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
Claims 1-20 are pending.
This action is in response to the application filed on December 30 September 2025.
CROSS-REFERENCE TO RELATED APPLICATION(S)
This patent application is a continuation of and claims the benefit of priority to U.S. Nonprovisional Patent Application No. 18/603,686, filed on March 13, 2024, and to U.S. Provisional Patent Application No. 63/490,131, filed on March 14, 2023, the entireties of which are incorporated herein by reference.
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 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 7, 9-10, and 16, 18-19 are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention. Because claims 1, 11 and 17 recite, " …. likely next search query …. “, which render indefinite claimed invention scope. Demonstrative pronouns are not permitted, only what is being referred by “those” and “they” should be set forth in the claim. May be renders the claim indefinite by failing to point out that is being performed. Thus, applicants are required to define the statutory claim class type for claims 1-21. Applicants are advised to amend the claim so solve the 112 rejection set forth in the claim.
2173.05(b) Relative Terminology [R-07.2022]
The use of relative terminology in claim language, including terms of degree, does not automatically render the claim indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Seattle Box Co., Inc. v. Industrial Crating & Packing, Inc., 731 F.2d 818, 221 USPQ 568 (Fed. Cir. 1984). Acceptability of the claim language depends on whether one of ordinary skill in the art would understand what is claimed, in light of the specification. TERMS OF DEGREE
Terms of degree are not necessarily indefinite. "Claim language employing terms of degree has long been found definite where it provided enough certainty to one of skill in the art when read in the context of the invention." Interval Licensing LLC v. AOL, Inc., 766 F.3d 1364, 1370, 112 USPQ2d 1188, 1192-93 (Fed. Cir. 2014) (citing Eibel Process Co. v. Minnesota & Ontario Paper Co., 261 U.S. 45, 65-66 (1923) (finding ‘substantial pitch’ sufficiently definite because one skilled in the art ‘had no difficulty … in determining what was the substantial pitch needed’ to practice the invention)). Thus, when a term of degree is used in the claim, the examiner should determine whether the specification provides some standard for measuring that degree. Hearing Components, Inc. v. Shure Inc., 600 F.3d 1357, 1367, 94 USPQ2d 1385, 1391 (Fed. Cir. 2010); Enzo Biochem, Inc., v. Applera Corp., 599 F.3d 1325, 1332, 94 USPQ2d 1321, 1326 (Fed. Cir. 2010); Seattle Box Co., Inc. v. Indus. Crating & Packing, Inc., 731 F.2d 818, 826, 221 USPQ 568, 574 (Fed. Cir. 1984). If the specification does not provide some standard for measuring that degree, a determination must be made as to whether one of ordinary skill in the art could nevertheless ascertain the scope of the claim (e.g., a standard that is recognized in the art for measuring the meaning of the term of degree). For example, in Ex parte Oetiker, 23 USPQ2d 1641 (Bd. Pat. App. & Inter. 1992), the phrases "relatively shallow," "of the order of," "the order of about 5mm," and "substantial portion" were held to be indefinite because the specification lacked some standard for measuring the degrees intended.
The claim is not indefinite if the specification provides examples or teachings that can be used to measure a degree even without a precise numerical measurement (e.g., a figure that provides a standard for measuring the meaning of the term of degree). See, e.g., Interval Licensing LLC v. AOL, Inc., 766 F.3d 1364, 1371-72, 112 USPQ2d 1188, 1193 (Fed. Cir. 2014) (observing that although there is no absolute or mathematical precision required, "[t]he claims, when read in light of the specification and the prosecution history, must provide objective boundaries for those of skill in the art").
During prosecution, an applicant may also overcome an indefiniteness rejection by providing evidence that the meaning of the term of degree can be ascertained by one of ordinary skill in the art when reading the disclosure. For example, in Enzo Biochem, the applicant submitted a declaration under 37 CFR 1.132 showing examples that met the claim limitation and examples that did not. Enzo Biochem, 599 F.3d at 1335, 94 USPQ2d at 1328 (noting that applicant overcame an indefiniteness rejection over "not interfering substantially" claim language by submitting a declaration under 37 CFR 1.132 listing eight specific linkage groups that applicant declared did not substantially interfere with hybridization or detection).
Even if the specification uses the same term of degree as in the claim, a rejection is proper if the scope of the term is not understood when read in light of the specification. While, as a general proposition, broadening modifiers are standard tools in claim drafting in order to avoid reliance on the doctrine of equivalents in infringement actions, when the scope of the claim is unclear a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, is proper. See In re Wiggins, 488 F. 2d 538, 541, 179 USPQ 421, 423 (CCPA 1973).
When relative terms are used in claims wherein the improvement over the prior art rests entirely upon size or weight of an element in a combination of elements, the adequacy of the disclosure of a standard is of greater criticality.
Claim Rejections - 35 USC § 102
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 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 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)(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al (US-20230394295-A1).
With respect to claims 1, 10 and 19, Wang et al teaches
a memory storing instructions ([0080] processor, memory storing instructions);
a trained machine learning model having been trained, based on training sequences of user search queries ([0003] providing related content recommendations in response to a user search query by supervising the training), to predict a likely next search query in response to input of a sequence of search queries; and a processor operatively connected to the memory and configured to execute the instructions to perform operations ([0019] receive the user search query from the network 120. The related content recommendation engine 130, machine learning tower for representation learning of the search query and another machine learning tower for the related content response to the search query. [0049] occur such that step 210 occurs first followed sequentially to step 214 then to step 204 through step 208) including
receiving the sequence of search queries from a user device associated with a user ([0019] receive the user search query from network 120. The related content recommendation engine 130, machine learning tower for representation learning of the search query and another machine learning tower for the related content response to the search query. [0049] occur that step 210 occurs sequentially to step 214 then to step 204 through step 208);
predicting the likely next search query from the user by inputting the received sequence of search queries into the trained machine learning model ([0034] evaluated by applying the model to user search queries (received from user device 102), generating ground-truth next click predictions for the search query, recording the results of the users next click and comparing the results to the predictions to see if the prediction was correct);
generating predicted search results by applying the likely next search query ([0048] At operation 222, applying the model to user search queries, generating ground-truth next click predictions for the search query, recording the result of the user's next click (e.g., a positive and/or negative interaction with recommended content), and/or comparing the result to the predictions to see if the prediction was correct. To evaluate the effectiveness of the trained related content model).
generating the personalized search results by appending the predicted search results to search results from a most recent query of the sequence of queries from the user ([0048] At operation 222, applying the model to user search queries, generating ground-truth next click predictions for the search query, recording the result of the user's next click (e.g., a positive and/or negative interaction with recommended content), and/or comparing the result to the predictions to see if the prediction was correct. To evaluate the effectiveness of the trained related content model. [0049] performed sequentially as the functionality and linear input to output flow); and
causing the user device to display the personalized search results (FIG. 4, [0017] entry of text and/or image content into a search field or as a user selection of an image or a link displayed on the application. [0022] determining the trained joined feature vector for the search query as a final output).
With respect to claims 2 and 11, Wang et al teaches sequence of search queries is in a sequential order ([0049] performed sequentially as functionality and linear input to output flow).
With respect to claims 3, 12 and 20, Wang et al teaches using at least one search query in a training sequence as training input, and using a next query as training ground truth; and until the next query is a last query in the training sequence, adding the next query to the training input and replacing the next query with a subsequent query as the training ground truth ([0025] ] complexity is added to the input. [0049] performed sequentially as the functionality and linear input to output flow).
With respect to claims 4 and 13, Wang et al teaches serializing the at least one search query such that the at least one search query is in sequential order; and encoding the at least one serialized query to generate the at least one encoded query ([0048] At operation 222, the related content model is evaluated. related content model is evaluated by applying the model to user search queries, generating ground-truth next click predictions for the search query, recording the result).
With respect to claim 5 and 14, Wang et al teaches assigning a pre-defined encoded input sequence to each input sequence ([0048] At operation 222, the related content model is evaluated. related content model is evaluated by applying the model to user search queries, generating ground-truth next click predictions for the search query, recording the result).
With respect to claims 6 and 15, Wang et al teaches machine learning model has been further trained on input data associated with one or more parameter or preference of a user account associated with the sequence of search queries ([0048] At operation 222, the related content model is evaluated. related content model is evaluated by applying the model to user search queries, generating ground-truth next click predictions for the search query, recording the result).
With respect to claims 7 and 16, Wang et al teaches developing a vector based on at least one encoded query; and predicting the likely next search query based on the vector via the trained machine learning model ([0008] FIG. 3 illustrates an overview of an example method for modeling joined feature vectors using a single cascaded multilayer perceptron (MLP) neural network).
With respect to claims 8 and17, Wang et al teaches statistical vector is a standardized length derived based on the search queries ([0008] FIG. 3 illustrates an overview of an example method for modeling joined feature vectors using a single cascaded multilayer perceptron (MLP) neural network).
With respect to claims 9 and 18, Wang et al teaches ranked prioritization of one or more parameters associated with the sequence of search queries, or a predictive value for each of the one or more parameters associated with the sequence of search queries ([0008] FIG. 3 illustrates an overview of an example method for modeling joined feature vectors using a single cascaded multilayer perceptron (MLP) neural network).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KUROMATSU (US-20170372230-A1). MACHINE LEARNING MANAGEMENT METHOD AND MACHINE LEARNING MANAGEMENT APPARATUS.
Considered for teachings related generally machine learning management apparatus calculates, for each of a plurality of second models that are generated by model searches by a plurality of algorithms using a plurality of sets of second training data and based on prediction performance of first models, an index value used to determine whether to generate each second model. The machine learning management apparatus then sets the number of second models that are generated using a set of second training data and have an index value at least equal to a threshold as the priority for caching that second training data. The machine learning management apparatus then decides, when a model search has been executed using second training data, whether to cache the second training data based on the priority and stores the second training data in a memory when the decision to cache the data is taken.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC M WOO whose telephone number is (571)272-4043. The examiner can normally be reached 9:00 to 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISAAC M WOO/ Primary Examiner, Art Unit 2163
