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
1. The following Office action is in response to communications filed on 1/2/2026. Claims 1-33 are currently pending within this application.
Information Disclosure Statement
2. The information disclosure statement(s) (IDS) submitted on 1/19/2024 and 4/20/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Claim Informalities
3. Claims 8-18 and 23-27 are objected to because of the following informalities: dependent claims 8, 13, and 23 are further dependent upon future claims 17, 19, and 32 respectively. Claims 9-12, 14-18, and 24-27 are objected to for being dependent upon objected base claims 8, 13, 23. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claims 1-7, 13-22, and 28-33 are rejected under rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Independent claim 1 is directed towards a system, which is a recognized statutory category of invention.
Step 2A, Prong One: The above mentioned independent claim(s) recites the abstract ideas of mental processes which are concepts performed in the human mind (including an observation, evaluation, judgement, and opinion). For example, to “identify, on the digital image of the jet dispenser, an identified pattern of features present on the jet dispenser; compare the identified patter with a stored pattern, the stored pattern being stored in the memory; calculate a similarity between the identified pattern and the stored pattern” encompasses observing an image/data and performing an evaluation/making a determination regarding the contents of the image which may be practically performed in the human mind using observation, evaluation, judgement, and opinion which falls within the “mental process” grouping of abstract ideas.
Step 2A, Prong Two: The abstract ideas, as claimed, are not integrated into a practical application and thus do not provide an inventive concept. The above mentioned independent claim(s) recite additional elements of “a camera”, “a controller”, “a processor”, “a memory”, and “a jet dispenser” which are recited at a high level of generality and amount to no more than components that apply/execute the abstract ideas without limiting how they function and thus can be performed by any generic computer capable of applying the abstract ideas and are at best the equivalent of merely adding the words “apply it” to the judicial exception. Furthermore, to “acquire a digital image of a jet dispenser” and “provide an identifier value associated with the stored pattern” are mere data gathering and input/output activities recited at a high level of generality, thus are insignificant extra-solution activities.
Step 2B: As explained in Step 2A, Prong Two above, the independent claims recite additional elements of “a camera”, “a controller”, “a processor”, “a memory”, and “a jet dispenser” recited at a high level of generality such that they amount to no more than generic components to implement the abstract idea on a conventional computer, while “acquire a digital image of a jet dispenser” and “provide an identifier value associated with the stored pattern” are insignificant extra-solution data gathering and input/output activities which are well-understood, routine, and conventional activities that even when considered in combination represent instructions to apply an exception and insignificant extra-solution activity which cannot provide an inventive concept. Even when considered in combination, the additional elements represent mere instructions to apply the judicial exceptions and insignificant extra-solution activities which cannot provide an inventive concept. The claim does not point to a specific improvement in computers in their communication role or provides a specific improvement in the way computers operate (See MPEP 2016.05(g), MPEP 2106.05(d), and Berkheimer Memo).
Therefore, based on the above analysis in conjunction with the 2019 Revised Patent Subject Matter Eligibility Guidance, it is determined that the independent claim(s) are directed towards ineligible subject matter of an abstract idea without significantly more.
Dependent claims 2-7, 13-22, and 28-33 are also rejected for being directed towards the abstract idea(s) of mental processes as well as insignificant pre-solution data gathering and post-solution data input/output activities that are ineligible subject matter of an abstract idea without adding significantly more than the judicial exceptions present within the independent claims.
Claim Rejections – 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
5. Claims 1, 5-7, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Suzuki (US PGPub 2022/0080726) [hereafter Suzuki].
6. As to claim 1, Suzuki discloses a jet dispenser identification system (information processing system 200 shown in Figures 1-2), the jet dispenser identification system comprising a camera (second imaging unit 75) configured to acquire a digital image of a jet dispenser (print head unit 30 shown in Figures 2-4); and a controller (controller 100) having a memory (memory 103) and a processor (processing portion 220 realized by processor 102 and/or second image processing portion 77), the processor configured to: identify, on the digital image of the jet dispenser, an identified pattern of features (nozzle plate surface image information) present on the jet dispenser; compare the identified pattern with a stored pattern (nozzle plate surface image information of reference images used as training data), the stored pattern being stored in the memory; calculate a similarity between the identified pattern and the stored pattern (probability that maintenance is necessary); and provide an identifier value (necessity of maintenance of print head or recommendation for execution timing of maintenance) associated with the stored pattern (Paragraphs 0030-0032, 0037, 0039, 0044, 0047, 0053, 0061-0062, 0065, 0074, 0078-0079, 0087-0088, 0091-0094, 0103-0104, 0107-0112, 0122-0126, a printing device includes a camera that captures an image of a print head with head units including a plurality of nozzles for dispensing ink onto a recording medium and a controller including a processor and memory that implement the functionalities of an information processing system 200 and learning device 400 that are used to identify, on a digital image of the nozzle plate surface of the print head, patterns of features of foreign matter or droplets constituting defect factor information which are compared to stored reference images of print head patterns having various degrees of features of foreign matter or droplets present thereon used to train a neural network model where a similarity between the input captured image to the reference images enables the calculation of probability that maintenance is necessary and probability that maintenance is not necessary, where if the probability that maintenance is necessary is above a threshold, maintenance information and execution timing of the maintenance associated with the reference images is provided in order to enable the printer to perform the prescribed maintenance at the prescribed timing).
7. As to claim 5, Suzuki discloses the system is configured to be in wired communication with a jet dispenser configured to receive a fluid material therein (Paragraph 0094, the information processing system and learning device components are connected to the printing device having the print head via a network that is wired or wireless).
8. As to claim 6, Suzuki discloses the system is configured to be in wireless communication with a jet dispenser configured to receive a fluid material therein (Paragraph 0094, the information processing system and learning device components are connected to the printing device having the print head via a network that is wired or wireless).
9. As to claim 7, Suzuki discloses the jet dispenser identification system of claim 1 as explained above, and further discloses a dispensing system (printing device 1 with print head as shown in Figures 1 and 4) for dispensing a fluid material (ink) onto a substrate (medium), the dispensing system comprising: a jet dispenser (print head 31) configured to receive the fluid material therein, the jet dispenser having a jet cartridge (pressure chamber 331 with ink chamber 333) operably connected thereto, the jet cartridge being configured to receive the fluid material and having a nozzle (Nz) configured to discharge the fluid material (Paragraphs 0057-0060).
10. As to claim 19, Suzuki discloses the jet dispenser identification system of claim 1 as explained above, and further discloses a manufacturing system (printing device 1 with print head as shown in Figures 1 and 4) for dispensing a fluid material (ink) comprising: a jet dispenser (print head 31) configured to receive the fluid material therein, the jet dispenser having a dispenser component (pressure chamber 331) operably connected thereto, the dispenser component being configured to receive the fluid material from the dispenser (common ink chamber 333) and having a nozzle (Nz) configured to discharge the fluid material (Paragraphs 0057-0060).
16Claim 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 of this title, 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.
11. Claims 2-4 and 20-22 are rejected under 35 U.S.C 103 as being unpatentable over Suzuki (US PGPub 2022/0080726) [hereafter Suzuki] in view of Koushik (US PGPub 2020/0019819) [hereafter Koushik].
12. As to claims 2 and 20, it is noted that Suzuki fails to particularly disclose the identifier value comprises at least one of the following associated with the jet dispenser: a product name, a product type, a product serial number, a product number, and a product manufacturing lot number.
On the other hand, Koushik discloses providing an identifier value associated with a stored patter, wherein the identifier value comprises at least one of the following associated with server components: a product name, a product type, a product serial number, a product number, and a product manufacturing lot number (Paragraphs 0020, 0029, 0035-0039, 0042, 0044-0050, a part identification system uses a machine learning object detection module trained to compare extracted features in specific regions of interest of an input image to server component data information stored within a database in order to output a classifier 220 that comprises the various names and types of multiple imaged server components identified on an input image of a specific server model).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to include the technique of generating an identifier value comprising at least one of a product name, a product type, a product serial number, a product number, and a product manufacturing lot number as taught by Koushik with the system of Suzuki because the cited prior art references are directed towards systems that implement neural network models for extracting specific patterns/features of an input image in order to identify imaged components similar to reference patterns/features and because each of the claimed limitations are fully disclosed within the cited prior art references and would yield predictable results of enabling dynamic component look-up and identification capabilities to distinguish identified parts from various other imaged components.
13. As to claims 3 and 21, it is noted that Suzuki fails to particularly disclose the processor is configured to compare the identified pattern with a plurality of stored patterns and calculate the similarity between the identified pattern and each of the plurality of stored patterns, the processor being further configured to select one of the plurality of stored patterns, the selected one of the plurality of stored patterns being most similar to the identified pattern out of the plurality of stored patterns.
On the other hand, Koushik discloses the processor (processor 120 shown in Figure 1) is configured to compare the identified pattern (characteristics of proposed regions of input image) with a plurality of stored patterns (server component data 107 in database 106) and calculate the similarity between the identified pattern and each of the plurality of stored patterns, the processor being further configured to select one of the plurality of stored patterns, the selected one of the plurality of stored patterns being most similar (confident) to the identified pattern out of the plurality of stored patterns (Paragraphs 0020, 0029, 0035-0039, 0042, 0044-0050, a part identification system uses a machine learning object detection module trained to compare extracted features in specific regions of interest of an input image to server component data information stored within a database in order to output a classifier 220 that comprises the various names and types of multiple imaged server components identified on an input image of a specific server model, where the output classifier is associated with a confidence value attributed to the identification of each object for a server model being determined to be closest to matching the model being imaged in the input image).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to include the technique of comparing the identified pattern with a plurality of stored patterns and calculate the similarity between the identified pattern and each of the plurality of stored patterns, and selecting one of the plurality of stored patterns, the selected one of the plurality of stored patterns being most similar to the identified pattern out of the plurality of stored patterns as taught by Koushik with the system of Suzuki because the cited prior art references are directed towards systems that implement neural network models for extracting specific patterns/features of an input image in order to identify imaged components similar to reference patterns/features and because each of the claimed limitations are fully disclosed within the cited prior art references and would yield predictable results of improving the identification of foreign particles and droplets affecting the operation of the print head by deriving the most accurate defect factor information and corresponding maintenance information from the training/reference image that most closely matches the current image of the printhead being captured.
14. As to claims 4 and 22, it is noted that Suzuki fails to particularly disclose the processor is configured to implement a neural network to compare the identified pattern with a plurality of stored patterns and calculate the similarity between the identified pattern and each of the plurality of stored patterns, the processor being further configured to implement the neural network to select one of the plurality of stored patterns, the selected one of the plurality of stored patterns being most similar to the identified pattern out of the plurality of stored patterns.
On the other hand, Koushik discloses the processor (processor 120 shown in Figure 1) is configured to implement a neural network (machine learning object detection module 130) to compare the identified pattern (characteristics of proposed regions of input image) with a plurality of stored patterns (server component data 107 in database 106) and calculate the similarity between the identified pattern and each of the plurality of stored patterns, the processor being further configured to implement the neural network to select one of the plurality of stored patterns, the selected one of the plurality of stored patterns being most similar (confident) to the identified pattern out of the plurality of stored patterns (Paragraphs 0020, 0029, 0035-0039, 0042, 0044-0050, a part identification system uses a machine learning object detection module trained to compare extracted features in specific regions of interest of an input image to server component data information stored within a database in order to output a classifier 220 that comprises the various names and types of multiple imaged server components identified on an input image of a specific server model, where the output classifier is associated with a confidence value attributed to the identification of each object for a server model being determined to be closest to matching the model being imaged in the input image).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to include the technique of including a neural network for comparing the identified pattern with a plurality of stored patterns and calculate the similarity between the identified pattern and each of the plurality of stored patterns, and selecting one of the plurality of stored patterns, the selected one of the plurality of stored patterns being most similar to the identified pattern out of the plurality of stored patterns as taught by Koushik with the system of Suzuki because the cited prior art references are directed towards systems that implement neural network models for extracting specific patterns/features of an input image in order to identify imaged components similar to reference patterns/features and because each of the claimed limitations are fully disclosed within the cited prior art references and would yield predictable results of improving the identification of foreign particles and droplets affecting the operation of the print head by deriving the most accurate defect factor information and corresponding maintenance information from the training/reference image that most closely matches the current image of the printhead being captured.
Claim Objections
15. Claims 8-12 and 23-27 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.
Conclusion
16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL S OSINSKI whose telephone number is (571) 270-3949. The examiner can normally be reached on Monday - Friday, 10:00am - 6:00pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oneal Mistry can be reached on (313) 446-4912. The fax phone number for the organization where this application or proceeding is assigned is (571)-273-8300.
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MO
/MICHAEL S OSINSKI/Primary Examiner, Art Unit 2674
4/27/2026
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