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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/17/2025 has been entered.
Status of the Claims
The claims 1-9, 11-18 are currently pending and have been examined. Applicant amended claims 1 and 16 and cancelled claim 10.
Response to Arguments/Amendments
The amendment filed December 17, 2025 has been entered. Claims 1-9, 11-18 are currently pending in the Application.
Applicant’s arguments with respect to claim(s) 1-9, 11-18 under 103 have been have been fully considered but they are not persuasive.
The Examiner has carefully considered applicant’s arguments and respectfully disagrees. Applicant argues that amended independent claim 1 (and claim 16) are not rendered obvious by KOANG in view of LIM. Applicant argues that KOANG fails to disclose: (A) a pre-stored fishing plan; (B) acquiring a fishing plan selection operation in which a pre-stored target fishing plan is selected to generate navigation information; and (C) generating navigation according to a time sequence derived from multiple fishing time periods and corresponding locations (See pages 13-16 of Applicant’s remarks). Applicant further argues that LIM also fails to disclose these features, asserting that LIM generates fishing information instantaneously rather than from pre-stored fishing plans, does not generate navigation to multiple fishing points, and therefore does not cure the alleged deficiencies of KOANG (See pages 16-20 of Applicant’s remarks).
The Examiner has considered such arguments; however, the claims are rendered obvious by the combination of KOANG and LIM, and each of the alleged distinguishing features of the amended claims is taught by the cited references. Applicant argues that neither KOANG nor LIM discloses a pre-stored fishing plan. However, LIM expressly discloses estimating fish species activity based on accumulated historical fishing data using big data analysis and machine learning, and further discloses storing the estimated fish species activity information in a storage unit (See paragraphs [0066], [0077], [0078]). The system estimates fish species activity based on historical fishing data by fishing point and time using big data analysis and machine learning and stores the estimated fish species activity information in a storage unit, thereby providing a pre-stored fishing plan. Accordingly, LIM teaches the generation and storage of fishing plans prior to user selection, as recited in amended claim 1.
Applicant argues that KOANG discloses selecting multiple fishing routes to generate a guide, while amended claim 1 requires selecting a pre-stored target fishing plan to generate navigation information. However, KOANG discloses establishing a fishing plan based on user selection of fishing area, data, and time and acquiring a user selection of operation routes to provide corresponding navigation guidance (See paragraph [0018], [0059], [0061], [0062]). In particular, KOANG teaches that when a ranked or recommended operation route is selected, the system displays the selected route and guides the user to the starting point of that route (See paragraph [0059]). The claims do not require a particular selection modality, nor do they exclude embodiments in which multiple candidate routes are selectable prior to determining a target route or plan. Accordingly, selecting operation routes in KOANG reasonably corresponds to acquiring a fishing plan selection operation and determining a target fishing plan according to the selection, as recited in amended claim 1.
Applicant argues that KOANG generates guidance based on selection order or marine environment and weather information, rather than based on a time sequence derived from fishing time periods. The argument is not persuasive. KOANG discloses user input of fishing data and time and generating navigation guidance based on marine environment and weather prediction information corresponding to the fishing period (See paragraph [0061], [0062]). KOANG further discloses generating guidance among multiple fishing routes based on selection order and time-dependent marine conditions (See paragraph [0059]). Thus, KOANG teaches navigation guidance that is generated in a time-dependent context. LIM further discloses estimating fishing information by fishing point and time based on historical fishing data (See paragraph [0077]), thereby providing explicit fishing time periods corresponding to fishing locations. When considered together, KOANG and LIM teach generating navigation according to fishing time periods and corresponding locations, which reasonably corresponds to determining a time sequence of fishing spots and generating a fishing route as recited in amended claim 1.
Applicant’s arguments analyze KOANG and LIM in isolation and rely on distinctions not recited in the claims. When considered together, KOANG and LIM teach or render obvious each of the limitations of amended claim 1, including the generation and storage of fishing plans based on historical data using big data analysis and machine learning, user selection of a target fishing plan, and navigation generated according to fishing time periods and corresponding locations. Accordingly, the rejection of claims 1-9, 11-18 under 35 U.S.C. 103 is maintained.
Applicant's arguments regarding the 35 U.S.C. 101 mental process rejection have been fully considered but they are not persuasive.
The Examiner has carefully considered applicant’s arguments and respectfully disagrees. Applicant argues that the amended claims include additional elements, such as using big data analysis and machine learning to pre-generate the at least one fishing plan and storing the generated fishing plan in advance, then selecting a target fishing plan from the pre-stored at least one fishing plan, and then generating a fishing route according to a time sequence and a first plurality of locations of a first plurality of target fishing spots in the target fishing plan, which Applicant asserts integrate the judicial exception into a practical application. Applicant further asserts that these elements provide an improved fishing navigation method and amount to significantly more than the abstract idea because retrieving a pre-defined plan avoids processing load and latency associated with real-time calculation, leading to faster plan retrieval, and because direct selection of a complete and pre-configured plan results in a more intuitive and less-error prone interface. Applicant additionally asserts that generating navigation based on a time sequence derived from the plan’s fishing periods guides the user between locations in an order that meets scheduled time periods, executing the pre-determined fishing plan in a timely manner and improving fishing navigation (See pages 10-13 of Applicant’s remarks).
The Examiner has considered such arguments; however, when given their broadest reasonable interpretation in light of the specification, the claims remain directed to a judicial exception—specifically, to methods of organizing human activity and mental processes. The claimed steps of acquiring plans, selecting among plans, determining a target plan, generating navigation guidance, acquiring fishing time periods, determining a time sequence, and generating the fishing route based on selected times and locations reflect data collection, evaluation, and presentation of results—activities that can be performed mentally or with pen and paper. Performing these steps on through big data analysis and machine learning on an electronic device merely automates what a person could do mentally or manually and does not transform the nature of the claim into a technological process. Any alleged technical improvement is not shown in the steps of the claims, which merely describe generic data collection, analysis, and presentation operations performed by a conventional electronic device using big data analysis and machine learning. While Applicant argues the method improves efficiency, any such benefit is directed to the user’s fishing experience rather than to an improvement in the functioning of the computer or navigation technology itself. Generating guidance more conveniently or saving time in planning a trip does not represent a technical improvement in how data is processed or displayed. Accordingly, the Examiner finds that the amended claims do not include additional elements that meaningfully integrate the judicial exception into a practical application or that amount to significantly more than the exception itself. The rejection under 35 U.S.C. 101 is therefore maintained for claims 1-9, 11-18.
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 1-9, 11-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1 and 16-18 are directed toward non-statutory subject matter, as shown below:
STEP 1: Do claims 1 and 16-18 fall within one of the statutory categories? Yes. The claims are directed toward a method including at least one step, an apparatus, an apparatus, and an apparatus.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claims are directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claim 1. A fishing navigation method, performed by an electronic device, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation method comprises:
acquiring at least one fishing plan corresponding to the target fishing area, wherein the at least one fishing plan is generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of a first plurality of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots, different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area;
acquiring a fishing plan selection operation of the user;
determining a target fishing plan from the at least one fishing plan according to the fishing plan selection operation;
and generating navigation information according to the first plurality of each locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan;
wherein the navigation information comprises a fishing route;
and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises:
acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots;
determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods;
and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots.
The method in claim 1, specifically the limitations emphasized above, is a mental process that can be practicably performed in the human mind and, therefore, an abstract idea. It merely consists of determining a target fishing plan and determining a time sequence. This is equivalent to a person mentally viewing the fishing plan, deciding on the fishing plan, and deducing a time sequence.
Claim 16. A fishing navigation apparatus, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation apparatus comprises at least one processor, and the at least one processor is configured to:
acquire at least one fishing plan corresponding to the target fishing area, wherein the at least one fishing plan is generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of a first plurality of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots, different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area;
acquire a fishing plan selection operation of the user;
determine a target fishing plan from the at least one fishing plan according to the fishing plan selection operation;
and generate navigation information according to the first plurality of each locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan;
wherein the navigation information comprises a fishing route;
and wherein the at least one processor is further configured to generate the navigation information in the following manner:
acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots;
determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods;
and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots.
The method in claim 16, specifically the limitations emphasized above, is a mental process that can be practicably performed in the human mind and, therefore, an abstract idea. It merely consists of determining a target fishing plan and determining a time sequence. This is equivalent to a person mentally viewing the fishing plan, deciding on the fishing plan, and deducing a time sequence.
Claim 17. An electronic device, comprising: at least one processor; and a memory, which is communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program, when executed by the at least one processor, causes the at least one processor to perform the fishing navigation method according to claim 1.
The method in claim 17, specifically the limitations emphasized above, is a mental process that can be practicably performed in the human mind and, therefore, an abstract idea. It merely consists of performing the fishing navigation method. This is equivalent to a person mentally viewing the area and using the fishing navigation method.
Claim 18. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions, when executed by a processor, cause the processor to perform the fishing navigation method according to claim 1.
The method in claim 18, specifically the limitations emphasized above, is a mental process that can be practicably performed in the human mind and, therefore, an abstract idea. It merely consists of performing the fishing navigation method. This is equivalent to a person mentally viewing the area and using the fishing navigation method.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
In the present case, the additional limitations beyond the above-noted abstract ideas are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the abstract “idea”).
Claim 1. A fishing navigation method, performed by an electronic device, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation method comprises:
acquiring at least one fishing plan corresponding to the target fishing area, wherein the at least one fishing plan is generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of a first plurality of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots, different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area;
acquiring a fishing plan selection operation of the user;
determining a target fishing plan from the at least one fishing plan according to the fishing plan selection operation;
and generating navigation information according to the first plurality of each locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan;
wherein the navigation information comprises a fishing route;
and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises:
acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots;
determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods;
and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots.
Claim 1 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. The steps of “…acquiring at least one fishing plan…”, “acquiring a fishing plan selection operation…”, and “acquiring the first plurality of fishing time periods…” are recited at a high level of generality and amount to mere data gathering, which are a form of extra solution activity. The steps of “generating navigation information…” and “…generating the fishing route…” are recited at a high level of generality and amount to mere post solution actions, which are a form of extra solution activity. The limitation “…performed by an electronic device…” is claimed generically and is operating in its ordinary capacity such that it does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The electronic device merely describes how to generally “apply” the otherwise mental judgments in a generic or general purpose computing environment. The electronic device is recited at a high level of generality and merely automate the acquiring, determining, and generating steps. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 16. A fishing navigation apparatus, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation apparatus comprises at least one processor, and the at least one processor is configured to:
acquire at least one fishing plan corresponding to the target fishing area, wherein the at least one fishing plan is generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of a first plurality of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots, different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area;
acquire a fishing plan selection operation of the user;
determine a target fishing plan from the at least one fishing plan according to the fishing plan selection operation;
and generate navigation information according to the first plurality of each locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan;
wherein the navigation information comprises a fishing route;
and wherein the at least one processor is further configured to generate the navigation information in the following manner:
acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots;
determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods;
and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots.
Claim 16 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. The steps of “…acquire at least one fishing plan…”, “acquire a fishing plan selection operation…”, and “acquiring the first plurality of fishing time periods…” are recited at a high level of generality and amounts to mere data gathering, which is a form of extra solution activity. The steps of “generate navigation information…” and “…generating the fishing route…” are recited at a high level of generality and amount to mere post solution actions, which are a form of extra solution activity. The limitation “A fishing navigation apparatus, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation apparatus comprises at least one processor, and the at least one processor is configured to” is claimed generically and is operating in its ordinary capacity such that it does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The fishing navigation apparatus and processor merely describe how to generally “apply” the otherwise mental judgments in a generic or general purpose computing environment. The fishing navigation apparatus and processor are recited at a high level of generality and merely automate the acquiring, determining, and generating steps. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 17. An electronic device, comprising: at least one processor; and a memory, which is communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program, when executed by the at least one processor, causes the at least one processor to perform the fishing navigation method according to claim 1.
Claim 17 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. The limitation “An electronic device, comprising: at least one processor; and a memory, which is communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program, when executed by the at least one processor, causes the at least one processor to” is claimed generically and is operating in its ordinary capacity such that it does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The electronic device, processor, memory, and computer program merely describe how to generally “apply” the otherwise mental judgments in a generic or general purpose computing environment. The electronic device, processor, memory, and computer program are recited at a high level of generality and merely automate the acquiring, determining, and generating steps. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 18. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions, when executed by a processor, cause the processor to perform the fishing navigation method according to claim 1.
Claim 18 does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. The limitation “A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions, when executed by a processor, cause the processor to” is claimed generically and is operating in its ordinary capacity such that it does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The non-transitory computer-readable storage medium, computer instructions, and processor merely describe how to generally “apply” the otherwise mental judgments in a generic or general purpose computing environment. The non-transitory computer-readable storage medium, computer instructions, and processor are recited at a high level of generality and merely automate the acquiring, determining, and generating steps. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Regarding Step 2B of the 2019 PEG, independent claims 1 and 16-18 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claims do not integrate the abstract idea into a practical application.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation(s) of “…performed by an electronic device…”, “A fishing navigation apparatus, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation apparatus comprises at least one processor, and the at least one processor is configured to”, “An electronic device, comprising: at least one processor; and a memory, which is communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program, when executed by the at least one processor, causes the at least one processor to”, and “A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions, when executed by a processor, cause the processor to” is/are merely means to apply the exception and do not amount to “significantly more”, as 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, are not sufficient to amount to significantly more than the judicial exception.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “…acquiring at least one fishing plan…”, “acquiring a fishing plan selection operation…”,“acquiring the first plurality of fishing time periods…”, “generating navigation information…”, “…generating the fishing route…”, “…acquire at least one fishing plan…”, “acquire a fishing plan selection operation…”, and “generate navigation information…” are well-understood, routine, and conventional activities because the specification does not provide any indication that the acquiring, determining, and generating steps are performed using anything other than a conventional computer. See also MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures |, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TL! Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and O/P Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere performance of an action is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Hence, the claim is not patent eligible.
CONCLUSION
Thus, since claims 1 and 16-18 are: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claims 1 and 16-18 are directed towards non-statutory subject matter.
Dependent claims 2-9, 11-15 further limit the abstract idea without integrating the abstract idea into practical application or adding significantly more, such as the limitations in claim 7 that amount to insignificant extra solution activity using a similar analysis applied to claim 1 above.
As such, claims 1-9, 11-18 are rejected under 35 USC 101 as being drawn to an abstract idea without significantly more, and thus are ineligible.
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 factual inquiries 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 the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 6-9, 11, 12, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over KOANG (KR 2022048861 A) in view of LIM (US 20230185841 A1).
Regarding Claim 1, KOANG teaches A fishing navigation method, performed by an electronic device, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation method comprises: acquiring at least one fishing plan corresponding to the target fishing area (See at least paragraph [0001], “The present invention relates to a navigation terminal for fishing”, paragraph [0015], “When the navigation terminal for phishing displays the electronic chart, it is configured to overlay the tide information and maritime weather information at the current location along with the position and speed of the own ship and display it”, paragraph [0018], “the present invention is designed to recommend and guide the fishing route with the highest probability according to the marine environment information and weather information of the fishing area by selecting only the fishing date, fish species, and fishing area when establishing a fishing plan”, paragraph [0051], “At this time, the operation route 14 is displayed on the electronic chart 10, and when the automatically generated operation route 14 is selected, the pop-up menu 40 is exposed and configured to register, delete, edit, or edit the operation route”, and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”), acquiring a fishing plan selection operation of the user (See at least paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable.”); determining a target fishing plan from the at least one fishing plan according to the fishing plan selection operation (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”); wherein the navigation information comprises a fishing route (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”).
KOANG does not explicitly disclose, however, LIM, in the same field of endeavor, teaches wherein the at least one fishing plan is generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots (See at least paragraph [0066], “The fishing guiding server 100 recognizes fish species from the collected images in the online community channels and determines the fish species information representing the recognized fish species (S105). For example, the control unit 170 executes the extraction module for extracting fish species information by applying a machine learning technique, recognizes a fish species for each collected image stored in the storage unit 130, and provides fish species information indicating the recognized fish species. Fish species represent the types of live fish that can be caught by fishing, such as flounder, flatfish, yellowtail, mullet, rockfish, black sea bream, and red sea bream”, paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks”, and paragraph [0078], “As such, the control unit 170 may estimate the fish species activity of each fish species associated with the fishing point, time, weather information, etc. based on big data and store the estimated fish species activity information in the storage unit 130, or may prepare to immediately estimate the fish species activity.” The system estimates fish species activity based on historical fishing data by fishing point and time using big data analysis and machine learning and stores the estimated fishing species activity information in a storage unit, thereby providing a pre-stored fishing plan.), different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks.”); and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks” and paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response.”); and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks.”); determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods (See at least paragraph [0078], “As such, the control unit 170 may estimate the fish species activity of each fish species associated with the fishing point, time, weather information, etc. based on big data and store the estimated fish species activity information in the storage unit 130, or may prepare to immediately estimate the fish species activity”, paragraph [0079], “Through the control flow shown in FIG. 3, the fishing guiding server 100 may estimate and update at least the fish species activity for each fishing point through fishing photos, etc. that may be secured from online community channels”, paragraph [0080], “FIG. 4 is a diagram showing an exemplary control flow for recommending a fishing point for a requested target fish species according to a user's input using estimated fish species activity for each fishing point”, and paragraph [0081], “The control flow shown in FIG. 4 is performed by the fishing guiding server 100 and is preferably performed by the control unit 170 of the fishing guiding server 100 executing the recommendation module. The control flow shown in FIG. 4 is preferably performed after the fish species activity is estimated or may be immediately estimated according to the control flow of FIG. 3.”); and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots (See at least paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response” and paragraph [0083], “The fishing guiding server 100 receives a fishing point recommendation request including a fish to be caught and an expected fishing date, which is a fishing date, from the user terminal 200 through the Internet (S203). The control unit 170 may receive a fishing point recommendation request including input data for specifying the target fish species (e.g., target fish species identifier (name)) and input data for specifying the expected fishing date (e.g., date of year, month, day) through the communication unit 110.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].).
Regarding Claim 2, KOANG and LIM teach The fishing navigation method according to claim 1, as set forth in the obviousness rejection above. KOANG teaches further comprising: acquiring a fishing area selection operation of the user (See at least paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable.”); and determining the target fishing area from at least one fishing area according to the fishing area selection operation (See at least paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable.”).
Regarding Claim 3, KOANG and LIM teach The fishing navigation method according to claim 2, as set forth in the obviousness rejection above. KOANG teaches further comprising: acquiring current weather information of each of the at least one fishing area (See at least paragraph [0018], “the present invention is designed to recommend and guide the fishing route with the highest probability according to the marine environment information and weather information of the fishing area by selecting only the fishing date, fish species, and fishing area when establishing a fishing plan.”); and determining first candidate fishing areas from the at least one fishing area according to the current weather information (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information” and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”); wherein determining the target fishing area from the at least one fishing area according to the fishing area selection operation comprises: determining the target fishing area from the first candidate fishing areas according to the fishing area selection operation (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information”, paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable”, and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”).
Regarding Claim 4, KOANG and LIM teach The fishing navigation method according to claim 2, as set forth in the obviousness rejection above. KOANG teaches further comprising: acquiring a current location of the user (See at least paragraph [0054], “That is, as shown in FIG. 3b, when the edit button 42 is pressed, the movement route and the operation route are displayed centered on the current location along with the electronic chart.”); and determining second candidate fishing areas from the at least one fishing area according to the current location (See at least paragraph [0054], “That is, as shown in FIG. 3b, when the edit button 42 is pressed, the movement route and the operation route are displayed centered on the current location along with the electronic chart” and paragraph [0055], “At this time, the fishing route 14 is automatically generated, so the starting point 51 and the finishing point 52 on the movement path are designated so that the fishing route can be registered as accurately as possible as a fishing point.”); wherein determining the target fishing area from the at least one fishing area according to the fishing area selection operation comprises: determining the target fishing area from the second candidate fishing areas according to the fishing area selection operation (See at least paragraph [0054], “That is, as shown in FIG. 3b, when the edit button 42 is pressed, the movement route and the operation route are displayed centered on the current location along with the electronic chart”, paragraph [0055], “At this time, the fishing route 14 is automatically generated, so the starting point 51 and the finishing point 52 on the movement path are designated so that the fishing route can be registered as accurately as possible as a fishing point”, paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information”, and paragraph [0061], “In addition, when the setting of the marine environment information window 60 is automatically made by the check button 62, the filter range is automatically set according to the similar conditions registered in the setting.”).
Regarding Claim 6, KOANG and LIM teach The fishing navigation method according to claim 2, as set forth in the obviousness rejection above. KOANG teaches further comprising: acquiring fishing data related to the at least one fishing area (See at least paragraph [0049], “In particular, when the track 16 is created, the marine environment information 12 and the marine weather information 11 at the current location are collected and stored together so that you can find or analyze the fish that are active in the region with a similar pattern later.”); acquiring a fishing data selection operation of the user (See at least paragraph [0050], “At this time, the navigation terminal for fishing automatically includes the fishing route 14 based on the speed of the ship, the time of stay, or whether or not information on the amount of catch is input, etc. 16) will be created.”); and determining third candidate fishing areas from the at least one fishing area according to the fishing data selection operation (See at least paragraph [0056], “Figure 4 shows the setting of marine environment information in order to receive recommendations for fishing points”, paragraph [0057], “At this time, the navigation terminal for fishing can receive recommendations for fishing points through the setting of the period 61 and the marine environment information window 60”, paragraph [0058], “The marine environment information window 60 is configured to automatically or manually set and register the tide level, the water temperature, etc. by the slide moving bar 61. When the setting is completed, the setting and registered marine environment information and Based on the similarity of”, and paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”); wherein determining the target fishing area from the at least one fishing area according to the fishing area selection operation comprises: determining the target fishing area from the third candidate fishing areas according to the fishing area selection operation (See at least paragraph [0056], “Figure 4 shows the setting of marine environment information in order to receive recommendations for fishing points”, paragraph [0057], “At this time, the navigation terminal for fishing can receive recommendations for fishing points through the setting of the period 61 and the marine environment information window 60”, paragraph [0058], “The marine environment information window 60 is configured to automatically or manually set and register the tide level, the water temperature, etc. by the slide moving bar 61. When the setting is completed, the setting and registered marine environment information and Based on the similarity of”, and paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”).
Regarding Claim 7, KOANG and LIM teach The fishing navigation method according to claim 6, as set forth in the obviousness rejection above. KOANG teaches wherein the fishing data comprise at least one of a species, a size, a sex, or a color of a fish comprised in a fishing area related to the fishing data (See at least paragraph [0045], “At this time, you can select and input the fish species of the oyster button/floating button, but if you select any one of a number of fish species in advance and operate a wirelessly connected button or earphone, the current position is designated as a point and the current position It is also possible to easily set and register the target fish caught in”, paragraph [0046], “At this time, the tide button/floating button 30 automatically stores marine weather information and marine environment information based on the current location information through GPS when a target fish species is selected, so that the fish species, latitude, longitude, scale, and lunar calendar are automatically stored. , water temperature, temperature, barometric pressure, waves, wind, tide, and speed, etc., to easily form catch information, which increases convenience”, and paragraph [0049], “In particular, when the track 16 is created, the marine environment information 12 and the marine weather information 11 at the current location are collected and stored together so that you can find or analyze the fish that are active in the region with a similar pattern later.”).
Regarding Claim 8, KOANG and LIM teach The fishing navigation method according to claim 1, as set forth in the obviousness rejection above. KOANG teaches further comprising: acquiring a target fishing spot selection operation of the user (See at least paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable.”); and determining a second plurality of target fishing spots from the plurality of fishing spots corresponding to the target fishing area according to the target fishing spot selection operation (See at least paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable.”); wherein acquiring the at least one fishing plan corresponding to the target fishing area comprises: determining the at least one fishing plan corresponding to the target fishing area according to the second plurality of target fishing spots (See at least paragraph [0050], “At this time, the navigation terminal for fishing automatically includes the fishing route 14 based on the speed of the ship, the time of stay, or whether or not information on the amount of catch is input, etc. 16) will be created” and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”).
Regarding Claim 9, KOANG and LIM teach The fishing navigation method according to claim 8, as set forth in the obviousness rejection above. KOANG does not explicitly disclose, however, LIM, in the same field of endeavor, teaches further comprising: acquiring a second plurality of locations of the second plurality of target fishing spots and a second plurality of fishing time periods corresponding to the second plurality of target fishing spots (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks.”); wherein determining the at least one fishing plan corresponding to the target fishing area according to the second plurality of target fishing spots comprises: determining the at least one fishing plan according to the second plurality of locations of the second plurality of target fishing spots and the second plurality of fishing time periods corresponding to the second plurality of target fishing spots (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks” and paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots; acquiring a second plurality of locations of the second plurality of target fishing spots and a second plurality of fishing time periods corresponding to the second plurality of target fishing spots; and determining the at least one fishing plan corresponding to the target fishing area according to the second plurality of target fishing spots comprises: determining the at least one fishing plan according to the second plurality of locations of the second plurality of target fishing spots and the second plurality of fishing time periods corresponding to the second plurality of target fishing spots, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].).
Regarding Claim 11, KOANG and LIM teach The fishing navigation method according to claim 1, as set forth in the obviousness rejection above. KOANG teaches wherein the navigation information comprises a fishing map; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring a location of the target fishing area and surrounding environment information of the target fishing area (See at least paragraph [0026], “In particular, on the electronic chart (10), high tide time and tide difference, high and low tide, low tide time, etc. can be checked along with maritime meteorological information (11) such as weather, wind direction, wind speed, wave direction, wave height, temperature, etc. 13) is not only configured to be checked by overlaying the marine environment information 12, including”, paragraph [0027], “On the electronic chart 10, the track 16 that was actually operated according to the movement route is also configured to be able to be checked automatically”, paragraph [0033], “In addition, marine weather information 11 and marine environment information 12 are transmitted from the marine server and displayed through the display panel on the terminal”, paragraph [0050], “At this time, the navigation terminal for fishing automatically includes the fishing route 14 based on the speed of the ship, the time of stay, or whether or not information on the amount of catch is input, etc. 16) will be created”, and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”).
KOANG does not explicitly disclose, however, LIM, in the same field of endeavor, teaches and marking the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots on the fishing map (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks”, paragraph [0078], “As such, the control unit 170 may estimate the fish species activity of each fish species associated with the fishing point, time, weather information, etc. based on big data and store the estimated fish species activity information in the storage unit 130, or may prepare to immediately estimate the fish species activity”, and paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots; and marking the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots on the fishing map, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].).
Regarding Claim 12, KOANG and LIM teach The fishing navigation method according to claim 1, as set forth in the obviousness rejection above. KOANG teaches wherein the navigation information comprises a guidance route and an estimated arrival time, and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring a current time and a current location (See at least paragraph [0050], “At this time, the navigation terminal for fishing automatically includes the fishing route 14 based on the speed of the ship, the time of stay, or whether or not information on the amount of catch is input, etc. 16) will be created”, paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information”, paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable”, and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”); determining one of the first plurality of target fishing spots corresponding to a fishing time period that matches the current time to be a navigation fishing spot (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information” and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”); determining a location of the navigation fishing spot from the plurality of locations of the first plurality of target fishing spots (See at least paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable” and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”); and generating the guidance route between the current location and the location of the navigation fishing spot (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”), and determining the estimated arrival time according to a distance between the current location and the location of the navigation fishing spot (See at least paragraph [0050], “At this time, the navigation terminal for fishing automatically includes the fishing route 14 based on the speed of the ship, the time of stay, or whether or not information on the amount of catch is input, etc. 16) will be created” and paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”).
Regarding Claim 16, KOANG teaches A fishing navigation apparatus, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation apparatus comprises at least one processor, and the at least one processor is configured to: acquire at least one fishing plan corresponding to the target fishing area (See at least paragraph [0001], “The present invention relates to a navigation terminal for fishing”, paragraph [0018], “the present invention is designed to recommend and guide the fishing route with the highest probability according to the marine environment information and weather information of the fishing area by selecting only the fishing date, fish species, and fishing area when establishing a fishing plan”, paragraph [0025], “If described with reference to this, the navigation terminal for phishing of the present invention detects the location and movement of own ships based on the electronic chart and GPS when the phishing app is installed on an Internet-connectable smartphone or tablet PC or laptop and the phishing app is activated. It is configured so that it can be checked”, paragraph [0051], “At this time, the operation route 14 is displayed on the electronic chart 10, and when the automatically generated operation route 14 is selected, the pop-up menu 40 is exposed and configured to register, delete, edit, or edit the operation route”, and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”), acquire a fishing plan selection operation of the user (See at least paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable.”); determine a target fishing plan from the at least one fishing plan according to the fishing plan selection operation (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”); wherein the navigation information comprises a fishing route (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”).
KOANG does not explicitly disclose, however, LIM, in the same field of endeavor, teaches wherein the at least one fishing plan is generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of a first plurality of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots (See at least paragraph [0066], “The fishing guiding server 100 recognizes fish species from the collected images in the online community channels and determines the fish species information representing the recognized fish species (S105). For example, the control unit 170 executes the extraction module for extracting fish species information by applying a machine learning technique, recognizes a fish species for each collected image stored in the storage unit 130, and provides fish species information indicating the recognized fish species. Fish species represent the types of live fish that can be caught by fishing, such as flounder, flatfish, yellowtail, mullet, rockfish, black sea bream, and red sea bream”, paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks”, and paragraph [0078], “As such, the control unit 170 may estimate the fish species activity of each fish species associated with the fishing point, time, weather information, etc. based on big data and store the estimated fish species activity information in the storage unit 130, or may prepare to immediately estimate the fish species activity.” The system estimates fish species activity based on historical fishing data by fishing point and time using big data analysis and machine learning and stores the estimated fishing species activity information in a storage unit, thereby providing a pre-stored fishing plan.), different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks.”); and generate navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks” and paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response.”); and wherein the at least one processor is further configured to generate the navigation information in the following manner: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks.”); determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods (See at least paragraph [0078], “As such, the control unit 170 may estimate the fish species activity of each fish species associated with the fishing point, time, weather information, etc. based on big data and store the estimated fish species activity information in the storage unit 130, or may prepare to immediately estimate the fish species activity”, paragraph [0079], “Through the control flow shown in FIG. 3, the fishing guiding server 100 may estimate and update at least the fish species activity for each fishing point through fishing photos, etc. that may be secured from online community channels”, paragraph [0080], “FIG. 4 is a diagram showing an exemplary control flow for recommending a fishing point for a requested target fish species according to a user's input using estimated fish species activity for each fishing point”, and paragraph [0081], “The control flow shown in FIG. 4 is performed by the fishing guiding server 100 and is preferably performed by the control unit 170 of the fishing guiding server 100 executing the recommendation module. The control flow shown in FIG. 4 is preferably performed after the fish species activity is estimated or may be immediately estimated according to the control flow of FIG. 3.”); and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots (See at least paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response” and paragraph [0083], “The fishing guiding server 100 receives a fishing point recommendation request including a fish to be caught and an expected fishing date, which is a fishing date, from the user terminal 200 through the Internet (S203). The control unit 170 may receive a fishing point recommendation request including input data for specifying the target fish species (e.g., target fish species identifier (name)) and input data for specifying the expected fishing date (e.g., date of year, month, day) through the communication unit 110.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generate navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein the at least one processor is further configured to generate the navigation information in the following manner: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].).
Regarding Claim 17, KOANG teaches An electronic device, comprising: at least one processor (See at least paragraph [0025], “If described with reference to this, the navigation terminal for phishing of the present invention detects the location and movement of own ships based on the electronic chart and GPS when the phishing app is installed on an Internet-connectable smartphone or tablet PC or laptop and the phishing app is activated. It is configured so that it can be checked.”); and a memory, which is communicatively connected to the at least one processor (See at least paragraph [0025], “If described with reference to this, the navigation terminal for phishing of the present invention detects the location and movement of own ships based on the electronic chart and GPS when the phishing app is installed on an Internet-connectable smartphone or tablet PC or laptop and the phishing app is activated. It is configured so that it can be checked.”); wherein the memory stores a computer program executable by the at least one processor, and the computer program, when executed by the at least one processor, causes the at least one processor to perform the fishing navigation method according to claim 1: Regarding Claim 1, KOANG teaches A fishing navigation method, performed by an electronic device, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation method comprises: acquiring at least one fishing plan corresponding to the target fishing area (See at least paragraph [0001], “The present invention relates to a navigation terminal for fishing”, paragraph [0015], “When the navigation terminal for phishing displays the electronic chart, it is configured to overlay the tide information and maritime weather information at the current location along with the position and speed of the own ship and display it”, paragraph [0018], “the present invention is designed to recommend and guide the fishing route with the highest probability according to the marine environment information and weather information of the fishing area by selecting only the fishing date, fish species, and fishing area when establishing a fishing plan”, paragraph [0051], “At this time, the operation route 14 is displayed on the electronic chart 10, and when the automatically generated operation route 14 is selected, the pop-up menu 40 is exposed and configured to register, delete, edit, or edit the operation route”, and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”), acquiring a fishing plan selection operation of the user (See at least paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable.”); determining a target fishing plan from the at least one fishing plan according to the fishing plan selection operation (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”); wherein the navigation information comprises a fishing route (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”).
KOANG does not explicitly disclose, however, LIM, in the same field of endeavor, teaches wherein the at least one fishing plan is generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots (See at least paragraph [0066], “The fishing guiding server 100 recognizes fish species from the collected images in the online community channels and determines the fish species information representing the recognized fish species (S105). For example, the control unit 170 executes the extraction module for extracting fish species information by applying a machine learning technique, recognizes a fish species for each collected image stored in the storage unit 130, and provides fish species information indicating the recognized fish species. Fish species represent the types of live fish that can be caught by fishing, such as flounder, flatfish, yellowtail, mullet, rockfish, black sea bream, and red sea bream”, paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks”, and paragraph [0078], “As such, the control unit 170 may estimate the fish species activity of each fish species associated with the fishing point, time, weather information, etc. based on big data and store the estimated fish species activity information in the storage unit 130, or may prepare to immediately estimate the fish species activity.” The system estimates fish species activity based on historical fishing data by fishing point and time using big data analysis and machine learning and stores the estimated fishing species activity information in a storage unit, thereby providing a pre-stored fishing plan.), different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks.”); and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks” and paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response.”); and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks.”); determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods (See at least paragraph [0078], “As such, the control unit 170 may estimate the fish species activity of each fish species associated with the fishing point, time, weather information, etc. based on big data and store the estimated fish species activity information in the storage unit 130, or may prepare to immediately estimate the fish species activity”, paragraph [0079], “Through the control flow shown in FIG. 3, the fishing guiding server 100 may estimate and update at least the fish species activity for each fishing point through fishing photos, etc. that may be secured from online community channels”, paragraph [0080], “FIG. 4 is a diagram showing an exemplary control flow for recommending a fishing point for a requested target fish species according to a user's input using estimated fish species activity for each fishing point”, and paragraph [0081], “The control flow shown in FIG. 4 is performed by the fishing guiding server 100 and is preferably performed by the control unit 170 of the fishing guiding server 100 executing the recommendation module. The control flow shown in FIG. 4 is preferably performed after the fish species activity is estimated or may be immediately estimated according to the control flow of FIG. 3.”); and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots (See at least paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response” and paragraph [0083], “The fishing guiding server 100 receives a fishing point recommendation request including a fish to be caught and an expected fishing date, which is a fishing date, from the user terminal 200 through the Internet (S203). The control unit 170 may receive a fishing point recommendation request including input data for specifying the target fish species (e.g., target fish species identifier (name)) and input data for specifying the expected fishing date (e.g., date of year, month, day) through the communication unit 110.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].).
Regarding Claim 18, KOANG teaches A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions, when executed by a processor, cause the processor to perform the fishing navigation method according to claim 1: Regarding Claim 1, KOANG teaches A fishing navigation method, performed by an electronic device, which is applied to a target fishing area, wherein the target fishing area comprises a plurality of fishing spots and is obtained based on a selection of a user, and wherein the fishing navigation method comprises: acquiring at least one fishing plan corresponding to the target fishing area (See at least paragraph [0001], “The present invention relates to a navigation terminal for fishing”, paragraph [0015], “When the navigation terminal for phishing displays the electronic chart, it is configured to overlay the tide information and maritime weather information at the current location along with the position and speed of the own ship and display it”, paragraph [0018], “the present invention is designed to recommend and guide the fishing route with the highest probability according to the marine environment information and weather information of the fishing area by selecting only the fishing date, fish species, and fishing area when establishing a fishing plan”, paragraph [0051], “At this time, the operation route 14 is displayed on the electronic chart 10, and when the automatically generated operation route 14 is selected, the pop-up menu 40 is exposed and configured to register, delete, edit, or edit the operation route”, and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”), acquiring a fishing plan selection operation of the user (See at least paragraph [0061], “In addition, as shown in Fig. 5, depending on the embodiment, in order to establish a sea-going plan, the area to be fished is configured to be touch-able on the electronic chart 10 of the open window 70 or through an input window. By configuring the location value (71) and date and time (72) of a specific area to be inputable.”); determining a target fishing plan from the at least one fishing plan according to the fishing plan selection operation (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”); wherein the navigation information comprises a fishing route (See at least paragraph [0059], “And at this time, if the ranking for each operation section or the recommended ranking 63 overlaid on the electronic chart is selected, the corresponding operation route is displayed in the center of the electronic chart and guides to the starting point of the corresponding operation route. At this time, if multiple fishing routes are selected, the optimal route or the shortest distance is guided based on the selection order or marine environment information or marine weather information.”).
KOANG does not explicitly disclose, however, LIM, in the same field of endeavor, teaches wherein the at least one fishing plan is generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots (See at least paragraph [0066], “The fishing guiding server 100 recognizes fish species from the collected images in the online community channels and determines the fish species information representing the recognized fish species (S105). For example, the control unit 170 executes the extraction module for extracting fish species information by applying a machine learning technique, recognizes a fish species for each collected image stored in the storage unit 130, and provides fish species information indicating the recognized fish species. Fish species represent the types of live fish that can be caught by fishing, such as flounder, flatfish, yellowtail, mullet, rockfish, black sea bream, and red sea bream”, paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks”, and paragraph [0078], “As such, the control unit 170 may estimate the fish species activity of each fish species associated with the fishing point, time, weather information, etc. based on big data and store the estimated fish species activity information in the storage unit 130, or may prepare to immediately estimate the fish species activity.” The system estimates fish species activity based on historical fishing data by fishing point and time using big data analysis and machine learning and stores the estimated fishing species activity information in a storage unit, thereby providing a pre-stored fishing plan.), different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks.”); and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks” and paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response.”); and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots (See at least paragraph [0077], “The control unit 170 may estimate fish species activity when the number of fishing event items in the big data DB 131 of the storage unit 130 exceeds the internally set number, when the internally set period for estimating the activity of fish species arrives, or when there is a user request. As the big data is sufficiently secured, the control unit 170 may estimate fish species activity by fish species (flatfish, flatfish, yellowtail, mullet, rockfish, black sea bream, red sea bream, etc.) by fishing point and time. In addition, since weather information variables such as water temperature, wave height, atmospheric pressure, and tide time are matched with big data along with fishing points, times, and species, the control unit 170 may estimate the fish species activity when a condition for combining any of the above variables is given. The activity of the fish species may represent the presence or absence, or the degree of activity (abundance) of the corresponding fish stocks.”); determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods (See at least paragraph [0078], “As such, the control unit 170 may estimate the fish species activity of each fish species associated with the fishing point, time, weather information, etc. based on big data and store the estimated fish species activity information in the storage unit 130, or may prepare to immediately estimate the fish species activity”, paragraph [0079], “Through the control flow shown in FIG. 3, the fishing guiding server 100 may estimate and update at least the fish species activity for each fishing point through fishing photos, etc. that may be secured from online community channels”, paragraph [0080], “FIG. 4 is a diagram showing an exemplary control flow for recommending a fishing point for a requested target fish species according to a user's input using estimated fish species activity for each fishing point”, and paragraph [0081], “The control flow shown in FIG. 4 is performed by the fishing guiding server 100 and is preferably performed by the control unit 170 of the fishing guiding server 100 executing the recommendation module. The control flow shown in FIG. 4 is preferably performed after the fish species activity is estimated or may be immediately estimated according to the control flow of FIG. 3.”); and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots (See at least paragraph [0082], “First, a user logs in to the fishing guiding server 100 using the user terminal 200 (S201). The user inputs a user ID and password to the input interface of the user terminal 200, and the control unit 170 of the fishing guiding server 100 may receive this and authenticate the user by comparing the user ID and password stored in the storage unit 130 or the like. The user may log in to the fishing guiding server 100 through a web program or an app program, request a fishing point recommendation, and receive a response” and paragraph [0083], “The fishing guiding server 100 receives a fishing point recommendation request including a fish to be caught and an expected fishing date, which is a fishing date, from the user terminal 200 through the Internet (S203). The control unit 170 may receive a fishing point recommendation request including input data for specifying the target fish species (e.g., target fish species identifier (name)) and input data for specifying the expected fishing date (e.g., date of year, month, day) through the communication unit 110.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].).
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over KOANG (KR 2022048861 A) in view of LIM (US 20230185841 A1) and DePasqua (US 20150063059 A1).
Regarding Claim 5, KOANG and LIM teach The fishing navigation method according to claim 4, as set forth in the obviousness rejection above. KOANG teaches further comprising: acquiring a location of each of the at least one fishing area (See at least paragraph [0049], “In particular, when the track 16 is created, the marine environment information 12 and the marine weather information 11 at the current location are collected and stored together so that you can find or analyze the fish that are active in the region with a similar pattern later.”).
KOANG and LIM do not explicitly disclose, however, DePasqua, in the same field of endeavor, teaches wherein determining the second candidate fishing areas from the at least one fishing area according to the current location comprises: determining a distance between the current location and each of the at least one fishing area according to the current location and the location of each of the at least one fishing area (See at least paragraph [0004], “As such, the present invention is directed towards an improved method for determining the GPS position of an object in the water up to hundreds of feet or more away from the boat. This is done with aid of long range side scan sonar technology and by mounting a side/forward scan sonar transducer to the lower unit of an electric trolling motor. The fisherman can then direct the sonar beam by slowly spinning/steering the trolling motor lower unit, scanning the lake bottom 360 degrees and hundreds of feet from the boat. This system works whether the boat is moving or standing still. The sonar computer continuously stores compass heading, distance, and current GPS position as the lake bottom is scanned. The user can select any object (current or past view) on the screen to determine the GPS position of that object and use the GPS data to navigate the boat or correct previously stored object positions as a form of GPS error correction. The system determines the GPS position of objects with the aid of a compass linked or inline with the sonar transducers scanning direction and pointing device (trolling motor) that may be in the form of a steerable trolling motor connected to a sonar unit. The GPS position is calculated by the sonar computer knowing the distance the object is from the boat and the compass heading direction pointing to the object. The computer then reads the boat's current longitude and latitude position and formulates the object's position using the distance and compass direction to the object. The underwater object GPS position data can be used for the boat's auto pilot navigation to guide the boat to the under water object. The user can set a distance parameter keeping the boat within a set distance away from the object, allowing the fisherman to cast to the object but keeping a far enough distance not to scare off the fish hiding in the object. The system may also incorporate a wind detection device to properly keep the boat pointing into the wind allowing for accurate boat navigation. The forward/side scan sonar transducer may be attached to the trolling motor lower with a removable bracket or may be manufactured permanently into the trolling motor lower unit casing.”); and determining one of the at least one fishing area having a distance smaller than a set distance threshold to be a second candidate fishing area of the second candidate fishing areas (See at least paragraph [0019], “The sonar system seen in FIG. 3 is also capable of keeping the boat at a specified distance from the boat. The user can select how many feet they would like the boat to be away from an object and the GPS navigation system will keep the boat within that distance. This will be useful while fishing as the fisherman may want to stay away from an object to keep from chasing the fish away do to the fisherman's presents.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM and DePasqua such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), and determine a distance between the current location and each of the at least one fishing area according to the current location and the location of each of the at least one fishing area and determine one of the at least one fishing area having a distance smaller than a set distance threshold to be a second candidate fishing area of the second candidate fishing area, as taught by DePasqua (See paragraph [0004], [0019].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].). The motivation for doing so would be increasing navigation accuracy, as taught by DePasqua (See paragraph [0003].).
Regarding Claim 13, KOANG and LIM teach The fishing navigation method according to claim 12, as set forth in the obviousness rejection above. KOANG and LIM do not explicitly disclose, however, DePasqua, in the same field of endeavor, teaches further comprising: acquiring a motion speed of the user (See at least paragraph [0004], “As such, the present invention is directed towards an improved method for determining the GPS position of an object in the water up to hundreds of feet or more away from the boat. This is done with aid of long range side scan sonar technology and by mounting a side/forward scan sonar transducer to the lower unit of an electric trolling motor. The fisherman can then direct the sonar beam by slowly spinning/steering the trolling motor lower unit, scanning the lake bottom 360 degrees and hundreds of feet from the boat. This system works whether the boat is moving or standing still. The sonar computer continuously stores compass heading, distance, and current GPS position as the lake bottom is scanned. The user can select any object (current or past view) on the screen to determine the GPS position of that object and use the GPS data to navigate the boat or correct previously stored object positions as a form of GPS error correction. The system determines the GPS position of objects with the aid of a compass linked or inline with the sonar transducers scanning direction and pointing device (trolling motor) that may be in the form of a steerable trolling motor connected to a sonar unit. The GPS position is calculated by the sonar computer knowing the distance the object is from the boat and the compass heading direction pointing to the object. The computer then reads the boat's current longitude and latitude position and formulates the object's position using the distance and compass direction to the object. The underwater object GPS position data can be used for the boat's auto pilot navigation to guide the boat to the under water object. The user can set a distance parameter keeping the boat within a set distance away from the object, allowing the fisherman to cast to the object but keeping a far enough distance not to scare off the fish hiding in the object. The system may also incorporate a wind detection device to properly keep the boat pointing into the wind allowing for accurate boat navigation. The forward/side scan sonar transducer may be attached to the trolling motor lower with a removable bracket or may be manufactured permanently into the trolling motor lower unit casing.”); wherein determining the estimated arrival time according to the distance between the current location and the location of the navigation fishing spot comprises: determining the estimated arrival time according to the motion speed and the distance between the current location and the location of the navigation fishing spot (See at least paragraph [0004], “As such, the present invention is directed towards an improved method for determining the GPS position of an object in the water up to hundreds of feet or more away from the boat. This is done with aid of long range side scan sonar technology and by mounting a side/forward scan sonar transducer to the lower unit of an electric trolling motor. The fisherman can then direct the sonar beam by slowly spinning/steering the trolling motor lower unit, scanning the lake bottom 360 degrees and hundreds of feet from the boat. This system works whether the boat is moving or standing still. The sonar computer continuously stores compass heading, distance, and current GPS position as the lake bottom is scanned. The user can select any object (current or past view) on the screen to determine the GPS position of that object and use the GPS data to navigate the boat or correct previously stored object positions as a form of GPS error correction. The system determines the GPS position of objects with the aid of a compass linked or inline with the sonar transducers scanning direction and pointing device (trolling motor) that may be in the form of a steerable trolling motor connected to a sonar unit. The GPS position is calculated by the sonar computer knowing the distance the object is from the boat and the compass heading direction pointing to the object. The computer then reads the boat's current longitude and latitude position and formulates the object's position using the distance and compass direction to the object. The underwater object GPS position data can be used for the boat's auto pilot navigation to guide the boat to the under water object. The user can set a distance parameter keeping the boat within a set distance away from the object, allowing the fisherman to cast to the object but keeping a far enough distance not to scare off the fish hiding in the object. The system may also incorporate a wind detection device to properly keep the boat pointing into the wind allowing for accurate boat navigation. The forward/side scan sonar transducer may be attached to the trolling motor lower with a removable bracket or may be manufactured permanently into the trolling motor lower unit casing” and paragraph [0019], “The sonar system seen in FIG. 3 is also capable of keeping the boat at a specified distance from the boat. The user can select how many feet they would like the boat to be away from an object and the GPS navigation system will keep the boat within that distance. This will be useful while fishing as the fisherman may want to stay away from an object to keep from chasing the fish away do to the fisherman's presents.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM and DePasqua such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), and acquire a motion speed of the user and determine the estimated arrival time according to the motion speed and the distance between the current location and the location of the navigation fishing spot, as taught by DePasqua (See paragraph [0004], [0019].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].). The motivation for doing so would be increasing navigation accuracy, as taught by DePasqua (See paragraph [0003].).
Claim(s) 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over KOANG (KR 2022048861 A) in view of LIM (US 20230185841 A1) and Yokota (US 20100026526 A1).
Regarding Claim 14, KOANG and LIM teach The fishing navigation method according to claim 12, as set forth in the obviousness rejection above. KOANG and LIM do not explicitly disclose, however, Yokota, in the same field of endeavor, teaches after generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots, further comprising: sending reminder information to the user based on real-time positioning information (See at least paragraph [0010], “One aspect of the present invention is a method for generating a location based reminder by a navigation system. The method includes the steps of: creating reminder message data which include a reminder message, a primary location, a secondary location, and a validation rule where the primary location is a location associated with the reminder message, a secondary location is a location where a user conducts an activity defined by the reminder message, and the validation rule defines condition for generating the reminder message; comparing a reference location with the primary location where the reference location is a destination for a route guidance operation of the navigation system or a current vehicle and/or user position; applying the validation rule in the reminder message data to determine whether a relationship between the primary location and the reference location satisfies the condition in the validation rule for generating the reminder message; displaying the reminder message associated with the primary location when the condition defined in the validation rule is satisfied where the display includes one or more candidate secondary locations; and conducting a route guidance operation to reach a location selected by the user”, paragraph [0011], “The method of the present invention further includes a step of accepting a user's selection of one of the candidate secondary locations for the route guidance operation”, paragraph [0012], “In the method of the present invention, the reminder message data further include data related to an effective time which specifies a time range or a time limit for generating the reminder message which will be evaluated in combination with the validation rule. The method of the present invention further includes a step of determining whether the effective time has been met before displaying the reminder message associated with the primary location”, paragraph [0015], “In the method of the present invention, the step of conducting a route guidance operation includes a step of selecting a candidate secondary location as a destination (final destination) or a waypoint (intermediate destination) for the route guidance operation by the navigation system”, and paragraph [0062], “Based on this, it is possible to establish a fishing plan by collecting marine environment prediction information and marine weather prediction information on the date of departure and receiving recommendations and guidance on fishing routes and fishing points.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM and Yokota such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), and send reminder information to the user based on real-time positioning information, as taught by Yokota (See paragraph [0010]-[0012], [0015].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].). The motivation for doing so would be enhancing navigation capabilities, as taught by Yokota (See paragraph [0004].).
Regarding Claim 15, KOANG, LIM, and Yokota teach The fishing navigation method according to claim 14, as set forth in the obviousness rejection above. KOANG and LIM do not explicitly disclose, however, Yokota, in the same field of endeavor, teaches wherein the reminder information comprises at least one of: a route deviation reminder, which is used for reminding the user of a deviation from a navigation route when the current location deviates from the guidance route (See at least paragraphs [0009], “It is a further object of the present invention to provide a method and apparatus for a navigation system to generate a reminder message based on the reminder message data which is created directly by operating the navigation system or indirectly via a personal computer and transferred to the navigation system”, paragraph [0010], “One aspect of the present invention is a method for generating a location based reminder by a navigation system. The method includes the steps of: creating reminder message data which include a reminder message, a primary location, a secondary location, and a validation rule where the primary location is a location associated with the reminder message, a secondary location is a location where a user conducts an activity defined by the reminder message, and the validation rule defines condition for generating the reminder message; comparing a reference location with the primary location where the reference location is a destination for a route guidance operation of the navigation system or a current vehicle and/or user position; applying the validation rule in the reminder message data to determine whether a relationship between the primary location and the reference location satisfies the condition in the validation rule for generating the reminder message; displaying the reminder message associated with the primary location when the condition defined in the validation rule is satisfied where the display includes one or more candidate secondary locations; and conducting a route guidance operation to reach a location selected by the user”, paragraph [0012], “In the method of the present invention, the reminder message data further include data related to an effective time which specifies a time range or a time limit for generating the reminder message which will be evaluated in combination with the validation rule. The method of the present invention further includes a step of determining whether the effective time has been met before displaying the reminder message associated with the primary location”, paragraph [0014], “In the method of the present invention, the reminder message data include two or more different effective times for generating the reminder message and two or more different conditions in the validation rule for triggering a reminder operation so that the reminder message will be generated depending on the different times and conditions”, and paragraph [0089], “The reminder data may also contain information on the time or duration, which can be used to define the effective time of the message, such as "effective after 10:30 AM today", "valid until 7:00 PM tomorrow", "between 8:00 AM and 6:00 PM on weekdays", "this weekend", "on Tuesdays", "on the third Monday of the month", "during construction at Magnolia/Bake intersection", etc. The reminder data may contain additional validation rules, such as "if rainy", "if traffic congestion causes at least 30 minutes of delay in an estimated time of arrival", "if the destination is within 20 miles from the current position", etc. The user may optionally search for a nearby POIs of a suggested location. The navigation system may also be equipped with a function to receive information on traffic incidents and conditions around the suggested location.”); a remaining time reminder, which is used for reminding the user of a remaining fishing time of a fishing spot where the user is located when a remaining time of a fishing time period corresponding to the fishing spot where the user is located is less than a first time threshold; or a rest reminder, which is used for reminding the user to rest when a continuous fishing time of the user is greater than a second time threshold.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of KOANG with the teachings of LIM and Yokota such that the navigation terminal of KOANG is further configured to utilize at least one fishing plan generated according to historical fishing data through big data analysis and machine learning, the at least one fishing plan is pre-stored, each of the at least one fishing plan comprises a first plurality of locations of target fishing spots and a first plurality of fishing time periods corresponding to the first plurality of target fishing spots; different target fishing spots correspond to different fishing time periods, and the first plurality of target fishing spots are selected from the plurality of fishing spots of the target fishing area; and generating navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots to guide the user to perform the target fishing plan; and wherein generating the navigation information according to the first plurality of locations of the first plurality of target fishing spots and the first plurality of fishing time periods corresponding to the first plurality of target fishing spots comprises: acquiring the first plurality of fishing time periods corresponding to the first plurality of target fishing spots; determining a time sequence corresponding to the first plurality of target fishing spots according to the first plurality of fishing time periods; and generating the fishing route according to the time sequence and the first plurality of locations of the first plurality of target fishing spots, as taught by LIM (See paragraph [0066], [0077], [0078], [0080]-[0083].), and a route deviation reminder, which is used for reminding the user of a deviation from a navigation route when the current location deviates from the guidance route, as taught by Yokota (See paragraph [0009], [0010], [0012], [0014], [0089].), with a reasonable expectation of success. The motivation for doing so would be increasing accuracy of fishing recommendations, as taught by LIM (See paragraph [0009].). The motivation for doing so would be enhancing navigation capabilities, as taught by Yokota (See paragraph [0004].).
Conclusion
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/JEWEL A KUNTZ/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666