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 .
This office action is in response to submission of application on 11/28/2023
Claims 1-19 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a printing condition input reception unit configured to…” in claim 1 and 18
“and a recommended value output unit configured to output…” in claim 1 and 18
“and a recommended candidate value evaluation unit configured to…” in claim 1
Paragraphs [0094], [0145], and [0144], provides sufficient structure.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 11 and 12 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 invention.
Claim 11 includes limitation, “and excludes a recommended candidate objective variable value set excluding top K recommended candidate objective variable value sets for each of which a high recommendation degree is obtained,” This limitation is indefinite because the double negative is grammatically ambiguous leading to multiple contradictory interpretations, and it relies on a “high recommendation degree” which is a term of degree without a relative basis for comparison.
Claim 12 includes limitations, “variable value sets is high or low on a basis of the quality data…” This limitation is indefinite because it’s unclear if these are qualitative or quantitative. In either case it would be unclear as there is no objective boundary for making the determination of high or low.
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.
(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.
The following references are being used:
Aoki et al., (INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM THEREOF, US 20220138520 A1, herein Aoki)
Claim(s) 1, 18, and 19 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Aoki.
Regarding claim 1,
Aoki teaches,
A printing system that includes a printing apparatus provided with a (Aoki, FIG. 1, “printing system 10” and “inkjet device 12”)
conveyance mechanism configured to convey a print medium, (Aoki, paragraph [0025], “The transport unit 17 is configured to transport the fabric 99. The transport unit 17 is, for example, a belt, a roller, etc.”, note: Aoki identifies a physical component, a belt or roller, whose stated purpose is moving the fabric through the machine. Aoki’s fabric maps to the print medium because it is the material the machine prints on. A belt or roller that moves the material being printed on is what the claim calls a conveyance mechanism conveying a print medium)
a conveyance controller configured to control a conveyance speed at which the conveyance mechanism conveys the print medium, (Aoki, paragraph [0047], “The drawing processing parameter includes, for example, a transport speed of the fabric 99 by the transport unit 17,” paragraph [0045], “The information processing device 30 may also serve as a control device for controlling the printing system 10”, paragraph [0071], “The control unit 34 controls the printing system 10, for example, by transmitting the printing parameter to the printing system 10” note: The speed at which Aoki’s transport unit moves the fabric is not built in and fixed, it is a parameter, meaning a value the system sets. Aoki’s control device sends those parameter values to the printing system, and the printing system then operates accordingly. For a machine to receive commanded speed and run at it, something inside it must turn the command into motor behavior. That structure is the claims conveyance controller.)
a printing unit configured to perform printing by ejecting ink onto the print medium being conveyed by the conveyance mechanism, (Aoki, paragraph [0038], “The printing system 10 discharges the ink from the head 18 onto the fabric 99 transported by the transport unit 17 in step S14 to draw an image on the fabric 99.”, note: the head is the part that sprays ink, the fabric is what receives it, and an image is then formed. That is the printing by ejecting ink onto a print medium. The fabric is being moved by the transport unit at the moment the ink is applied)
a drying mechanism configured to dry the print medium after printing by the printing unit, (Aoki, paragraph [0031], “The post-processing drying unit 24 dries the fabric 99 to which the ink has been discharged, for example, by heating the fabric 99. The post-processing drying unit 24 includes, for example, a heater.”, and FIG. 2, S14, “Draw Image on Fabric” and S15, “Supply steam to fabric”, note: Aoki’s post-processing drying unit is a heater, so it is structurally a drying mechanism.)
and a drying controller configured to control a drying temperature at which the drying mechanism dries the print medium, (Aoki, paragraph [0048], “The post-processing parameter includes, for example, a drying time of the post-processing drying unit 24, a drying temperature of the post-processing drying unit 24”, paragraph [0045], “The information processing device 30 may also serve as a control device for controlling the printing system 10” and paragraph [0071], “The control unit 34 controls the printing system 10, for example, by transmitting the printing parameter to the printing system 10” note: Aoki treats the drying temperature the same way it treats the transport speed, as a value that get specified rather than a fixed characteristic of the heater. Since the system transmits that temperature value to the printing system to control it, the heater must have structure that hold it at the commanded temperature. A heater that runs at whatever temperature it is told to run at necessarily has a temperature-regulating element, and that element maps to the drying controller)
the printing system comprising:
a printing condition input reception unit configured to receive an input of a printing condition; (Aoki, paragraph [0055], “The acquisition unit 33 acquires data indicating information related to the printing process, for example.” and paragraph [0126], “the acquisition unit 33 configured to acquire the pre-printing image data and the ink data” note: Aoki’s acquisition unit is the way through which information about the job enters the system. What comes through it is a scan of the particular fabric to be printed and the identity of the ink loaded in the machine.)
and a recommended value output unit configured to output a plurality of recommended values for at least one of a conveyance speed and a drying temperature as a print parameter on a basis of an input printing condition that is a printing condition received by the printing condition input reception unit, (Aoki, paragraph [0089], “In step S24, the control unit 34 outputs the recommended parameter through the output unit 32. When the recommended parameter is output through the output unit 32, the user can grasp the recommended parameter recommended for obtaining predetermined image quality.”, paragraph [0087], “the control unit 34 derives, in step S22 and step S23, the recommended parameter for at least one of the pre-processing device 11 and the post-processing device 13 from the pre-printing image data and the ink data”, paragraph [0046], “a drying temperature of the pre-processing drying unit 16, etc.”, paragraph [0048], “a drying temperature of the post-processing drying unit” and paragraph [0080], “The second data 49 may indicate a correspondence relationship between the fabric data and ink data, and the recommended parameter for the inkjet device 12”, note: Aoki displays recommended settings to the operator and computes them from the fabric scan and ink type that came in at input. Aoki has two drying units and recommends a temperature for each, giving a plurality of drying-temperature values. Conveyance speed is also reached, since Aoki extends its recommendations to the inkjet device and transport speed is one of that device’s parameters.)
wherein the recommended value output unit includes a recommended candidate value search model learned by machine learning, the recommended candidate value search model being configured to obtain a plurality of recommended candidate values that are candidates for the plurality of recommended values on a basis of the input printing condition, (Aoki, paragraph [0118], “Examples of learning techniques include, for example, deep learning. Such learning results in a learned model that outputs the printing parameter recommended for obtaining predetermined image quality when the fabric data and the ink data is input.”, and paragraph [0122], “from the large amount of data, a population, i.e., unit space, is created in which the image quality of the post-printing image data is greater than or equal to predetermined image quality. Then, the Mahalanobis distance to the unit space is calculated.”, note: Aoki’s model is built by deep learning on past job records and produces recommended setting from the current job’s inputs. To reach its answer it scores combinations of setting by how far each sits from a reference group of jobs that printed well, so multiple combinations are under consideration, functioning as candidates.)
and a recommended candidate value evaluation unit configured to select output targets as the plurality of recommended values from the plurality of recommended candidate values by evaluating each of the plurality of recommended candidate values. (Aoki, paragraph [0122], “The greater the Mahalanobis distance, the lower the image quality. Next, the threshold value of the Mahalanobis distance with respect to the unit space is determined” and paragraph [0089], “the control unit 34 outputs the recommended parameter through the output unit 32”, note: Each candidate carries a score, with a higher score meaning worse expected print quality, and Aoki sets a cutoff against it. Applying a cutoff evaluates every candidate and splits them into those that pass and those that do not, the ones that pass are shown to the operator.)
Claim 18 recites a subset of the limitations of claim 1. Claim 18 recites, “A printing system that includes a printing apparatus provided with a conveyance mechanism configured to convey a print medium, a conveyance controller configured to control a conveyance speed at which the conveyance mechanism conveys the print medium, a printing unit configured to perform printing by ejecting ink onto the print medium being conveyed by the conveyance mechanism, a drying mechanism configured to dry the print medium after printing by the printing unit, and a drying controller configured to control a drying temperature at which the drying mechanism dries the print medium, the printing system comprising: a printing condition input reception unit configured to receive an input of a printing condition; and a recommended value output unit configured to output a plurality of recommended values for at least one of a conveyance speed and a drying temperature as a print parameter on a basis of a printing condition received by the printing condition input reception unit.” Each of the limitations is recited in claim 1 and is addressed in the rejection of claim 1. Therefore, claim 18 is rejected for the same reasons as claim 1.
Claim 19 is a method claim that corresponds to system claim 1 respectively. Otherwise, they are not patentably distinguishable. Therefore, claim 19 is rejected for the same reasons as claim 1, respectively.
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.
The following references are being used:
Aoki et al., (INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM THEREOF, US 20220138520 A1, herein Aoki)
Imai et al., (Image Forming Apparatus, Control Method Of Image Forming Apparatus, And Storage Medium, US 10445041 B2, herein Imai)
Mori et al., (Printing System And Print Setting Proposal Method, US 8705125 B2, herein Mori)
Vankouwenberg et al., (System and Method for Optimizing a Printer Setup, US 11650766 B2, herein Vankouwenberg)
Selensky et al., (Use Of Densitometer For Adaptive Control Of Printer Heater Output To Optimize Drying Time For Different Print Media, US 5784090 A, herein Selensky)
Lu et al., (A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm, herein Lu)
Bleiholder et al., (Conflict Handling Strategies in an Integrated Information System, herein Bleiholder)
Claim(s) 2-7, 12, 14, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aoki in view of Imai.
Regarding claim 2,
Imai teaches,
The printing system according to claim 1, wherein the recommended value output unit further outputs a recommendation degree for each of the plurality of recommended values. (Imai, claim 12, “displays a print setting value to which an automatic setting accuracy for the print setting value is added as a candidate that can be changed as a print setting value of the print job… which holds a plurality of print setting values and an automatic setting accuracy for each of the plurality of print setting values” note: The limitation needs two things, a number expressing recommendation strength, and one such number per recommended value. “Holds a plurality of print setting values and an automatic setting accuracy for each” states one per value requirement directly. That the figure measures recommendation strength comes from the Imai’s claim 6, defining it as “reliability indicating whether or not the changed print setting value is appropriate.” And “displays a print setting value to which an automatic setting accuracy… is added” establishes the figure is output alongside the value, not kept internally. FIG. 14 gives the numbers: one sided 80%, double-sided long-edge 15%, double-sided short-edge 5%.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to combine the teaching of Aoki and Imai because both supply an operator with recommended print setting values, and Imai teaches displaying a reliability figure alongside each recommended value so that the user can see how reliable the automatically set value is.
Regarding claim 3,
Imai teaches,
wherein the recommended candidate value search model includes aggregated print result data including information on an appearance frequency for each combination of an explanatory variable value set and an objective variable value set, (Imai, “it is possible to calculate an automatic setting accuracy by collecting log information in the case where some print job is determined to be an estimation request sheet and by counting the frequency of appearance of each setting value of the log information,” note: the limitation needs a stored count of how many past jobs used each condition and parameter pairing. Imai does this in two steps, it gathers the past jobs sharing one condition (document type), then counts how often each parameter value was used within that group, of 100 logs, 8 one-sided, 15 double-sided long-edge, 5 short-edge. Those counts are held in data table inside the rule store the recommendation comes from, which is what “the model includes the data” requires.)
and the recommended value output unit outputs a plurality of recommended objective variable value sets including the plurality of recommended values on a basis of the aggregated print result data. (Imai, FIG. 10B, “symbol 1013 refers to a pull-down menu indicating that it is possible to select not only the rule of “estimation request sheet”, which is an automatically set candidate, but also the rules of “specification sheet” and “contract sheet”, which are other candidates” note: the limitation needs several alternative sets of parameter values output, drawn from the accumulated data. Imai offers three candidates at once, and each is a set rather than one value. The menu is built from the sort of rules by their accuracy figures, which are the frequency counts from the first limitation.)
Aoki teaches,
the explanatory variable value set being a combination of a plurality of explanatory variable values, (Aoki, paragraph [0079], “when the fabric data and the ink data are input,” note: The limitation requires the condition side to be several values taken together, not one. Aoki’s model receives two distinct items, fabric data and ink data, and Aoki adds device data and ambient temperature and humidity among what acquisition unit gathers.)
the plurality of explanatory variable values each being a value obtained on a basis of a value of a condition item that is an item related to a printing condition or being the value of the condition item, (Aoki, paragraph [0077], “outputs fabric data indicating a feature value of the fabric 99 when the pre-printing image data is input”, note: The limitation allows each condition value to be either a condition item’s value directly or a value derived from one. Aoki’s fabric data is derived, the first model computes it from the pre-printing image data. Aoki’s ink data, identifying the ink type, is the other option, a condition item’s value used as-is.)
the objective variable value set being a value of one print parameter or a combination of values of a plurality of print parameters, (Aoki, paragraph [0048], “The post-processing parameter includes, for example, a drying time of the post-processing drying unit 24, a drying temperature of the post-processing drying unit 24, a temperature of the steam supplied by the steam unit” note: the limitation allows the parameter side to be one value or several together. Aoki’s post-processing parameter is a group of several, drying time, drying temperature, steam temperature, which meets the second option.)
Regarding claim 4,
Imai teaches,
wherein the recommended value output unit further outputs a recommendation degree for each of the plurality of recommended objective variable value sets. (Imai, FIG. 10B, “Further, in each item of the pull-down menu, an automatic setting accuracy is also displayed, not only the name of the rule” note: Each item in Imai’s menu is a rule, and a rule is a full set of parameter values. The quote says an accuracy figure is displayed for every item, so each set is output with its own figure. “Not only the name of the rule” shows the figure is displayed to the user rather than kept internally.)
Regarding claim 5,
Aoki teaches,
wherein the printing apparatus further includes a first drying mechanism and a second drying mechanism as the drying mechanism, (Aoki, paragraph [0031], “The post-processing drying unit 24 may be the same drying unit as the pre-processing drying unit 16” note: The quote names two drying units and says they may be the same one, which means that absent that option they are two separate units.)
and each of the plurality of recommended objective variable value sets includes a recommended value of a first drying temperature at which the first drying mechanism dries the print medium and a recommended value of a second drying temperature at which the second drying mechanism dries the print medium. (Aoki, paragraph [0046], “a drying temperature of the pre-processing drying unit 16,” and paragraph [0048], “a drying temperature of the post-processing drying unit 24,” note: each quote shows a drying temperature listed as a settable parameter for one of the two units, one among the pre-processing parameters, one among the post-processing parameters. Aoki provides that the model “outputs the recommended parameter for at least one of the pre-processing device and the post-processing device” so recommending for both devices means a recommended temperature is produced for each dryer.)
Regarding claim 6,
Imai teaches,
wherein the recommended candidate value search model obtains a plurality of recommended candidate objective variable value sets that are candidates for the plurality of recommended objective variable value sets on a basis of the input printing condition and the aggregated print result data, (Imai, FIG. 3, “The automatic setting value set acquisition unit 309 acquires “Job setting value set” corresponding to “Document type” based on the rule saved in the automatic setting rule saving unit”, note: The quote shows both required inputs feeding one retrieval. “Corresponding to ‘Document type’” is the incoming job’s condition, and “based on the rule saved in the automatic setting rule saving unit” is the accumulated data, that store holding the frequency counts. What is retrieved is a job setting value set, a set of parameter values offered as a candidate.)
and the recommended candidate value evaluation unit selects output targets as the plurality of recommended objective variable value sets from the plurality of recommended candidate objective variable value sets by evaluating each of the plurality of recommended candidate objective variable value sets. (Imai, FIG. 11B, “the job control unit 705 sorts the automatic setting rules associated with the print job data according to the automatic setting accuracy” note: Sorting the rules by accuracy requires a figure for each rule, so every candidate is evaluated. The ordering that results is what step S1113 uses to build the displayed candidate list, so the evaluation determines what is output)
Regarding claim 7,
Imai teaches,
wherein the recommended candidate value search model selects an explanatory variable value set similar to an explanatory variable value set corresponding to the input printing condition as a similar explanatory variable value set, (Imai, FIG. 3, “A document type specification unit 308 specifies a document type by using the feature amount extracted by the feature amount extraction unit 307” note: the feature amount is the incoming job’s condition values, extracted by unit 307. Unit 308 uses those values to determine which stored document type they correspond to. Identifying the stored condition group that the incoming values answer to is the selection of a similar explanatory variable value set.)
and obtains the plurality of recommended candidate objective variable value sets in consideration of an appearance frequency of a combination of the similar explanatory variable value set and each objective variable value set. (Imai, FIG. 11B, “the job control unit 705 sorts the automatic setting rules associated with the print job data according to the automatic setting accuracy (S1110)” note: the accuracy figure sorted on is the frequency count established, computed from the logs sharing the matched document type. Ordering the candidate rules by that figure before the candidate list is generated at step S1113 means the candidates are obtained in consideration of that frequency.)
Regarding claim 12,
Aoki teaches,
wherein the recommended candidate value evaluation unit includes a print quality determination model learned by machine learning, the print quality determination model being configured to output quality data representing print quality on a basis of a combination of the explanatory variable value set and the objective variable value set, (Aoki, paragraph [0122], “The greater the Mahalanobis distance, the lower the image quality” note: The quote ties the distance the model produces to image quality, a larger value means a worse print. So the figure is quality data rather than an arbitrary number. What the figure is computed from explained by, “the multivariate analysis on the fabric data, the ink data, the printing parameter, and the image quality parameter are performed.” The fabric and ink data are the condition values and the printing parameter is the parameter values, so the figure comes from a combination of the two sets.)
determines whether print quality for each of the plurality of recommended candidate objective variable value sets is high or low on a basis of the quality data outputted by inputting a combination of an explanatory variable value set corresponding to the input printing condition and each of the plurality of recommended candidate objective variable value sets to the print quality determination model, (Aoki, paragraph [0122], “the threshold value of the Mahalanobis distance with respect to the unit space is determined” note: setting a threshold on the distance divides candidates into those above it and those below. Because the distance tracks image quality per the preceding quote, the threshold sorts candidates into high and low predicted quality.)
and excludes a recommended candidate objective variable value set for which print quality is determined to be low from selection targets as the output targets. (Aoki, paragraph [0122], “a population, i.e., unit space, is created in which the image quality of the post-printing image data is greater than or equal to predetermined image quality” note: the reference group is built only from jobs that cleared a quality standard. A candidate far from that group is one with low predicted quality, and it falls outside the threshold, so it is not among the combinations identified as the recommended parameter that step S24 outputs.)
Regarding claim 14,
Imai teaches,
wherein the printing apparatus further includes a log output unit configured to output, after completion of printing, a print result log including a value of a condition item that is a source of the explanatory variable value set and a value of a print parameter that is a source of the objective variable value set, (Imai, “A job execution log saving unit 712 saves log information at the time of execution of printing in the case where the printing device 207 performs printing. This log information is the information the same as the log information received by the data reception unit 701 and saved in the job data saving unit 710 and includes the print setting value settled by execution of printing, the PDL data, the document type and so on” note: The quote gives the timing, the log is made when the print engine performs the job, and the contents. “The PDL data, the document type” is the condition side, the document type being what the condition values were derived from. “The print setting value settles by execution of printing” is the parameter side, and “settled by execution” shows these are the values actually used rather than the ones originally requested.)
and the aggregated print result data is generated by using a combination of the explanatory variable value set and the objective variable value set as teacher data on a basis of the print result log. (Imai, “It is also possible to manually register the automatic setting rule by a user using a rule registration I/F, not shown schematically, or to automatically register by causing the image forming apparatus 102 to learn job execution logs in the past” note: the rule store is built by having the apparatus learn from the past execution logs. Those logs pair document type with print setting value, so the pairs are what the learning consumes, which is what the claim means by using the combination as teacher data)
Regarding claim 17,
Aoki teaches,
comprising the printing apparatus that is only one printing apparatus (Aoki, paragraph [0015],
“a printing system 10 is constituted by a pre-processing device 11, an inkjet device 12, and a post-processing device” note: the quote lists every device in Aoki’s system. Only inkjet device ejects ink, and the claim defines the printing apparatus as one having a printing unit that ejects ink. Devices 11 and 13 apply pre-processing liquid and perform post-treatment, so neither qualifies. The count of printing apparatuses is one.)
Aoki does not teach,
wherein the printing apparatus includes the printing condition input reception unit and the
recommended value output unit.
Imai teaches,
wherein the printing apparatus includes the printing condition input reception unit and the
recommended value output unit. (Imai, FIG. 2, “The CPU 201 implements a function configuration of the image forming apparatus 102 and processing of flowcharts, to be described later, by reading programs stored in the auxiliary storage device 203 onto the RAM 202 and by executing the programs” note: the quote shows every functional unit running as a program on the image forming apparatus’s own processor and memory, including the rule acquisition and the display of recommended values. The input side sits in the apparatus as well, “An operation unit 204 is, for example, a liquid crystal display, a touch panel, and so on, and displays the state of the image forming apparatus 102, an error message, and so on, and receives an input relating to setting of a desired print job,” Both recite units are therefore inside the printing apparatus.)
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aoki in view of Imai and in further view of Mori.
Regarding claim 10,
Mori teaches,
The printing system according to claim 6, wherein the recommended candidate value evaluation unit determines whether a value of a print parameter included in each of the plurality of recommended candidate objective variable value sets satisfies a predetermined threshold condition, (Mori, “only in the case of the predetermined number or more” note: a measured value is compared against a number fixed in advance, and that comparison dictates what happens to the candidate)
and excludes a recommended candidate objective variable value set including a value of a print parameter that does not satisfy the predetermined threshold condition from selection targets as the output targets. (Mori, “eliminates a candidate of N-up from the recommendation setting candidates” note: the failing candidate is removed from the set that would otherwise be presented to the operator)
It would be obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to combine the teaching of Aoki, Imai, and Mori because all are printing systems that produce candidate print setting values for an operator, and Mori teaches testing a value in each candidate against a preset threshold and dropping the candidates that fail, so that candidate settings that would produce an unacceptable result are not offered.
Regarding claim 11,
Imai teaches,
wherein the recommended candidate value evaluation unit calculates a recommendation degree for each of the plurality of recommended candidate objective variable value sets on a basis of the aggregated print result data, (Imai, FIG. 11A, “the automatic setting accuracy calculation unit 713 acquires one of the rules (S1106) and further calculates an automatic setting accuracy from a comparison with the document type specified” note: The accuracy is computed one rule at a time. Because it sits inside the loop, every candidate receives its own figure, derived from the log counts.)
Imai does not teach,
and excludes a recommended candidate objective variable value set excluding top K recommended candidate objective variable value sets for each of which a high recommendation degree is obtained, where K is an integer, from selection targets as the output targets.
Mori teaches,
and excludes a recommended candidate objective variable value set excluding top K recommended candidate objective variable value sets for each of which a high recommendation degree is obtained, where K is an integer, from selection targets as the output targets. (Mori, FIG. 5, “the server 12 extracts top three items of the histogram, which are served as recommendation setting candidates” note: the histogram holds every candidate with its count, and only the three highest are extracted. “Which are served as recommendation setting candidates” means only those three become candidates, so everything below the third is excluded.)
Claim(s) 9 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aoki in view of Imai and in further view of Vankouwenberg.
Regarding claim 9,
Vankouwenberg teaches,
wherein a plurality of explanatory variable value sets based on the aggregated print result data are classified into a plurality of clusters, (Vankouwenberg, FIG. 2, “the generated recommended printer settings 300 (along with related information, e.g., whether or not the recommended setting were in fact selected or used for the job; the user input settings 120 for the job; the classification determined for the job by the job classification engine 500, etc.) from run print jobs can be supplied as feed-back to the historical knowledge DB 500”, note: Each completed job is stored in the historical database together with the classification assigned to it. Because every stored record carries a class, the accumulated records are divided amount the classification engine’s classes.)
and the recommended candidate value search model obtains a cluster to which an explanatory variable value set corresponding to the input printing condition belongs from the plurality of clusters, and selects an explanatory variable value set belonging to the cluster obtained, as the similar explanatory variable value set. (Vankouwenberg, FIG. 2, “the classification engine 600 analyze the content 104 of the submitted print job 100, and based thereon, using a suitable classification algorithm or the like designates the print job 100 as having a determined classification” and “the metadata or other like information from the knowledge DB 500 may be used to look for historical print jobs that are substantially similar to or the same as the one that the user is currently trying to run” note: the incoming job is assigned to one of the classes by analyzing its content, and the stored records resembling it are then retrieved to inform the recommendation.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to combine the teaching of Aoki, Imai, and Vankouwenberg because all are printing systems that recommend print setting values from records of past print jobs, and Vankouwenberg teaches storing each past job with an assigned classification and drawing on the stored jobs sharing the incoming job’s classification, so that the recommendation is based on past jobs of the same classification rather than on the stored records as a whole.
Regarding claim 15,
Vankouwenberg teaches,
comprising a plurality of the printing apparatuses and a management server, (Vankouwenberg, claim 20, “the historical data is related to prior print jobs run on a fleet of printers including the printer and indicates printer settings used when running the prior print jobs thereon” note: the stored records come from multiple printers rather than one, and are held in a database serving them.)
wherein the print result log is transmitted from the plurality of printing apparatuses to the management server, (Vankouwenberg, “the DB 500 stores and maintains historical printer setting knowledge supplied by and/or gleaned from operation of the printer 10 and/or the printer server 12 themselves” note: the records reach the central database from the operation of the printers themselves, which is the transmission of job records from the apparatuses up to the server.)
and each of the plurality of printing apparatuses includes the printing condition input reception unit, and the recommended value output unit including the recommended candidate value search model transmitted from the management server. (Vankouwenberg, claim 6, “the apparatus is incorporated in at least of one: the printer itself”, note: the recommendation engine and the units receiving the job settings reside in the printer, so each printer in the fleet holds both recited units)
Vankouwenberg does not teach,
the management server includes a model construction unit configured to construct the recommended candidate value search model on a basis of the print result log transmitted from each of the plurality of printing apparatuses,
the recommended candidate value search model constructed by the model construction unit is transmitted from the management server to the plurality of printing apparatuses,
Aoki teaches,
the management server includes a model construction unit configured to construct the recommended candidate value search model on a basis of the print result log transmitted from each of the plurality of printing apparatuses, (Aoki, paragraph [0081], “Such a learned model can be generated, for example, by a server 50 calculating based on the data set 46”, note: the model-building calculation occurs at the server, and what it calculates rom is the job record transmitted to it)
the recommended candidate value search model constructed by the model construction unit is transmitted from the management server to the plurality of printing apparatuses, (Aoki, paragraph [0104], “The server 50 may transmit the generated derivation data 47 to the information processing device 30. In this case, the derivation data 47 stored in the storage unit 35 of the information processing device 30 can be updated” note: what travels back down is the generated derivation data, the model itself, not a result computed from it. The second sentence confirms this by showing it replacing the copy already held in the device’s storage.)
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aoki in view of Imai and in further view of Lu.
Regarding claim 8,
Lu teaches,
wherein the recommended candidate value search model calculates a distance between a position vector representing an explanatory variable value set corresponding to the input printing condition and a position vector of each of a plurality of explanatory variable value sets based on the aggregated print result data, (Lu, page 6, section 3.1, “where similarities between ua and any other users are calculated by similarity measurement metric” note: each stored record is represented as a vector of its values, and a metric is computed between the target record’s vector and every other stored record’s vector.)
and selects, as the similar explanatory variable value set, top N explanatory variable value sets for each of which a short distance is obtained, where N is an integer. (Lu, page 6, section 3.1, “At the Neighbour Selection stage, k nearest candidates are selected from the target user ua’s candidate list Sa,” note: after the similarity to each stored record is computed, the k records with the highest similarity are selected as the set the recommendation is built from. K is the integer count.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to combine the teaching of Aoki, Imai, and Lu because all are directed to producing a recommended value for a user based on data from past activity, and Lu teaches computing similarity between the target record and each stored record and selecting the nearest k, so that the recommendation draws on the individual stores records closest to the target rather than on a group of records treated alike. Selecting the closest records rather than an entire group excludes records that are less similar to the target job, so the recommendation is drawn from data that better reflects the condition of the job at hand.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aoki in view of Imai and in further view of Selensky.
Regarding claim 13,
Aoki teaches,
wherein the explanatory variable value set includes a value related to the print medium (Aoki, paragraph [0077], “The fabric data is data indicating the feature value of the fabric 99, such as a thickness, density, and surface roughness of yarns constituting the fabric 99” note: thickness, density, and surface roughness are physical properties of the fabric itself, and the fabric is the print medium. This data is supplied to the model as input, so it is a value related to the print medium within the explanatory variable set.
Aoki does not teach,
and a value representing an amount of ink ejected by the printing unit.
Selensky teaches,
and a value representing an amount of ink ejected by the printing unit. (Selensky, claim 1, “counting the dots in a plurality of overlapping grid portions of said plot file to thereby locate a grid portion having a respective maximum density value… for determining a respective optimal heater output value based upon the said maximum density value, upon said selected print mode, and upon said selected print medium” note: each dot counted is one ink drop, so the count measures how much ink will be put down. It also shows that value used as an input alongside the print medium to determine a heater output, the same pairing of ink-amount and medium values.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to combine the teaching of Aoki, Imai, and Selensky because all are inkjet printing systems in which a drying parameter is set according to the characteristics of the job, and Selensky teaches counting the dots in the data to be printed and using that count together with the print medium to determine the dryer output. Setting the dryer output from the actual ink laid down rather than from the medium alone accounts for the fact that a lightly inked page and a heavily inked page on the same medium require different drying.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aoki in view of Imai and in further view of Bleiholder.
Regarding claim 16,
Bleiholder teaches,
wherein when there are a plurality of print result logs based on a same job, a combination of the explanatory variable value set and the objective variable value set based on a latest print result log is used as the teacher data. (Bleiholder, page 5, section 2.1, “Keep up to date. This strategy uses the most recent value and requires some additional timestamp information about the recency” and Table 1, “Most Recent Returns the most recent value. Recency is evaluated with the help of another attribute or other metadata about tuples/values” note: the limitation applies when more than one stored record describes the same job. Bleiholder addresses that same situation, where several stored records describe one entity and their values conflict. The strategy resolves it by taking the value from the most recent record, with recency determined from timestamp information. Applying that rule to duplicate print results logs means the values from the newest log are the ones used as the teacher data.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the
claimed invention to combine the teaching of Aoki, Imai, and Bleiholder because Aoki and Imai build their recommendation data from stored records and completed print jobs and Bleiholder teaches taking the value from the most recent record, identified by its timestamp. The benefit is that a single consistent value is used where the records disagree, rather than leaving it undetermined which of several conflicting records supplied the training data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UYEN-NHU PHAM TRAN whose telephone number is (571)272-1559. The examiner can normally be reached Monday - Friday 7:30-5.
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, Miranda Huang can be reached at (571) 270-7092. 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.
/U.P.T./ Examiner, Art Unit 2124
/MIRANDA M HUANG/ Supervisory Patent Examiner, Art Unit 2124
Prosecution Insights