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 .
DETAILED ACTION
This office action is in response to communication filed 4/4/2024. Claims 21-36 are pending for examination, the rejection cited as stated below.
Double Patenting
2. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
3. Claims 21-23, 26-31 and 34-36 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of US Patent 11979637 (hereafter Patent’637) in view of Vishnu Narayanan et al (US 10997500 B1, hereafter Vishnu).
As to claim 21, Patent’637 discloses a method comprising:
receiving, via control circuitry, a plurality of parameter weight ranges, each parameter weight range including a respective minimum value and a respective maximum value, each parameter weight range corresponding to a respective parameter of a plurality of parameters for generating recommendations of media content (Claim 1, “receiving a plurality of weight ranges, each weight range corresponding to a parameter of a plurality of parameters; selecting a weight value for each parameter based on a respective weight range; determining a recommendation score based on the selected weight values for the plurality of parameters; generating for display a content recommendation to a user device according to the recommendation score determined”, wherein a minimum value and a maximum value are implied by range);
transmitting the plurality of parameter weight ranges to a computing entity (claim 1, “selecting a weight value for each parameter based on a respective weight range”, wherein transmitting the ranges g to a computing entity is implied in order to select based on the ranges);
receiving a first media content recommendation generated, using the plurality of parameter weight ranges, by the computing entity (claim 1, “determining a recommendation score based on the selected weight values for the plurality of parameters”, wherein generating the recommendation by the computing entity is implied);
generating, for display by a user media device, the first media content recommendation (claim 1, “generating for display a content recommendation to a user device according to the recommendation score determined”);
obtaining a user engagement level by analyzing user behavior in response to the first media content recommendation (claim 1, “receiving input from the user device in response to the content recommendation”);
adjusting via the control circuitry, using the user engagement level, at least one parameter weight range of the plurality of parameter weight ranges to obtain an adjusted plurality of parameter
weight ranges (claim 1, “adjusting, using control circuitry, each weight range of the plurality of weight ranges based on the received input from the user device”);
transmitting the adjusted plurality of parameter weight ranges to the computing entity (claim 1, “generating for display a second content recommendation to the user device based on the adjusted weight ranges” therefore transmitting the adjusted weight ranges to the computing entity is implied in order for it to generate the second content recommendation based on the adjust weight ranges);
receiving a second media content recommendation generated, using the adjusted plurality of parameter weight ranges, by the machine learning model (claim 1, “generating for display a second content recommendation to the user device based on the adjusted weight ranges”); and
generating, for display by the user media device, the second media content recommendation (claim 1, “generating for display a second content recommendation to the user device based on the adjusted weight ranges”).
However, Patent’637 does not expressly disclose that the computing entity is a machine learning model.
Vishnu discloses a concept for a computing entity to be a machine learning model (col. 1, lines 10-15, “Artificial neural networks are artificial in the sense that they are computational entities”). Specifically, Vishnu discloses performing recommendation generation with a machine learning model such as a neural network and using engagement metrics to adjust controlling parameters/hyperparameters that are applied to that ML model so that subsequent recommendations are produced from the adjusted parameters (see abstract; and col. 2, lines 30-55, “user engagement can represent a viewing duration with respect to an item, a listening duration with respect to an item, a watching duration with respect to an item, an interaction with content regarding the item (e.g., clicking on an image representing the item), or any combination thereof. …a machine learning model can be trained to optimize for one metric, such as the generation of personalized item recommendations optimized for recommending the items most likely to be acted upon (e.g., purchased) by a user… This second metric can be an engagement metric which can capture user engagement (e.g., usage) associated with the various items that may be recommended… the machine learning model can be associated with one or more hyperparameters, which can be used to modify the engagement metric prior to the output of the machine learning model is modified..”; col. 1, lines 20-30, “NN-based models may generate probability scores via the forward pass using content interaction histories. The probability scores may indicate the probabilities of a user performing some future interaction”; col. 8, lines 30-40, “Advantageously, by modifying and re-ranking values of elements of the output vector 112 (shown in FIG. 1A), recommendations for a user can be tailored to optimized for both precision and the engagement metric (e.g., by changing the hyperparameters ). The balance between precision and the engagement metric can be modified using the hyperparameters without having to retrain the NN 102”).
Before the effectively filing date of the invention, it would have been obvious for an ordinary skilled in the art to combine Patent’637 with Vishnu. The suggestion/motivation of the combination would have been to utilize NN-based models to generate probability scores via the forward pass using content interaction histories (Vishnu, col. 1, lines 22-28).
As to claim 29, see similar rejection to claim 21. For Memory and Control Circuity, see Vishnu , Figure 6. The limitation of “to store in the memory the plurality of parameter weight ranges” is implied, in order to transmit the same to a machine learning model.
As to claim 22, Patent’637 in view of Vishnu discloses the method of claim 21, wherein the analyzing the user behavior comprises:
tracking user interaction with the first media content recommendation; and analyzing the user interaction to determine the user engagement level with the first media content recommendation (Patent’637, claim 3. See also Vishnu as cited in rejection to claim 1).
As to claim 30, see similar rejection to claim 22.
As to claim 23, Patent’637 in view of Vishnu discloses the method of claim 22, wherein the adjusting the at least one parameter weight range comprises shifting the at least one parameter weight range based on the user engagement level with the first media content recommendation (Patent’637, claim 4).
As to claim 31, see similar rejection to claim 23.
As to claim 26, Patent’637 in view of Vishnu discloses the method of claim 21, wherein the plurality of parameter weight ranges is received from a media content provider (Patent’637, claim 5).
As to claim 34, see similar rejection to claim 26.
As to claim 27, Patent’637 in view of Vishnu discloses the method of claim 21, wherein parameters of the plurality of parameter weight ranges comprise one or more of popularity of media content among users, recency of media content, media content indicated as favorite, media content liked by users, media content recommended by media content critics (Patent’637, claim 7, 8, or 9).
As to claim 35, see similar rejection to claim 27.
As to claim 28, Patent’637 in view of Vishnu discloses the method of claim 21, wherein the analyzing and the adjusting are performed according to a media content recommendation updating schedule (Patent’637, claim 10).
As to claim 36, see similar rejection to claim 28.
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4. Claims 21-36 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of US Patent 11509965 (hereafter Patent’965) in view of Vishnu Narayanan et al (US 10997500 B1, hereafter Vishnu).
As to claim 21, Patent’965 discloses a method comprising:
receiving, via control circuitry, a plurality of parameter weight ranges, each parameter weight range including a respective minimum value and a respective maximum value, each parameter weight range corresponding to a respective parameter of a plurality of parameters for generating recommendations of media content (Claim 1, “receiving a plurality of parameter ranges, each parameter range including a respective minimum value and a respective maximum value, each parameter range corresponding to a respective parameter of a plurality of parameters for recommending the content”);
transmitting the plurality of parameter weight ranges to a computing entity (claim 1, “selecting a value for the parameter based on the value being between the respective minimum value and the respective maximum value”, wherein transmitting the ranges g to a computing entity is implied in order to select based on the ranges);
receiving a first media content recommendation generated, using the plurality of parameter weight ranges, by the computing entity (claim 1, “providing a content recommendation to users based on the value for the parameter”, wherein generating the recommendation by the computing entity is implied);
generating, for display by a user media device, the first media content recommendation (claim 1, “providing a content recommendation to users based on the value for the parameter”, wherein displaying by a user media device is implied);
obtaining a user engagement level by analyzing user behavior in response to the first media content recommendation (claim 1, “analyzing users' behavior in response to receiving the content recommendation”);
adjusting via the control circuitry, using the user engagement level, at least one parameter weight range of the plurality of parameter weight ranges to obtain an adjusted plurality of parameter
weight ranges (claim 1, “updating, via control circuitry, at least one of the respective minimum value and the respective maximum value based on the analyzing users' behavior, the value, the respective minimum value, and the respective maximum value”);
transmitting the adjusted plurality of parameter weight ranges to the computing entity (claim 1, “updating, via control circuitry, at least one of the respective minimum value and the respective maximum value based on the analyzing users' behavior, the value, the respective minimum value, and the respective maximum value”), but does not expressly disclose that the computing entity is a machine learning model, or receiving a second media content recommendation generated, using the adjusted plurality of parameter weight ranges, by the machine learning model; or generating, for display by the user media device, the second media content recommendation.
Vishnu discloses a concept for a computing entity to be a machine learning model (col. 1, lines 10-15, “Artificial neural networks are artificial in the sense that they are computational entities”), receiving a second media content recommendation generated, using an adjusted plurality of parameter weights, by a machine learning model; and generating, for display by a user media device, the second media content recommendation (see abstract; and col. 2, lines 30-55, “user engagement can represent a viewing duration with respect to an item, a listening duration with respect to an item, a watching duration with respect to an item, an interaction with content regarding the item (e.g., clicking on an image representing the item), or any combination thereof. …a machine learning model can be trained to optimize for one metric, such as the generation of personalized item recommendations optimized for recommending the items most likely to be acted upon (e.g., purchased) by a user… This second metric can be an engagement metric which can capture user engagement (e.g., usage) associated with the various items that may be recommended… the machine learning model can be associated with one or more hyperparameters, which can be used to modify the engagement metric prior to the output of the machine learning model is modified..”; col. 1, lines 20-30, “NN-based models may generate probability scores via the forward pass using content interaction histories. The probability scores may indicate the probabilities of a user performing some future interaction”; col. 8, lines 30-40, “Advantageously, by modifying and re-ranking values of elements of the output vector 112 (shown in FIG. 1A), recommendations for a user can be tailored to optimized for both precision and the engagement metric (e.g., by changing the hyperparameters ). The balance between precision and the engagement metric can be modified using the hyperparameters without having to retrain the NN 102”).
Before the effectively filing date of the invention, it would have been obvious for an ordinary skilled in the art to combine Patent’965 with Vishnu. The suggestion/motivation of the combination would have been to utilize NN-based models to generate probability scores via the forward pass using content interaction histories (Vishnu, col. 1, lines 22-28).
As to claim 29, see similar rejection to claim 21. For Memory and Control Circuity, see Vishnu , Figure 6. The limitation of “to store in the memory the plurality of parameter weight ranges” is implied, in order to transmit the same to a machine learning model.
As to claim 22, Patent’965 in view of Vishnu discloses the method of claim 21, wherein the analyzing the user behavior comprises:
tracking user interaction with the first media content recommendation; and analyzing the user interaction to determine the user engagement level with the first media content recommendation (Patent’965, claim 3. See also Vishnu as cited in rejection to claim 1).
As to claim 30, see similar rejection to claim 22.
As to claim 23, Patent’965 in view of Vishnu discloses the method of claim 22, wherein the adjusting the at least one parameter weight range comprises shifting the at least one parameter weight range based on the user engagement level with the first media content recommendation (Patent’965, claim 4).
As to claim 31, see similar rejection to claim 23.
As to claim 24, Patent’965 in view of Vishnu discloses the method of claim 23, wherein the shifting further comprises:
moving the respective minimum value higher when the user engagement level with a plurality of media content recommendations, including the first media content recommendation, is determined to be higher for higher selected values (see Patent’965, claim 5).
As to claim 32, see similar rejection to claim 24.
As to claim 25, Patent’965 in view of Vishnu discloses the method of claim 23, wherein the shifting further comprises:
moving the respective minimum value lower and moving the respective maximum value
lower when the user engagement level with a plurality of media content recommendations including
the first media content recommendation is determined to be lower (see 112 rejection and Examiner’s interpretation stated therein. See Patent’965, claim 6).
As to claim 33, see similar rejection to claim 25.
As to claim 26, Patent’965 in view of Vishnu discloses the method of claim 21, wherein the plurality of parameter weight ranges is received from a media content provider (Patent’965, claim 7).
As to claim 34, see similar rejection to claim 26.
As to claim 27, Patent’965 in view of Vishnu discloses the method of claim 21, wherein parameters of the plurality of parameter weight ranges comprise one or more of popularity of media content among users, recency of media content, media content indicated as favorite, media content liked by users, media content recommended by media content critics (Patent’965, claim 9).
As to claim 35, see similar rejection to claim 27.
As to claim 28, Patent’965 in view of Vishnu discloses the method of claim 21, wherein the analyzing and the adjusting are performed according to a media content recommendation updating schedule (Patent’965, claim 10).
As to claim 36, see similar rejection to claim 28.
Claim Rejections - 35 USC § 112
5. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
6. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
7. Claims 25 and 33 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 25 recites “moving the respective minimum value lower and moving the respective maximum value lower when the user engagement level with a plurality of media content recommendations including the first media content recommendation is determined to be lower” which does not find support in the originally filed application. Specifically, the instant application claims priority to parent application cases 17970267 and the grandparent application cases 17091080. However, neither the parent application nor the grandparent application, or the specification of the instant application discloses the claimed limitation. For example, the specification of the parent application, grandparent application, and the instant application discloses instead, “if it is determined that the users’ engagement level with the recommended media content items is low, then the content recommendation application determines that the media critic recommendation parameter is triggering the user’s engagement level to be low and shifts the higher end of candidate weight range (e.g. via a right slider 605 on the display screen 600) corresponding to the media critic recommendation parameter lower towards the minimum range values. In one example, the candidate weight range .3 to .5 of the media critic recommendation parameter is changed to .3 to .4” indicating that the minimum value is not moved to lower at all. Claims 33 is similarly rejected.
8. 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.
9. 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.
10. Claims 25 and 33 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 25 recites “moving the respective minimum value lower and moving the respective maximum value lower when the user engagement level with a plurality of media content recommendations including the first media content recommendation is determined to be lower” which conflicts with the disclosure in the specification. For example, the specification discloses instead, “if it is determined that the users’ engagement level with the recommended media content items is low, then the content recommendation application determines that the media critic recommendation parameter is triggering the user’s engagement level to be low and shifts the higher end of candidate weight range (e.g. via a right slider 605 on the display screen 600) corresponding to the media critic recommendation parameter lower towards the minimum range values. In one example, the candidate weight range .3 to .5 of the media critic recommendation parameter is changed to .3 to .4” indicating that the minimum value is not moved to lower at all. Due to the conflicting disclosure, the exact scope of the claimed limitation cannot be definitely determined. For the sake of the examination, Examiner interprets in light of the specification that the claimed limitation reads “moving the respective maximum value lower when the user engagement level with a plurality of media content recommendations including the first media content recommendation is determined to be lower”. Claims 33 is similarly rejected.
Prior Art Cited but not Applied in the Rejection
11. Below is a list of prior art reference(s) cited but not applied in the rejection:
a) LuVogt et al (US 20130290110), disclosing that users receive content recommendations from a personalized, generalized recommendation service that aggregates and selects content of high personal relevance to each individual user from a large pool of both personal and public content. The received content is filtered and the content determined to be relevant is cached. When a user request for content is received, the cached content is rescored and the content determined to be most relevant based on satisfaction of a relevance threshold is selected and forwarded to the user. Feedback methodologies are also implemented so that a user's actions are taken into consideration in real time and can affect subsequent recommendations to the user (see abstract).
b) Zhang et al (US 20100070498 A1), disclosing automatically adjusting a plurality of weight values for a plurality of parameters for inputting into a ranking engine that is adapted to rank a plurality of web objects based on such weight values and their corresponding parameters, to generated a ranked set of web objects based on such adjusted weights values and their corresponding parameters as well as particular query (see abstract).
c) Kalmes et al (US 2015/0019573), disclosing that a weighting factor based on the feedback data for the media content item presented in association with the content management account may be applied to produce a recommendation (see abstract).
d) Blundell et al (US 10438114 B1), disclosing content recommendation using neural networks, to generate for each of the actions one or more parameters of a distribution over possible action scores for the action and selecting an action from plurality of possible actions using the parameters of the distributions over the possible action scores for the action (see abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUA FAN whose telephone number is (571)270-5311. The examiner can normally be reached on 9-6.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nasser Goodarzi, can be reached at (571) 272-4195. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUA FAN/Primary Examiner, Art Unit 2426
