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 action is responsive to the Application filed on August 11, 2023 and Response to Requirement for Restriction/Election filed on April 8, 2026. Claims 1-6 are elected. Claim 7 is non-elected and therefore withdrawn. Therefore, Claims 1-6 are pending in the case. Claim 1 is the independent claim.
This action is non-final.
Election/Restrictions
Applicant’s election without traverse of claims 1-6 in the reply filed on April 9, 2026 is acknowledged.
Claim 7 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on April 9, 2026.
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.
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental steps) without significantly more. This judicial exception is not integrated into a practical application because any additional elements amount to implementing the abstract idea on a generic computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding independent claim 1, and relying on the evaluation flowchart in MPEP 2106:
Step 1 (Is the claim to a process, machine, manufacture, or composition of matter?): Yes. Claim 1 is an apparatus (machine).
Step 2a Prong One (Does the claim recite an abstract idea?): Yes. Claim 1 recites:
acquire evaluation input and odor information corresponding to the evaluation (a mental process of observation and evaluation);
determine odor information of a determination target for the each index (a mental process of observation and evaluation).
Under the broadest reasonable interpretation, these steps may be performed mentally, using mental observation and mental determination, including by a human using a physical aid such as pen and paper, including a human mentally performing observations and mentally performing mathematical calculations, and therefore correspond to the Mental Processes grouping.
Step 2a Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?): No. Claim 1 additionally recites:
the apparatus is an information processing apparatus, comprising: a memory configured to store a program; and a processor configured to execute the program so as to perform the recited processes/steps (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
generate a template that allows input of evaluation for each index according to a specified number of index, where the evaluation input is to the template (insignificant extra-solution activity as discussed in MPEP 2106.05(g));
the odor information is a detection result detected by an odor sensor (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
perform machine learning using a set of the evaluation and the odor information as teaching data (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
the determining of the odor information is based on a result of the machine learning (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components.
Step 2b (Does the claim recite additional elements that amount to siqnificantly more than the judicial exception): No. Relying on the same analysis as Step 2a Prong Two (see MPEP 2106.05.I.A: Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:…Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP 2106.05(f));…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception...; Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g);…)), claims 1 and 11 do not recite any additional elements that amount to significantly more than the abstract idea. As discussed above, Claim 1 additionally recites:
the apparatus is an information processing apparatus, comprising: a memory configured to store a program; and a processor configured to execute the program so as to perform the recited processes/steps (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
generate a template that allows input of evaluation for each index according to a specified number of index, where the evaluation input is to the template (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering);
the odor information is a detection result detected by an odor sensor (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
perform machine learning using a set of the evaluation and the odor information as teaching data (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f));
the determining of the odor information is based on a result of the machine learning (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea.
Regarding dependent claim 2:
Step 2a Prong One: incorporates the rejection of claim 1. The claim additionally recite wherein: the index corresponds to word or phrase representing human sensation (a mental process or observation or determination, i.e. when read in context with the relevant determination step of claim 1, i.e. the determination of odor information for each index, where the index corresponds to a word or phrase representing human sensation; i.e. the mental determination by a human regarding a word or phrase representing human sensation with respect to an odor).
Step 2a Prong Two: the claim recites no additional limitations.
Step 2b: the claim recites no additional limitations.
Regarding dependent claim 3:
Step 2a Prong One: incorporates the rejection of claim 2.
Step 2a Prong Two: the claim additionally recites wherein the template is configured in such a manner that the evaluation is input by input or selection of a value, and the value corresponds to specified number of evaluation stage (insignificant extra-solution activity as discussed in MPEP 2106.05(g)).
Step 2b: the claim additionally recites wherein the template is configured in such a manner that the evaluation is input by input or selection of a value, and the value corresponds to specified number of evaluation stage (insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering).
Regarding dependent claim 4:
Step 2a Prong One: incorporates the rejection of claim 2.
Step 2a Prong Two: the claim additionally recites wherein: the processor is configured to execute the program so as to perform the machine learning based on a selected learning algorithm among two or more learning algorithms (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Step 2b: the claim additionally recites wherein: the processor is configured to execute the program so as to perform the machine learning based on a selected learning algorithm among two or more learning algorithms (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)).
Regarding dependent claim 5:
Step 2a Prong One: incorporates the rejection of claim 2.
Step 2a Prong Two: the claim additionally recites wherein: the processor is configured to execute the program so as to generate a graph based on a determination result by the processor and present the generated graph in a visible manner (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f), with respect to performing the recited steps using a processor, and insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to generating and presenting a graph in a visible manner).
Step 2b: the claim additionally recites wherein: the processor is configured to execute the program so as to generate a graph based on a determination result by the processor and present the generated graph in a visible manner (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f), with respect to performing the recited steps using a processor, and insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting, with respect to generating and presenting a graph in a visible manner).
Regarding dependent claim 6:
Step 2a Prong One: incorporates the rejection of claim 5.
Step 2a Prong Two: the claim additionally recites wherein: the processor is configured to execute the program so as to present respective determination result of two or more determination targets by the processor in a comparable manner (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f), with respect to performing the recited steps using a processor, and insignificant extra-solution activity as discussed in MPEP 2106.05(g), with respect to generating and presenting respective determination results of determination targets in a comparable manner).
Step 2b: the claim additionally recites wherein: the processor is configured to execute the program so as to present respective determination result of two or more determination targets by the processor in a comparable manner (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f), with respect to performing the recited steps using a processor, and insignificant extra-solution activity as discussed in MPEP 2106.05(g), such as mere data gathering and outputting, with respect to generating and presenting respective determination results of determination targets in a comparable manner).
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.
(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.
Claims 1-3 and 5 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gafsou (US 20160091470 A1).
With respect to claim 1, Gafsou teaches an information processing apparatus, comprising: a memory configured to store a program; and a processor configured to execute the program (e.g. paragraph 0162, described functions controlled through instructions executed by computer-based control system including processor connected to memories, etc.; paragraph 0163, computer programs loaded from secondary memory into main memory for execution by processors) so as to
generate a template that allows input of evaluation for each index according to specified number of index (e.g. paragraph 0015, predetermined descriptors for odorant samples; paragraph 0016, providing definition of data record in database, including olfactive index/indices; paragraph 0038, predetermined number of descriptors to describe sample for database record; Fig. 3, showing format of database record for an odorant sample; paragraph 0071, human machine interface for receiving data inputs and instructions from a user; i.e. a database record format for storing odorant sample ratings is defined, and a corresponding human machine interface is provided for entering the defined values, analogous to a template allowing input of evaluations for indices of the odorant),
acquire evaluation input to the template and odor information corresponding to the evaluation (e.g. paragraph 0038, samples directed to electronic sensing/analyzing unit and to panelist; sensing unit generates odorant signature and panelist provides the values for predetermined number of descriptors which are input into the computer device to create a corresponding record in a database; paragraph 0041, descriptors comprise ratings of various olfactive indices obtained from human subjects; paragraph 0042, updating database with data obtained from panelists/professionals; i.e. human or electronic entities provide evaluation of the odorant as input to the corresponding database record), wherein
the odor information is a detection result detected by an odor sensor (e.g. paragraph 0018, biologic nose refers to receptors/sensors of living creature; paragraph 0020, electric nose comprising group of sensors configured to provide scent intensity indications such that electronic signature data used to determine olfactive descriptors; paragraph 0021, olfactive perception obtained from biologic nose (panelist); paragraph 0026, measurement of olfactive descriptors using panelists or electronic signature generated by electronic nose unit; paragraph 0027, electronic nose is a device to detect volatile components; electronic signature is a set of measurement values obtained from various sensors of the device in response to the odorant);
perform machine learning using a set of the evaluation and the odor information as teaching data (e.g. paragraph 0005, neural network for outputting odor assessment; paragraph 0042, expert system capable of self-learning and thus updating selected model for scent measurements (olfactive descriptors, olfactive index, olfactive perception/naming, and final score); paragraph 0068, neural network used for scent descriptor analysis of odorant sample; paragraph 0135, forecasting algorithm built based on recorded olfactive perception data; i.e. the provided odorant data records are utilized as training/teaching data in a machine learning process to generate/update a corresponding model of an expert system), and
determine odor information of a determination target for the each index based on a result of the machine learning (e.g. paragraph 0005, neural network which maps extracted structure to location on pre-learned axis of odor pleasantness, and outputting an assessment of an applied odor based on the location; paragraph 0020, electronic signature data used to determine olfactive descriptors, i.e. using neural network; paragraph 0068, after completing construction of scent database, system adapted for scent descriptor analysis of an odorant sample using different techniques including neural network; paragraph 0071, determining perceptive descriptors by forecasting from electronic signature; paragraph 0135, forecasting of properties of odorant materials by the system based on olfactive perception/descriptors; i.e. the trained expert system/model is utilized to determine odor information for subsequent odorant samples).
With respect to claim 2, Gafsou teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein: the index corresponds to word or phrase representing human sensation (e.g. paragraph 0017, olfactive descriptor refers to data that describes scent characteristics, e.g. intensity, pleasantness, olfactive families such as defined by descriptors in Dravnieks atlas for example, fruity, floral, etc.; each olfactive descriptor has a certain value/score obtained from the biologic nose on an objective scale; properties of a scent influencing and describing its perception, such as cognitive perception, pleasantness, repulsion, intensity, irritation level, etc.; may mainly refer to human perception but can also refer to neural/brain activity measured responsive to presence of an odorant; paragraph 0041, olfactive indices based on the olfactive descriptors; paragraph 0046, descriptors data comprising ratings of various olfactive indices from human subjects such as scent intense, pleasantness, emotional association, cognitive perception, etc.).
With respect to claim 3, Gafsou teaches all of the limitations of claim 2 as previously discussed, and further teaches wherein: the template is configured in such a manner that the evaluation is input by input or selection of a value, and the value corresponds to specified number of evaluation stage (e.g. paragraph 0015, predetermined descriptors for odorant samples; paragraph 0016, providing definition of data record in database, including olfactive index/indices; paragraph 0017, olfactive descriptor has a value/score obtained from biologic nose on an objective scale; paragraph 0019, olfactive index refers to quantifying scale of olfactive descriptors and their values; paragraph 0023, olfactive perception value including a numerical component; paragraph 0038, panelist provides the values for predetermined number of descriptors which are input into the computer device to create a corresponding record in a database; paragraph 0041, descriptors comprise ratings of various olfactive indices obtained from human subjects; paragraph 0072, olfactive descriptors/index/perception may include a numerical component; paragraph 0124, panelists providing olfactive descriptors for sample and a rating such as on a scale from 1 to 10, etc. for each descriptor; paragraph 0139, olfactive descriptors and olfactive indexes composed by letters or numbers or both; compare with specification of the instant application at paragraph 0050, describing column 313 of Fig. 10 as providing an evaluation where the value corresponds to specified number of evaluation stage; i.e. based on the description of the instant application’s specification, Examiner interprets the limitation “the value corresponds to specified number of evaluation stage” as including a specified/selected number within an evaluation/rating scale, as shown in Fig. 10 of the instant application; Gafsou teaches human panelists/users entering olfactive evaluations, including providing an olfactive descriptor and further providing a corresponding value within a scale corresponding to that descriptor, which is analogous to the limitations of claim 3)
With respect to claim 5, Gafsou teaches all of the limitations of claim 2 as previously discussed, and further teaches wherein: the processor is configured to execute the program so as to generate a graph based on a determination result by the processor and present the generated graph in a visible manner (e.g. paragraph 0071, generating olfactive perception map representing by 2D or 3D graphs, the perceptive descriptor levels determined by the panelists or by forecasting from the electronic signature; creating visual display of the olfactive perception, assigning specific color to each perceptive descriptor).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102€, (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Gafsou in view of Watanabe et al. (US 20200336543 A1).
With respect to claim 4, Gafsou teaches all of the limitations of claim 2 as previously discussed, and further teaches wherein: the processor is configured to execute the program so as to perform the machine learning based on a selected learning algorithm among two or more learning algorithms (e.g. paragraph 0005, neural network for outputting odor assessment; paragraph 0042, expert system capable of self-learning and thus updating selected model; paragraph 0068, neural network used for scent descriptor analysis of odorant sample; paragraph 0135, forecasting algorithm built based on recorded olfactive perception data; i.e. the provided odorant data records are utilized as training/teaching data in a machine learning process to generate/update a corresponding model of an expert system).
Assuming arguendo that Gafsou does not explicitly disclose selection from among two or more learning algorithms, Watanabe teaches selection from among two or more learning algorithms (e.g. paragraph 0006, performing analysis through machine learning to generate an odor analyzer for every target; paragraph 0076-0078, each analyzer is an analysis model created in advance by machine learning; performing machine learning using only specific/effective portion of learning data (i.e. to train a specific analysis model among a plurality of analysis models); paragraphs 0080-0081, Fig. 5, upon selecting analyzer, specifying feature amount to be extracted form sensor data according to selected analyzer, corresponding to the effective portion of the sensor data utilized at the time of machine learning of that analyzer; index list/table showing details for plurality of analyzers/analysis models).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Gafsou and Watanabe in front of him to have modified the teachings of Gafsou (directed to scent perception measurements for construction of a scent database), to incorporate the teachings of Watanabe (directed to odor sensor data analysis) to include the capability to select the learning algorithm (of Gafsou) from among two or more learning algorithms/models (as taught by Watanabe). One of ordinary skill would have been motivated to perform such a modification in order to, in the case where substance detection is performed using an odor sensor whose analysis target is not fixed, utilize an analyzer adapted to the situation, without implementing a large number of analyzers as described in Watanabe (paragraph 0038).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Gafsou in view of Ogata et al. (US 20230152290 A1).
With respect to claim 6, Gafsou teaches all of the limitations of claim 5 as previously discussed, and further teaches wherein: the processor is configured to execute the program so as to present respective determination result of two or more determination targets by the processor in a comparable manner (e.g. paragraph 0071, generating olfactive perception map representing by 2D or 3D graphs, the perceptive descriptor levels determined by the panelists or by forecasting from the electronic signature; creating visual display of the olfactive perception, assigning specific color to each perceptive descriptor).
Assuming arguendo that Gafsou does not explicitly disclose that the presentation of the respective determination result is of two or more determination targets in a comparable manner, Ogata teaches that the presentation of the respective determination result is of two or more determination targets in a comparable manner (e.g. paragraphs 0084-0085, storing patterns of reaction values for substances in odors A, B, C; Fig. 2, corresponding to detected patters for each of odors A, B, and C; paragraph 0089, displaying percentages of reaction values corresponding to the odors so that comparison of the percentages is enabled between the odors; displaying bar graph, such as illustrated in Fig. 5; paragraph 0090, bar graph enabling comparison of reaction values between odors; paragraph 0091, displaying bar graph of Fig. 4 indicating reaction values corresponding to odors A, B, and graph of Fig. 5 illustrates percentage corresponding to the odors such that comparison of the bar graphs is enabled).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Gafsou and Ogata in front of him to have modified the teachings of Gafsou (directed to scent perception measurements for construction of a scent database), to incorporate the teachings of Ogata (directed to odor detection) to include the capability to display, as the output graph (of Gafsou), a graph including representations of multiple different odor samples/determination targets, such that comparison of respective evaluations of the different odor samples can be compared (as taught by Ogata). One of ordinary skill would have been motivated to perform such a modification in order to reduce variations in results of detection of an odor regardless of the number of measurements or condition of an environment as described in Ogata (paragraph 0060).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain,” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting in re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (GCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co, v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F,3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir, 2005): Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L STANLEY whose telephone number is (469)295-9105. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM CST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar, can be reached at telephone number (571) 270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEREMY L STANLEY/
Primary Examiner, Art Unit 2127
Prosecution Insights