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 .
Claim Interpretation
Claim(s) 1-15 do not use “means for” (or “step for”) language, or generic placeholders for "means” coupled with functional language without recitation of sufficient structure for carrying out the claimed functions and therefore do not invoke 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph).
While the claims recite “data processing unit” and “processing unit” the term “processing unit” would be considered a structural term (e.g. a processor), therefore the claims are not considered to invoke 35 USC 112(f).
Claim Objections
Applicant is advised that should claim 11 be found allowable, claim 12 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
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 (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 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, 2, 6-9, 11, 12, 16 and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ide et al. (US 2013/0197890 A1).[claim 1]
Regarding claim 1, Ide discloses a time-series data processing device (Figures 1-2; Paragraphs 0127-0137) comprising:
a data processing unit configured to process a plurality of pieces of time-series data indicating a user action based on time lengths associated with the time-series data (Figure 1, 21-22; Figures 7-10; receiving and annotating time-series data); and
a processing unit configured to perform processing associated with machine learning based on the processed plurality of pieces of time-series data (Figure 1, 23-24; Paragraphs 0112-0117).[claim 2]
Regarding claim 2, Ide discloses wherein the data processing unit inserts an action identifier indicating a break between the pieces of time-series data based on time intervals between the pieces of time-series data (Figure 9; LOST identifier indicating a break between the pieces of time-series data).[claim 6]
Regarding claim 6, Ide discloses wherein the data processing unit processes the time-series data based on time lengths of actions of a user indicated by the time-series data (Figures 8 and 9; dividing and providing movement attributes according to lengths of actions in the time-series data).[claims 7, 11, 12, 16 and 17]
Regarding claims 7, 11, 12, 16 and 17, Ide discloses wherein the data processing unit adds identifiers indicating the time lengths or identifiers indicating the actions to the time-series data according to the time lengths of the actions indicated by the time-series data (Figures 8 and 9; movement attributes indicating actions added to the time-series data).[claim 8]
Regarding claim 8, Ide discloses wherein the processing unit performs processing associated with machine learning on the time-series data (Paragraphs 0112-0117; learning performed by a machine).[claim 9]
Regarding claim 9, Ide discloses wherein the processing unit calculates weights of attention of the pieces of time-series data based on a self-attention function (Paragraph 0112-0116; obtaining parameters or “weights” associated with a learning model), and wherein the processing unit acquires one or more other pieces of time-series data with a high degree of association with arbitrary time-series data based on the weights of attention (Figure 1, 33-35; Paragraphs 0123-0126; using obtained model parameters to generate routes, arrival probabilities, and time required to the destination as prediction results).
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.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over by Ide et al. (US 2013/0197890 A1) in view of Geipel et al. (US 2022/0128987 A1).[claim 10]
Regarding claim 10, Ide discloses acquiring one or more pieces of time-series data (Figure 1, MOVEMENT HISTORY DATA input to 31), but does not disclose wherein the processing unit identifies another piece of time-series data with the elapse of predetermined time out of the other pieces of time-series data of which the degree of association is determined to be high based on the weights of attention, and wherein the one or more pieces of time-series data generated are acquired after the other piece of time-series data.
Geipel discloses a system for identifying and labeling pieces of time-series data by using a model for segmenting the data and comparing the data to previously labeled pieces of time-series data to determine a piece of labeled time-series data with a high degree of association and applying the label to the new piece of time-series data (e.g. Figure 3; Paragraphs 0047-0055). Geipel discloses that the system allows for efficient labeling of new time series data (Paragraph 0018).
Therefore, it would have been obvious to detect and label new time-series movement history data in Ide as taught by Geipel using the trained probability model to identify movement events and automatically select an appropriate label by comparison with previous time-series data to detect labeled time-series data with a high degree of association and applying the label to the new time-series data so that it may be efficiently labeled. Doing so would assist in labeling the new time-series data in the system of Ide (e.g. Figure 1, 32; Figure 54).
Additionally note that as one piece of movement history data is received and labeled, additional movement history data obtained at a later time may be received and labeled, thus the system would additionally receive one or more pieces of time-series data generated are acquired after the other piece of time-series data as claimed.
Allowable Subject Matter
Claims 3-5 and 13-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.[claims 3-5 and 13-15]
Regarding claims 3-5 and 13-15 the prior art does not teach or reasonably suggest time-series data processing device according to claim 2, wherein the data processing unit inserts action identifiers of a number corresponding to lengths of the time intervals.
While Ide discloses inserting an action identifier indicating a break between the pieces of time-series data, Ide or the other cited prior art does not teach or reasonably suggest inserting action identifiers of a number corresponding to lengths of the time intervals as claimed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following show additional prior art systems for processing time-series data:
Khan et al. US 2023/0014795 A1
Togawa US 2022/0253461 A1
Qiao et al. US 2022/0197977 A1
Zhuo et al. US 2022/0066900 A1
Kurauchi et al. US 2021/0201204 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J HENN whose telephone number is (571)272-7310. The examiner can normally be reached Monday-Friday ~10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Twyler Haskins can be reached at (571) 272-7406. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Timothy J Henn/Primary Examiner, Art Unit 2639
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