DETAILED ACTION
This office action is responsive to the Amendment/Request for Reconsideration-After Non-Final filed 6/17/2026. The application contains claims 1-20, all examined and rejected.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim limitation “4” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Claim 4 recites the limitation “a feature extractor” coupled with functional language without reciting sufficient structure to achieve the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “9” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Claim 9 recites the limitation “an encoder” and “a decoder” coupled with functional language without reciting sufficient structure to achieve the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claim limitations in amended claim 1 has been interpreted under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, because it uses a non-structural term “module” coupled with functional language without reciting sufficient structure to achieve the function. Furthermore, the non-structural term is not preceded by a structural modifier.
Claim 1 recites the limitation "a server” coupled with functional language without reciting sufficient structure to achieve the function.
Since these claim limitations invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, claim 1 is interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph limitation: Page 1 states, “The server may comprise at least one processor coupled to memory for training” Based on the guidelines announced from Federal Register Vol. 76, No. 27, this has been interpreted as encompassing a hardware or hardware in combination with software implementation of the module, but not a pure software implementation.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action. Claimed modules also trigger interpretation of the claim language under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph since they are considered a place holder for a corresponding structure in the specification.
If applicant does not wish to have the claim limitation treated under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, applicant may amend the claim so that it will clearly not invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, or present a sufficient showing that the claim recites sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance with 35 U.S.C. § 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 16-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Malik et al. [US 2021/0117780 A1, hereinafter D1] further in view Chakraborty et al. [US 20230419339, hereinafter D2].
With regard to Claim 16,
D1 teach a computer-implemented method training task-independent personal machine learning (ML), models for a user, the method comprising:
obtaining a training dataset specific to the user (¶117, “Each of the client systems 130a, 130b, 130c, and 130d may have stored in a local data store a respective plurality of examples 530a, 530b, 530c and 530d”, “ Client system 130a may then retrieve the plurality of examples 530 a from the local data store“);
obtaining, from a task-independent shared ML model, a set of shared features (¶117, “global neural network model 820 having a plurality of federated model parameters stored on a server 510”, “ Client system 130 a may then receive a current version of the global neural network model 820 a from server 510”, ¶119, “ federated parameters that were shared across all client systems …”);
training, using the training dataset and the set of shared features, the task-independent personal ML model to learn a set of personal features specific to the user (¶109, “ the model parameters … of the machine-learning model may include both a global neural network model and a private local personalization model”, ¶117, “ Client system 130 a may then train the received global neural network model 820 a together with the local personalization model 830 a on the pluralities of examples 530 a to generate a plurality of updated federated model parameters and a plurality of updated local model parameters. Client system 130 a may then store in the local data store the trained local personalization model 830 a including the updated local model parameters”, ¶108, “jointly training private local model parameters and federated parameters of a machine-learning model locally on the client systems, storing updated local parameters on the client systems ..”).
D1 does not explicitly teach training, based on the set of personal features obtained from the task-independent personal ML model, a task-specific personal ML model for the user.
D2 teach training, based on the set of personal features obtained from the task-independent personal ML model, a task-specific personal ML model for the user (¶51, “The task agnostic embedding 203 generated by the task agnostic learning model 215 can be used by the task prediction model 113 to predict tasks downstream from the user event sequence. In this case, the task prediction model 113 includes a 2-layered fully connected network, similar to the configuration used with the task specific learning model 205. However, here the task prediction model 113 is trained separately from the task agnostic learning model 215 and uses categorical cross-entropy loss 214 as the loss function”, ¶50, “ task agnostic model 215 generates the task agnostic embedding 203 using an autoencoder model. … the user event sequence is input to the encoder LSTM, which generates the task agnostic embedding 203”, ¶57, “transmitting the user representation 117 to the edge system 130, wherein the edge system 130 applies one or more machine learning based service 133 to the user representation 117 to generate a prediction 139”, ¶33, “hub system 110 applies a user representation model 112 to user event sequence data 115 stored in a data store 114, either as part of or associated with a user profile 118 of a user, to generate a user representation”, ¶30, ““tasks” are specific actions that a user may take in response to being presented with certain content or services”).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of providing machine learning systems for generating and using personalized user models or user representations based on user’s data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above because training a task-specific personal machine learning (ML) model using rich features extracted from a broader, task-independent personal model significantly boosts performance, efficiency, and adaptability as Incorporating pre-established personal traits reduces errors and delivers context-aware, highly accurate predictions tailored to an individual, transferring generalized representations means the task-specific model requires far less user data and compute time to converge during training, and specialized and streamlined models reduce computational overhead, leading to faster, real-time responses during inference. This is simply combining prior art elements according to known methods to yield predictable results; use of known technique to improve similar devices (methods, or products) in the same way; and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 17,
D1-D2 teach the method as claimed in claim 16,wherein the training using an encoder to encode features of data in the training dataset and the shared features, and using a decoder to decode the encoded features (D1, ¶117, “global neural network model 820 having a plurality of federated model parameters stored on a server 510”, “ Client system 130 a may then receive a current version of the global neural network model 820 a from server 510”, ¶119, “ federated parameters that were shared across all client systems …”, D2, ¶50, “the task agnostic model 215 generates the task agnostic embedding 203 using an autoencoder model. In certain embodiments, the task agnostic learning model 215 uses a single layered LSTM as the architecture for the encoder and decoder”, “user event sequence is input to the encoder LSTM”, “task agnostic embedding 203 is input to the decoder LSTM”).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of providing machine learning systems for generating and using personalized user models or user representations based on user’s data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to allow higher flexibility as the model can be adapted to different tasks by simply changing the input and output formats, as the encoder-decoder architecture is flexible and can be used for various applications, encoder captures the context of the input data, allowing the decoder to generate outputs that are contextually appropriate, also encoder-decoder models have been shown to outperform traditional models in tasks requiring sequence-to-sequence learning, such as natural language processing and machine translation. These benefits make the encoder-decoder architecture a powerful tool in deep learning, enabling the development of advanced models capable of handling complex data and generating meaningful outputs. This simply combining prior art elements according to known methods to yield predictable results; use of known technique to improve similar devices (methods, or products) in the same way; and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 19,
D1-D2 teach the method as claimed in claim 16, wherein the
training dataset comprises labelled and unlabeled data items, and wherein the training comprises using any one of the following: supervised learning, unsupervised learning, semi-supervised (D1, ¶94, “each input example may include features xk and labels yk “, D2, ¶50, “the task agnostic model 215 generates the task agnostic embedding 203 using an autoencoder model”, “user event sequence is input to the encoder LSTM”, “The task agnostic embedding 203 is input to the decoder LSTM”, Autoencoder training reconstruct input sequences and does not require labels, ¶45, “ training data set 116 includes target labels to indicate this correspondence”, ¶47).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of providing machine learning systems for generating and using personalized user models or user representations based on user’s data and learning user representation from user specific data on distributed client systems. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to incorporate the unsupervised representation learning technique of D2 into the federated learning personalization system of D1 to improve personalization performance when labeled examples are limited or unavailable. This is simply combining prior art elements according to known methods to yield predictable results; use of known technique to improve similar devices (methods, or products) in the same way; and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 20,
Claim 20 is similar in scope to claim 16; therefore it is rejected under similar rationale. D1 further teach a non-transitory computer readable medium for storing computer readable program code or instructions which are executable by a processor to perform a method for suggesting at least one modality of interaction See at least Fig. 16, ¶¶156-157,” processor 1602 includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions, processor 1602 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 1604, or storage”, ¶163, “computer-readable non-transitory storage medium or media”). The same motivation to combine for claim 16 equally applies for current claim.
Claim 18 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Malik et al. [US 2021/0117780 A1, hereinafter D1] further in view Chakraborty et al. [US 20230419339, hereinafter D2] further in view of Gheorghita et al. [US 2022/0093270A1, hereinafter Gheorghita].
With regard to Claim 18,
D1-D2 teach the method as claimed in claim 16, wherein the training dataset comprises labelled data items (D1, ¶123, “each of the plurality of examples 530 comprises one or more features and one or more labels”).
D2 does not explicitly teach using zero-shot or few-shot learning.
Gheorghita disclose the training comprises using zero-shot or few-shot learning (¶5, “The trained model is then adapted using few-shot learning to predict …”, ¶6, “machine-learned model having been trained for classification with few-shot learning”).
D1-D2 and Gheorghita are analogous art to the claimed invention because they are from a similar field of endeavor of training machine learning model. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by Gheorghita with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-D2 as described above to allow training of machine learning models using a fewer number of samples of training data (Gheorghita ¶9, “The few-shot learning allows for a fewer number of samples of training data”). This is simply combining prior art elements according to known methods to yield predictable results; use of known technique to improve similar devices (methods, or products) in the same way; and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Allowable Subject Matter
Claim 1-2, 5-6, 10-13, 15, and 21-22 allowed. Claims 4 and 9 would be allowable if the claims overcome the 35 USC 112(b) rejection.
The following is an examiner’s statement of reasons for allowance:
Upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of applicant's invention as the noted features amount to more than a predictable use of elements in the prior art. The allowable features include “A system for providing personal machine learning (ML) models for users, the system comprising: a server comprising a task-independent shared ML model, wherein the server is configured to train the task-independent shared ML model to learn a set of shared features; a user platform device comprising a task-independent personal ML model for a user, wherein the task-independent personal ML model is trained, based on the shared features received from the task-independent shared ML model, to learn a set of personal features specific to the user; and a user device comprising a task-specific personal ML model for the user, wherein the task-specific personal ML model is trained based on the personal features received from the task-independent personal ML model”.
Claims disclose individual components (three tier servers, personalized models, task independent shared ML models, task specific personalized models) are individually known, but the specific way they are combined especially the distribution of the models in a specific architecture is not an obvious combination. A skilled person in the art would generally need the specific claimed invention to be laid out to arrive at this architecture or structure rather than naturally deriving it from the prior art alone. In other words, while the individual components of the claims as a server hosted shared model, a personal model, and a task specific personal model, are each independently known in the art, no reference teaches distributing a task independent model specifically to an intermediate device positioned between the server and the end user device.
For example, one of the most related references is Malik et al. [US 2021/0117780 A1, hereinafter Malik], Malik teach a client system that stores and trains a local personalization model comprising a plurality of local model parameters, distinct from the global neural network model federated parameters, with private parameters trained locally instead of being transferred to remote server. This is a personal ML model, specific to a user using a device separated from the server. However, Malik personal model is expressly described as learning task specific user representations, and it resides on the same end user client system that also runs the downstream prediction there is no intermediate user platform device between the server and the user device. Therefore, Malik does not teach that model residing on a distinct user platform device as recited.
Another related teaching, Chakraborty et al. [US 20230419339, hereinafter D1], D1 teach a hub system that trains a user representation model as a task agnostic learning model, generating a task agnostic embedding that does not learn dependency between events and downstream tasks, and which is transferable to machine learning services not associated with its training data. This is task independent and the reused representation concept generally. However, the task agnostic model is the shared server side model applied across all users and the edge system between the server and user device store only the resulting user representation vector, not a trained model with its own parameters. D1 does not teach a task independent personal ML model in the user platform device and the intermediate tier holds data not models.
Another related teaching, Zyglowicz et al. [US 20170293846, hereinafter D2], D2 teach a three tier hardware chain wearable device, smart phone or hub and cloud computing platform. The smart phone or hub is personal user device that receive updates from the cloud and forward them to the wearable. However, every model in the disclosure perform the same function at every tier. Algorithm output is trained centrally in the cloud on user data and pushed to update the same algorithm at the wearable. Nothing disclose a task independent model, or personal model hosted by hub, what is stored and delivered are firmware updates, not a distinct trained representation. Therefore, there is no disclosure of task independent model and that the platform device itself train a personal model.
In addition to the above, the Examiner emphasizes the interrelation of the above distinguishing elements with the remainder of each respective claim element, and further notes that it is the interrelation that truly distinguishes Applicant's invention from the evidence at hand. Moreover, none of the evidence at hand teaches or suggests the combination of features claimed, nor does there exist an appropriate rationale for further modification of the evidence at hand.
It is hereby asserted by the Examiner that, in light of the above and in further deliberation over all of the evidence at hand, that the claims are allowable as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art. However, further consideration will be provided upon receiving the applicant’s respond.
Response to Arguments
Examiner respectfully withdraw the 35 U.S.C. 112(b) rejection for claims 8, 9, 14, and 17-19 based on the applicant’s amendments. However new rejection is raised for claims 4 and 9 based on the applicant’s amendments.
Applicant arguments related to claims 1-16 are moot as there is no 35 U.S.C. 102(a)(1) 35 U.S.C. 102(a)(2), or 35 U.S.C. 103 rejection applied to the claims. Examiner provided reason of withdrawing the rejections under the section of “Allowable Subject Matter“.
Applicant argue that claims 17 and 20 recite features similar to claim 1 therefore they are allowable.
Examiner respectfully disagrees, claims 17 and 20 do not recite features similar to claim 1; therefore they are rejected as detailed in the Office Action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 20220300804 filed by Guan et al. that disclose training using few shot learning See at least ¶5, ¶19, ¶¶35-36
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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