based on the ratio of a scale of the content from start of the video to a part currently viewed by the user to a scale of the whole video. (See Liu ¶0020, “With regard to the interest level, interests may be explicitly or implicitly identified via the user based on activity such as content consumed and actions taken in or outside the content library. For example, the user may post content about the entity "orange juice", may search for the entity "orange" in the past and consume (e.g., read/purchase/interact with) an article about "orange juice". Recent interests based on recent activity may tend to be weighted higher. For example, news that isn't recent but is considered very interesting based on user activity or interaction may still be chosen to show. Long-term interests may comprise interests that the user consistently has over time. For example, the user may search for "orange" related objects every day for the past 2 years. With regard to the clustered interests, interests that are related to each other may make sense to consider them together. For example, "oranges" may be related to an entity (e.g., a company) which provides orange juice and "oranges" may also be related to an entity (e.g., a place) where oranges are grown. With regard to the library activity, for example, the user may recently click to access (e.g., to play) the particular content such as the documentary movie entitled "Processing Orange Juice", the user may have shared content (e.g., a link or a post) to someone else or to public, the user may have watched a given percentage of the full content (such as 3% of a play time of the documentary movie entitled "Processing Orange Juice") a month ago, the user may have watched the particular content a number of times such as watching 3% but watching that part 100 times due to an awesome view of an "orange grove", and/or the user may have watched related videos such as a video clip of "orange farmer" describing a process for harvesting.” Liu teaches the concept of tracking a ratio of content consumption to determine interest.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of Liu with the invention of Yi. As shown, Yi discloses the concept of determining interest including tracking user interaction and viewership of content. Liu further teaches the concept of determining interest for video content based on a ratio/percentage of time viewing the content. Liu teaches this concept to further determine user interest as one of a plurality of user interactions and information considered to further fine-tune to determination of interest in both content and specific subject matter (See Liu ¶0004). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to further utilize the teachings of Liu to further determine and fine tune user interest.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sato et al. (US 20180336578 A1), which talks about determining interest levels for content during time intervals.
Kozloski et al. (US 20170269814 A1), which talks about user sentiment analysis including tracking user interaction with content.
Tomkins et al. (US 8984098 B1), organizing streams of content including tracking and utilizing content interest.
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/VINCENT M CAO/Primary Examiner, Art Unit 3622
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