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 18/727,001 last filed on January 22nd, 2026.
Claims 7-9, 12, and 13 were amended.
1-13 remain pending and have been examined, directed to METHOD FOR IDENTIFYING ITEMS OF EQUIPMENT PRESENT IN A HOME NETWORK.
Upon further review of the latest claim amendments along with the applicant’s representative’s response, the examiner reviewed the applied references and respectfully disagrees and remain unpersuaded.
With respect to the 35 U.S.C. § 102 rejection, using Suski, and referencing independent claim 1 for discussion purposes, the applicant’s representative argued that Suski did not expressly disclose of the first, third and fourth limitation steps, which were in reference to identifying an item of equipment using received identification data and later determining the distance between a computed digital fingerprint compared against a reference digital fingerprint and then concluding and identifying the item of equipment as something that is known, because the difference or distance is minimal (under a threshold).
The representative argued that Suski’s “identification data” was only wired or wireless signals (physical communication data from ¶ 33), but failed to address the other cited sections like ¶¶ 34 and 37 that were also relevant to the claimed concept as this “identification data” is converted into device fingerprint data. In response, the Examiner would argue that the claimed term “identification data” is very broad and does not limit or restrict the current interpretations to any particular type of data. Also, the claimed steps immediately convert this data into a different form which Suski readily teaches. The claim language establishes that this identification data is converted into a digital fingerprint and the system is manipulating and handling the digital fingerprint from then onwards, all of which Suski is also disclosing as Suski’s system can handle the various types of physical communication signals, get processed by a model to determine a device fingerprint, which would then identify and classify the device.
Next, when calculating the distances between a computed and a reference digital fingerprint value, the representative argued that cited ¶ 121 only described of scores and not distances. And following up with the last step, which is a positive outcome from the identification process, resulting in a known item of equipment, the representative argued that in the present claims, no models are used for any binary classification, and instead the claim required a closet-item matching for any known equipment.
In response, the Examiner would argue that with these last two steps, Suski’s ¶ 121 does provide enough details to teach of the claimed concept. The representative acknowledged here that there are generated device fingerprints, which have an associated score, which represents how close it is to a known reference item/equipment, This concept can be described of as determining how close of a match (or “distance”) some computed device fingerprint is compared to a reference, when there are scores or values associated. Also, Suski’s ¶ 121 provided examples highlighting at least two scenarios. In one scenario, after a machine learning model processes and outputs a high score and several low scores, the high score (or highest out of the group) can be evaluated as the closest to a known (or reference) device. This is not a binary classification, because there is a sliding scale of finding the closest or highest scored device digital fingerprint and then determining that it is a match to a known reference, which is exactly as the represent argued to be in the claims, as in closest item matching. There was another described scenario where when evaluating an unknown device and all the scores were low compared to the reference score of a known device - the scores needed to be high, to be considered “close” to the high reference score/value, and in this scenario, since all the scores were low, the determination ends with a negative classification, which . The first scenario already sufficiently addresses the limits and bounds of the current claimed language, as having found a close match to a reference digital fingerprint, the item of equipment is determined to a known item of equipment.
The representative further argued about the rejection of dependent claim 3 (rejected under 35 U.S.C. 103 obviousness using Suski in view of Sawal), following claims 1 and 2, which adds a further limiting definition for digital fingerprints which are now further defined as vectors from the (generic) identification data. The representative argued that the claimed identification data should be somehow interpreted as network frames of OSI layer 2 and above. If the intent was for identification data to be interpreted that specifically, then please amend claim 1 to include such definitions.
In response, the Examiner would argue that the claim language from claims 1 up to 3 do not specifically define or require the interpretation for “identification data” to be that specific related to OSI layers. Claim 3 was directed to converting the identification data into vectors or sub-vectors format, which the Examiner established. Suski disclosed about the use of vectors in models and neural networks that were utilized to identify the devices from the digital fingerprints (e.g., ¶ 50). The Examiner also established that Suski did not teach of the rest of the additional features in claim 3 regarding ordinal encoding for creating additional codes and sub-vectors, which were further supplemented using the secondary reference Sawal. The representative did not comment on the specifics of those features and primarily focused on the dependency of claim 1’s contents, which were already addressed in claim 1.
Additional dependent claims 4-6 and 9 were mentioned, but not specifically argued, except for their respective dependency back to claim 1. None of the specifics for each of the named dependents were argued. The Examiner would recommend further amendments to include specific claim language into claim 1. As for the additional comments about hindsight reasoning, none of the specifics for any of the dependents were specifically argued, so it’s not clear what specific feature(s) or value or benefits were considered improperly applied or combined within the base Suski’s reference, when Suski already covers the core concept related to using digital fingerprints and neural network models to identify devices, given those forms of identifiers. Any additional features that were directly or indirectly related, that arose from the mentioned dependent claims were also related to those core concepts. For example, since Suski was already disclosing of using neural network and modeling data and vectors, dependent claim 3’s additional sub-vectors and performing ordinal encoding on such data from Sawal’s incorporated teachings were an obvious application (e.g., encoding methodologies) for one of ordinary skill in the art, when working with such datasets.
Also, it is recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning, and taking into account of the knowledge which was within the level of ordinary skill at the time the claimed invention was made (the dates on the prior art references were before the present application’s priority dates), and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
The remaining dependent claims were not specifically mentioned or argued at this time.
Applicant's arguments were considered but they were not found persuasive. See the following claim rejections for further clarifications with added emphasis on the points previously disclosed.
Claim Objections
Claim 12 is objected to because of the following informalities:
In the latest amendment, regarding the section “…when the program is executed by the non-transitory medium, cause the non-transitory medium to implement the method according to claim 1” – the underlined section should be replaced with a hardware processor. The medium itself is for storing the program product or instructions, and does not replace a computer processing unit.
Appropriate corrections are required.
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 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.
Claims 1, 2, 7, 8, and 10-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Publication No. US 2021/0326644 A1 to Suski et al. (referred to hereafter as “Suski”).
As to claim 1, Suski further discloses a method for identifying a first item of equipment present in a communication network, this method being implemented by a processing unit and comprising the following steps:
receiving identification data from said first item of equipment (Suski discloses of an overall system that can receive identifying information about a first device’s characteristics, and then proceed with determining the digital fingerprint of said device’s received characteristics, e.g., Suski: ¶¶ 33-34 and 37);
using a neural network-based statistical model to compute a digital fingerprint of the first item of equipment based on the identification data (A neural network can be utilized to compute the digital fingerprint, e.g., Suski: ¶¶ 43, 45-47, 53, and 90);
successively determining distances between the computed digital fingerprint and, respectively, digital fingerprints pre-recorded in a reference base (The system can make the determinations on identifying the device, when taking the device’s determined digital fingerprint and compared against reference values or dataset in the model data, e.g., Suski: ¶¶ 49, 62, 108, and 121);
these pre-recorded digital fingerprints being digital fingerprints of known items of equipment (There are datasets of known devices within the model data, e.g., Suski: ¶¶ 108 and 116-118); and
identifying the first item of equipment as being a known item of equipment when the distance between the digital fingerprint of the first item of equipment and the pre-recorded digital fingerprint of said known item of equipment is less than a predetermined threshold (There can be scores or percentages, with established threshold levels, that would lead to a resulting category or identifier or label for the evaluated device/system, e.g., Suski: ¶ 121 and Fig. 17A-C).
As to claim 2, Suski further discloses the method according to claim 1, characterized in that the pre-recorded digital fingerprints are vectors obtained from identification data of known items of equipment (e.g., Suski: ¶ 53).
As to claim 7, Suski further discloses the method according to claim 1, characterized in that the pre-recorded digital fingerprints are predetermined by the neural network from the following data of known items of equipment
(Following claim 1, Suski discloses that the system can support and identify data details including MAC addresses, port information, wired and wireless information, and frequencies characteristics (e.g., Suski: ¶¶ 35-36, 40, and 63), out of the following list of possibilities):
Dynamic Host Configuration Protocol identifiers including hostname, options, vendor class and list of options in a request packet,
the first three bytes of the Media Access Control address,
service names of Multicast Domain Name System announcements,
WiFi data,
Transport Layer Security client and server fingerprints,
the list of domain names contacted,
the number of different domain names contacted,
list of network ports used,
list of open network ports,
network communication time information including WiFi and Dynamic Host Configuration Protocol server connection frequency, and/or domain name network access frequency, and
network connection type: WiFi or Ethernet.
As to claim 8, Suski further discloses the method according to claim 7, characterized in that the WiFi data comprise (Following claims 1 and 7, Suski discloses that the system can support and identify details that include wireless signal types, and schemes, and supported signal standards (e.g., Suski: ¶¶ 36, 46, and 107), out of the following list of possibilities):
High Throughput/Very High Throughput/High Efficiency capacities, the first three bytes of the supplier-specific label,
the number of antennas,
the list of supported Modulation and Coding Scheme,
maximum bandwidth supported,
Unlicensed National Information Infrastructure band capacities,
spatial flow: maximum rx/tx supported,
supported standards,
supported radio standards.
As to claim 10, Suski further discloses the method according to claim 1, characterized in that before using the statistical model, the identification data are first fed to an expert system capable of identifying the item of equipment or transmitting the identification data to the statistical model if identification fails, the expert system comprising an equipment recognition algorithm based on regular expression rules (The system is first trained/re-trained with a set of known device fingerprints and trained model datasets, before it is used against new devices that the system is now trying to determine and identify (within claim 1), e.g., Suski: ¶¶ 49-52, 108, and 116).
As to claim 11, Suski further discloses the method according to claim 1, characterized in that the reference base comprises digital fingerprints obtained from data gathered from information collections and digital fingerprints obtained from data synthesized from a generator (Similar to claim 10, the system is trained with some dataset and can generate additional model data, e.g., Suski: ¶¶ 49-52, 108, and 116).
As to claim 12, Suski further discloses a non-transitory-medium storing a computer program product comprising instructions which, when the program is executed by the non-transitory medium, cause the non-transitory medium to implement the method according to claim 1 (e.g., Suski: ¶¶ 92-95).
As to claim 13, Suski further discloses a data processing system comprising a hardware processor adapted to the method according to claim 1 (e.g., Suski: ¶¶ 92-95).
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 3 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. US 2021/0326644 A1 to Suski in view of U.S. Patent Publication No. US 2024/0169120 A1 to Sawal et al. (referred to hereafter as “Sawal”).
As to claim 3, Suski does not fully disclose of the method according to claim 2, characterized in that each vector is determined from:
on the one hand, identification data containing textual information whereupon processing is applied by means of a subset of the neural network based on recurrent neurons to determine a first sub-vector (Following claims 1 and 2, Suski discloses that the system can have appropriate labels or identifiers for various vectors that represent some data, e.g., Suski: ¶¶ 50, 52, and 81-83);
identification data containing categorical information, whereupon ordinal encoding is applied to determine a unique code for each category, followed by embedding to associate each category with a second sub-vector (Suski does not expressly discloses of utilizing ordinal encoding to creating another vector/sub-vector.
Sawal more expressly discloses of using machine learning models and being able to manipulate date including ordinal encoding and turning the data into vectors (e.g., Sawal: ¶¶ 55 and 57).
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 Sawal’s teachings of the ordinal encoding on the dataset to form more vectors, all within Suski’s overall system and teachings, because the resulting combined system can benefit from additional layer(s) of data to be incorporated into the model; and
the first and second sub-vectors are then combined by means of at least one dense layer to form said vector (Following the above steps and interpretations, Suski discloses about combining the data/vectors and describe of layers of data (e.g., Suski: ¶ 48). This concept can still take into account of Sawal’s incorporated teachings of vectors/sub-vectors that represent the unique category identifiers/labels from the previous step, all of which can be incorporated within a model of Suski’s).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. US 2021/0326644 A1 to Suski in view of U.S. Patent Publication No. 2024/0169120 A1 to Sawal and further in view of U.S. Patent Publication No. US 2019/0114320 A1 to Patwardhan et al. (referred to hereafter as “Patwardhan”).
As to claim 4, Suski does not fully disclose of the method according to claim 3, characterized in that the subset of the neural network based on recurrent neurons is a recurrent network of the LSTM (“Long Short Term Memory”) or GRU (“Gated Recurrent Unit”) type (Following claims 1-3, Suski and Sawal both do not expressly discloses of LSTM or GRU types.
Patwardhan more expressly discloses of utilizing LSTM type neural networks (e.g., Patwardhan: ¶¶ 22, 25, and 40).
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 Patwardhan’s teachings altogether within Suski’s overall system, because it would lead to increased accuracy with the overall identification and classification).
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. US 2021/0326644 A1 to Suski in view of U.S. Patent Publication No. 2024/0169120 A1 to Sawal and further in view of U.S. Patent Publication No. US 2021/0398183 A1 to Jain et al. (referred to hereafter as “Jain”).
As to claim 5, Suski does not fully disclose of the method according to claim 3, characterized in that, for a given item of equipment, the statistical model comprises a triplet loss function to generate a vector closer to the vectors of items of equipment identical to said given item of equipment and further away from the vectors of items of equipment different from said given item of equipment (Following claims 1-3, Suski and Sawal both do not expressly discloses of triplet loss functions.
Jain more expressly discloses of triplet loss functions, used/implemented to determine similarities vs differences in data models (e.g., Jain: ¶¶ 92 and 128).
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 Jain’s teachings altogether within Suski’s overall system, because it provides a better analysis in terms of identifying and categorizing data).
As to claim 6, Suski does not fully disclose of the method according to claim 3, characterized in that, for a given item of equipment, the statistical model comprises a contrastive loss function to generate a vector closer to the vectors of items of equipment identical to said given item of equipment and further away from the vectors of items of equipment different from said given item of equipment (Following claims 1-3, Suski and Sawal both do not expressly discloses of contrastive loss functions.
Jain more expressly discloses of contrastive loss functions, used/implemented to determine similarities vs differences in data models (e.g., Jain: ¶¶ 92 and 128).
See the previously stated reasons for combining and incorporating Jain’s teachings within Suski’s overall system and teachings).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. US 2021/0326644 A1 to Suski in view of U.S. Patent Publication No. US 2021/0398183 A1 to Jain.
As to claim 9, Suski does not fully disclose of the method according to claim 1, characterized in that the identification data comprises at least one of the following data:
a user agent in a Hyper Text Transfer Protocol or a Quick User Datagram Internet Connections protocol,
Dynamic Host Configuration Protocol identifiers comprising hostname, vendor class, user class and vendor specific information,
service names of Multicast Domain Name System announcements, and
Universal Plug and Play protocol data comprising: manufacturer, familiar name, model, description, model number (Suski does not expressly disclose of these specific data elements.
Jain more expressly discloses of identifiable data including device types, vendor, brand, categories, HTTP, etc. (e.g., Jain: ¶¶ 11, 44, 58, and 116).
See the previously stated reasons for combining and incorporating Jain within Suski’s overall system and teachings).
Conclusion
THIS ACTION IS MADE FINAL. 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).
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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.
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/X.Y./Examiner, Art Unit 2455
/EMMANUEL L MOISE/Supervisory Patent Examiner, Art Unit 2455
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