Detailed Action
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 .
Claims 1-20 are pending for examination. Claims 1, 8, and 15 are independent.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/01/2026 has been entered.
Response to Amendment
The office action is responsive to the amendments filed on 07/01/2026. As
directed by the amendments claims 1, 6-8, and 15 are amended.
Response to Arguments
Applicant's arguments filed 07/01/2026 have been fully considered.
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “the user actions comprising user navigations to the plurality of token sets and user selections of subjects of the plurality of token sets;”. The claim defines a token set as a plurality of words representing a respective document. However, it does not explain how a user navigates to the set of words through the electronic interface. The specification para 0011 and para 0020 describe user activity include navigating documents and selecting subjects of the documents. It is unclear if the user navigates a document or webpage represented by the token set or the user navigates directly to a token set comprising descriptive words. Claim 1 is indefinite because it is unclear whether the claimed navigation is directed to a document accessible through the interface or the claimed navigation is directed to specific token-set representation.
Claim 1 in the same limitation also states, “user selections of subjects of the plurality of token sets”. The claim establishes that token sets represent documents but does not establish what constitutes a subject of a token set. A document can have a subject, for example a product described on a webpage. A token set, however, is a representation of the document and it is unclear what the subject of tokens could be. The specification describes actions such as purchasing a product and adding a product to a shopping cart. These are selection or interaction including subjects of documents. It is unclear what exactly the relationship between the selectable subject and token set is.
For purpose of examination, examiner interprets the claim limitation as user actions navigating a document and user selections of subjects of the document.
Independent claims 8 and 15 recite similar limitations and are also rejected under 112(b) for the same reasons described for claim 1.
Dependent claims 2-7, 9-14, and 16-20 do not resolve the 112(b) rejection
from independent claims 1, 8, and 15 and are also rejected under 112(b).
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (US 2015/0363688 A1, hereinafter "Gao") in view of Dai (US 9,691,096 B1, hereinafter "Dai").
Regarding Claim 1
Gao discloses: A method for predicting a next user selection in an electronic user interface ([Abstract, para 0044, 0109, and Fig 3] describes predicting target documents that would interest users (i.e. next selection/interest).), the method comprising:
training a machine learning model according to a training data set ([Para 0041-0042, and Fig 1] describes training a deep semantic model DSM (i.e. machine learning model) with browser logs (i.e. training data).) to create a trained model to predict sequences of user actions based on a content of documents ([Para 0033-0035, and 0117] describes a deep semantic model trained from browsing transition between source and target document.), the training data set comprising a plurality of token sets each token set representative of a respective document accessible through the interface, each token set comprising a plurality of words, each word describing a characteristic of the respective document, ([Para 0028-0029, 0044, 0061-0071, 0091-0093, Fig 1 and Fig 3-5] describes extracting words/tokens form a document when training a deep neural network (DSM). Para 0044 and Fig 3 disclose selecting tokens/words from document 310 accessible through user interface module 320. Examiner interprets words from the document as describing context (i.e. characteristics) of the document.)
receiving, from a user, a sequence of selections of documents ([Para 0028-0029, 0035-0038, 0044, 0068, 0111, Fig 1 and Fig 3-5] describes extracting/receiving click transitions or context and focus (i.e. selections of document) from documents. [Para 0117] also describes user sessions consisting of a sequence of browsing events.);
inputting the sequence of selections of documents to the trained model ([Para 0005, 0020-0021, 0028-0029, 0035-0038, 0044, 0068, 0111, Fig 1 and Fig 3-5] describe click transitions and Context and Focus extraction that evaluates selected words from documents and inputs to the DSM model. [Para 0117] also describes a sequence of browsing events.); and
outputting to the user, in response to the sequence of selections, a predicted next document selection according to an output of the trained model, the output being based on a content of the sequence of selections of documents. ([Para 0025, 0056, 0117, and Fig 3] describes the trained DSM predicting target documents (i.e. next document selection) in response to context and focus (i.e. user selection). [Para 0060-0065 and 0098-0103]).
Gao does not explicitly discloses: the training data set comprising a plurality of and user actions associated with the plurality of token sets,
the user actions comprising user navigations to the plurality of
However, Dai discloses in the same field of endeavor: the training data set comprising a plurality of and user actions associated with the plurality of token sets, ([Col 18 lines 54-67, Col 22 lines 27, Claim 1, and Fig 10-12] describes training the model with navigation patterns (i.e. user actions) associated with at least one item.)
the user actions comprising user navigations to the plurality of ([Col 16 lines 42-67, Col 17 lines 1-52, and Fig 8] describes navigation through a webpage and patterns comprising user selection. The pattern describes a viewed/navigated product page associated with a product purchase (i.e. selection).)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Item Recommendations through Navigational Patterns disclosed by Dai into the method of Modeling Interestingness disclosed by Gao to analyze user actions on documents. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Item Recommendations through Navigational Patterns disclosed by Dai as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to provide a more accurate measure of a classifiers performance for training and evaluating the classifier with different sets of patterns.
Regarding Claim 8
Gao in view of Dai discloses: A system comprising: a non-transitory, computer-readable medium storing instructions; and a processor configured to execute the instructions to ([Para 0126-0131 and Fig 6], Gao): (Claim 8 is a system claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 15
Gao discloses: A method for predicting a next user selection in an electronic user interface ([Abstract, para 0044, 0109, and Fig 3] describes predicting target documents that would interest users (i.e. next selection/interest).), the method comprising:
training a machine learning model according to a training data set to create a trained model to predict sequences of user actions based on a content of documents, ([Para 0025, 0041-0042, 0056, 0117, Fig 1 and Fig 3] describes training a deep semantic model DSM (i.e. machine learning model) with browser logs (i.e. training data), the trained DSM predicting target documents (i.e. next document selection).) the training data set comprising a plurality of token sets and sequences of each token set representative of a respective document accessible through the interface, each token set comprising a plurality of words, each word describing a characteristic of the document ([Para 0028-0029, 0044, 0061-0071, 0091-0093, Fig 1 and Fig 3-5] describes extracting words/tokens form a document when training a deep neural network (DSM). Para 0044 and Fig 3 disclose selecting tokens/words from document 310 accessible through user interface module 320. Examiner interprets words from the document as describing context (i.e. characteristics) of the document.), wherein training the machine learning model according to the training data set comprises:
conducting a first training round in which token sets are used independent of user selections of documents; ([Para 0027, 0061-0067, 0071, 0090-0093, Fig 1 and Fig 5] describes extracting words/tokens to train the DSM model.)
conducting a second training round in which the sequences of user actions are used in conjunction with token sets; ([Para 0005, 0020-0021, 0028-0029, 0035-0038, 0044, 0068, 0111, Fig 1 and Fig 3] describe click transitions and Context and Focus extraction that evaluates selected words from documents and trains the DSM model.) and
deploying the trained model to output to a user, in response to a sequence of user actions, a predicted next document selection. ([Para 0025, 0056, 0117] describes the trained DSM predicting target documents (i.e. next document selection) in response to context and focus (i.e. user selection).).
Gao does not explicitly discloses: the training data set comprising a plurality of and sequences of user actions associated with the plurality of token sets,
the sequences of user actions comprising user navigations to the plurality of
However, Dai discloses in the same field of endeavor: the training data set comprising a plurality of and sequences of user actions associated with the plurality of token sets, ([Col 18 lines 54-67, Col 22 lines 27, Claim 1, and Fig 10-12] describes training the model with navigation patterns (i.e. user actions) associated with at least one item.)
the sequences of user actions comprising user navigations to the plurality of ([Col 16 lines 42-67, Col 17 lines 1-52, and Fig 8] describes navigation through a webpage and patterns comprising user selection. The pattern describes a viewed/navigated product page associated with a product purchase (i.e. selection).)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Item Recommendations through Navigational Patterns disclosed by Dai into the method of Modeling Interestingness disclosed by Gao to analyze user actions on documents. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Item Recommendations through Navigational Patterns disclosed by Dai as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to provide a more accurate measure of a classifiers performance for training and evaluating the classifier with different sets of patterns.
Regarding Claim 2
Gao in view of Dai discloses: The method of claim 1, wherein outputting the predicted next document selection comprises one or more of:
displaying a link to the predicted next document in response to a user search;
displaying a link to the predicted next document in response to a user navigation; or
displaying a link to the predicted next document in response to a user click.
([Para 044-0045, 0099, 0103, 0109], Gao describes displaying links to predicted documents based on highlighted text (i.e. user click/navigation).)
Regarding Claim 3
Gao in view of Dai discloses: The method of claim 1, wherein each word describes a characteristic of a subject of the document. ([Para 0060-0068, 0071-0072, Fig 1, and Fig 4], Gao describes words from a document and extracting features from the words (i.e. characteristics of a subject). Examiner also interprets words from the document as describing context (i.e. characteristics) of a subject of the document.)
Regarding Claim 4
Gao in view of Dai discloses: The method of claim 1, wherein a plurality of the words describing a characteristic of the document are contained in the document. ([Para 0028-0029, 0035-0038, 0044, 0060-0072, 0111, Fig 1 and Fig 3-5], Gao describe a number of words representing context/focus (i.e. characteristics) found within the document.)
Regarding Claim 5
Gao in view of Dai discloses: The method of claim 1, wherein training the machine learning model according to the training data set comprises conducting a training round in which token sets are used independent of user selections of documents ([Para 0027, 0061-0067, 0071, 0090-0093, Fig 1 and Fig 5], Gao describes extracting words/tokens to train the DSM model.).
Regarding Claim 6
Gao in view of Dai discloses: The method of claim 5, wherein the training round is a first training round, wherein training the machine learning model according to the training data set further comprises conducting a second training round in which token sets are used in conjunction with corresponding sequences of user actions. ([Para 0005, 0020-0021, 0028-0029, 0035-0038, 0044, 0068, 0111, Fig 1 and Fig 3], Gao describe click transitions and Context and Focus extraction that evaluates selected words from documents and trains the DSM model.).
Regarding Claim 7
Gao in view of Dai discloses: The method of claim 1, wherein training the machine learning model according to the training data set comprises conducting a training round in which token sets are used in conjunction with corresponding sequences of user actions. ([Para 0041-0042, 0092-0093, 0106, Fig 2, and Fig 3-5], Gao describes training the DSM with both word/token sets and context/focus (i.e. user selections).).
Regarding Claim 9
(Claim 9 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.)
Regarding Claim 10
(Claim 10 recites analogous limitations to claim 3 and therefore is rejected on the same ground as claim 3.)
Regarding Claim 11
(Claim 11 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Regarding Claim 12
(Claim 12 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Regarding Claim 13
(Claim 13 recites analogous limitations to claim 6 and therefore is rejected on the same ground as claim 6.)
Regarding Claim 14
(Claim 14 recites analogous limitations to claim 7 and therefore is rejected on the same ground as claim 7.)
Regarding Claim 16
(Claim 16 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.)
Regarding Claim 17
(Claim 17 recites analogous limitations to claim 3 and therefore is rejected on the same ground as claim 3.)
Regarding Claim 18
(Claim 18 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Regarding Claim 19
Gao in view of Dai discloses: The method of claim 15, wherein both the first training round and the second training round comprise a plurality of epochs ([Para 0088] describes DSM training within epochs.).
Regarding Claim 20
Gao in view of Dai discloses: The method of claim 15, wherein the first training round is before the second training round. ([Para 0041-0042, 0092-0093, 0106, Fig 2, and Fig 3-5] describes extracting words/tokens first then evaluating context/focus after.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang et al. (US 2021/0358007 A1) describes training user actions on a webpage and evaluating tokens.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TEWODROS E MENGISTU whose telephone number is (571)270-7714. The examiner can normally be reached Mon-Fri 9:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABDULLAH KAWSAR can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TEWODROS E MENGISTU/ Primary Examiner, Art Unit 2127
