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 .
Response to Remarks/Arguments
This Office Action is in response to the communications for the present US application number 17/740,912 last filed on August 04th, 2025.
Claims 1-25, 30, 37, and 44 were cancelled.
Claims 26-29, 33-36, and 40-43 were amended.
Claims 46-48 were newly added.
26-29, 31-36, 38-43, and 45-48 remain pending and have been examined, directed to METHOD, APPARATUS, AND SYSTEM FOR IMPLEMENTING SERVICE FUNCTION DEPLOYMENT.
Upon further review of the latest claim amendments along with the applicant’s representative’s response, the examiner reviewed and updated the searching on the amended language and responds with the following.
With respect to the previous 35 U.S.C. § 112 rejection, due to the substantive amendments towards clarifying and separately out the functions between a main apparatus and a standby apparatus, the § 112 rejection is currently withdrawn.
With respect to the 35 U.S.C. § 103 rejection, using Ikeda, and using amended independent claim 26 for example, the applicant’s representative primarily argued about the newly amended concepts with further emphasis on “raw data” and “feature data”. The amended language was also directed towards steps/processes carried out “…on the active apparatus” before sending the “feature data” to the standby apparatus for inferencing.
In response, the Examiner reviewed the filed Specifications for further clarity and support, found within ¶¶ [0061], [0062], [0067], [0069], and [0091]. Mainly, the “second service” or data processing (which was established to be on the standby apparatus), may be an AI service, and feature extracting is one of those tasks, but if its an AI service, it may be deployed on either the standby or the main apparatus.
The Examiner then also reviewed Ikeda’s teachings and what was previously already established, and would further contend that there is an intermediate step after the main apparatus obtaining the raw camera image data and before sending it off to the one or more inference/standby sub-systems. The raw data or camera image is supplied with image identifiers/labels (Ikeda: ¶ 85). This goes towards how “features data” is supplied to the standby systems. Since there are no further definitions in the current claim language on this term, “features data” and because Ikeda distinctly has this an intermediate step, versus directly sending the raw data straight through, the Examiner would contend there is a difference between the raw data versus the supplied images with identifiers. For example, a descriptive label or identifier on some data or camera images can be indicative of some “features”. Due to the substantive amendments and to further supplement this obvious aspect, the Examiner has updated the response with another reference that more specifically describes of extracting features from a dataset. For at least these reasons, the Examiner remains unpersuaded at this time.
The other independent claim 33 and 40 were similarly amended and argued following claim 26 and thus was similarly rejected under the same rationale.
The newly added dependent claims 46-48 were also reviewed and addressed accordingly.
The remaining dependent claims were not specifically argued at this time.
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument
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 26-29, 31-36, 38-43, 45, and 46 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. US 2020/0257993 A1 to Ikeda et al. (referred to hereafter as “Ikeda”) in view of U.S. Patent Publication No. 2019/0295282 A1 to Smolyanskiy et al. (referred to hereafter as “Smolyanskiy”).
As to claim 26, Ikeda discloses a method implemented by an active apparatus, wherein the method comprises:
processing a first service (Ikeda discloses of an overall system with distributed components/module, where each component/module can be assigned to handle different functions/services, and collectively to work towards some goal(s), directed to different fields of endeavor. A first main or “active” apparatus can be broadly interpreted as an encompassing entire “system of components” or starting with a “main” information processing apparatus 100 (i.e., apparatus/element 100 – in Figs. 1, 2, and/or 5-10), that can perform a first task/service, such as acquiring . See also related relevant sections in ¶¶ 29-32 and 85);
deploying a first function module for a second service on a standby apparatus for the first service, wherein the first function module is configured to process data of the second service, wherein the second service is an artificial intelligence (AI) service, and wherein the first function module comprises an inference module configured to draw an inference based on feature data of the second service to obtain an inference result (Examiner’s Note: This limitation establishes deploying a “second”/AI service, on a standby apparatus, which is to perform a “first”/inference function based upon some (obtained) “feature data” that results in some inferred result. The term “feature data” however can be broadly interpreted, which as currently claimed appears to be coming from the “active apparatus” as the next limitation step establishes.
With that in mind, Ikeda also discloses of utilizing or deploying one or more (so “second” or “third” or etc.) separate AI inference processing apparatus units/components/modules, that are on “standby” (i.e., 300-1, 300-2, 300-3, etc.), wherein each would be running inference models based upon gathered model data files. And furthermore, a model file can be further split into smaller files, (e.g., model file 304 can be split into 304-1, 304-2, etc.), and wherein each of the files can be directed to different inferencing tasks, to obtain separate inferred results (e.g., Ikeda: 37-42));
deploying a feature extraction module for the second service on the active apparatus (Following the above established interpretations, here we’re circling back to the active apparatus, and one or more “features” or piece of data is to be identified or “extracted” by the active or “main” information processing apparatus, as part of the “system”.
The main information processing apparatus can obtain or acquire some data, such as the one or more raw camera images G1 (or “raw data”). The raw data is stored in storage medium M11. Before the main apparatus sends the raw camera data images to the one or more standby systems for further inferencing, Ikeda discloses of outputting the camera image G1 together with image identifier(s) in step P3. This is an extra intermediate step before the AI inference system(s) get the “feature data” which is not exactly the same as getting the raw data (e.g., Ikeda: ¶ 85). While Ikeda does not explicitly disclose and use the terms of a feature extraction process/function/module, it would still have been obvious to one of ordinary skill in the art, before the effective filing date of the present application, that the main apparatus was able to perform something similar to “extracting features.” The act of associating or applying a label or identifier to the raw camera image data is a form of feature extraction, because for example the label could be very descriptive.
Smolyanskiy more expressly discloses of a similar distributed system, that also has a feature extraction mechanism or layer(s). In Smolyanskiy’s system and example, the feature extraction layer(s) can be further concatenated or combined into cost volume layers (e.g., Smolyanskiy: ¶¶ 39-43). This illustrates that the extracted features can be further manipulated in various ways.
And therefore, based upon these Smolyanskiy’s teachings, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the present application, to combine and incorporate Smolyanskiy’s teachings within Ikeda’s overall system and teachings, wherein the resulting combined system can take these extracted features or feature layers and apply further AI inferencing or other types of analysis on them, thus enhancing the overall capabilities of the system);
obtaining raw data (As already explained above within a complete example, the system is obtaining the raw camera image data G1 or generically some model file 304 (e.g., Ikeda: ¶¶ 37 and 85). This step could also be interpreted as overlapping with “processing” the first “service” by the first main apparatus);
processing, by the feature extraction module, the raw data to obtain the feature data (Similar to what was established and explained above within a complete example, the system can process the raw camera image(s) data G1, and further determine and/or identify (or “extract”) some “features” and adds labels or identifiers to that data, before sending it onwards to one or more inference processing apparatuses (e.g., Ikeda: ¶¶ 85-89).
Once again, while Ikeda does not explicitly describe or use the terms of extracting “features” it would still have been obvious to one of ordinary skill in the art, before the effective filing date of the present application, that the main apparatus was able to perform something similar to “extracting features.” The act of associating or applying a label or identifier to the raw camera image data is a form of feature extraction, because for example the label could be very descriptive.
Smolyanskiy more expressly discloses of extracting features with the use of DNN features layers or cost volume layers (e.g., Smolyanskiy: ¶¶ 39-43).
See the previously stated reasons for combining and incorporating Smolyanskiy’s teachings together within Ikeda’s overall system and teachings); and
sending the feature data to the standby apparatus to obtain the inference result based on the feature data (Following the above steps and interpretations, Ikeda discloses that the manipulated or “extracted” camera data images with identifiers or labels on the “features” (and/or any other “features” from Smolyanskiy’s incorporated teachings) are then sent to the one or more standby inference apparatus systems (i.e., 300-1, 300-2, 300-3, etc.), e.g., Ikeda: ¶¶ 85-89).
As to claim 27, Ikeda further discloses the method of claim 26, wherein the active apparatus comprises an active main processing unit (MPU), wherein the standby apparatus comprises a standby MPU, wherein the method further comprises deploying a data collection module on a line processing unit (LPU), wherein the data collection module is configured to collect data and wherein the feature extraction module is configured to extract a feature of the data collected by the data collection module to obtain the feature data (Examiner’s Note: A “line processing unit” or LPU was reviewed in the filed Specifications and it appears to be just a label for another processing unit that operates in the same fashion as a backup or standby processing unit, wherein any task assigned to the LPU would expect some returned results back from it, such as a feature extraction module/function, to be handled by a LPU. The LPU can then pass on the features and have it sent to a “second service” which was interpreted and understood to be an AI inference processing unit/module/system (which is already addressed and covered in claim 26).
With that in mind, Ikeda also discloses of a main information processing apparatus/unit, along with any number of additional standby inference processing apparatus/units. The data collecting that’s occurring can be interpreted as some intermediary data which is the manipulated camera image data G1 in step P2, which is then supplied to the one or more inference processing apparatuses/units, e.g., Ikeda: ¶¶ 85-89 and Fig. 5).
As to claim 28 Ikeda further discloses the method of claim 26, wherein the first function module further comprises the feature extraction module, wherein the feature extraction module is configured to extract a feature of the raw data to obtain the feature data, and wherein the inference module is configured to draw the inference based on the feature data obtained by the feature extraction module (Following claim 26, each of the inference processing apparatuses (i.e., 300-1, 300-2, 300-3, etc.) can infer additional results, given the supplied respective camera/captured imaging data. Ikeda discloses of model data files (e.g., model data file(s) – 304-1, 304-2, 304-3, etc.), which can be interpreted as a form or example of the respective feature data that is fed to each inference processing apparatus/system, e.g., Ikeda: ¶¶ 85-89 and 34-42).
As to claim 29, Ikeda further discloses the method of claim 26, further comprising
receiving the inference result from the standby apparatus (Following claim 26, the main active apparatus (or information processing apparatus), element 100, would be getting the inferred results from the one or more standby inference processing apparatuses (300-1, 300-2, 300-3, etc.), such as files M12, M13, and R1 for example, e.g., Ikeda: ¶¶ 85-89 and Fig. 5);
deploying a result application module for the second service on the active apparatus (There is an intermediary results acquisitions unit (123) as a storage means for receiving any data or results from one or more inference apparatuses, e.g., Ikeda: ¶¶ 85-89 and Fig. 5); and
applying the inference result using the result application module (those intermediary inferred results would be applied or fed back into the end result and add additional value/details to the overall captured camera image, e.g., Ikeda: ¶¶ 85-89, 40-42 and Fig. 5).
As to claim 31, Ikeda further discloses the method of claim 26, further comprising sending a start instruction comprising the first function module when starting the second service (Following claim 26, the “first function module” was interpreted as the “second” AI inferencing service, which Ikeda clearly discloses of with the multiple standby inference processing apparatuses.
When the system determines that the need arises, one or more inference processing units can be assigned with the task to determine or infer some feature or to carry out some other task(s), (e.g., Ikeda: ¶¶ 39-42 and 47-48). And, while Ikeda does not expressly emphasize a start instruction, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the present application, that once a task was delegated/deployed/assigned to one or more inference processing units, then that would also mean the task has started).
As to claim 32, Ikeda further discloses the method of claim 26, wherein the first service comprises a firewall service, a protocol processing service, a route calculation service, a security authentication service, or a user access service (While Ikeda does not expressly further disclose of these types of services, Smolyanskiy more expressly discloses of a similar distributed system, which can be applicable towards several different types of services and/or applications that can encompass one or more of these services, including general user accessing system services, routing services, and possibly utilizing different protocols, (e.g., Smolyanskiy: ¶¶ 93, 120, 126, 131, 157-158, and 188).
Based upon Smolyanskiy’s teachings, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the present application, to combine and incorporate Smolyanskiy’s teachings within Ikeda’s overall system and teachings, which would result in an expanded capacity of providing more varied services).
As to claims 33-36, 38, and 39, see the similar corresponding rejections of claims 26-29, 31, and 32 respectively.
As to claims 40-43 and 45, see the similar corresponding rejections of claims 26-29 and 31 respectively.
As to claim 46, Ikeda further discloses the method of claim 26, wherein processing the raw data to obtain the feature data comprises processing the raw data to obtain a statistical feature, and wherein sending the feature data comprises sending the statistical feature to the standby apparatus (Following claim 26’s example and interpretations, the inference systems with their individual task can be configured to identify statistical data/feature. In the example with the raw camera imaging data, obtaining/inferring “humans” out of the image, or inferring “age” or “gender” can all be interpreted as a form of statistical data, e.g., Ikeda: ¶¶ 85-89 and Fig. 5).
Claims 47 and 48 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. US 2020/0257993 A1 to Ikeda in view of U.S. Patent Publication No. US 2019/0295282 A1 to Smolyanskiy and further in view of U.S. Patent No. US 10,380,997 B1 to Ward et al. (referred to hereafter as “Ward”).
As to claim 47, Ikeda and Smolyanskiy both do not fully further disclose of the method of claim 26, wherein processing the raw data to obtain the feature data comprises processing the raw data to obtain a fitting feature, and wherein sending the feature data comprises sending the fitting feature to the standby apparatus (While Ikeda and Smolyanskiy both do not expressly further disclose of a fitting feature, Ward more expressly discloses of quantizing results, which is a form fitting approach applied towards the dataset (e.g., Ward: col. 34, ll. 61 – col. 35, ll. 1-13).
Based upon Ward’s teachings, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the present application, to combine and incorporate Ward’s technique altogether within Ikeda’s overall system and teachings, as this can be readily incorporated within the main apparatus or as a separate functionality within a standby, and wherein the resulting combined system would be more capable with increased functionality can utilize this functionality to gain more meaningful analysis on the data).
As to claim 48, Ikeda and Smolyanskiy both do not fully further disclose of the method of claim 26, wherein processing the raw data to obtain the feature data comprises processing the raw data to obtain a frequency domain feature, and wherein sending the feature data comprises sending the frequency domain feature to the standby apparatus (Similar to claim 47, while Ikeda and Smolyanskiy both do not expressly further disclose of a fitting feature, Ward more expressly discloses of different identifying and analyzing different frequency domains (e.g., Ward: col. 22, ll. 35 - col. 23, ll. 5, and col. 24, ll. 8-33).
Based upon Ward’s teachings, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the present application, to combine and incorporate Ward’s teachings here altogether within Ikeda’s overall system and teachings, as providing discrete frequency domain samples off to standby apparatuses can be an efficient form of delegating and parallelism).
Conclusion
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 Xiang Yu whose telephone number is (571)270-5695. The examiner can normally be reached M-F 9:30-3:00 (PST/PDT).
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, Emmanuel Moise can be reached at (571)272-3865. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/X.Y./Examiner, Art Unit 2455
/EMMANUEL L MOISE/Supervisory Patent Examiner, Art Unit 2455
Prosecution Insights